ORIGINAL_ARTICLE
A mathematical model to optimize debris clearance problem in the disaster response Phase: A case study
The post-disaster response phase aims to reduce casualties by accessing critical areas to transfer relief aid, search and rescue operations to the injured as soon as possible. Debris from the disaster blocks roads and prevents rescue teams from reaching critical areas. It is crucial to decide which routes should be cleared for relief aid transportation to reduce the negative effects of the disaster. In this study, a model for debris removal is presented to minimize access time to critical areas such as hospitals and maximize coverage of the areas. The AUGMECON 2 method has been used to solve this problem. Also, the efficiency of this solution method in Tehran has been studied, and its results have been analyzed. The results of this study indicate the importance of considering a comprehensive plan and several sites for debris removal in the disaster response phase.
https://www.jise.ir/article_137242_612dad3b6b0fb05f0f1cf0c927dd8a5f.pdf
2021-01-01
1
34
Debris removal
Emergency relief
Disaster Management
Hanieh
Heydari
hanieh.heidari@ut.ac.ir
1
School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Amir
Aghsami
a.aghsami@ut.ac.ir
2
School of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
Masoud
Rabani
mrabani@ut.ac.ir
3
School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
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1
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50
ORIGINAL_ARTICLE
Dual-purpose model of energy consumption-construction cost to evaluate the construction methods of the outer shell of residential buildings: A real case study
The outer shell is the primary protection of the building against adverse weather conditions and determines the heat exchange rate to the environment. Evaluating and optimizing the outer shell design of residential buildings due to multiple and conflicting criteria such as energy consumption, costs, and environmental impacts is a multi-objective challenge. In this paper, a bi-objective model is presented to evaluate different methods of constructing the outer shell of residential buildings to reduce energy consumption at the lowest possible cost significantly. So that, minimizing the heat transfer from the outer shell as a function of the energy target and minimizing the cost of fabricating the components of the outer shell as a function of the cost and the augmented epsilon constraint method are used to solve the model and determine Pareto’s solutions. The results show that by determining the appropriate thickness and density of the walls and the appropriate ratio of walls’ permeable surface while spending reasonable costs, it is possible to reduce required energy for cooling and heating the house.
https://www.jise.ir/article_137245_c5281a991eafe55703a053c18077913d.pdf
2021-12-26
35
50
Residential Building
outer shell
Multi-Objective Optimization
energy consumption
Construction cost
Augmented ε-constraint
Ahmad
Madadi
a.madadi@haierasa.ir
1
Department of Industrial Management, Islamic Azad University, Science and Research, Najafabad branch, ,Esfahan, Iran
LEAD_AUTHOR
Masoud
Barati
barati_masoud@yahoo.com
2
Department of Industrial Management, Islamic Azad University, Science and Research, Najafabad branch, ,Esfahan, Iran
AUTHOR
Rasoul
Baharloo
rasoul.baharloo@gmail.com
3
Department of Industrial Management, Islamic Azad University, Science and Research, Najafabad branch, ,Esfahan, Iran
AUTHOR
Omid
Solgi
omidsolg72@gmail.com
4
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Albertini, F., Gomes, L. P., Grondona, A. E. B., & Caetano, M. O. (2021). Assessment of environmental performance in building construction sites: Data envelopment analysis and Tobit model approach. Journal of Building Engineering, 44, 102994.
1
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2
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3
Azadeh, A., Rezaei-Malek, M., Evazabadian, F., & Sheikhalishahi, M. (2015). Improved design of CMS by considering operators decision-making styles. International Journal of Production Research, 53(11), 3276-3287.
4
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5
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6
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8
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9
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20
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21
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24
ORIGINAL_ARTICLE
Data driven approaches for customer centric and service dominant value propositions: A systematic literature review
Novel marketing theories that focus on service dominant approaches require to deeply consider customer specifications and needs within using products and services by customers. In this way, data driven approaches that focus on analyzing customer behavior are critically important to realize service dominant logic of marketing. Although previous studies have proposed different approaches to enhance dynamic and customer centric value propositions, there is not a comprehensive view on data-driven approaches that can be used within this context. The main research question that is addressed in this paper is "what are the data-driven approaches, concepts, and practical domains that are addressed for customer centric value propositions to enable service ecosystems to co-create value with customers”. To answer this research question, a systematic literature review is conducted. Based on the relevant evidence extracted from 124 papers, the approaches, core concepts, and key practical domains of customer centric value propositions are described. The paper aims to systematically bridge between prescriptive approaches and tools that have emerged in the field of data analytics and descriptive concepts that have introduced by novel marketing theories.
https://www.jise.ir/article_137483_372ef3050984b29c5bb6c0bf76c9a54b.pdf
2021-12-31
51
75
Service dominant logic
Value Co-creation
Value Proposition
data driven approach
Machine Learning
Mahshad
Mohammadi
mh.mahshad@gmail.com
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
mohammad Reza
Rasouli
rasouli@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Abdelkafi, N., Raasch, C., Roth, A., & Srinivasan, R. (2019). Multi-sided platforms. Electronic Markets, 29(4), 553-559.
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ORIGINAL_ARTICLE
Risks and buffer analysis in critical chain management by system dynamics (Case study: Oil refinery)
In this paper, risks in oil construction projects are identified, analyzed and considered for buffers in system dynamics modeling. To do so, all important and effective variables and risks within an oil construction project are determined, including internal and external variables and then, conceptual model is developed using system thinking approach. Based on the conceptual model, state and flow model is obtained and the relations between variables are established. In order to show efficiency and effectiveness of the proposed method, a real case study is considered and solved. Moreover, sensitivity analysis is provided, by using real data and this model. The primary goal of this research is to investigate the impact of different risks that exist in oil construction risks on key variables; these key variables include human resources, facilities and materials. The results demonstrate that the initial plan of each resource is not consistent with the actual need of them. In other words, based on the existing risks in the model, the proposed approach determines what level the actual resources requirement would be placed at and given existing risks, which buffer should be considered for each resource. Furthermore, the impact of risks in performing activities is forecasted and the model shows what impact the risks have on the delay in initial progress of the project as time passes. Finally, it is studied how changes in key and input variables affect the all project.
https://www.jise.ir/article_142949_842f93d07305141e7b389d8c4f25212e.pdf
2022-01-08
76
110
Critical chain
buffer management
Dynamic system
Risk Management
Oil refinery
Behnam
Faizabadi
bfaizabadi@gmail.com
1
Department of Industrial Management, Islamic Azad University, Science and Research branch, Tehran, Iran
AUTHOR
Mahmood
Alborzi
mahmood_alborzi@yahoo.com
2
Department of Industrial Management, Islamic Azad University, Science and Research branch, Tehran, Iran
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Abbas
Toloie Ashlaghi
toloie@srbiau.ac.ir
4
Department of Industrial Management, Islamic Azad University, Science and Research branch, Tehran, Iran
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30
ORIGINAL_ARTICLE
Heuristic approach for a pessimistic robust closed loop supply chain network considering commercial, end-of-use and end-of-life returns and quality constraint
Nowadays, due to the environmental issues, governmental regulations and economic benefits, focus on collecting and recovery of products has increased. Recovered products can be reused or sold in secondary markets. In this paper, we consider a given structure for a closed loop supply chain, including a manufacturer, distributer and retailer in the forward logistic; the original products are given to the primary market. In the reverse logistic of the given structure, the returned products are disassembled and some obtained parts are used in the manufacturer. We assume that the produced products from returned parts can be given to a secondary market. A minimum quality level is considered for the returned parts. A collection site, and a repair site is added to the initial structure and it is assumed that the disassembled parts to be categorized into end-of-use, end-of-life and disposals. Some products called commercial returns are not assembled and can be given to the secondary market after a simple repair. Furthermore, uncertainty on the demand and return rates are considered and the operational decision variables of the models which are mainly the flow values in the chain and opening some facilities are determined. Electronic devices such as mobile phones and printers are suitable examples for the studied supply chain. The robust counterpart of the model is developed and a solution approach based on the Lagrangian relaxation is developed for solving the problem. Two heuristics based on partial derivations are developed to solve the sub problems and results are analyzed.
https://www.jise.ir/article_137484_19f10ecefb4eacc485d8362a53d00397.pdf
2021-01-01
111
136
Closed loop supply chain
end-of-use
end-of-life
robust optimization
quality level
Lagrangian Relaxation
Mehdi
Seifbarghy
m.seifbarghy@alzahra.ac.ir
1
Department of Industrial Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Leyla
Ahmadpour
leyla.ahmadpour@yahoo.com
2
Department of Industrial Engineering, Alzahra University, Tehran, Iran
AUTHOR
Ahmadzadeh, E., & Vahdani, B. (2017). A location-inventory-pricing model in a closed loop supply chain network with correlated demands and shortages under a periodic review system. Computers and Chemical Engineering, 101, 148–166.
1
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2
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3
Beamon, B.M., & Fernandes, C. (2004). Supply-chain network configuration for product recovery. Production Planning & Control: The Management of Operations, 15(3), 270-281.
4
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6
Biçe, K., & Batun, S. (2021). Closed-loop supply chain network design under demand, return and quality uncertainty. Computers & Industrial Engineering, 155, 107081.
7
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8
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10
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Garg, K., Kannan, D., Diabat, A., & Jha, P.C. (2015). A multi-criteria optimization approach to manage environmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297-314.
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Gong, Y., Huang, D., Wang, E., & Peng, Y. (2009). A fuzzy chance constraint programming approach for location-allocation problem under uncertainty in a closed-loop supply chain. International Joint Conference on Computational Sciences and Optimization, 836-840, doi: 10.1109/CSO.2009.151.
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Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
15
Guang-zhi, Y., Shu-shi, N., & Qing, L. (2009). Genetic local search for facility location-allocation problem in closed-loop supply chains, The 1st International Conference on Information Science and Engineering, 4316-4319, doi: 10.1109/ICISE.2009.624.
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20
Jangali, M.T., Makui, A., & Dehghani, E. (2020). Designing a closed loop supply chain network for engine oil in an uncertain environment: A case study in Behran Oil Company. Journal of Industrial and Systems Engineering, 13(2), 49-64.
21
Jayaraman, V. (2006). Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach. International Journal of Production Research, 44(5), 981-998.
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Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93–103.
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29
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31
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33
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36
Wang, H.F., & Hsu, H.W. (2012). A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics. Fuzzy Optimization and Decision Making, 11, 177–208.
37
ORIGINAL_ARTICLE
A possibilistic-stochastic programming approach to resilient natural gas transmission network design problem under disruption: A case study
Resilient natural gas production and transmission pipeline for minimum cost and minimum the maximum cumulative fraction of unsupplied demand related to the met demand before disruption) are two essential goals of natural gas transmission network design. This paper develops a multi-objective multi-period mixed possibilistic-stochastic programming model to form a trade-off between resiliency and cost. In the presented model, the uncertainty of natural gas consumptions is considered as an operational risk while disruption risks are accounted for the failure of refinery production capacity and pipeline transmission capacity. The proposed model utilizes mitigation strategy such as extra capacities in the refinery, backup and fortified pipelines before disruption event and recovery strategy for restoring lost capacities of facilities to reach normal performance after disruption event. Finally, the performance of the proposed model is validated by executing a computational analysis using the data of a real case study. Our analysis shows that the efficiency of the natural gas transmission network is highly vulnerable to failure of pipeline and refinery capacity as well as demand fluctuations. Also, results indicate that utilizing extra refinery production capacity, fortified pipeline and backup pipeline options have numerous influences in raising the resiliency of the NG network.
https://www.jise.ir/article_137485_5d5a6ad22797b0153a8c633a43c475f4.pdf
2022-02-09
137
162
Natural gas transmission network
resilient natural gas network
Possibilistic programming
two-stage scenario-based stochastic programming
Multi-Objective Optimization
Rozita
Daghigh
r_daghigh@iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad Saeed
Jabalameli
jabal@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Saeed
Pakseresht
s.pakseresht110@gmail.com
4
Research Institute of Petroleum Industry, Tehran, Iran
AUTHOR
Azadeh, A., Shabanpour, N., Gharibdousti, M.S. and Nasirian, B., (2016). Optimization of supply chain based on macro ergonomics criteria: A case study in gas transmission unit. Journal of Loss Prevention in the Process Industries, 43, pp.332-351.
1
Babazadeh, R., Ghaderi, H. and Pishvaee, M.S., (2019). A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty. Computers & Chemical Engineering, 124, pp.364-380.
2
Biringer, B., Vugrin, E. and Warren, D., (2013). Critical infrastructure system security and resiliency. CRC press.
3
California. Seismic Safety Commission and ASCE-25 Task Committee on Earthquake Safety Issues for Gas Systems, (2002). Improving Natural Gas Safety in Earthquakes (No. 2). Seismic Safety Commission.
4
Cimellaro, G.P., Villa, O. and Bruneau, M., 2014. Resilience-based design of natural gas distribution networks. Journal of Infrastructure systems, 21(1), p.05014005.
5
da Silva Alves, F., de Souza, J.N.M. and Costa, A.L.H., (2016). Multi-objective design optimization of natural gas transmission networks. Computers & Chemical Engineering, 93, pp.212-220.
6
Emenike, S.N. and Falcone, G., (2020). A review on energy supply chain resilience through optimization. Renewable and Sustainable Energy Reviews, 134, p.110088.
7
Fan, M.W., Gong, J., Wu, Y. and Kong, W.H., (2017). The gas supply reliability analysis of natural gas pipeline network based on simplified topological structure. Journal of Renewable and Sustainable Energy, 9(4), p.045503.
8
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Ghavamifar, A., Sabouhi, F. and Makui, A., (2018). An integrated model for designing a distribution network of products under facility and transportation link disruptions. Journal of Industrial and Systems Engineering, 11(1), pp.113-126.
10
Hamedi, M., Farahani, R.Z., Husseini, M.M. and Esmaeilian, G.R., (2009). A distribution-planning model for natural gas supply chain: A case study. Energy Policy, 37(3), pp.799-812.
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12
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Karmon, E., (2002). The risk of terrorism against oil and gas pipelines in Central Asia. The Oil and Gas Routed from Caspian-Caucasus Region: Geopolitics of Pipelines, Stability and International Security.
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Li, X., Armagan, E., Tomasgard, A. and Barton, P.I., (2011). Stochastic pooling problem for natural gas production network design and operation under uncertainty. AIChE Journal, 57(8), pp.2120-2135.
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Liang, Y., Zheng, J., Wang, B., Zheng, T. and Xu, N., (2020). Optimization Design of Natural Gas Pipeline Based on a Hybrid Intelligent Algorithm. In Recent Trends in Intelligent Computing, Communication and Devices (pp. 1015-1025). Springer, Singapore.
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Liu, W., Li, Z., Song, Z. and Li, J., (2018). Seismic reliability evaluation of gas supply networks based on the probability density evolution method. Structural safety, 70, pp.21-34.
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22
Omidvar, B. and Kivi, H.K., (2016). Multi-hazard failure probability analysis of gas pipelines for earthquake shaking, ground failure and fire following earthquake. Natural hazards, 82(1), pp.703-720.
23
Pishvaee, M.S. and Torabi, S.A., (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), pp.2668-2683.
24
Pishvaee, M.S., Razmi, J. and Torabi, S.A., (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, pp.1-20.
25
Papageorgiou, L.G., (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), pp.1931-1938.
26
Sabouhi, F. and Jabalameli, M.S., (2019). A stochastic bi-objective multi-product programming model to supply chain network design under disruption risks. Journal of Industrial and Systems Engineering, 12(3), pp.196-209.
27
Su, H., Zio, E., Zhang, J., Li, X., Chi, L., Fan, L. and Zhang, Z., (2019). A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency. Computers & Chemical Engineering, 131, p.106584.
28
Su, H., Zio, E., Zhang, J. and Li, X., (2018). A systematic framework of vulnerability analysis of a natural gas pipeline network. Reliability Engineering & System Safety, 175, pp.79-91.
29
Sesini, M., Giarola, S. and Hawkes, A.D., (2020). The impact of liquefied natural gas and storage on the EU natural gas infrastructure resilience. Energy, 209, p.118367.
30
Su, H., Zhang, J., Zio, E., Yang, N., Li, X. and Zhang, Z., (2018). An integrated systemic method for supply reliability assessment of natural gas pipeline networks. Applied Energy, 209, pp.489-501.
31
Tabatabaee, M., (2016). Resilience assessment of the natural gas supply system of the Country and Proposals to increase its Resiliency. The Center for Energy Technology Development, No. of research agreement: 193010.
32
Tsinidis, G., Di Sarno, L., Sextos, A. and Furtner, P., (2019). A critical review on the vulnerability assessment of natural gas pipelines subjected to seismic wave propagation. Part 1: Fragility relations and implemented seismic intensity measures. Tunnelling and Underground Space Technology, 86, pp.279-296.
33
Üster, H. and Dilaveroğlu, Ş., (2014). Optimization for design and operation of natural gas transmission networks. Applied Energy, 133, pp.56-69.
34
Yu, W., Gong, J., Song, S., Huang, W., Li, Y., Zhang, J., Hong, B., Zhang, Y., Wen, K. and Duan, X., (2019). Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages. Applied Energy, 252, p.113418.
35
Zamanian, M.R., Sadeh, E., Sabegh, Z.A. and Rasi, R.E., (2020). A Multi-Objective Optimization Model for the Resilience and Sustainable Supply Chain: A Case Study. International Journal of Supply and Operations Management, 7(1), pp.51-75.
36
Zhang, H., Liang, Y., Liao, Q., Chen, J., Zhang, W., Long, Y. and Qian, C., (2019). Optimal design and operation for supply chain system of multi-state natural gas under uncertainties of demand and purchase price. Computers & Industrial Engineering, 131, pp.115-130.
37
Zhu, Y., Wang, P., Wang, Y., Tong, R., Yu, B. and Qu, Z., (2021). Assessment method for gas supply reliability of natural gas pipeline networks considering failure and repair. Journal of Natural Gas Science and Engineering, 88, p.103817.
38
ORIGINAL_ARTICLE
A robust multi-objective optimization approach for construction project portfolio by considering sustainability
Macroeconomic investments in recent years has grown dramatically. Since the number of sources are usually less than the number of proposing projects to the organization, project selection and decision-making in this regard is considered as an inevitable issue. Wrong selection, will have negative consequences, such as wasting resources and also eliminate resources which can be properly used in a more appropriate project results in benefits for the organization. Therefore, a method for selecting project portfolio using a mathematical model and focusing on sustainability factors is proposed. In this paper, we present a multi-objective mathematical programming model that is a comprehensive and also a practical model for portfolio selection of construction projects because it uses sustainability criteria to evaluate projects as one of the objective functions. Multi-objective models can also be used to contrast the objectives with each other in project portfolio selection. Other innovations of the proposed model in this paper are multi-period modeling that specifies the precise timing of the selection of selected projects over 10 defined periods. A robust model is then proposed in order to considering the uncertainty, in this paper contains the uncertainty is the duration of the project. The results show that the robust model in terms of mean objective function under different realizations performs better than the deterministic model and may be because the robust model unlike the deterministic model considers the uncertainties caused by the disturbances.
https://www.jise.ir/article_137602_252164ad477f785663a5f3fd34252c03.pdf
2022-02-10
163
186
Project portfolio
sustainability criteria
loss
Benefit
Project selection
zahra
jalilibal
zjalili222@ut.ac.ir
1
Department of Industrial Engineering, Shahed University, Tehran, Iran
AUTHOR
Ali
Bozorgi-Amiri
alibozorgi@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Alyamani, R., & Long, S. (2020). The application of fuzzy Analytic Hierarchy Process in sustainable project selection. Sustainability, 12(20), 8314.
1
Allen, M., Alleyne, D., Farmer, C., McRae, A., & Turner, C. (2014). A framework for project success. Journal of Information Technology and Economic Development, 5(2), 1-17.
2
Bernhardi, L., Beroggi, G. E., & Moens, M. R. (2000). Sustainable water management through flexible method management. Water resources management, 14(6), 473-495.
3
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
4
Bochini, G. L., Fransozo, A., Castilho, A. L., Hirose, G. L., & Costa, R. C. (2014). Temporal and spatial distribution of the commercial shrimp Litopenaeus schmitti (Dendrobranchiata: Penaeidae) in the south-eastern Brazilian coast. Journal of the Marine Biological Association of the United Kingdom, 94(5), 1001-1008.
5
Bodea, C. N., Elmas, C., Tănăsescu, A., & Dascălu, M. (2010). An ontological-based model for competences in sustainable development projects: a case study for project’s commercial activities. Amfiteatru economic, 12(27), 177-189.
6
Bozorgi-Amiri, A., Jalilibal, Z., & Hahi Yakhchali, S. (2020). Balancing construction projects by considering resilience factors in crisis. Journal of Industrial and Systems Engineering, 12(Special issue on Project Management and Control), 100-109.
7
Carazo, A. F., Gómez, T., Molina, J., Hernández-Díaz, A. G., Guerrero, F. M., & Caballero, R. (2010). Solving a comprehensive model for multiobjective project portfolio selection. Computers & operations research, 37(4), 630-639.
8
Chen, Z., Li, H., & Wong, C. T. (2005). EnvironalPlanning: analytic network process model for environmentally conscious construction planning. Journal of construction engineering and management, 131(1), 92-101.
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Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (1997). Portfolio management in new product development: Lessons from the leaders—II. Research-Technology Management, 40(6), 43-52.
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11
Doerner, K. F., Gutjahr, W. J., Hartl, R. F., Strauss, C., & Stummer, C. (2006). Pareto ant colony optimization with ILP preprocessing in multiobjective project portfolio selection. European Journal of Operational Research, 171(3), 830-841.
12
Drenovak, M., & Ranković, V. (2014). Markowitz portfolio rebalancing with turnover monitoring. Ekonomski horizonti, 16(3), 211-223.
13
Fernández-Sánchez, G., & Rodríguez-López, F. (2010). A methodology to identify sustainability indicators in construction project management—Application to infrastructure projects in Spain. Ecological Indicators, 10(6), 1193-1201.
14
Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International Journal of Production Economics, 140(1), 149-159.
15
Ghahtarani, A., & Najafi, A. A. (2013). Robust goal programming for multi-objective portfolio selection problem. Economic Modelling, 33, 588-592.
16
Ghapanchi, A. H., Tavana, M., Khakbaz, M. H., & Low, G. (2012). A methodology for selecting portfolios of projects with interactions and under uncertainty. International Journal of Project Management, 30(7), 791-803.
17
Griffith, A., Stephenson, P., & Bhutto, K. (2005). An integrated management system for construction quality, safety and environment: a framework for IMS. International Journal of Construction Management, 5(2), 51-60.
18
Hassanzadeh, F., Nemati, H., & Sun, M. (2014). Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection. European Journal of Operational Research, 238(1), 41-53.
19
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20
Jones, B. (2006, May). Trying harder: Developing a new sustainable strategy for the UK. In Natural Resources Forum (Vol. 30, No. 2, pp. 124-135). Oxford, UK: Blackwell Publishing Ltd.
21
Keshavarz Hadadha, A., Jalili Bal, Z., & Haji Yakhchali, S. (2018). Multi Criteria Decision Making Techniques and Knapsack Approach for Clustering, Evaluating and Selecting Projects. Industrial Management Studies, 16(50), 229-255.
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Li, B., Zhu, Y., Sun, Y., Aw, G., & Teo, K. L. (2018). Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint. Applied Mathematical Modelling, 56, 539-550.
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25
Martens, M. L., Brones, F., & de Carvalho, M. M. (2013). Lacunas e tendências na literatura de sustentabilidade no gerenciamento de projetos: uma revisão sistemática mesclando bibliometria e análise de conteúdo. Gestão e Projetos: GeP, 4(1), 165-195.
26
Ma, J., Harstvedt, J. D., Jaradat, R., & Smith, B. (2020). Sustainability driven multi-criteria project portfolio selection under uncertain decision-making environment. Computers & Industrial Engineering, 140, 106236.
27
Mousavi, S. M., & Jalilibal, Z. (2021). A multi-period multi-objective mathematical planning model for construction project portfolio selection considering sustainability factors. 17 th. International Conference of Industrial Engineering.
28
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30
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47
ORIGINAL_ARTICLE
Repairable spare part supply chain: A hybrid priority-based particle swarm approach
The industry life highly depends on spare parts since it is vital to perform maintenance operations, especially in strategic industries. The expensive and low-demand spare parts are a must for the continuation of the production; therefore, they are held in warehouses to meet unexpected demand. These spare parts cause high inventory costs also they require human resources, energy, and budget for the repair operations. It is important to point out that separate optimization of decisions in spare part supply chain leads to sub-optimality so, an integrated mathematical model can outperform a routine model. In this paper, we present a network design and planning model that is integrated with the METRIC model (Multi-Echelon Technique for Recoverable Item Control) that formulates inventory management decisions of the repairable spare parts. This model covers different decisions such as supplier order assignment, stock level in warehouses, flows among the facilities, and location of facilities. Due to the np-hardness of the problem, a hybrid approach is presented that incorporates heuristic and meta-heuristic methods. This approach is used to solve the proposed model that has been never applied in previous researches for such a model.
https://www.jise.ir/article_138842_4f6b73eaab7a221c2f7aaab121f6932f.pdf
2022-02-11
187
204
Supply chain
spare part
Meta-heuristic
PSO
Inventory management
Gholamreza
Moini
gr.moini@gmail.com
1
Department of Management and Economics, Science and Research Branch, Islamic Azad University Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ebrahim
Teimoury
teimoury@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Seyed Mohammad
Seyedhosseini
seyedhoseini@yahoo.com
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Reza
Radfar
r.radfar@srbiau.ac.ir
4
Department of Management and Economics, Science and Research Branch, Islamic Azad University Iran University of Science and Technology, Tehran, Iran
AUTHOR
Mahmood
Alborzi
mahmood_alborzi@yahoo.com
5
Department of Management and Economics, Science and Research Branch, Islamic Azad University Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ahmadi kurd, H., Yaghobi, S., & Taghan zadeh, A. hakim. (1396). A robust optimization Model for designing reverse water network for Agricultural comsumption (Case Study: Tehran Province). 28(4), 633–647.
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Alborzi, M. (2019). Management information system (2nd ed., Vol. 1). Andishehaye goharbar.
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3
Babaveisi, V., Paydar, M. M., & Safaei, A. S. (2018). Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms. Journal of Industrial Engineering International, 14(2), 305–326. https://doi.org/10.1007/s40092-017-0217-7
4
Carrasco-Gallego, R., Ponce-Cueto, E., & Dekker, R. (2012). Closed-loop supply chains of reusable articles: A typology grounded on case studies. International Journal of Production Research, 50(19), 5582–5596.
5
Driessen, M. A. (2018). Integrated capacity planning and inventory control for repairable spare parts.
6
Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942–1948.
7
Fathollahi-Fard, A. M., Ahmadi, A., & Al-e-Hashem, S. M. J. M. (2020). Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty. Journal of Environmental Management, 275, 111277. https://doi.org/10.1016/j.jenvman.2020.111277
8
Fonseca, M. C., García-Sánchez, Á., Ortega-Mier, M., & Saldanha-da-Gama, F. (2010). A stochastic bi-objective location model for strategic reverse logistics. Top, 18(1), 158–184.
9
Frandsen, C. S., Nielsen, M. M., Chaudhuri, A., Jayaram, J., & Govindan, K. (2020). In search for classification and selection of spare parts suitable for additive manufacturing: A literature review. International Journal of Production Research, 58(4), 970–996. https://doi.org/10.1080/00207543.2019.1605226
10
González-Varona, J. M., Poza, D., Acebes, F., Villafáñez, F., Pajares, J., & López-Paredes, A. (2020). New Business Models for Sustainable Spare Parts Logistics: A Case Study. Sustainability, 12(8), 3071.
11
Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). Reliable design of an integrated forward-revere logistics network under uncertainty and facility disruptions: A fuzzy possibilistic programing model. KSCE Journal of Civil Engineering, 19(4), 1117–1128.
12
He, X., & Hu, W. (2014). Modeling Relief Demands in an Emergency Supply Chain System under Large-Scale Disasters Based on a Queuing Network. The Scientific World Journal, 2014, 195053. https://doi.org/10.1155/2014/195053
13
Hora, M. E. (1987). The unglamorous game of managing maintenance. Business Horizons, 30(3), 67–75.
14
Jain, S., & Raghavan, N. R. S. (2009). A queuing approach for inventory planning with batch ordering in multi-echelon supply chains. Central European Journal of Operations Research, 17(1), 95–110. https://doi.org/10.1007/s10100-008-0077-8
15
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Karamouzian, A., Teimoury, E., & Modarres, M. (2011). A model for admission control of returned products in a remanufacturing facility using queuing theory. The International Journal of Advanced Manufacturing Technology, 54(1–4), 403–412.
17
Karim, R., & Nakade, K. (2021). An integrated location-inventory model for a spare part’s supply chain considering facility disruption risk and CO2 emission. Journal of Industrial Engineering and Management, 14(2), 87–119.
18
Kim, J., Chung, B. D., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314–1328. https://doi.org/10.1016/j.jclepro.2018.06.157
19
Kosanoglu, F., Turan, H. H., & Atmis, M. (2018). A Simulated Annealing Algorithm for Integrated Decisions on Spare Part Inventories and Cross-Training Policies in Repairable Inventory Systems. Proceedings of International Conference on Computers and Industrial Engineering, 1–14.
20
Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers & Chemical Engineering, 104, 38–55. https://doi.org/10.1016/j.compchemeng.2017.04.005
21
Prasanna Venkatesan, S., & Kumanan, S. (2012). A multi-objective discrete particle swarm optimisation algorithm for supply chain network design. International Journal of Logistics Systems and Management, 11(3), 375–406.
22
Qin, X., Jiang, Z.-Z., Sun, M., Tang, L., & Liu, X. (2021). Repairable spare parts provisioning for multiregional expanding fleets of equipment under performance-based contracting. Omega, 102, 102328.
23
Rabbani, M., Hosseini-Mokhallesun, S. A. A., Ordibazar, A. H., & Farrokhi-Asl, H. (2020). A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design. International Journal of Systems Science: Operations & Logistics, 7(1), 60–75. https://doi.org/10.1080/23302674.2018.1506061
24
Sadeghi, A., Mina, H., & Bahrami, N. (2020). A mixed integer linear programming model for designing a green closed-loop supply chain network considering location-routing problem. International Journal of Logistics Systems and Management, 36(2), 177–198.
25
Sarrafha, K., Kazemi, A., & Alinejad, A. (1394). Designing and optimizing the integrated production-distribution planning problem in a multi-level supply chain network: A multi-objective evolutionary approach. 26(3), 283–298.
26
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27
Talbi, E.-G. (2009). Metaheuristics: From design to implementation (Vol. 74). John Wiley & Sons.
28
Topan, E., & van der Heijden, M. C. (2020). Operational level planning of a multi-item two-echelon spare parts inventory system with reactive and proactive interventions. European Journal of Operational Research, 284(1), 164–175. https://doi.org/10.1016/j.ejor.2019.12.022
29
Tosarkani, B. M., & Amin, S. H. (2019). An environmental optimization model to configure a hybrid forward and reverse supply chain network under uncertainty. Computers & Chemical Engineering, 121, 540–555. https://doi.org/10.1016/j.compchemeng.2018.11.014
30
Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model. Transportation Research Part E: Logistics and Transportation Review, 48(6), 1152–1168. https://doi.org/10.1016/j.tre.2012.06.002
31
Wang, F., & Lin, L. (2021). Spare parts supply chain network modeling based on a novel scale-free network and replenishment path optimization with Q learning. Computers & Industrial Engineering, 157, 107312.
32
Wilson, W. (2020). What’s the real cost of spare parts inventory? Resource Library. https://www.lce.com/Whats-the-real-cost-of-spare-parts-inventory-1189.html
33
Zhao, Y., Shi, Y., & Karimi, H. R. (2012). Entry-item-quantity-ABC analysis-based multitype cigarette fast sorting system. Mathematical Problems in Engineering, 2012.
34
ORIGINAL_ARTICLE
The selection of healthcare waste treatment technologies by a multi-criteria group decision-making method with intuitionistic fuzzy sets
Nowadays, healthcare waste (HCW) management has been received attention by increasing the rate of the population and the usage of services. Meanwhile, one of the significant challenges is to select the appropriate treatment technology for decision-makers (DMs) in the HCW industry. In this respect, this paper proposes a new multi-criteria decision-making (MCDM) approach to compute criteria weights, DMs' weights, and alternative ranking methods for assessing and selecting the best HCW treatment technology from various stakeholders. The proposed structure deals with uncertain evaluations of alternatives by using intuitionistic fuzzy (IF)’ linguistic variables to show criteria weights and to extend two new weighting and ranking methods to obtain DMs' weight and rank the HCW disposal alternatives based on uncertain conditions. Eventually, an empirical case in Shanghai, China, from the recent literature, is applied to determine the feasibility, validation, and effectiveness of the proposed model. Results demonstrate that the introduced model is proper and efficient to handle the HCW treatment technology selection problem under an uncertain information condition. According to the final comparative results, the first alternative and the first DM have a high preference than others, respectively. Furthermore, the sensitivity analysis determines that the final ranking results are reliable with changing the criteria' weights regarding four various kinds of states.
https://www.jise.ir/article_139146_8db576d558dbae51b97acd7c81f432c1.pdf
2022-03-02
205
220
Healthcare waste management
technology selection
intuitionistic fuzzy sets
weights of decision-makers
Ranking method
Sina
Salimian
snsalimian422.ss@gmail.com
1
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
Seyed Meysam
Mousavi
mousavi.sme@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Adar, T., & Delice, E. K. (2019). New integrated approaches based on MC-HFLTS for healthcare waste treatment technology selection. Journal of Enterprise Information Management.
1
Atanassov, K. (1986). Intuitionistic fuzzy sets. fuzzy sets and systems 20 (1), 87-96.
2
Atanassov, K. T. (1994). New operations defined over the intuitionistic fuzzy sets. Fuzzy sets and Systems, 61(2), 137-142.
3
Caniato, M., Tudor, T., & Vaccari, M. (2015). International governance structures for health-care waste management: A systematic review of scientific literature. Journal of Environmental Management, 153, 93-107.
4
Dorfeshan, Y., Tavakkoli-Moghaddam, R., Mousavi, S.M., & Vahedi-Nouri, B. (2020). A new weighted distance-based approximation methodology for flow shop scheduling group decisions under the interval-valued fuzzy processing time. Applied Soft Computing, 91, 106248.
5
Davoudabadi, R., Mousavi, S.M., & Mohagheghi, V. (2020). A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty. Knowledge and Information Systems, 62(4), 1371-1391.
6
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7
Dorfeshan, Y., & Mousavi, S. M. (2019). A group TOPSIS-COPRAS methodology with Pythagorean fuzzy sets considering weights of experts for project critical path problem. Journal of Intelligent & Fuzzy Systems, 36(2), 1375-1387.
8
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Mishra, A. R., & Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex & Intelligent Systems, 1-16.
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24
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25
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26
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27
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28
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44
ORIGINAL_ARTICLE
A novel approach for solving the fully fuzzy bi-level linear programming problems
Bi-level linear programming (BLP) is a problem with two decision makers and two levels: the Leader in the upper and the Follower in the lower levels. Decision on one level affects the other one. In this respect, finding an optimal solution for BLP problems with inexact parameters and variables (proposed in many real-world applications) is non-convex and very hard to solve regarding its structure. In the present study, Multi-Objective Linear Programming (MOLP) is applied to offer a new approach is proposed to find an optimal fuzzy solution for the BLP problems, in which all parameters and variables have fuzzy nature. The main contribution of this research can be described as follows. First based on lexicographic ordering and using triangular fuzzy numbers, the given fully fuzzy BLP problem is converted into its equivalent multi-objective BLP problem. Then, the lexicographic method is used to solve the obtained model in the previous step. Subsequently, the optimal solution of the multi-objective BLP problem is obtained. However the answer to the main question is given in Theorem 1 if the optimal solution of the multi-objective BLP problem can be considered an optimal solution of the fully fuzzy BLP problem.. Finally, to demonstrate the applicability of the proposed approach, it is run to solve some examples, and its results are compared with one of the existing methods.
https://www.jise.ir/article_139837_29b29d4f5eca9e95ea9ce40f0fd82565.pdf
2022-03-19
221
237
solving approach
fully fuzzy bi-level linear programming
multi-objective linear programming
lexicographic method
Hajar
Aghapour
aghapour@yahoo.com
1
Faculty of Science, Urmia, University of Technology, Urmia, Iran
AUTHOR
Elnaz
Osgooei
e.osgooei@uut.ac.ir
2
Faculty of Science, Urmia, University of Technology, Urmia, Iran
LEAD_AUTHOR
Akbari, F., & Osgooei, E. (2020). Solving linear equation system based on Z-numbers using big-M method. Journal of Industrial and Systems Engineering.
1
Alessa, N. A. (2021). Bi-level linear programming of intuitionistic fuzzy. Fuzzy Systems and their Mathematics, 25, 8635-8641.
2
Allahviranloo, T., Lotfi, F. H., Kiasary, M. K., Kiani, N. A., & Alizadeh, L. (2008). Solving fully fuzzy linear programming problem by the ranking function. Applied Mathematical Sciences, 2(1), 19-32.
3
Allahviranloo, T., Shamsolkotabi, K. H., Kiani, N. A., & Alizadeh, L. (2007). Fuzzy integer linear programming problems. International Journal of Contemporary Mathematical Sciences, 2(4), 167-181.
4
Ayas, S., Dogan, H., Gedikli, E., & Ekinci, M. (2018). A novel approach for Bi-level segmentation of Tuberculosis bacilli based on meta- Heuristic algorithms. Advances in Electrical and Computer Engineering, 18(1), 113-121.
5
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141-B-164.
6
Buckley, J. J., & Feuring, T. (2000). Evolutionary algorithm solution to fuzzy problems: fuzzy linear programming. Fuzzy Sets and Systems, 109(1), 35-53.
7
Campos, L., & Verdegay, J. L. (1989). Linear programming problems and ranking of fuzzy numbers. Fuzzy Sets and Systems, 32(1), 1-11.
8
Chalmardi, M. K., & Camacho-Vallejo, J. F. (2019). A bi-level programming model for sustainable supply chain network design that considers incentives for using cleaner technologies. Journal of Cleaner Production, 213, 1035-1050.
9
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12
Ebrahimnejad, A., & Nasseri, S. H. (2009). Using complementary slackness property to solve linear programming with fuzzy parameters. Fuzzy Information and Engineering, 1(3), 233-245.
13
Einaddin, A. H, & Yazdankhah, A. S. (2020). A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework. International Journal of Electrical Power & Energy Systems, 117, 105617.
14
Elsisy, M. A., El Sayed, M. A., & Abo-Elnaga, Y. (2021). A novel algorithm for generating pareto frontier of bi-level multi-objective rough nonlinear programming problem. Ain Shams Engineering Journal, 12(2), 2125-2133.
15
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17
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18
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19
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20
Jafarzadeh-Ghoushchi, S. (2018). Qualitative and quantitative analysis of Green Supply Chain Management (GSCM) literature from 2000 to 2015. International Journal of Supply Chain Management, 7(1), 77-86.
21
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39
ORIGINAL_ARTICLE
Sustainable fuel supply network design by integrating gas oil and biodiesel supply chains under uncertainty
In recent years, research has shown that biomass as an alternative energy source for fossil fuels can be effective in decreasing recent environmental crises. Next, the researchers examined how biofuels are produced through the oil supply chain infrastructure and came up with useful results. This paper is the first study to present the decisions of both chains simultaneously through a mathematical optimization model for the gas oil and biodiesel supply chains. The model proposed in this paper determines the connection point of two chains and other decisions related to network design with a sustainable development approach. The method used in this paper for solving the multi-objective model is the augmented epsilon constraint method. Also, to consider the uncertainty in export demand, the two-stage scenario-based stochastic programming method has been used. Finally, the performance of the mathematical programming model has been investigated through a case study in Iran, and its sensitivity analyzes have been performed.
https://www.jise.ir/article_141965_ff2b27e7269ca9eccc82c11d6c2958d2.pdf
2022-03-19
238
262
Gas-oil supply chain
bioenergy supply chain
optimization
Sustainability
Uncertainty
Seyed Meysam
Rafie
rafieiseyedmeisam@gmail.com
1
School of Industrial Engineering,Iran University of Science and Technology, Tehran, Iran
AUTHOR
Hadi
Sahebi
hadi_sahebi@iust.ac.ir
2
School of Industrial Engineering,Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Achten, W. M., Verchot, L., Franken, Y. J., Mathijs, E., Singh, V. P., Aerts, R., & Muys, B. (2008). Jatropha bio-diesel production and use. Biomass and bioenergy, 32(12), 1063-1084.
1
Akbarian Saravi, N., Yazdanparast, R., Momeni, O., Heydarian, D., & Jolai, F. (2018). Location optimization of agricultural residues-based biomass plant using Z-number DEA. Journal of Industrial and Systems Engineering, 12(1), 39-65.
2
Atabani, A. E., Badruddin, I. A., Badarudin, A., Khayoon, M. S., & Triwahyono, S. (2014). Recent scenario and technologies to utilize non-edible oils for biodiesel production. Renewable and Sustainable Energy Reviews, 37, 840-851.
3
Babazadeh, R., Ghaderi, H., & Pishvaee, M. S. (2019). A benders-local branching algorithm for second-generation biodiesel supply chain network design under epistemic uncertainty. Computers & Chemical Engineering, 124, 364-380.
4
Babazadeh, R. (2017). Optimal design and planning of biodiesel supply chain considering non-edible feedstock. Renewable and Sustainable Energy Reviews, 75, 1089-1100.
5
Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega, 66, 258-277.
6
Bairamzadeh, S., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach. Renewable energy, 116, 500-517.
7
Ezzati, F., Babazadeh, R., & Donyavi, A. (2018). Optimization of multimodal, multi-period and complex biodiesel supply chain systems: Case study. Renewable Energy Focus, 26, 81-92.
8
Farahani, M., & Rahmani, D. (2017). Production and distribution planning in petroleum supply chains regarding the impacts of gas injection and swap. Energy, 141, 991-1003.
9
Fattahi, M., & Govindan, K. (2018). A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study. Transportation Research Part E: Logistics and Transportation Review, 118, 534-567.
10
Fernandes, L. J., Relvas, S., & Barbosa-Póvoa, A. P. (2013). Strategic network design of downstream petroleum supply chains: single versus multi-entity participation. Chemical engineering research and design, 91(8), 1557-1587.
11
Ghadami, M., Sahebi, H., Pishvaee, M., & Gilani, H. (2021). A sustainable cross-efficiency DEA model for international MSW-to-biofuel supply chain design. RAIRO: Recherche Opérationnelle, 55, 2653.
12
Ghaderi, H., Moini, A., & Pishvaee, M. S. (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. Journal of cleaner production, 179, 368-406.
13
Ghani, N. M. A. M. A., Vogiatzis, C., & Szmerekovsky, J. (2018). Biomass feedstock supply chain network design with biomass conversion incentives. Energy Policy, 116, 39-49.
14
Ghelichi, Z., Saidi-Mehrabad, M., & Pishvaee, M. S. (2018). A stochastic programming approach toward optimal design and planning of an integrated green biodiesel supply chain network under uncertainty: A case study. Energy, 156, 661-687.
15
Ghezavati, V. R., Ghaffarpour, M. H., & Salimian, M. (2015). A hierarchical approach for designing the downstream segment for a supply chain of petroleum production systems. Journal of Industrial and Systems Engineering, 8(4), 1-17.
16
Gupta, V., & Grossmann, I. E. (2014). Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties. Journal of Petroleum Science and Engineering, 124, 180-197.
17
Habib, M. S., Asghar, O., Hussain, A., Imran, M., Mughal, M. P., & Sarkar, B. (2021). A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. Journal of Cleaner Production, 278, 122403.
18
Huber, G. W., & Corma, A. (2007). Synergies between bio‐and oil refineries for the production of fuels from biomass. Angewandte Chemie International Edition, 46(38), 7184-7201.
19
Kang, S., Heo, S., Realff, M. J., & Lee, J. H. (2020). Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system. Applied Energy, 265, 114773.
20
Kazemi, Y., & Szmerekovsky, J. (2015). Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning. Transportation Research Part E: Logistics and Transportation Review, 83, 111-125.
21
Leiras, A., Ribas, G., Hamacher, S., & Elkamel, A. (2013). Tactical and operational planning of multirefinery networks under uncertainty: an iterative integration approach. Industrial & Engineering Chemistry Research, 52(25), 8507-8517.
22
Lima, C., Relvas, S., & Barbosa-Póvoa, A. (2021). Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach. Computers & Chemical Engineering, 151, 107373.
23
Lima, C., Relvas, S., & Barbosa-Póvoa, A. (2018). Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain. Computers & Chemical Engineering, 108, 314-336.
24
Lin, T., Rodríguez, L. F., Shastri, Y. N., Hansen, A. C., & Ting, K. C. (2014). Integrated strategic and tactical biomass–biofuel supply chain optimization. Bioresource technology, 156, 256-266.
25
Mahjoub, N., Sahebi, H., Mazdeh, M., & Teymouri, A. (2020). Optimal design of the second and third generation biofuel supply network by a multi-objective model. Journal of Cleaner Production, 256, 120355.
26
Mohtashami, Z., Bozorgi-Amiri, A., & Tavakkoli-Moghaddam, R. (2021). A two-stage multi-objective second generation biodiesel supply chain design considering social sustainability: A case study. Energy, 233, 121020.
27
Mousavi Ahranjani, P., Ghaderi, S. F., Azadeh, A., & Babazadeh, R. (2018). Hybrid multiobjective robust possibilistic programming approach to a sustainable bioethanol supply chain network design. Industrial & Engineering Chemistry Research, 57(44), 15066-15083.
28
Nasab, N. M., & Amin-Naseri, M. R. (2016). Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain. Energy, 114, 708-733.
29
Oliveira, F., Grossmann, I. E., & Hamacher, S. (2014). Accelerating Benders stochastic decomposition for the optimization under uncertainty of the petroleum product supply chain. Computers & Operations Research, 49, 47-58.
30
Öztürkoğlu, Ö., & Lawal, O. (2016). The integrated network model of pipeline, sea and road distribution of petroleum product. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 6(2), 151-165.
31
Peri, M., & Baldi, L. (2013). The effect of biofuel policies on feedstock market: Empirical evidence for rapeseed oil prices in EU. Resource and Energy Economics, 35(1), 18-37.
32
Rabani, M., Moazam, E., Akbarian Saravi, N., & Farrokhi-Asl, H. (2020). A mathematical model for a hybrid first/second generation of biodiesel supply chain design with limited and reliable multimodal transport. Journal of Industrial and Systems Engineering, 13(2), 105-120.
33
Razm, S., Nickel, S., Saidi-Mehrabad, M., & Sahebi, H. (2019). A global bioenergy supply network redesign through integrating transfer pricing under uncertain condition. Journal of Cleaner Production, 208, 1081-1095.
34
Sahebi, H., Nickel, S., & Ashayeri, J. (2014). Environmentally conscious design of upstream crude oil supply chain. Industrial & Engineering Chemistry Research, 53(28), 11501-11511.
35
Tong, K., Gleeson, M. J., Rong, G., & You, F. (2014). Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty. biomass and bioenergy, 60, 108-120.
36
Tong, K., Gong, J., Yue, D., & You, F. (2014). Stochastic programming approach to optimal design and operations of integrated hydrocarbon biofuel and petroleum supply chains. ACS Sustainable Chemistry & Engineering, 2(1), 49-61.
37
Tong, K., You, F., & Rong, G. (2014). Robust design and operations of hydrocarbon biofuel supply chain integrating with existing petroleum refineries considering unit cost objective. Computers & Chemical Engineering, 68, 128-139.
38
Wang, B., Van Fan, Y., Chin, H. H., Klemeš, J. J., & Liang, Y. (2020). Emission-cost nexus optimisation and performance analysis of downstream oil supply chains. Journal of Cleaner Production, 266, 121831.
39
Ward, H., Radebach, A., Vierhaus, I., Fügenschuh, A., & Steckel, J. C. (2017). Reducing global CO2 emissions with the technologies we have. Resource and Energy Economics, 49, 201-217.
40
Xie, F., Huang, Y., & Eksioglu, S. (2014). Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California. Bioresource technology, 152, 15-23.
41
Zhang, Y., & Jiang, Y. (2017). Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty. Waste Management, 60, 329-339.
42
Zhou, X., Zhang, H., Xin, S., Yan, Y., Long, Y., Yuan, M., & Liang, Y. (2020). Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines. Journal of Cleaner Production, 244, 118866.
43
ORIGINAL_ARTICLE
A supply chain coordination model using buyback contract considering effort dependent demand
This paper deals with the coordination of a two-stage supply chain, including a supplier and a retailer. The final demand is sensitive to the sales promotion and the quality improvement done by the retailer and the supplier, respectively. In the standard newsvendor setting, a buyback contract integrates the decentralized system where both members try to optimize their own profit. We showed that a buyback contract could not thoroughly coordinate the supply chain even though it enhances the whole supply chain profit. Therefore, in this research, we extended a new contract based on a buyback contract with which both members share the costs of efforts. The results showed that this contract can coordinate the channel and provide a win-win condition for supply chain components. The numerical example is used to indicate the results and obtain more insights. The optimal sales and quality efforts and the optimal order level are also determined, resulting in the optimal supply chain profit. Sensitivity analyses are performed in order to investigate the effects of different parameters on decision variables and profit. The results showed that the supply chain performance decreases by incrementing the cost coefficients of sales effort and quality efforts.
https://www.jise.ir/article_141967_c3eeedb52a06a20e0f536aa85d7138f6.pdf
2022-03-21
263
278
Buyback contract
supply chain coordination
sales effort
quality improvement effort
Mohammad Bagher
Fakhrzad
mfakhrzad@yazd.ac.ir
1
Department of industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Abolfazl
Dehghan
abolfazldehghan@stu.yazd.ac.ir
2
Department of industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
Abbasali
Jafari-Nodoushan
a.jafari@meybod.ac.ir
3
Department of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran
AUTHOR
Cachon, G.P. (2003). Supply chain coordination with contracts. Handbooks in operations research and management science, 11, 227-339.
1
Cachon, G.P., & Lariviere, M.A. (2005). Supply chain coordination with revenue-sharing contracts: strengths and limitations. Management science, 51(1), 30-44.
2
Cao, X., Qin, Y., & Lu, R. (2009). Quantity flexibility contract with effort cost sharing in perishable product's supply chain. 2009 Second International Symposium on Knowledge Acquisition and Modeling, IEEE, 312-315.
3
Duc, T.T.H., Loi, N.T., Buddhakulsomsiri, J. (2018). Buyback contract in a risk-averse supply chain with a return policy and price dependent demand. International Journal of Logistics Systems and Management, 30(3), 298-329.
4
Ebrahimi, S., Hosseini-Motlagh, S.M., Nematollahi, M. (2019). Proposing a delay in payment contract for coordinating a two-echelon periodic review supply chain with stochastic promotional effort dependent demand. International Journal of Machine Learning and Cybernetics, 10, 1037-1050.
5
Ebrahimzadeh-Afruzi, M., Aliahmadi, A. (2020). A credit mechanism in coordinating quality level, pricing and replenishment decisions with deteriorating items. Journal of Industrial and Systems Engineering, 13(1), 76-91.
6
Ghosh, P.K., Manna, A.K., Dey, J.K., Kar, S. (2021). Supply chain coordination model for green product with different payment strategies: A game theoretic approach. Journal of Cleaner Production, 290, 125734.
7
Gurnani, H., & Erkoc, M. (2008). Supply contracts in manufacturer‐retailer interactions with manufacturer‐quality and retailer effort‐induced demand. Naval Research Logistics (NRL), 55(3), 200-217.
8
Heydari, J., Govindan, K., et al. (2021). Balancing price and green quality in presence of consumer environmental awareness: a green supply chain coordination approach. International Journal of Production Research, 59(7), 1957-1975.
9
Hosseini-Motlagh, S.M., Johari, M., Ebrahimi, S., Rogetzer, P. (2020). Competitive channels coordination in a closed-loop supply chain based on energy-saving effort and cost-tariff contract. Computers & Industrial Engineering, 149, 106763.
10
Hosseini-Motlagh, S.M., Nouri, M., Pazari, p. (2018). Coordination of promotional effort, corporate social responsibility and periodic review replenishment decisions in a two-echelon socially responsible supply chain. Journal of Industrial and Systems Engineering, 11(3), 60-83.
11
Jian, J., Li, B., Zhang, N., Su, J. (2021). Decision-making and coordination of green closed-loop supply chain with fairness concern. Journal of Cleaner Production, 298, 126779.
12
Jiang, G., & Liu, J. (2014). Research on the supply chain coordination of the buyback contract based on sales effort. Lecture Notes in Electrical Engineering, 242, 827-836.
13
Li, Q., & Liu, Z. (2015). Supply chain coordination via a two-part tariff contract with price and sales effort dependent demand. Decision Science Letters, 4(1), 27-34.
14
Ma, P., Wang, H., Shang, J. (2013). Contract design for two-stage supply chain coordination: Integrating manufacturer-quality and retailer-marketing efforts. International Journal of Production Economics, 146(2), 745-755.
15
Nikkhoo, F., Bozorgi-Amiri, A. (2018). A Procurement-distribution Coordination Model in Humanitarian Supply Chain Using the Information-sharing Mechanism. International Journal of Engineering(IJE), 31(7), 1057-1065
16
Pang, Q., Chen, Y., Hu, Y. (2014). Coordinating three-level supply chain by revenue-sharing contract with sales effort dependent demand. Discrete Dynamics in Nature and Society, 2014(1), 1-10.
17
Ranjan, A. & Jha, J. (2019). Pricing and coordination strategies of a dual-channel supply chain considering green quality and sales effort. Journal of cleaner production, 218, 409-424.
18
Setak, M., Kafshian Ahar, H., Alaei, S. (2017). Coordination of Information Sharing and Cooperative Advertising in a Decentralized Supply Chain with Competing Retailers Considering Free Riding Behavior. Journal of Industrial and Systems Engineering, 10(2), 151-168.
19
Taylor, T.A. (2002). Supply chain coordination under channel rebates with sales effort effects. Management science, 48(8), 992-1007.
20
Tian, Y., Ma, J., Xie, L., Koivumäki, T., Seppänen, V. (2020). Coordination and control of multi-channel supply chain driven by consumers’ channel preference and sales effort. Chaos, Solitons & Fractals, 132, 109576.
21
Wang, X., Liu, Z., Chen, H. (2019). A composite contract for coordinating a supply chain with sales effort-dependent fuzzy demand. International Journal of Machine Learning and Cybernetics, 10(5), 949-965.
22
Wang, Z. & Liu, S. (2018). Supply chain coordination under trade credit and quantity discount with sales effort effects. Mathematical Problems in Engineering, 2018(4), 1-15.
23
Zhang, X.M., Li, Y.Y., et al. (2021). Coordination Contracts of Dual-Channel Supply Chain Considering Advertising Cooperation. International Journal of Information Systems and Supply Chain Management (IJISSCM), 14(1), 55-89.
24
ORIGINAL_ARTICLE
Key performance indicators of HSE in the hospital management system during corona virus pandemic
Healthcare is considered as one of the most important issues of today's societies. In recent years, healthcare economy has found a special status worldwide. Meanwhile, hospitals as the important arm of providing healthcare services and the first level of referral for healthcare services, with their specific areas and responsibilities, are considered the most important healthcare Institute in any country. This paper examines key performance indicators (KPIs) of HSE in the hospital management system through creating a strategic agenda and set of strategic decisions during corona virus pandemic. Using the available multiple decision-making tools based on the criteria of interest to patients, the research first deals with selecting the effective indicators in assessing the HSE management system of the hospital management by experts through a bank of collected indicators. It then ranks the KPIs of HSE using fuzzy TOPSIS method. The results indicated that TOPSIS algorithm is one of the most reliable, scientific, and managerial methods for decision-making. Also, based on the results, the most important factor in the HSE performance in the hospital management system was determined as the Absenteeism from work due to illness indicator.
https://www.jise.ir/article_141973_6665c8d5359a8dd4b6a2cc4720329f61.pdf
2021-01-01
279
291
Key Performance Indicators
Health
safety and environment
hospital management system
Seyed Farid
Mousavi
mousavifarid@khu.ac.ir
1
Department of Information Technology and Operations Management, Kharazmi University, Tehran, Iran
AUTHOR
Arash
Apornak
arash.apornak@ut.ac.ir
2
Department of Industrial Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mohammad Reza
Pourhassan
st_m_poorhassan@azad.ac.ir
3
Department of Industrial Engineering - Ershad University Of Damavand – Tehran – Iran
AUTHOR
Sadigh
Raissi
raissi@azad.ac.ir
4
Department of industrial engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Ahmadi Choukolaei, H., Jahangoshai Rezaee, M., Ghasemi, P., & Saberi, M. (2021). Efficient crisis management by selection and analysis of relief centers in disaster integrating GIS and multicriteria decision methods: a case study of Tehran. Mathematical Problems in Engineering, 2021.
1
Ahmadizadeh-Tourzani, N., Keramati, A., & Apornak, A. (2018). Supplier selection model using QFD-ANP methodology under fuzzy multi-criteria environment. International Journal of Productivity and Quality Management, 24(1), 59-83.
2
Apornak, A. (2021). Human resources allocation in the hospital emergency department during COVID-19 pandemic. International Journal of Healthcare Management, 14(1), 264-270.
3
Apornak, A., Raissi, S., Keramati, A., & Khalili-Damghani, K. (2020). Human resources optimization in hospital emergency using the genetic algorithm approach. International Journal of Healthcare Management, 1-8.
4
Apornak, A., Raissi, S., Keramati, A., & Khalili-Damghani, K. (2020). Optimisation nursing employees in a hospital emergency department by using linear programming. International Journal of Management Concepts and Philosophy, 13(3), 184-195.
5
Babaeinesami, A., & Ghasemi, P. (2021). Ranking of hospitals: A new approach comparing organizational learning criteria. International Journal of Healthcare Management, 14(4), 1031-1039.
6
Bielicki, J. A., Duval, X., Gobat, N., Goossens, H., Koopmans, M., Tacconelli, E., & van der Werf, S. (2020). Monitoring approaches for health-care workers during the COVID-19 pandemic. The Lancet Infectious Diseases.
7
Bisbe, Josep, and Ricardo Malagueño. "Using strategic performance measurement systems for strategy formulation: Does it work in dynamic environments?" Management Accounting Research 23.4 (2012): 296-311.
8
Chandra, D., & Kumar, D. (2021). Evaluating the effect of key performance indicators of vaccine supply chain on sustainable development of mission indradhanush: A structural equation modeling approach. Omega, 101, 102258.
9
Dabliz, R., Poon, S. K., Ritchie, A., Burke, R., & Penm, J. (2021). Usability evaluation of an integrated electronic medication management system implemented in an oncology setting using the unified theory of the acceptance and use of technology. BMC Medical Informatics and Decision Making, 21(1), 1-11.
10
Ding, B. (2018). Pharma industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains. Process Safety and Environmental Protection, 119, 115-130.
11
Ghasemi, P., Mehdiabadi, A., Spulbar, C., & Birau, R. (2021). Ranking of Sustainable Medical Tourism Destinations in Iran: An Integrated Approach Using Fuzzy SWARA-PROMETHEE. Sustainability, 13(2), 683.
12
Ghasemi, P., & Babaeinesami, A. (2020). Simulation of fire stations resources considering the downtime of machines: A case study. Journal of Industrial Engineering and Management Studies, 7(1), 161-176.
13
Ghasemi, P., & Babaeinesami, A. (2019). Estimation of relief supplies demands through fuzzy inference system in earthquake condition. Journal of Industrial and Systems Engineering, 12(3), 154-165.
14
Ghasemi, P., & Talebi Brijani, E. (2014). An integrated FAHP-PROMETHEE approach for selecting the best flexible manufacturing system. European Online Journal of Natural and Social Sciences, 3(4), pp-1137.
15
Kang, J., Zhang, J., & Gao, J. (2016). Improving performance evaluation of health, safety and environment management system by combining fuzzy cognitive maps and relative degree analysis. Safety science, 87, 92-100.
16
Kejriwal, M., Shen, K., Ni, C. C., & Torzec, N. (2021). An evaluation and annotation methodology for product category matching in e-commerce. Computers in Industry, 131, 103497.
17
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18
Khalili-Damghani, K., Tavana, M., & Ghasemi, P. (2021). A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, 1-39.
19
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20
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Moktadir, M. A., Dwivedi, A., Rahman, A., Chiappetta Jabbour, C. J., Paul, S. K., Sultana, R., & Madaan, J. (2020). An investigation of key performance indicators for operational excellence towards sustainability in the leather products industry. Business Strategy and the Environment, 29(8), 3331-3351.
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36
ORIGINAL_ARTICLE
Truncated life testing under resubmitted sampling plans for Weibull distribution
In a truncated life testing, the test of each item is terminated at a predetermined time which is usually a coefficient of mean, median or other percentiles of lifetime. Life testing and acceptance sampling plans are two major fields of reliability theory and statistical quality control. In a reliability acceptance sampling (RAS) plan the quality characteristic of interest is lifetime. Thus, in designing RAS plans, two subjects of life testing and acceptance sampling plans should be taken into consideration. In this paper, one type of sampling plans, which is known as resubmitted sampling (RS) plans, is proposed for truncated life testing. The items are considered Weibull distributed with a known shape parameter. To obtain the operating characteristic (OC) curve of the RS plan, an equation is derived and to optimize the value of average sample number (ASN), three models are proposed: (I) minimizing ASN in acceptable quality level (AQL), (II) minimizing ASN in limiting quality level (LQL) and (III) minimizing ASN based on the both AQL and LQL. In optimizing the models, the constraints related to the consumer’s and producer’s risks are taken into consideration. Finally, numerical examples and sensitivity analyses are conducted. According to the results of comparison of RS and single sampling (SS) plans, it cannot be concluded that one scheme monotonically outperforms the other. Moreover, from the aspect of OC curve, the acceptance probability of a given lot under the RS plan is slightly larger than the corresponding value in the SS plans.
https://www.jise.ir/article_142948_f2eed42a7c7bf862a0b0d770cbc22de8.pdf
2022-03-23
292
306
Life testing
Lifetime
Reliability
Weibull distribution
Acceptance Sampling Plan
Hiwa
Farughi
h.farughi@uok.ac.ir
1
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Hasan
Rasay
hasan.rasay@gmail.com
2
Department of Industrial Engineering, Kermanshah University of Technology, Kermanshah, Iran
AUTHOR
Faeze
Advay
fa.advay.4@gmail.com
3
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Al-Omari, A., Al-Nasser, A., & Ciavolino, E. (2019). Acceptance sampling plans based on truncated life tests for Rama distribution. International Journal of Quality & Reliability Management.
1
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2
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3
Aslam, M., Azam, M., & Jun, C. H. (2013). A mixed repetitive sampling plan based on process capability index. Applied Mathematical Modelling, 37(24), 10027-10035.
4
Aslam, M., & Jun, C. H. (2010). A double acceptance sampling plan for generalized log-logistic distributions with known shape parameters. Journal of Applied Statistics, 37(3), 405-414.
5
Aslam, M., & Jun, C. H. (2009a). A group acceptance sampling plan for truncated life test having Weibull distribution. Journal of Applied Statistics, 36(9), 1021-1027.
6
Aslam, M., Jun, C. H., & Ahmad, M. (2009b). A double acceptance sampling plan based on the truncated life tests in the Weibull model. Journal of Statistical Theory and Applications, 8(2), 191-206.
7
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8
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9
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10
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11
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14
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15
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16
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17
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18
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19
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20
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21
ORIGINAL_ARTICLE
Impact of fuel prices on electricity price using the predictive power of ANN-GA, LRM: Evidence from Iran
In Iran, the energy price is very much influenced by the dollar price. However, this price is highly fluctuated due to various reasons. The emergence of the pandemic, the covid-19, from one part and the financial sanctions on the economy from another, cause the high volatility on this foreign currency. First, in this study, we converted the IRR (Iranian currency) into the same dollar rate of the year, contributing to the impact of exchange rate volatility in the model. Then, we forecast the price of all three principal fuels that affect the cost of electricity production, and then we forecast the electricity prices using ANN_GA and the historical data. This study also examines the fundamental and exacerbating causes in recent years, especially in 2018 when we faced an unprecedented increase in dollar prices in the Iranian market when the U.S. withdrew from the joint comprehensive plan of action (JCPA), and its effects are still visible. We intend to investigate the impact of these fluctuations on the future electricity market. In the end, we examine that which variables (fuel prices) would affect electricity prices the most using a linear regression model.
https://www.jise.ir/article_145393_7c6f92a0f59198232eded5c433de2d3e.pdf
2022-03-24
307
319
Forecasting
Fuel Prices
ANN
GA
Energy Prices
Linear Regression Model
Alireza
Rokhsari
ali96@aut.ac.ir
1
MS.c of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Akbar
Esfahanipour
esfaha@aut.ac.ir
2
Department of Industrial engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Hassan
Tanha
hasan.tanha@vu.edu.au
3
Business School, Victoria University, Australia
AUTHOR
Mehrzad
Saremi
mehrzad.saremy@aut.ac.ir
4
MS.c of Artificial intelligence Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
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25