ORIGINAL_ARTICLE
Tackling uncertainty in safety risk analysis in process systems: The case of gas pressure reduction stations
Industrial plants are subjected to very dangerous events. Therefore, it is very essential to carry out an efficient risk and safety analysis. In classical applications, risk analysis treats event probabilities as certain data, while there is much penurious knowledge and uncertainty in generic failure data that will lead to biased and inconsistent alternative estimates. Then, in order to achieve a better fitting with systems condition, uncertainty needs to be considered. One of the most usual analytical methods that have been widely applied in the field of risk analysis is the technic of failure mode and effects analysis (FMEA). The usage of this method is in identifying and abolishing the multiple failure modes in various phases of system, from the product design to production of the industries system operation. To solve the shortcomings in the traditional FMEA method, we propose an innovative approach consisted of Dempster Shafer evidence theory (DST) and FMEA to provide a more efficient way for failure mode identification and prioritization. The proposed methodology in this study can well capture imprecise opinions and can prioritize failure modes considering uncertainties. City Gate Station (CGS) of Yazd Province was used to prove the practical application and validity of the proposed risk analysis methodology. Results showed that the proposed method is effective and practical for real engineering purposes.
https://www.jise.ir/article_111692_78fefe4c8d0d7c2243f75c620fa39863.pdf
2020-02-01
1
15
Risk Analysis
Failure mode and effect analysis (FMEA)
Uncertainty
Dempster Shafer evidence theory (DST)
City Gate Station (CGS)
Batool
Rafiee
batoolrafiee@stu.yazd.ac.ir
1
Department of industrial engineering, Yazd University, Yazd, Iran
AUTHOR
Davood
Shishehbori
shishebori@yazd.az.ir
2
Department of industrial engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Hassan
Hosseini Nasab
hhn@yazd.ac.ir
3
Department of industrial engineering, Yazd University, Yazd, Iran
AUTHOR
Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
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Colli, M., Sala, R., Pirola, F., Pinto, R., Cavalieri, S., & Wæhrens, B. V. (2019). Implementing a dynamic FMECA in the digital transformation era. IFAC-PapersOnLine, 52(13), 755-760.
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Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67.
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Shishebori, D., & Zeinal Hamadani, A. (2010). The effect of gauge measurement capability and dependency measure of process variables on the MCp. Journal of Industrial and Systems Engineering, 4(1), 59-76.
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Wang, Z., Gao, J. M., Wang, R. X., Chen, K., Gao, Z. Y., & Jiang, Y. (2018). Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: Application to the gas insulated metal enclosed transmission line (GIL). Applied Soft Computing, 70, 633-647.
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Yager, R. R. (1987). On the Dempster-Shafer framework and new combination rules. Information sciences, 41(2), 93-137.
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33
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34
ORIGINAL_ARTICLE
Computing optimal subsidies for Iranian renewable energy investments using real options
For the valuation of the renewable energy investments, providing private investors with a financial incentive to accelerate their investment is a very significant issue. Financial subsidies are known by the majority of the people to be one of the most important drivers in renewable energy expansion and one of the main reasons which result in the development of any industry. In this paper, we present a new approach to compute the optimal subsidies over a specific time period by using the Binomial model for the Valuation of Real Options for Iranian renewable energy investments adjusted with Tax rate. We also apply linear regression method for predicting energy prices in order to allow an investor to exercise the relevant option over the timeline of the project at the optimal price. To evaluate our proposed approach, we apply it using predicted electricity prices for the next 16 years and electricity generation cost for Seid Abad, Damghan solar power plant. Our results in comparison of the base paper show that our proposed approach improves the error of subsidy’s computation by 1.57 percent since we used the predicted energy prices rather than the spot price as used before in real options’ valuation.
https://www.jise.ir/article_111693_882106c3440c82cff80ab5c2c2499e6f.pdf
2020-02-01
16
29
Real Options
Subsidy
renewable energy investment
binomial method
Alireza
Rokhsari
ali96@aut.ac.ir
1
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Akbar
Esfahanipour
esfahaa@aut.ac.ir
2
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Morteza
Ardehali
ardehali@aut.ac.ir
3
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Fazelpour, F. Soltani, N. Soltani, S. Rosen, M,A. (2015). Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil, Renew. Sustain. Energy Rev., vol. 45, pp. 87–99.
1
Fleten, S,E. Linnerud, K. Molnár, P. Nygaard, M,T. (2016). Green electricity investment timing in practice: Real options or net present value?, Energy, vol. 116, pp. 498–506.
2
Gatzert, N. Kosub, T. (2016). Risks and risk management of renewable energy projects: The case of onshore and offshore wind parks, Renew. Sustain. Energy Rev., vol. 60, pp. 982–998.
3
Insley, M. (2002). A real options approach to the valuation of a forestry investment, J. Environ. Econ. Manage., vol. 44, no. 3, pp. 471–492.
4
Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: a simple least-squares approach. The review of financial studies, 14(1), 113-147.
5
Liu, X. Ronn, E,I. (2018). Using the Binomial Model for the Valuation of Real Options in Computing Optimal Subsidies for Chinese Renewable Energy Investments, Available SSRN 3186274.
6
McDonald, R. Siegel, D. (1986). The value of waiting to invest, Q. J. Econ., vol. 101, no. 4, pp. 707–727.
7
Page, M. (1998). Investment Basics: XXXVIII. Options pricing using a binomial lattice, Invest. Anal. J., vol. 27, no. 48, pp. 67–69.
8
Pringles, R., Olsina, F., & Garcés, F. (2014). Designing regulatory frameworks for merchant transmission investments by real options analysis. Energy Policy, 67, 272-280.
9
“Real Option.” [Online]. Available: https://www.investopedia.com/terms/r/realoption.asp.
10
Ronn, E., & Books, R. (2004). Valuation of Oil Fields as Optimal Exercise of the Extraction Option. Managing Energy Price Risk.
11
Rouder, J,N. Engelhardt,C,R. McCabe,S. Morey,RD. (2016). Model comparison in ANOVA, Psychon. Bull. Rev., vol. 23, no. 6, pp. 1779–1786.
12
Routledge, B. R., Seppi, D. J., & Spatt, C. S. (2001). The spark spread: Cross-commodity equilibrium restrictions and electricity. mimeo, Carnegi Mellon.
13
Schwartz, F. A. L. E. S. (2001). Valuing American Options by Simulation: A Simple Least—Squares.
14
Schwartz, E., & Smith, J. E. (2000). Short-term variations and long-term dynamics in commodity prices. Management Science, 46(7), 893-911.
15
Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. The Journal of finance, 52(3), 923-973.
16
Tofigh, A,A. Abedian, M. (2016). Analysis of energy status in Iran for designing sustainable energy roadmap, Renew. Sustain. Energy Rev., vol. 57, pp. 1296–1306.
17
Yang, M., & Blyth, W. (2007). Modeling investment risks and uncertainties with real options approach. International Energy Agency, 23.
18
Zeng, M. Lu, W. Duan, J,H. Li, N (2012). Study on the cost of solar photovoltaic power generation using double-factors learning curve model, Mod Electr Power, vol. 29, no. 5, pp. 72–76.
19
Zhang, M,M. Zhou, D,Q. Zhou, P. Chen, H,T. (2017). Optimal design of subsidy to stimulate renewable energy investments: The case of China, Renew. Sustain. Energy Rev., vol. 71, pp. 873–883.
20
Zhang, M,M. Zhou, D,Q. Zhou, P. Liu, G,Q. (2016). Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis, Energy Policy, vol. 97, pp. 181–192.
21
Zhang, M. Zhou, D. Zhou, P. (2014). A real option model for renewable energy policy evaluation with application to solar PV power generation in China, Renew. Sustain. Energy Rev., vol. 40, pp. 944–955.
22
Zhao, Z,Y. Chen, Y,L. Chang, R,D. (2016). How to stimulate renewable energy power generation effectively?–China’s incentive approaches and lessons,” Renew. energy, vol. 92, pp. 147–156.
23
Zhou, W. Zhu, B. Chen, D. Zhao, F. Fei, W. (2014). How policy choice affects investment in low-carbon technology: The case of CO2 capture in indirect coal liquefaction in China, Energy, vol. 73, pp. 670–679.
24
Zhu, L. Fan, Y. (2011). A real options–based CCS investment evaluation model: Case study of China’s power generation sector, Appl. Energy, vol. 88, no. 12, pp. 4320–4333.
25
ORIGINAL_ARTICLE
Reliability estimation of Iran's power network
Today, the electricity power system is the most complicated engineering system has ever been made. The integrated power generating stations with power transmission lines has created a network, called complex power network. The reliability estimation of such complex power networks is a very challenging problem, as one cannot find any immediate solution methods in current literature. In this paper, we advanced a new method for estimating the reliability of such networks, which is based on 1) decomposition of the whole network into sub-networks called islands, 2) estimating each island’s reliability in exact form using the network reliability theory, and 3) assembling the islands back together to estimate the whole network reliability, again in exact form. We applied the new method on Iran’s power network with 105 generation stations and 16460 kilometres of transmission lines.
https://www.jise.ir/article_111694_203f414035a1270a89805f13d5a366e2.pdf
2020-02-01
30
40
Reliability estimation
power network
Network Reliability
Graph theory
Complex systems
Mahdiyeh
Kalaei
m.kalaei@alzahra.ac.ir
1
Industrial Engineering Department, Alzahra University, Tehran, Iran
AUTHOR
mohammad ali
Saniee monfared
mas_monfared@alzahra.ac.ir
2
Industrial Engineering Department, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Alipour Z., Saniee Monfared M.A., and Zio E. (2014), “Comparing topological and reliability-based vulnerability analysis of Iran power transmission network, Proceedings of the Institution of Mechanical Engineers”, Part O: Journal of Risk and Reliability, 228, 139-151.
1
Alipour Z (2010), “Modeling of Iran's Complex Power Network Systems Reliability”, Master Thesis in Farsi, under supervision of Dr. Monfared, M.A.S., Department of Ind. Eng., Alzahra University .
2
Beichelt F, Tittmann P. (2012), “Reliability and Maintenance Networks and Systems”, Taylor and Francis.
3
Boccaletti S., Latora V., Moreno Y., Chavez M. (2006), and Hwang D-U., “Complex networks: structure and dynamics”, Physics Reports 424 , 175-308.
4
Cadini, F.,,Zio E., and Petrescu, C.A. (2010) “Optimal expansion of an existing electrical power transmission network by multi-objective genetic algorithms”, Reliability Engineering and System Safety 95, 173-181.
5
Leslie G. V. (1979), “The complexity of enumeration and reliability problems”, SIAM J. Comput. 8:410.421.
6
Saniee Monfared M.A., Jalili M., Alipour Z. (2014), ‘ Topology and vulnerability of the Iranian power grid’, Physica A: Statistical Mechanics and its Applications, 406, 24-33
7
Saniee Monfared M.A., Alipour Z. (2013), “Structural Properties and Vulnerability of Iranian 400kv Power Transmission Grid: A Complex Systems Approach”, Industrial Engineering & Management 2
8
Solé, R. V. -Casals. M.R, Corominas-Murtra, B. and Valverde S.(2008), “Robustness of the European power grids under intentional attack”, Physical Review E 77.
9
ORIGINAL_ARTICLE
A service decomposition and definition model in cloud manufacturing systems using game theory focusing on cost accounting perspectives
Cloud manufacturing is a new paradigm which has been under study since 2010 and a vast body of research has been conducted on this topic. Among them, service composition problems are of utmost importance. However, most studies only focused on private clouds meaning the objective function is defined for just one component of the supply chain. This paper attempts to consider service composition problem by using the concept of game theory in cloud manufacturing which is the main contribution. This issue is investigated by introducing a bi-level mathematical model with emphasizing on the realization of public clouds, in which the preferences of all stakeholders in the cloud manufacturing system have been taken into consideration. Concretely, the first level is defined based on manufacturer company’s perspective while the second level is a game designed to obtain a feasible solution by making trade-offs among costs and revenues of service providers. Manufacturer tends to optimize the quality of service metrics by producing a package of operations inside the company’s environment or assigning a combination of service providers with considering clustering. Results show the model will be able to enable the trade-off mechanism among the compositions of all stakeholders’ preferences in cloud manufacturing system with focusing on cost accounting.
https://www.jise.ir/article_111695_74d1df1c8e200f0002f4fb5760673501.pdf
2020-02-01
41
51
Cloud manufacturing
service decomposition
game theory
public manufacturing cloud
Nayereh
Keramatnezhad
n.keramatnezhad@ie.sharif.edu
1
Department of Industrial Engineering, Sharif University, Tehran, Iran
AUTHOR
Omid
Fatahi Valilai
o.fatahivalilai@jacobs-university.de
2
Department of Mathematics & Logistics, Jacobs University Bremen gGmbH, Bremen, Germany
LEAD_AUTHOR
Ahmadreza
Jafarikia
ahmadreza.jafarikia@yahoo.com
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Aghamohammadzadeh, E., & Valilai, O. F. (2020). A novel cloud manufacturing service composition platform enabled by Blockchain technology. International Journal of Production Research, 1-19; https://doi.org/10.1080/00207543.00202020.01715507.
1
Assari, M., Delaram, J., & Valilai, O. F. (2018). Mutual manufacturing service selection and routing problem considering customer clustering in Cloud manufacturing. Production & Manufacturing Research, 6(1), 345-363; http://doi.org/310.1080/21693277.21692018.21517056
2
Chen, F., Dou, R., Li, M., & Wu, H. (2016). A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing. Computers & Industrial Engineering, 99, 423-431.
3
Chen, Q. Y., Wu, Z. H., Luo, L. C., & Tong, J. G. (2014). A Game Theoretic Analysis on Cloud Manufacturing Model. Paper presented at the Advanced Materials Research.
4
Delaram, J., & Valilai, O. F. (2018). A Mathematical Model for Task Scheduling in Cloud Manufacturing Systems focusing on Global Logistics. Procedia Manufacturing, 17, 387-394; https://doi.org/310.1016/j.promfg.2018.1010.1061.
5
Delaram, J., & Valilai, O. F. (2018a). An Architectural Solution for Virtual Computer Integrated Manufacturing Systems using ISO Standards. Scientia Iranica, 26(6), 3712-3727; http://dx.doi.org/3710.24200/SCI.22018.20799
6
Delaram, J., & Valilai, O. F. (2018b). An architectural view to computer integrated manufacturing systems based on Axiomatic Design Theory. Computers in Industry, 100, 96-114; https://doi.org/110.1016/j.compind.2018.1004.1009.
7
Delaram, J., & Valilai, O. F. (2017). A Novel Solution for Manufacturing Interoperability Fulfillment using Interoperability Service Providers. Procedia CIRP, 63, 774-779; https://doi.org/710.1016/j.procir.2017.1003.1141. Retrieved from http://www.sciencedirect.com/science/article/pii/S2212827117302925
8
Gutierrez-Garcia, J. O., & Sim, K. M. (2013). Agent-based cloud service composition. Applied intelligence, 38(3), 436-464.
9
Houshmand, M., & Valilai, O. F. (2013). A layered and modular platform to enable distributed CAx collaboration and support product data integration based on STEP standard. International Journal of Computer Integrated Manufacturing, 26(8), 731-750; http://dx.doi.org/710.1080/0951192X.0952013.0766935.
10
Li, F., Zhang, L., Liu, Y., Laili, Y., & Tao, F. (2017). A clustering network-based approach to service composition in cloud manufacturing. International Journal of Computer Integrated Manufacturing, 30(12), 1331-1342.
11
Li, Bo Hu, Zhang, Leven, Wang, S.-L., Chai, X.-D., (2010). Cloud manufacturing: A new service-oriented networked manufacturing model, Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS 16(1):1-7.
12
Liu, Z. Z., Song, C., Chu, D. H., Hou, Z. W., & Peng, W. P. (2017). An approach for multipath cloud manufacturing services dynamic composition. International journal of intelligent systems, 32(4), 371-393.
13
Liu, W., Liu, B., Sun, D., Li, Y., & Ma, G. (2013). Study on multi-task oriented services composition and optimisation with the ‘Multi-Composition for Each Task’pattern in cloud manufacturing systems. International Journal of Computer Integrated Manufacturing, 26(8), 786-805.
14
Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., . . . Payne, T. (2004). OWL-S: Semantic markup for web services. W3C member submission, 22(4).
15
Myerson, R. B. (2013). Game theory: Harvard university press, London
16
Oh, S.-C., Lee, D., & Kumara, S. R. (2008). Effective web service composition in diverse and large-scale service networks. IEEE Transactions on Services Computing, 1(1), 15-32.
17
Osborne, M. J. (2004). An introduction to game theory (Vol. 3): Oxford university press New York.
18
Rao, J., Küngas, P., & Matskin, M. (2006). Composition of semantic web services using linear logic theorem proving. Information Systems, 31(4-5), 340-360.
19
Singh, A., Juneja, D., & Malhotra, M. (2017). A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. Journal of King Saud University-Computer and Information Sciences, 29(1), 19-28.
20
Song, L.-j. (2016). A Two-stage Biform Game model Study between Service Providers in Cloud Manufacturing. Journal of Residuals Science & Technology, 13(5).
21
Stachtiari, E., Mentis, A., & Katsaros, P. (2012). Rigorous analysis of service composability by embedding WS-BPEL into the BIP component framework. Paper presented at the Web Services (ICWS), 2012 IEEE 19th International Conference on.
22
Su, K., Xu, W., & Li, J. (2015). Manufacturing resource allocation method based on non-cooperative game in cloud manufacturing. Jisuanji Jicheng Zhizao Xitong/computer integrated manufacturing systems, CIMS, 21, 2228-2239.
23
Tao, F., LaiLi, Y., Xu, L., & Zhang, L. (2013). FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics, 9(4), 2023-2033.
24
Valilai, O. F., & Houshmand, M. (2013). A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm. Robotics and Computer-Integrated Manufacturing, 29(1), 110-127; http://dx.doi.org/110.1016/j.rcim.2012.1007.1009.
25
Valizadeh, S., Valilai, O. F., Houshmand, M., & Vasegh, Z. (2019). A novel digital dentistry platform based on cloud manufacturing paradigm. International Journal of Computer Integrated Manufacturing, 1-19; http://doi.org/10.1080/0951192X.0952019.1686170.
26
Wang, L., Guo, C., Guo, S., Du, B., Li, X., & Wu, R. (2018). Rescheduling strategy of cloud service based on shuffled frog leading algorithm and Nash equilibrium. The International Journal of Advanced Manufacturing Technology, 94(9-12), 3519-3535.
27
Wang, Y., & Peng, T. (2016). Speculations on the market evolution of cloud manufacturing. Paper presented at the ASME 2016 11th International Manufacturing Science and Engineering Conference.
28
Xiang, F., Hu, Y., Yu, Y., & Wu, H. (2014). QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system. Central European Journal of Operations Research, 22(4), 663-685.
29
Zhang, Y., Wang, J., Liu, S., & Qian, C. (2016). Game Theory Based Real‐Time Shop Floor Scheduling Strategy and Method for Cloud Manufacturing. International journal of intelligent systems, 32(4), 437-463.
30
Zheng, H., Feng, Y., & Tan, J. (2016). A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. The International Journal of Advanced Manufacturing Technology, 84(1-4), 371-379.
31
ORIGINAL_ARTICLE
Valuing flexibility in demand-side response: A real options approach
The investment interests in the electricity industry are transmitted through various mechanisms to other economic activities. This paper considers methods for esteeming the adaptability of demand-side response (DSR) in its capacity to react to future uncertainties. The capacity to evaluate this adaptability is particularly critical for vitality frameworks speculations given their extensive and irreversible capital expenses. The primary result of this exploration is a broad survey of current real options (RO) strategies that elucidate the suppositions and use of RO for basic leadership in engineering applications. The second result is the structure of a probabilistic RO framework and operational model for DSR that evaluates its advantages as a vitality benefit for supporting diverse market price risks. The third result of this work is the improvement of a total, general and viable apparatus for making long haul multi-arranged speculation choices in future power organizes under numerous vulnerabilities.
https://www.jise.ir/article_111696_bba86def9f10ba0a12825a240b0d7d6b.pdf
2020-02-01
52
65
electricity
Investment
Uncertainty
real options analysis
demand-side response
Abdollah
Arasteh
arasteh@nit.ac.ir
1
Industrial Engineering Department, Babol Noshirvani University of Technology, Babol, Iran
LEAD_AUTHOR
Agnetis, A., Dellino, G., De Pascale, G., Innocenti, G., Pranzo, M., & Vicino, A. (2011, October). Optimization models for consumer flexibility aggregation in smart grids: The ADDRESS approach. In 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS) (pp. 96-101). IEEE.
1
Alstad, R. M., & Foss, J. T. (2003). Real option analysis of gas fired power plants. NTNU, Norwegian University of Science and Technology, Department of Industrial Economics and Technology Management.
2
Anderson, E. J., & Philpott, A. B. (2002). Using supply functions for offering generation into an electricity market. Operations research, 50(3), 477-489.
3
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of political economy, 81(3), 637-654.
4
Boyle, P. P. (1977). Options: A monte carlo approach. Journal of financial economics, 4(3), 323-338.
5
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Chorn, L. G., & Shokhor, S. (2006). Real options for risk management in petroleum development investments. Energy Economics, 28(4), 489-505.
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de Moraes Marreco, J., & Carpio, L. G. T. (2006). Flexibility valuation in the Brazilian power system: A real options approach. Energy Policy, 34(18), 3749-3756.
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Fleten, S. E., Maribu, K. M., & Wangensteen, I. (2007). Optimal investment strategies in decentralized renewable power generation under uncertainty. Energy, 32(5), 803-815.
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32
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52
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53
ORIGINAL_ARTICLE
Assembly line balancing problem with skilled and unskilled workers: The advantages of considering multi-manned workstations
This paper address a special class of generalized assembly line balancing in which it is assumed that there are two groups of workers: skilled and unskilled ones. The skilled workers are hired permanently while the unskilled ones can be hired temporarily in order to meet the seasonal demands. It is also assumed that more than one worker may be assigned to each workstation. To show the advantages of assigning several workers instead of single workers to each workstation in such a class of problem, a mixed integer programming formulation is presented. This model minimizes the number of temporary workers on the line as the first objective and the number of workstations as the secondary one while cycle time and the number of permanent workers are fixed. The proposed formulation is applied to solve some experimental instances found in the literature. The comparison between the optimal solutions of the proposed model and those of traditional assembly lines with a single-manned workstation indicates that our model has been able to reduce the line length on average of 24.40 per cent while the number of unskilled workers remains optimal.
https://www.jise.ir/article_111697_64efe2ac6ed843a070ca77cc85ab6826.pdf
2020-02-01
66
77
mathematical programming
assembly line balancing problem
skilled and unskilled workers
multi-manned workstations
Azadeh
Esfandyari
azadeh.esfandyari@gmail.com
1
Department of computer engineering, Faculty of Engineering, Gilan-e-Gharb Branch, Islamic Azad University, Gilan-e-Gharb, Iran
AUTHOR
Abdolreza
Roshani
a.roshani@kut.ac.ir
2
Department of industrial Engineering, Faculty of engineering management, Kermanshah University of Technology, Kermanshah, Iran
LEAD_AUTHOR
Battaïa, O., and Dolgui, A. (2013). A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142(2), 259-277.
1
Baybars, I., 1986. A survey of exact algorithms for the simple assembly line balancing problem. Management science, 32(8), 909-932.
2
Becker, C. and Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research, 168(3), 694-715.
3
Becker, C. and Scholl, A. (2009). Balancing assembly lines with variable parallel workplaces: Problem definition and effective solution procedure. European Journal of Operational Research, 199(2), 359-374.
4
Bowman EH (1960) Assembly line balancing by linear programming. Operations Research, 8, 385–389.
5
Chang, H.-J., and Chang, T.-M. (2010). Simultaneous Perspective-based Mixed-model Assembly Line Balancing Problem. Tamkang Journal of Science and Engineering, 13, 327-336.
6
Cevikcan, E., Durmusoglu, M.B., and Unal, M.E. (2009). A team-oriented design methodology for mixed model assembly systems. Computers & Industrial Engineering, 56(2), 576-599.
7
Corominas, A., Pastor, R., & Plans, J. (2008). Balancing assembly line with skilled and unskilled workers. Omega, 36(6), 1126-1132.
8
Dimitriadis, S.G. (2006). Assembly line balancing and group working: A heuristic procedure for workers' groups operating on the same product and workstation. Computers & Operations Research 33(9), 2757-2774.
9
Fattahi, P., and Roshani, A. and Roshani, A. (2011). A mathematical model and ant colony algorithm for multi-manned assembly line balancing problem. The International Journal of Advanced Manufacturing Technology, 53 (1-4), 363-378.
10
Jackson JR (1956) A computing procedure for a line balancing problem. Management Science, 2, 261–272.
11
Jaeschke G (1964) Eine allgemaine Methode Zur Losung Kombinatoriiicher Probleme. Ablauf-Plan Forsch, 5,133–153.
12
Kellegöz, T. (2017). Assembly line balancing problems with multi-manned stations: a new mathematical formulation and Gantt based heuristic method. Annals of Operations Research, 253(1), 377-404.
13
Kellegoz, T. and Toklu, B. (2012). An efficient branch and bound algorithm for assembly line balancing problems with parallel multi-manned workstations. Computers & Operations Research, 39(12), 3344-3360.
14
Kellegoz, T. and Toklu, B. (2015). A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations. International Journal of Production Research, 53(3), 736-756.
15
Kim, D., Moon, D.H., Moon, I., (2018). Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study, Assembly Automation, https://doi.org/10.1108/AA-06-2017-070.
16
Mansoor EM (1964) Assembly line balancing-An Improvement on the Ranked Positional Weight Technique. Journal of Industrial Engineering, 15, 73–77.
17
Merten P (1967) Assembly line balancing by partial enumeration. Ablauf- und planungsforschung, 429-433.
18
Moon, I., Shin, S. and Kim, D., (2014), Integrated assembly line balancing with skilled and unskilled workers. IFIP International Conference on Advances in Production Management Systems, 459-466. Springer, Berlin, Heidelberg.
19
Roshani, A. and Giglio, D., (2015). A Mathematical Programming Formulation for Cost-oriented Multi-manned Assembly Line Balancing Problem. IFACPapersOnLine, 48(3), 2293-2298.
20
Roshani, A. and Giglio, D., (2015). A simulated annealing approach for multi-manned assembly line balancing problem type II. IFAC-PapersOnLine 48(3), 2299-2304.
21
Roshani, A., & Giglio, D. (2017). Simulated annealing algorithms for the multi-manned assembly line balancing problem: minimising cycle time. International Journal of Production Research, 55(10), 2731-2751.
22
Roshani, A., Paolucci, M., Giglio, D, and Tonelli, F., (2020) A hybrid adaptive variable neighbourhood search approach for multi-sided assembly line balancing problem to minimise the cycle time, International Journal of Production Research, DOI: 10.1080/00207543.2020.1749958
23
Roshani, A., Roshani, A., Roshani, A., Salehi, M., and Esfandyari, A. (2013). A simulated annealing algorithm for multi-manned assembly line balancing problem. Journal of Manufacturing Systems, 32(1), 238-247.
24
Salveson, M.E. (1955). The assembly line balancing problem. Journal of industrial engineering, 6(3), 18-25.
25
Sahin, M., Kellegz, T., (2019). A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations. Computers & Industrial Engineering, 133, 107 - 120.
26
Tonelli, F., Paolucci, M., Anghinolfi, D. and Taticchi, P. (2013). Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach. Production Planning & Control, 24(1), 110-127.
27
Yilmaz H. and Yilmaz M. 2015. Multi-manned assembly line balancing problem with balanced load density. Assembly Automation, 35(1), 137-142.
28
ORIGINAL_ARTICLE
A multi objective mixed integer programming model for design of a sustainable meat supply chain network
In the recent decades, rapid population growth has led to the significant increase in food demand. Food supply chain has always been one of the most important and challenging management issues. Product with short age, especially foodstuffs, is the most problematic challenges for supply chain management. These challenges are mainly due to the diversity in the number of these goods, the special need for tracking the flow of goods in the supply chain and the short age of products. Designing an appropriate supply chain network for the organization will increase profitability as well as customer satisfaction. It also helps organizations to achieve competitive advantage in market. In this research, a multi-objective planning model is presented in order to design a sustainable supply chain network. The first objective function minimizes costs, the second objective function minimizes network environmental impacts, the third objective function optimizes the productivity of facilities and the fourth objective function optimizes network social impacts. In this research, in order to deal with uncertainty, the robust optimization approach is implemented. Multi-criteria decision-making methods are also used to solve the multi-objective model.
https://www.jise.ir/article_111698_7337734c000b8da5c192a509165b72ca.pdf
2020-02-01
78
92
Supply chain management
Sustainable Supply Chain
multi objective
robust optimization
Fatemeh
Ghasemian Zarini
fatemeh.ghasemian95@gmail.com
1
Department of industrial engineering, Mazandaran University of Science and Technology, Babol, Iran
AUTHOR
Nikbakhsh
Javadian
nijavadian@ustmb.ac.ir
2
Department of industrial engineering, Mazandaran University of Science and Technology, Babol, Iran
LEAD_AUTHOR
Mohebalizadehgashti, F., Zolfagharinia, H., & Amin, S. H. (2020). Designing a green meat supply chain network: A multi-objective approach. International Journal of Production Economics, 219, 312-327.
1
Aras, N., & Bilge, Ü. (2018). Robust supply chain network design with multi-products for a company in the food sector. Applied Mathematical Modelling, 60, 526-539.
2
Vafaei, A., Yaghoubi, S., Tajik, J., & Barzinpour, F. (2020). Designing a sustainable multi-channel supply chain distribution network: A case study. Journal of Cleaner Production, 251, 119628.
3
Ghezavati, V. R., Hooshyar, S., & Tavakkoli-Moghaddam, R. (2017). A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato. Central European Journal of Operations Research, 25(1), 29-54.
4
Mogale, D. G., Cheikhrouhou, N., & Tiwari, M. K. (2019). Modelling of sustainable food grain supply chain distribution system: a bi-objective approach. International Journal of Production Research, 1-24.
5
Cruz, L., Pires-Ribeiro, J., & Barbosa-Póvoa, A. (2019). Design and Planning of Agri-Food Supply Chains. In Computer Aided Chemical Engineering (Vol. 46, pp. 55-60). Elsevier.
6
Soysal, M., Bloemhof-Ruwaard, J. M., & Van Der Vorst, J. G. (2014). Modelling food logistics networks with emission considerations: The case of an international beef supply chain. International Journal of Production Economics, 152, 57-70.
7
Mohammed, A., & Wang, Q. (2017). The fuzzy multi-objective distribution planner for a green meat supply chain. International Journal of Production Economics, 184, 47-58.
8
Bottani, E., Murino, T., Schiavo, M., & Akkerman, R. (2019). Resilient food supply chain design: Modelling framework and metaheuristic solution approach. Computers & Industrial Engineering, 135, 177-198.
9
Musavi, M., & Bozorgi-Amiri, A. (2017). A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. Computers & Industrial Engineering, 113, 766-778.
10
Mohammadi, Z., Barzinpour, F., & Teimoury, E. (2020). Designing sustainable supply chain network by considering direct and indirect shipment: Evidence from food industry. Decision Science Letters, 9(3), 323-336.
11
Bortolini, M., Galizia, F. G., Mora, C., Botti, L., & Rosano, M. (2018). Bi-objective design of fresh food supply chain networks with reusable and disposable packaging containers. Journal of cleaner production, 184, 375-388.
12
Validi, S., Bhattacharya, A., & Byrne, P. J. (2014). A case analysis of a sustainable food supply chain distribution system—A multi-objective approach. International Journal of Production Economics, 152, 71-87.
13
Zhang, X., Zhao, G., Qi, Y., & Li, B. (2019). A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability. Sustainability, 11(20), 5726.
14
Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276-292.
15
Yun, Y., Chuluunsukh, A., & Gen, M. (2019, August). Design and Implementation of Sustainable Supply Chain Model with Various Distribution Channels. In International Conference on Management Science and Engineering Management (pp. 469-482). Springer, Cham.
16
Pishvaee, Mir Saman, Masoud Rabbani, and Seyed Ali Torabi. "A robust optimization approach to closed-loop supply chain network design under uncertainty." Applied Mathematical Modelling 35.2 (2011): 637-649.
17
ORIGINAL_ARTICLE
Social sustainability assessment of conversion technologies: Municipal solid waste into bioenergy using Best Worst Method
The majority of sustainability assessments of the bio based industries are primarily focused on the environmental and economic aspects, while social impacts are rarely considered. While overlooking social dimension can have a serious harmful impact across supply chains. To address this issue, this study proposes a modified systemic approach for a social sustainability impact assessment of the technology treatment for converting municipal solid waste to bioenergy based on a review on the common methodologies for assessing social impacts. To show the applicability and efficiency of the proposed framework, a sample of 8 experts were used to evaluate and prioritize social sustainability criteria, using a multi-criteria decision-making method called the ‘best worst method’ (BWM). The criteria are ranked according to their average weight obtained through BWM. The results of this study help bio industry managers, decision-makers and practitioners decide where to focus their attention during the implementation stage, to increase social sustainability in their bioenergy supply chains derived waste and move towards sustainable development.
https://www.jise.ir/article_111699_c8e761e6e74855ba8964ed392500e807.pdf
2020-02-01
93
101
Social Sustainability
Bioenergy
Best Worst Method (BWM)
treatment technology
Zahra
Alidoosti
alidoostiza@yahoo.com
1
Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
Ahmad
Sadegheih
sadegheih@yazd.ac.ir
2
Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ali
Mostafaeipour
mostafaei@yazd.ac.ir
4
Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
Ahmadi, H. B., Kusi-Sarpong, S., and Rezaei, J., (2017), Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126: 99-106.
1
Black, A. W. (2004). The Quest for Sustainable, Healthy Communities. Australian Journal of Environmental Education, 20(01), 33–44. https://doi.org/10.1017/S0814062600002287
2
Blom M, Solmar C. (2009). How to socially assess biofuels: a case study of the UNEP/ SETAC code of practice for social-economical LCA – University Publication Master’s thesis in cooperation with the Division of Quality and Environmental Management at Luleå University of Technology, commissioned by Enact;
3
Dale VH, Efroymson RA, Kline KL, Langholtz MH, Leiby PN, Oladosu GA, Davis MR, Downing ME, Hilliard RE(2013). Indicators for assessing socioeconomic sustainability of bioenergy systems: a short list of practical measures. Ecol Indic;26:87–102
4
Efroymson RA, Dale VH, Langholtz MH. Socioeconomic indicators for sustainable design and commercial development of algal biofuel systems. GCB Bioenergy 2016. http://dx.doi.org/10.1111/gcbb.12359.
5
European Commission. HORIZON 2020, (2016). Work Programme 2016 – 2017 Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy. European Commission Decision C,4614. https://ec.europa.eu/research/participants/data/ref/ h2020/wp/2016_2017/main/h2020-wp1617-food_en.pdf
6
Hasenheit M, Gerdes H, Kiresiewa Z, Beekman V. Summary report on the social, economic and environmental impacts of the bioeconomy; (2016).
7
http://bio-step. eu/fileadmin/BioSTEP/Bio_documents/BioSTEP_D2.2_Impacts_of_the_ bioeconomy.pdf
8
Hosseinijou, S. A., Mansour, S., Akbarpour Shirazi, M. (2014), Social life cycle assessment for material selection: a case study of building materials, International Journal of Life Cycle Assess, 19: 620–645.
9
Ibáñez-Forésa,V., Boveaa, M.D., Coutinho-Nóbregab,C., Medeiros,H.R.D.(2019), Assessing the social performance of municipal solid waste management systems in developing countries: Proposal of indicators and a case study, Ecological Indicators,98:164-178.
10
Köppen S, Fehrenbach H, Markwardt S, Hennecke S. ,(2014), Implementing the GBEP Indicators for Sustainable Bioenergy in Germany. Final Report, ifeu - Institut für Energie- und Umweltforschung gGmbH and IINAS - International Institute for Sustainability Analysis and Strategy GmbH, Heidelberg, Darmstadt, Berlin; October. http://www.iinas.org/tl_files/iinas/downloads/bio/IFEU_ IINAS_2014_GBEP_
11
Kumara,A, Sahb,B., Singhc,A.R. Denga, Hea,X., Kumarb,P., Bansa, R.C.,(2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development, Renewable and Sustainable Energy Reviews ,69: 596-609. https://doi.org/10.1016/j.rser.2016.11.191
12
Rafiaani, P., Kuppens, T., Dael, M. V., Azadi, H., Lebailly, P., & Passel, S. V. (2018). Social sustainability assessments in the biobased economy: Towards a systemic approach. Renewable and Sustainable Energy Reviews, 82: 1839–1853, https://doi.org/10.1016/j.rser.2017.06.118
13
Rafiaani, P., Van Passel,S., Lebailly,P., Kuppens, T., Azadi, H.(2016). Social Life Cycle Assessment in Biobased Industries: Identifying Main Indicators and Impacts. Submitted communication, SLCA 2016, Cambridge (USA)
14
Rezaei,J.,(2015). Best-worst multi-criteria decision-making method, Omega53: 49-57.
15
Rezaei,J.,(2016). Best-worst multi-criteria decision-making method: Some properties and a linear model, Omega64: 126-130.
16
Salimi,N., Rezaei, J.,(2017). Evaluating firms’ R&D performance using best worst method, Evaluation and Program Planning,66:147-177, https://doi.org/10.1016/j.evalprogplan.2017.10.002
17
Siebert A, Sinéad O’Keeffe AB, Thrän D. (2016).Social life cycle assessment: in pursuit of a framework for assessing wood-based products from bioeconomy regions in Germany. Int J Life Cycle Assess http://dx.doi.org/10.1007/s11367-0161066-0.
18
Vanclay F, Esteves AM, Aucamp I, Franks DM.,(2015). Social Impact Assessment: Guidance for assessing and managing the social impacts of projects. International Association for Impact Assessment (IAIA). http://www.iaia.org/uploads/ pdf/SIA_Guidance_Document_IAIA.pdf.
19
Van Dam J, Faaij A, Rutz D, Janssen R. (2010), Socio-economic impacts of biomass feedstock production, Global BioPact project. Utrecht: Utrecht University;
20
Vis M, Dörnbrack A-S, Haye S.,(2014). Socio-economic impact assessment tools. Socio-Economic Impacts of Bioenergy Production. Switzerland: Springer International Publishing; pp. 1-16. http://dx.doi.org/ 10.1007/978-3-319-03829-2_1
21
UNEP-SETAC. Guidelines for social Life Cycle Assessment of products. United Nations Environmental Programme. Belgium: Druk in der weer; 2009. http://www.unep.fr/shared/publications/pdf/dtix1164xpa-guidelines_slca.pdf.
22
ORIGINAL_ARTICLE
Benders decomposition algorithm for a green closed-loop supply chain under a build-to-order environment
Nowadays, researches pay more attention to environmental concerns consisted of various communities. This study proposes a multi-echelon, multi-period closed-loop supply chain (CLSC). A comprehensive model considers the selection of selection of technology and environmental effects. The supply chain is under a build-to-order (BTO) environment. So, there is not a final product inventory. Also, the returned products disassembled into reused components. The bi-objective mixed-integer linear problem is solved by a Benders decomposition algorithm by validating some numerical experiments. The convergence is also shown in the property.
https://www.jise.ir/article_111700_bfa75126292b3dea687c5670f559f852.pdf
2020-02-01
102
111
Green supply chain
Closed-loop supply chain
technology
build-to-order
Benders decomposition algorithm
Malihe
Ebrahimi
maliheebrahimi@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Domiguez, R., Ponte, B., Cannella, S. and Framinan, J. M. (2019). On the dynamics of closed-loop supply chains with capacity constraint. Computers & Industrial Engineering, 128; 91-103.
1
Ebrahimi, M., and Tavakkoli-Moghaddam, R. (2020). A build to order green supply chain problem by concidering the technology, Proceeding of the 16th Iranian Internatioal Engineering Conference, Tehran, Iran, 22-23 January 2020.
2
Jabbarzadeh, A., Haughton, M. and Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: a robust approach with real world application. Computers & Industrial Engineering, 116; 178-191.
3
Ebrahimi, M., Tavakkoli-Moghaddam and R., Joli, F. (2018). Bi-objective build-to-order supply chain problem with customer utility. IJE Transactions A: Basics, 31; 1066-1073.
4
Ebrahimi, M., Tavakkoli-Moghaddam and R., Joli, F. (2019). Benders decomposition algorithm for a build-to-order supply chain problem under uncertainty. International Journal of Industrial Engineering & Production Research, 30; 173-186.
5
Gaur, J. and Mani, V. (2018). Antecedents of closed-loop supply chain in emerging economics: A conceptual framework using stakeholderʼs perspective. Resources, Conservation & Recycling, 139; 219-227.
6
Howang, C. L., Paius S. R., Yoon, K. and Masud, A. S. M. (1980). Mathematical programming withmultiple objectives: a tutorial. Computer & Operations Research, 7(1-2); 5-31.
7
Hassanpour, A., Bgherinejad, J. and Bashiri, M. (2019). A robust leader-follower approach for closed loop supply chain network design considering returns quality levels. Computers & Industrial Engineering, 136; 293-304.
8
Khalafi, S., Hafezalkotob, A., Mohammaditabar, D. and Sayadi, M. K., (2019). A novel model for a network of a closed-loop supply chain with recycling of returned perishable goods: a case study of dairy industry. Journal of Industrial and System Engineering, 12(4);136-153.
9
10
Liu, Z., Li, K. W., Li, B. Y., Huang, J. and Tang, J. (2019). Impact of product design strategies on the operations of a closed-loop supply chain. Transportation Research Part E, 124; 75-91.
11
Laimazloumian, M., Wong, K.Y., Govindan, K. and Kannan, D. (2013). A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research, 240; 435-470.
12
Ma, R., Yao, L., Jin, M., Ren, P. and LV, Z. (2016). Robust environmental closed-loop supply chain design under uncertainty. Chaos, Solitons & Fractals, 89; 195-202.
13
Mardan, E., Govindan, K., Mina, H. and Golami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed-loop supply chain network design problem. Journal of Cleaner Production, 235; 1499-1514.
14
Pishvaee, M. S., Razmi, J. and Torabi, S. A. (2014). An accelerated benders decomposition algorithm for sustainable supply chain network design under uncertainty: a case study of medical needle and syringe supply chain. Transportation Research Part E, 67; 14-38.
15
Salehi, H., Tavakkoli-Moghaddam, R., Taleizaheh, A. A., and Hafezalkotob, A. (2019). Solving a Location-Alocation problem by a fuzzy self-adaptive NSGA-II. Journal of Industrial and Systems Engineering, 12(4); 18-26.
16
Sadeghi Rad, R. and Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed-loop supply chain network design and quantity discount. Journal of Cleaner Production, 196; 1549-1565.
17
Schankel, M., Kirikke, H., Caniels, M. C. J. and Lumbrechts, W. (2019). Vicious cycles that hinder value creation in closed loop supply chain: experiences from the field. Journal of Cleaner Production, 223; 278-288.
18
Shaharudin, M. R., Tan, K. C., Kannan and V., Zailani, S. (2019). The mediating effects of products returns on the relationship between green capabilities and closed-loop supply chain adoption. Journal of Cleaner Production, 211, 233-246.
19
Shimada, T. and Van Wassenhove, L. N. (2019). Closed-loop supply chain activities in Japanese home appliance/personal computer manufacturers: a case study. International Journal of Production Economics, 212; 259-265.
20
Wan, N. and Hong, D. (2019). The impacts of subsidy policies and transfer pricing policies on the closed-loop supply chain with dual collection channels. Journal of Cleaner Production 224; 881-891.
21
Wang, Y., Li, B., Wang, Z., Liu, Z., Zhu, X. and Wang, Q. (2019). Closed-loop supply chain models with product recovery and donation. Journal of Cleaner Production, 227; 861-876.
22
Zhen, L., Huang, L. and Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of Cleaner Production, 227; 1195-1209.
23
Yavari, M. and Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226; 282-305.
24
Yousefi, Nejad Attari, M. and Ebadi Torkayesh, A. (2018). Developing benders decomposition algorithm for a green supply chain network of mine industry: case of Iranian industry. Operations Research Perspectives, 5; 371-382.
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ORIGINAL_ARTICLE
Single-machine scheduling considering carryover sequence-dependent setup time, and earliness and tardiness penalties of production
Production scheduling is one of the very important problems that industry and production are confronted with it. Production scheduling is often planned in the industrial environments while productivity in production can improve significantly the expansion of simultaneous optimization of the scheduling plan. Production scheduling and production are two areas that have attracted much attention in the industry literature and production and research in the operation systems. In this study, the problem of single-machine scheduling with linear earliness and tardiness costs considering the work failure, energy consumption restriction, and the allowed idleness have been investigated and a new nonlinear mathematical model has been presented for the single-machine scheduling problem. Considering complexity in solution, this problem has been regarded as NP-hard problem. However, using methods that produce optimized results, it is just suitable for small size problems. Based on this, a genetic algorithm has been presented for solving this problem in average and large sizes. Numerical samples show that the presented algorithm is effective and efficient.
https://www.jise.ir/article_111701_23e9aa4c3380c70770cd3557bc65c3c3.pdf
2020-02-01
112
120
Single-machine scheduling
energy consumption restriction
Earliness and tardiness penalties
Saeed
Mozaffariyan
saeed.mozafarian@shahed.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
Rashed
Sahraeian
sahraeian@shahed.ac.ir
2
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Alemão, D., Parreira-Rocha, M., & Barata, J. (2018, January). Production and Maintenance Scheduling Supported by Genetic Algorithms. In International Precision Assembly Seminar (pp. 49-59). Springer, Cham.
1
Aouam, T., Geryl, K., Kumar, K., & Brahimi, N. (2018). Production planning with order acceptance and demand uncertainty. Computers & Operations Research, 91, 145-159.
2
Cochran, W. G., & Cox, G. M. (1992). Notes on the statistical analysis of the results.
3
da Rocha, J. S. A., Sala, A. D., de Almeida, E. B., Mancusi, F. C. M., Gerolin, F. S. F., Bucione, F. T. S., ... & Moraes, S. (2016). Relato de experiência: construção do modelo assistencial Hospital Alemão Oswaldo Cruz. Revista Acreditação: ACRED, 6(11), 72-85.
4
da Silva, N. C. O., Scarpin, C. T., Pécora Jr, J. E., & Ruiz, A. (2019). Online single machine scheduling with setup times depending on the jobs sequence. Computers & Industrial Engineering, 129, 251-258.
5
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning.
6
Joo, C. M., & Kim, B. S. (2013). Genetic algorithms for single machine scheduling with time-dependent deterioration and rate-modifying activities. Expert Systems with Applications, 40(8), 3036-3043.. Expert Systems with Applications, 41 (2), 3136-3143.
7
Liu, Q., Dong, M., Chen, F. F., Lv, W., & Ye, C. (2019). Single-machine-based joint optimization of predictive maintenance planning and production scheduling. Robotics and Computer-Integrated Manufacturing, 55, 173-182.
8
Montgomery, D. T., & Reitz, R. D. (2000). Optimization of heavy-duty diesel engine operating parameters using a response surface method. SAE transactions, 1753-1765.
9
Niu, S., Song, S., Ding, J. Y., Zhang, Y., & Chiong, R. (2019). Distributionally robust single machine scheduling with the total tardiness criterion. Computers & Operations Research, 101, 13-28.
10
Pei, J., Cheng, B., Liu, X., Pardalos, P. M., & Kong, M. (2019). Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time. Annals of Operations Research, 272(1-2), 217-241.
11
Sabouni, M. Y., & Jolai, F. (2010). Optimal methods for batch processing problem with makespan and maximum lateness objectives. Applied Mathematical Modelling, 34(2), 314-324.
12
Sun, X., & Geng, X. N. (2019). Single-machine scheduling with deteriorating effects and machine maintenance. International Journal of Production Research, 57(10), 3186-3199.
13
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes (No. 658.562 T3).
14
Zhou, B., & Peng, T. (2019). New single machine scheduling with nonnegative inventory constraints and discretely controllable processing times. Optimization Letters, 13(5), 1111-1142.
15
ORIGINAL_ARTICLE
A multi-objective mathematical model for production-distribution scheduling problem
With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in recent years the pressures of global environmental organizations have prompted private and public organizations to take action to reduce environmental pollutants. This paper presents a nonlinear mixed integer model for the production and distribution of goods with specified shipping capacity and specific delivery time for customers. The proposed model is applicable to flexible production systems; it also provides routing for the means of transportation of products, as well as the reduction of emissions from production and distribution. The model is presented, and then by mathematical linearization is transformed into a mixed integer linear model. The data of a furniture company is used to solve the linear model, and then the linear model with the company data is solved by CPLEX software. The numerical results show that as costs increase, delays are reduced and consequently, customer satisfaction increases, and as costs increase the air pollution decreases.
https://www.jise.ir/article_111702_e5704efbf628cfa6b73a54fc6c8fb020.pdf
2020-02-01
121
132
Production-distribution integration
Production scheduling
routing
Job shop
Green supply chain
timely delivery
Farshad
Aghajani
faghajani@aut.ac.ir
1
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran
AUTHOR
Mohammad Javad
Mirzapour al-e-hashem
mirzapour@aut.ac.ir
2
Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Iran
LEAD_AUTHOR
Boutarfa, Y., Senoussi, A., Mouss, N. K., & Brahimi, N. (2016). A Tabu search heuristic for an integrated production-distribution problem with clustered retailers. IFAC-PapersOnLine, 49(12), 1514-1519.
1
Darvish, M., & Coelho, L. C. (2018). Sequential versus integrated optimization: Production, location, inventory control, and distribution. European Journal of Operational Research, 268(1), 203-214.
2
Devapriya, P., Ferrell, W., & Geismar, N. (2017). Integrated production and distribution scheduling with a perishable product. European Journal of Operational Research, 259(3), 906-916.
3
Fan, J., Lu, X., & Liu, P. (2015). Integrated scheduling of production and delivery on a single machine with availability constraint. Theoretical Computer Science, 562, 581-589.
4
Gao, S., Qi, L., & Lei, L. (2015). Integrated batch production and distribution scheduling with limited vehicle capacity. International Journal of Production Economics, 160, 13-25.
5
Gharaei, A., & Jolai, F. (2018). A multi-agent approach to the integrated production scheduling and distribution problem in multi-factory supply chain. Applied Soft Computing, 65, 577-589.
6
Li, K., Zhou, C., Leung, J. Y., & Ma, Y. (2016). Integrated production and delivery with single machine and multiple vehicles. Expert Systems with Applications, 57, 12-20.
7
Mirzapour Al-E-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134(1), 28-42.
8
Mirzapour Al-e-hashem, S. M. J., Baboli, A., & Sazvar, Z. (2013). A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchase and shortage cost functions. European Journal of Operational Research, 230(1), 26-41.
9
Mohammadi, S., Al-e-Hashem, S. M., & Rekik, Y. (2020). An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company. International Journal of Production Economics, 219, 347-359.
10
Rafiei, H., Safaei, F., & Rabbani, M. (2018). Integrated production-distribution planning problem in a competition-based four-echelon supply chain. Computers & Industrial Engineering, 119, 85-99.
11
Sreeram, K. Y., & Panicker, V. V. (2015). Clonal selection algorithm approach for multi-objective optimization of production-distribution system. Procedia Soc Behav Sci, 189(1).
12
Senoussi, A., Dauzère-Pérès, S., Brahimi, N., Penz, B., & Mouss, N. K. (2018). Heuristics based on genetic algorithms for the capacitated multi vehicle production distribution problem. Computers & Operations Research, 96, 108-119.
13
Wang, Y., & Wang, X. (2013). Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm. Applied Soft Computing, 13(3), 1400-1406.
14