ORIGINAL_ARTICLE
Hierarchical Facility Location and Hub Network Problems: A literature review
In this paper, a complete review of published researches about hierarchical facility location and hub network problems is presented. Hierarchical network is a system where facilities with different service levels interact in a top-down way or vice versa. In Hierarchical systems, service levels are composed of different facilities. Published papers from (1970) to (2015) have been studied and a comprehensively classified and surveys is presented. Mathematical models are classified based on different properties such as: input, output, objective functions, constraints, applications, some of the real world case studies and solution methods. At the ends, according to classification, a conclusion based on the literature and the future research to tackle real world of hierarchical facility location problems and hierarchical hub network problems is presented. This study may be used as a comprehensive reference for researchers in the hierarchical facility location problems, particularly those of hierarchical location-based on hub networks.
http://www.jise.ir/article_14005_81379dbfb519890a33bf4c7acd8d0e9f.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
1
22
Hub and Spoke
Hierarchical FacilityLocation (HFLPs)
Hierarchical Hub Network Problems (HHNPs)
review
Sara
Torkestani
storkestani@ind.iust.ac.ir
true
1
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
AUTHOR
Seyed Mohammad
Seyedhosseini
seyedhosseini@iust.ac.ir
true
2
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
LEAD_AUTHOR
Ahmad
Makui
amakui@iust.ac.ir
true
3
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
AUTHOR
Kamran
Shahanaghi
shahanaghi@iust.ac.ir
true
4
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
AUTHOR
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ORIGINAL_ARTICLE
Uncapacitated phub center problem under uncertainty
Hubs are facilities to collect arrange and distribute commodities in telecommunication networks, cargo delivery systems, etc. In this paper, an uncapacitated phub center problem in case of single allocation and also multiple allocation will be introduced in which travel times or transportation costs are considered as fuzzy parameters. Then, by proposing new methods, the presented problem is converted to deterministic mixed integer programming (MIP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy chance-constrained programming. Both possibility and necessity measures are considered separately in the proposed new methods. Finally, the proposed methods are applied on the popular CAB data set. The computational results of our study show that these methods can be implemented for the phub center problem with uncertain frameworks.
http://www.jise.ir/article_14002_dbec54b0ae1b05ae6571a5f5b0c96aba.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
23
39
phub center
fuzzy chance-constrained programming
Uncertainty
Hub Location
Javad
Nematian
jnematian@tabrizu.ac.ir
true
1
Department of Industrial Engineering, University of Tabriz
Department of Industrial Engineering, University of Tabriz
Department of Industrial Engineering, University of Tabriz
LEAD_AUTHOR
M.Mohammad
Musavi
mohammad.musavi@ut.ac.ir
true
2
Department of Industrial Engineering, University of Tabriz, Tabriz, Iran
Department of Industrial Engineering, University of Tabriz, Tabriz, Iran
Department of Industrial Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
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8
Farahani, R. Z., Hekmatfar, M., Arabani, A. B., & Nikbakhsh, E. 2013. Hub location problems: A review of models, classification, solution techniques, and applications. Computers & Industrial Engineering, 64(4), 1096-1109.
9
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Martins de Sá, E., Contreras, I., Cordeau, J.F., Saraiva de Camargo, R. and de Miranda, G., 2015. The hub line location problem. Transportation Science.
20
Mohammadi, M., Torabi, S. A., & Tavakkoli-Moghaddam, R. 2014. Sustainable hub location under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 62, 89-115.
21
Mohammadi, M. and Tavakkoli-Moghaddam, R., 2015. Design of a fuzzy bi-objective reliable p-hub center problem. Journal of Intelligent & Fuzzy Systems, (Preprint), pp.1-18.
22
Mulvey, J. M. & Ruszczynsk, A. 1995. A new scenario decomposition method for large-scalestochastic optimization. Operations Research, 43, 477–490
23
Nematian, J. 2015. A fuzzy robust linear programming problem with hybrid variables. International Journal of Industrial and Systems Engineering, 19(4), 515-546.
24
O’Kelly, M. E. 1987. A quadratic integer program for the location of interacting hub facilities. European Journal of Operational Research, 32, 393–404.
25
Shahabi, M., & Unnikrishnan, A. 2014. Robust hub network design problem.Transportation Research Part E: Logistics and Transportation Review, 70, 356-373.
26
Qin, Z., & Gao, Y. 2014. Uncapacitated p-hub location problem with fixed costs and uncertain flows. Journal of Intelligent Manufacturing, 1-12.
27
Zade, A. E., Sadegheih, A., & Lotfi, M. M. 2014. A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments. Journal of Industrial Engineering International, 10(4), 185-197.
28
ORIGINAL_ARTICLE
A multiobjective continuous covering location model
This paper presents a multiobjective continuous covering location problem in fuzzy environment. Because of uncertain covering radius, possibility of covering concept is introduced.Since, the uncertainty may cause risk of uncovering customers; the problemis formulated as a risk management model. The presented model is an extension of the discrete covering location models tocontinuous space. Two variables, namely, selecting zone variable and covering variableare introduced for extending the discrete model to the continuous one. In the model, a facility is locatedin a zone with a predetermined radius from its center and is determined by the selecting zonevariable. Allocating a customer to a facility is shown by the covering variable. Also, the paperintroduces the possibility of covering based on distance between the customers and the facilities. Two objectives are considered in the model; the first is the possibility of covering by each facility and the second is the risk cost of the uncovered customers.Then, a fuzzy programming is applied for converting the model to a single objective one. Finally, a numerical examplewith sensitivity analysis is expressed to illustrate the presented model.
http://www.jise.ir/article_14003_eab1dc22998b665af0ac0316e035df91.pdf
2016-05-01T11:23:20
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40
52
Continuous covering location problem (CCLP)
Risk Management
fuzzy covering radius
Multiobjective Problem
Seyed Javad
Hosseininezhad
hosseininezhad@kntu.ac.ir
true
1
Department of Industrial Engineering
K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering
K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering
K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
mohammad
Jabalameli
true
2
Department of Industrial engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Araz C., Selim H., Ozkarahan I., (2007), “A fuzzy multi-objective covering-based vehicle location model for emergency services”, Computers & Operations Research, 34, 705–726.
1
Azaron A. et al., (2008), “A multi-objective stochastic programming approach for supply chain design considering risk”, International Journal of Production Economics, 116, 129-138.
2
Batanovic V., PetrovicD., PetrovicR.(2009), “Fuzzy logic based algorithms for maximum covering location problems”, Information Sciences, 179, 120–129.
3
Berman O., Krass D., Drezner Z., (2003), The gradual covering decay location problem on a network, European Journal of Operational Research,151, 474–480.
4
Chen Z., Li H., Ren H., Xu Q., Hong J., (2011), “A total environmental risk assessment model for international hub airports”, International Journal of Project Management , 29, 856–866.
5
Chiang C. I. , Hwang M. J., Liu Y. H., (2004), “Solving a Fuzzy Set-Covering Problem”, Mathematical and Computer Modelling, 40, 861-865.
6
Chiang C. I. , Hwang M. J., Liu Y. H., (2005), “An Alternative Formulation for Certain Fuzzy Set-Covering Problems”, Mathematical and Computer Modelling, 42 , 363-365.
7
Church R, ReVelle C., (1974), “the maximal covering location problem”, Papers Region. Sci. Assoc., 32,101–118.
8
Cui T., Ouyang Y., Shen Z.-J. M., (2010), ”Reliable facility location design under the risk of disruption”, Operation Research, 58, 998-1011.
9
De Boer P., Kroese D.P., Mannor S, Rubinstein R.Y., (2005), “A tutorial on the cross-entropy method”,Annals of Operations Research, 2005,134(1):19–67.
10
Drezner Z., Wesolowsky G., (1999), “Allocation of discrete demand with changing costs”, Computers and Operations Research, 26, 1335–1349.
11
Francis, R.L., F. Leon, L.F. McGinnis, and J.A. White, (1992), “Facility Layout and Location: An Analytical Approach” ,NY: Prentice Hall.
12
Guillen G., Mele F.D., Bagajewicz M. J., Espuna A., Puigjaner L., (2005), “Multiobjective supply chain design under uncertainty”, Chemical Enginnering Science, 60, 1535-1553.
13
Hahn G.J., Kuhn H., (2012), “Value-based performance and risk management in supply chains: A robust optimization approach”, International Journal of Production Economics, 139, 135-144.
14
Huang B., Liu N., Chandramouli M., (2006), “A GIS supported Ant algorithm for the linear feature covering problem with distance constraints”, Decision Support Systems, 42, 1063–1075.
15
Hosseininezhad S. J.,Jabalameli M. S.,JalaliNaini S. G, (2013), “A continuous covering location model with risk consideration”, Applied Mathematical Modelling, 37, 9665-9676.
16
Hosseininezhad S. J.,JabalameliM. S.,JalaliNaini S. G, (2014), “A fuzzy algorithm for continuous capacitated location allocationmodel with risk consideration”, Applied Mathematical Modelling, 38, 983–1000.
17
Huang B., Liu N., Chandramouli M., (2006), “A GIS supported Ant algorithm for the linear feature covering problem with distance constraints”, Decision Support Systems, 42, 1063–1075.
18
Laporte G., Nickel N., Saldanha da Gama F.,Editors, (2015), Location Science, Springer, Switzerland.
19
Liu K., Zhou Y., Zhang, Z. (2010), “Capacitated location model with online demand pooling in a multi-channel, supply chain”, European Journal of Operational Research, 207, 218–231.
20
Lushu L., Kabadi S. N., Nair K.P.K. (2002), “Fuzzy versions of the covering circle problem”, European Journal of Operational Research, 137, 93-109.
21
Mirchandani P.B., Francis R.L., (1990), Discrete location theory. Wiley, New York.
22
Mete H. O., Zabinsky Z. B., (2010), “Stochastic optimization of medical supply location and distribution in disaster management”, International Journal of Production Economics, 126, 76-84.
23
Nickel S., Saldanha-da-Gama F., ZieglerH.-P., (2012), “A multi-stage stochastic supply network design problem with financial decisions and risk management”, Omega, 40, 511–524.
24
Owen S.H., Daskin M.S., (1998), “Strategic facility location: a review”, European Journal of Operational Research, 111, 423–447.
25
Ozsen L., Coullard C. R., Daskin M. S., (2008), “Capacitated Warehouse Location Model with Risk Pooling”, Naval Research Logistics, 55, 295-312.
26
Peng P., Snyder La. V., Lim A., Liu Z., (2011), “Reliable logistics networks design with facility disruptions”, Transportation Research Part B, 45, 1190–1211.
27
Perez J.A.M., Vega J.M.M., Verdegay J.L., (2004), “Fuzzy location problems on networks”, Fuzzy Sets and Systems, 142, 393–405.
28
Rubinstein R. Y., (1997), "Optimization of computer simulation models with rare events", European Journal of Operation Research, 99, 89-112.
29
Schilling D., Jayaraman V., Barkhi R., (1993), “A review of covering problems in facility location”, Location Science, 1, 25–55.
30
Shavandi H., Mahlooji H., (2006), “A fuzzy queuing location model with a genetic algorithm for congested systems”, Applied Mathematics and Computation, 181, 440–456.
31
Sirbiladze G., Ghvaberidze B., Latsabidze T., Matsaberidze B., (2009), “Using a minimal fuzzy covering in decision-making problems”, Information Sciences, 179, 2022–2027
32
Synder L. V., Daskin M. S., Teo C.-P., (2007), “The stochastic location model with risk pooling”, European Journal of Operational Research, 179, 1221-1238.
33
Toregas C., Swain R., ReVelle C., Bergman L., (1971),“The location of emergency service facilities”. Oper Res., 19, 1363–1373.
34
Wagner M. R., Bhaduryb J., Penga S., (2009), “Risk management in uncapacitated facility location models with random demands”, Computers & Operations Research, 36, 1002-1011.
35
Wang S., Watada J., (2012), “A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty”, Information Sciences, 192, 3-18.
36
Yaodong N. (2008), “Fuzzy minimum weight edge covering problem”, Applied Mathematical Modelling, 32, 1327–1337.
37
You F., Wassick J. M., Grossmann I. E., (2009), “Risk management for a global supply chain planning under uncertainty: models and algorithm”, AIChE Journal, 55, 931-946.
38
ORIGINAL_ARTICLE
Presenting a three-objective model in location-allocation problems using combinational interval full-ranking and maximal covering with backup model
Covering models have found many applications in a wide variety of real-world problems; nevertheless, some assumptions of covering models are not realistic enough. Accordingly, a general approach would not be able to answer the needs of encountering varied aspects of real-world considerations. Assumptions like the unavailability of servers, uncertainty, and evaluating more factors at the same time, are a sort of assumptions, with which covering models are always faced; however, these models are not able to find any answers for them. Therefore, how to deal with these sorts of assumptions has been always a big question. In this research, for facing unavailability and uncertainty in input data, backup covering and interval full-ranking model were addressed, respectively. Furthermore, by combining backup covering and interval full-ranking models (also conceptions), not only time was saved and more factors like efficiency and cost were simultaneously evaluated, but also covering considerations were reachable in real aspects.
http://www.jise.ir/article_14004_46d18285c2e51114d7a5be24b0c98704.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
53
70
Emergency Service
Backup coverage
Interval full-ranking
meta-heuristic algorithm
Multi-Objective Optimization
Majedeh
Kordjazi
majede.kord@gmail.com
true
1
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
Abolfazl
Kazemi
abkaazemi@gmail.com
true
2
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
Baron, B., Berman, O., Kim, S. and Krass, D. (2009). Ensuring feasibility in location problems with stochastic demands and congestions. IIE transactions, 41, 467-481.
1
Berman, O., Drenzer, Z., Krass, D. and Wesolowsky, G.O. (2009). The variable radius covering problem. European Journal of Operational Research, 196, 516-525.
2
Berman, O. and Wang, J. (2011). The mini-max regret gradual covering location problem on a network with incomplete information of demand weights. European Journal of Operational Research, 208, 233-238.
3
Chou, S.Y., Chang, Y.H. and Shen, C.Y. (2008). A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189, 132-145.
4
Church, R.L. and Revelle, C. (1974). The maximal covering location problem. Papers of the Regional Science Association, 32, 101-118.
5
Daskin, M.S. (1995). Networks and discrete location: Models, algorithms and applications. New York, US: John Wiley and Sons.
6
Daskin, M.S. and Stern, E.H. (1981). A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15, 137-152.
7
Erdemir, E.T., Batta, R., Spielman S., Rogerson, P.A., Blatt, A. and Flanigan, M. (2010). Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach. European Journal of Operational Research, 207, 736-749.
8
Ghodratnama, A., Tavakkoli-Mogaddam, R. and Azaron, A. (2015). Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview. Applied Soft Computing, 37, 255-276.
9
Hakimi, S.L. (1965). Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Operational Research, 13, 462-475.
10
Hogan, K. and Revelle, C. (1986). Concepts and applications of backup coverage. Management Science, 32, 1434-1444.
11
Hosseininezhad, J., Jabalameli, M.S. and Jalali Naini, GH. (2014). Fuzzy algorithm for continuous capacitated location allocation model with risk consideration. Applied Mathematical Modeling, 38, 983-1000.
12
Kolen, A. and Tamir, A. (1990). Covering problems, In: P.B. Mirchandani, R.L. Francies (Eds.), Conf. Discrete Location Theory, Wily, New York, 263-304.
13
Lannoni, A.P. and Morabito, R. (2007). A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways. Transportation Research, 43, 755-771.
14
Lee, J.M. and Lee, Y.H. (2010). Tabu based heuristics for the generalized hierarchical covering location problem. Computers and Industrial Engineering, 58, 638-645.
15
Martinez-Salazar, I., Molina, J., Ángel-Bello, F., Gómez, T. and Caballero, R. (2014). Solving a bi-objective Transportation Location Routing Problem by meta-heuristic algorithms. European Journal of Operational Research, 234, 25-36.
16
Moheb-alizade, H., Rasouli, S.M. and Tavakkoli-mogaddam, R. (2011). The use of multi-criteria data envelopment analysis for location-allocation problems in a fuzzy environment. Expert Systems with Applications, 38, 5687-5695.
17
Ni, Y., (2012). Minimum weight covering problems in stochastic environments. Information Sciences, 214, 91-104.
18
Owen, S.H. and Daskin, M.S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111, 423-447.
19
Peijun, G. (2009). Fuzzy data envelopment analysis and its application to location problems. Information Sciences, 179, 820-829.
20
Pereira, M. and Coelho, L. (2015). A hybrid method for the probabilistic maximal covering location-allocation problem. Computers and Operations Research, 57, 51-59.
21
Pirkul, H. and Schilling, D. (1989). The capacitated maximal covering location problem with backup service. Annals of Operational Research, 18, 141-154.
22
Revelle, C. and Hogan, K. (1989). The maximum reliability location problem and a-reliable p-center problems. Annals of Operational Research, 18, 155-174.
23
Shieh, B.S. (2013). Solution to the covering problem. Information Sciences, 222, 626-633.
24
Sohrabi Haghighat, M. and Khorram, E. (2005). The maximum and minimum number of efficient units in DEA with interval data. Applied Mathematics and Computation, 163, 919-930.
25
Thomas, P., Chan, Y., Lehmkuhl, L. and Nixon, W. (2002). Obnoxious-facility location and data envelopment analysis: A combined distance-based formulation. European Journal of Operational Research, 141, 495-514.
26
Toregas, C., Swain, R., Revelle C. and Berman L. (1971). The location of emergency service facilities. Operational Research, 19, 1363-1373.
27
Tsou, C.S. (2009). Evolutionary Pareto optimizers for continuous review stochastic inventory systems. European Journal of Operation Research, 195, 364-371.
28
Vidyarthi, N. and Jayaswal, S. (2014). Efficient solution of a class of location–allocation problems with stochastic demand and congestion. Computers and Operations Research, 48, 20-30.
29
Wen, M. and Kang, R. (2011). Some optimal models for facility location-allocation problem with random Fuzzy demands. Applied Soft Computing, 11, 1202-1207.
30
Zanjirani, R., Asgari, N., Heidari, N., Hosseininia, M. and Goh, M. (2012). Covering problems in facility location: A review. Computers and Industrial Engineering, 62, 368-407.
31
Zarandi, M.H., Davari, S. and Haddad Sisakht, A. (2013). The large-scale dynamic maximal covering location problem. Mathematical and Computer Modeling, 57, 710-719.
32
ORIGINAL_ARTICLE
A robust approach to multi period covering location-allocation problem in pharmaceutical supply chain
This paper proposes a discrete capacitated covering location-allocation model for pharmaceutical centers. In the presented model, two objectives are considered; the first one is minimization of costs and the second one try to maximize customer satisfaction by definition of social justice. Social justice in the model means that we consider customers satisfaction by using distance. the introduced model is an extension of the maximum covering model by adding zone constraint that Actually the distance between facility and customer zone is categorized as best, possible and not possible location. The model tries to locate facilities in best and possible location. In addition, number of missed customers is important and the model considered this issue. Since the nature of the demand is uncertain, a robust approach is proposed. The model could applied to other industries that have limitation about their product such as perishability foods or other perishability product. The model solved by GAMS Software. Finally, a numerical example with sensitivity analysis presented to illustrate the proposed model.
http://www.jise.ir/article_15109_9cc227f7b00548dc623d755b048c8a10.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
71
84
pharmaceutical centers
covering location-allocation problem
robust approach
Marjan
haji abbas
marjan.pesaran@gmail.com
true
1
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Seyed Javad
Hosseininezhad
hosseininezhad@kntu.ac.ir
true
2
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
1
Alnaji L., Ridha M., (2013), "The role of Supply Chain Applications in Jordanian Pharmacies: A case study on Pharmacies in the capital city Amman," Industrial Engineering Letters, vol. 3, pp. 65-71.
2
Ceselli A., Righini G., Tresoldi E., (2014), “Combined location and routing problems for drug distribution”, Discrete Applied Mathematics, vol.165, pp.130–145.
3
Chen Y., Mockus L., Orcun S., Reklaitis G.V., (2012), “Simulation-optimization approach to clinical trial supply chain management with demand scenario forecast”, Computers and Chemical Engineering, vol. 40, pp. 82– 96.
4
Costantino N., Dotoli M., Falagario M., Fanti M.P., Mangini A.M., Sciancalepore F., Ukovich W., (2013), “A hierarchical optimization technique for the strategic design of distribution networks”, Computers & Industrial Engineering, vol. 66 ,pp.849–864.
5
Fleischhacker A.J., Zhao Y., (2011) “Planning for demand failure: A dynamic lot size model for clinical trial supply chains”, European Journal of Operational Research, vol. 211, pp. 496–506.
6
Williams H.P., (2010), Model Building in mathematical programming. WILEY, london.
7
Hosseininezhad S.J., Jabalameli. M.S., Jalali Naini S.Gh., (2013), “A continuous covering location model with risk consideration”, Applied Mathematical Modelling, vol.37, pp.9665–9676.
8
Hosseininezhad S.J., Jabalameli M.S., Jalali Naini S.Gh., (2014), “A fuzzy algorithm for continuous capacitated location allocation model with risk consideration” , Applied Mathematical Modelling,vol. 38 ,pp. 983–1000
9
http://www.reportlinker.com/p0197979-summary/view-report.html
10
Jabbarzadeh A., Fahimnia B., Seuring S., (2014), “Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application”, Transportation Research Part E, vol.70, pp.225–244.
11
Jetly G., Rossetti C. L., Handfield R., (2012), "A multi-agent simulation of the pharmaceutical supply chain," Journal of Simulation, vol. 6, pp. 215-226.
12
Kelle P., Woosley J., Schneider H., (2012). “Pharmaceutical supply chain specifics and inventory solutions for a hospital case”, Operations Research for Health Care, vol. 1 ,pp. 54–63.
13
Masoumi A.H., Yu M., Nagurney A., (2012), “A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability”, Transportation Research Part E, vol. 48 , pp.762–780.
14
Mousazadeh M., Torabi S.A., Zahiri B., (2015), “A robust possibilistic programming approach for pharmaceutical supply chain network design”,Computers and Chemical Engineering, vol. 82, pp.115-128.
15
Mulvey J.M., Vanderbei R., Zenios S.A., (1995), “Robust Optimization of Large-Scale Systems” , Operations Research, Vol. 43, No.2 pp.264-281.
16
Pitta D.A., Laric M.V., (2004), “Value chains in health care”, Journal of ConsumerMarketing, vol.21 (7) pp.451–464.
17
Reinholdt K., Hansen N., Grunow M., (2015), “Planning operations before market launch for balancing time-to-market and risks in pharmaceutical supply chains”, Int. J. Production Economics, vol. 161, pp. 129–139.
18
Shah N., (2004), “Pharmaceutical supply chains: key issues and strategies for optimization”, Computers and Chemical Engineering, vol. 28, pp. 929–941.
19
Spiliotopoulou E., Boni M.F., Yadav P., (2013), “Impact of treatment heterogeneity on drug resistance and supply chain costs” , Socio-Economic Planning Sciences,vol.47 ,pp. 158-171.
20
Uthayakumar R., Priyan S., (2013), “Pharmaceutical supply chain and inventory management strategies for optimization: A study on pharmaceutical company and hospital”, Operations Research for Health Care, vol.2, pp. 52-64.
21
ORIGINAL_ARTICLE
A new stochastic location-allocation emergency medical services healthcare system model during major disaster
From the most important issues in the design of large logistics network in times of crisis are providing a timely quick reaction for treating of injured people and the rapid distribution of medicines and medical equipment. In this paper, a multi-objective model is presented that aims to determine the location of transfer points and hospitals to provide timely quick reaction for treating injured people as well as to determine unreliable and reliable depots for the distribution of medicines and medical equipment. Given that the dynamic nature of the great crises, the parameters of the model has considered as uncertain and dynamic. To solve the model, the hybrid meta-heuristic algorithm is proposed which is composed of simulated annealing algorithm and CPLEX. By comparing the results can be seen that the proposed meta-heuristic hybrid algorithm is efficient.
http://www.jise.ir/article_15110_f8bb7a0de93712d98af1ce53f3c45f70.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
85
99
location-allocation
Emergency medical services logistics
Hybrid meta-heuristic algorithm
Major crises
Ahmad
Mohamadi
mohamadi_a@ind.iust.ac.ir
true
1
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
AUTHOR
Saeed
Yaghoubi
yaghoubi@iust.ac.ir
true
2
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
School of Industrial Engineering, Iran University of science & Technology, Tehran, Iran
LEAD_AUTHOR
Guha-Sapir, D. Hoyois Ph, Below R (2015). Annual Disaster Statistical Review 2014: The Numbers and Trends. Brussels: CRED.
1
Swiss, R. Natural catastrophes and man-made disasters in 2014: convective and winter storms generate most losses. Sigma, (2). (2015)
2
Hashzemi, S., Shokri A, Amin Naseri, M. R & Akbaripour, H. (2014). Designing an Expert System for Management of Crowding and Overcrowding in Emergency Departments. J. of Industrial Engineering. 48(2) 281-292
3
Berman, O., Drezner, Z., & Wesolowsky, G. O. (2005).The facility and transfer points’ location problem. International Transactions in Operational Research, 12(4), 387-402.
4
Dessouky, M., Ordonez, F., Jia, H., & Shen, Z. (2006). Rapid distribution of medical supplies. In Patient Flow: Reducing Delay in Healthcare Delivery (pp. 309-338). Springer US.
5
Berman, O., Drezner, Z., Wesolowsky, G.O., (2007) “The multiple location of transfer points,” Journal of the Operational Research Society, 59, 6, 805–811.
6
Berman, O., Drezner, Z., Wesolowsky, G.O., (2007) “The transfer point location problem,” European Journal of Operational Research, 179, 978–989.
7
Mahmudian, M., Keivani, A., Davoudpour, H and Ardestani Jaafari, A. (2010)"Two Iterative Algorithms for Transfer Point Location Problem" Journal of American Science, vol 9:pp.827-830.
8
Furuta, T. and Tanaka,K,. (2013)"Minisum and minimax location models for helicopter emergency medical service systems" Journal of the Operations Research Society of Japan Vol. 56, No. 3, pp. 221–242,
9
Hosseinijou, S.A., Bashiri, M., (2011) “Stochastic models for transfer point location problem,” The International Journal of Advanced Manufacturing Technology, 58, 1–4, 211–225.
10
Kalantari, H., Yousefli, A., Ghazanfari, M. (2013) “Fuzzy transfer point location problem : a probabilistic unconstrained nonlinear programming approach,” The International Journal of Advanced Manufacturing Technology, 70, 1043–1051.
11
Mohamadi, A., Yaghoubi, S., & Derikvand, H. (2015).A credibility-based chance-constrained transfer point location model for the relief logistics design (Case Study: earthquake disaster on region 1 of Tehran city).International Journal of Supply and Operations Management, 1(4), 466-488.
12
Ebrahimi Zade, A., & Lotfi, M. M. (2015). Stochastic facility and transfer point covering problem with a soft capacity constraint. International Transactions in Operational Research.
13
Kohneh, J. N., Teymoury, E., & Pishvaee, M. S. (2016). Blood products supply chain design considering disaster circumstances (Case study: earthquake disaster in Tehran). Journal of Industrial and Systems Engineering, 9, 51-72.
14
Sasaki, M., Furuta, T. and Suzuki, A., (2008). "Exact optimal solutions of the minisum facility and transfer points location problems on a network," International Transactions in Operational Research, vol. 15, pp. 295-306.
15
Jabal Ameli, S., Araste, K., Bozorgi Amiri, A (2012).The location of facilities and transfer points in the network. First international conference on nonlinear optimization.
16
Araste, K., Jabal Ameli, S., Bozorgi Amiri, A .The location of the transfer point. MScs thesis, Iran University of Science and Technology. (2012).
17
Cochrane, J. L., & Zeleny, M. (1973). Multiple criteria decision making. Univ of South Carolina Pr.
18
Hwang, C. R. (1988). Simulated annealing: theory and applications. Acta Applicandae Mathematicae, 12(1), 108-111.
19
Barzinpour, F., Saffarian, M., Makoui, A., & Teimoury, E. (2014). Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics. Journal of Applied Mathematics, 2014.
20
ORIGINAL_ARTICLE
Reliable location-allocation model for congested systems under disruptions using accelerated Benders decomposition
This paper aims to propose a reliable location-allocation model where facilities are subject to the risk of disruptions. Since service facilities are expected to satisfy random and heavy demands, we model the congested situations in the system within a queuing framework which handles two sources of uncertainty associated with demand and service. To insure the service quality, a minimum limit reflected in the customers’ expected waiting time is considered in the model. We also consider the geographical accessibility of the service network in terms of the proximity of a facility to the potential demands. The model determines the optimal number and locations of facilities and the corresponding customer assignments in such a way as to minimize the fixed installation cost as well as expected traveling, serving and penalty costs. To obtain exact solution of the proposed model, a Benders decomposition algorithm enhanced by two efficient accelerating methods including valid inequalities and knapsack inequalities is proposed. Numerical results illustrate the applicability of the proposed model as well as the effectiveness of the designed solution procedure.
http://www.jise.ir/article_17231_dae9aded945096ecdce48290c5bb1971.pdf
2016-05-01T11:23:20
2020-01-25T11:23:20
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Location-allocation model
Disruption
Queuing system
Service Quality
Geographical accessibility
Benders Decomposition
Naeme
Zarrinpoor
naemezarrinpoor@gmail.com
true
1
Department of industrial engineering, Yazd University, Yazd, Iran.
Department of industrial engineering, Yazd University, Yazd, Iran.
Department of industrial engineering, Yazd University, Yazd, Iran.
AUTHOR
Mohammad
Fallahnezhad
fallahnezhad@yazd.ac.ir
true
2
Department of industrial engineering, Yazd University, Yazd, Iran.
Department of industrial engineering, Yazd University, Yazd, Iran.
Department of industrial engineering, Yazd University, Yazd, Iran.
LEAD_AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
true
3
Department of industrial engineering, Iran University of Science and Technology, Tehran, Iran.
Department of industrial engineering, Iran University of Science and Technology, Tehran, Iran.
Department of industrial engineering, Iran University of Science and Technology, Tehran, Iran.
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