2016
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Coordination of Pricing, Ordering, and Lead time Decisions in a Manufacturing Supply Chain
2
2
In this paper, an incentive policy is proposed to coordinate ordering, lead time, and pricing strategies in a twoechelon manufacturing supply chain (SC) consisting of one manufacturer and one retailer. The system is faced with a stochastic demand which depends on both price and lead time. The manufacturer decides on production size and manufacturing acceleration rate while the retailer determines the retail price and order size. A gametheory approach is proposed to analyze both members’ decision making process. An integrated decision making process where both members cooperate as a single entity aiming to maximize systemwide profit is formulated. Finally a coordination mechanism based on adjusting wholesale price is proposed to convince both members to decide jointly. Numerical experiments demonstrate that whole SC profitability as well as both members profitability is increased by applying the proposed scheme. Results indicate that coordinated decision making decreases both retail price and lead time length while it causes an increase in order size.
1

1
16


Jafar
Heydari
School of Industrial Engineering, College of Engineering, University of Tehran
School of Industrial Engineering, College
Iran
j.heydari@ut.ac.ir


Yousef
Norouzinasab
School of Industrial Engineering, College of Engineering, University of Tehran
School of Industrial Engineering, College
Iran
y.nnasab@alumni.ut.ac.ir
Manufacturing process acceleration
supply chain coordination
Stochastic price and lead timesensitive demand
Gametheory
lead time reduction
[Anli, O. M., M. C. Caramanis and I. C. Paschalidis (2007). "Tractable supply chain production planning, modeling nonlinear lead time and quality of service constraints." Journal of Manufacturing Systems 26(2): 116134.##BenDaya, M. and M. Hariga (2003). "Leadtime reduction in a stochastic inventory system with learning consideration." International Journal of Production Research 41(3): 571579.##Boyacı, T. and G. Gallego (2002). "Coordinating pricing and inventory replenishment policies for one wholesaler and one or more geographically dispersed retailers." International Journal of Production Economics 77(2): 95111.##Chang, H.C., L.Y. Ouyang, K.S. Wu and C.H. Ho (2006). "Integrated vendor–buyer cooperative inventory models with controllable lead time and ordering cost reduction." European Journal of Operational Research 170(2): 481495.##Chen, J. (2011). "Returns with wholesalepricediscount contract in a newsvendor problem." International Journal of Production Economics 130(1): 104111.##Chen, X. and D. SimchiLevi (2006). "Coordinating inventory control and pricing strategies: The continuous review model." Operations Research Letters 34(3): 323332.##Chiu, C.H., T.M. Choi, H.T. Yeung and Y. Zhao (2012). "Sales rebate contracts in fashion supply chains." Mathematical Problems in Engineering 2012.##Chung, W., S. Talluri and R. Narasimhan (2014). "Quantity Flexibility Contract in the Presence of Discount Incentive." Decision Sciences 45(1): 4979.##Du, R., Banerjee, A., Kim, S.L., (2013). “Coordination of twoechelon supply chains using wholesale price discount and credit option” International Journal of Production Economics, 143(2): 327334.##Emmons, H. and S. M. Gilbert (1998). "Note. The role of returns policies in pricing and inventory decisions for catalogue goods." Management science 44(2): 276283.##Ghotbi, E., W. A. Otieno and A. K. Dhingra (2014). "Determination of Stackelberg–Nash equilibria using a sensitivity based approach." Applied Mathematical Modelling 38(21): 49724984.##Glock, C. H. (2012). "The joint economic lot size problem: A review." International Journal of Production Economics 135(2): 671686.##Govindan, K., Diabat, A., & Popiuc, M. N. (2012). “Contract analysis: A performance measures and profit evaluation within twoechelon supply chains”. Computers & Industrial Engineering, 63(1), 5874.##Govindan, K., Popiuc, M. N., & Diabat, A. (2013). “Overview of coordination contracts within forward and reverse supply chains”. Journal of Cleaner Production, 47, 319334.##Goyal, S. (1977). "An integrated inventory model for a single manufaturersingle customer problem." The International Journal of Production Research 15(1): 107111.##Heese, H.S., KemahlıoğluZiya, E., (2016). “Don't ask, don't tell: Sharing revenues with a dishonest retailer” European Journal of Operational Research, 248(2): 580592.##Heydari, J. (2014a). "Coordinating manufaturer׳ s reorder point: A coordination mechanism for supply chains with long manufaturer lead time." Computers & Operations Research 48: 89101.##Heydari, J. (2014b). "Lead time variation control using reliable shipment equipment: An incentive scheme for supply chain coordination." Transportation Research Part E: Logistics and Transportation Review 63: 4458.##Hill, A. V. and I. S. Khosla (1992). "Models for optimal lead time reduction." Production and Operations Management 1(2): 185197.##Hou, J., A. Z. Zeng and L. Zhao (2010). "Coordination with a backup supplier through buyback contract under supply disruption." Transportation Research Part E: Logistics and Transportation Review 46(6): 881895.##Huang, K.L., Kuo, C.W., Lu, M.L., (2014). “Wholesale price rebate vs. capacity expansion: The optimal strategy for seasonal products in a supply chain” European Journal of Operational Research, 234(1): 7785.##Huang, Y.S., W.J. Su and Z.L. Lin (2011). "A study on leadtime discount coordination for deteriorating products." European Journal of Operational Research 215(2): 358366.##Jha, J. and K. Shanker (2014). "An integrated inventory problem with transportation in a divergent supply chain under service level constraint." Journal of Manufacturing Systems 33(4): 462475.##Johnson, D. J. (2003). "A framework for reducing manufacturing throughput time." Journal of manufacturing systems 22(4): 283298.##Khouja, M. (2006). "A joint optimal pricing, rebate value, and lot sizing model." European Journal of Operational Research 174(2): 706723.##Leng, M. and M. Parlar (2009). "Leadtime reduction in a twolevel supply chain: Noncooperative equilibria vs. coordination with a profitsharing contract." International Journal of Production Economics 118(2): 521544.##Li, J., & Liu, L. (2006). Supply chain coordination with quantity discount policy. International Journal of Production Economics, 101(1), 8998.##Li, Y., X. Xu, X. Zhao, J. H. Y. Yeung and F. Ye (2012). "Supply chain coordination with controllable lead time and asymmetric information." European Journal of Operational Research 217(1): 108119.##Lian, Z. and A. Deshmukh (2009). "Analysis of supply contracts with quantity flexibility." European Journal of Operational Research 196(2): 526533.##Linh, C. T. and Y. Hong (2009). "Channel coordination through a revenue sharing contract in a twoperiod newsboy problem." European Journal of Operational Research 198(3): 822829.##Masihabadi, S., Eshghi, K., (2011). “Coordinating a SellerBuyer Supply Chain with a Proper Allocation of Chain’s Surplus Profit Using a General SidePayment Contract” Journal of Industrial and Systems Engineering, 5(2): 6379.##Mokhlesian, M., Zogordi, S.H., Nakhai Kamal Abadi, I., Albadvi, A., (2015). “Pricing decisions in a twoechelon decentralized supply chain using bilevel programming approach” Journal of Industrial and Systems Engineering, 8(1): 106124.##Noori daryan, M., Taleizadeh, A.A. (2016). “Coordinating Pricing and Ordering Decisions in a MultiEchelon Pharmacological Supply Chain under Different Market Power using Game Theory” Journal of Industrial and Systems Engineering, In Press.##Ouyang, L.Y., C.H. Ho and C.H. Su (2009). "An optimization approach for joint pricing and ordering problem in an integrated inventory system with ordersize dependent trade credit." Computers & Industrial Engineering 57(3): 920930.##Ouyang, L.Y., K.S. Wu and C.H. Ho (2007). "An integrated vendor–buyer inventory model with quality improvement and lead time reduction." International Journal of Production Economics 108(1): 349358.##Pan, J. C.H. and Y.C. Hsiao (2005). "Integrated inventory models with controllable lead time and backorder discount considerations." International Journal of Production Economics 93: 387397.##Rhee, B.V.D., Schmidt, G., Veen, J.A.A.V.D., Venugopal, V., (2014). “Revenuesharing contracts across an extended supply chain” Business Horizons, 57(4):473482.##Sarkar, B., B. Mandal and S. Sarkar (2015). "Quality improvement and backorder price discount under controllable lead time in an inventory model." Journal of Manufacturing Systems 35: 2636.##Sethi, S. P., H. Yan and H. Zhang (2004). "Quantity Flexibility Contracts: Optimal Decisions with Information Updates." Decision Sciences 35(4): 691712.##Silver, E., D. F. Pyke and R. Peterson (1998). "Inventory management and production planning and scheduling."##Wang, C., R. Huang and Q. Wei (2015). "Integrated pricing and lotsizing decision in a twoechelon supply chain with a finite production rate." International Journal of Production Economics 161: 4453.##Wong, W.K., J. Qi and S. Leung (2009). "Coordinating supply chains with sales rebate contracts and vendormanaged inventory." International Journal of Production Economics 120(1): 151161.##Yıldırmaz, C., S. Karabatı and S. Sayın (2009). "Pricing and lotsizing decisions in a twoechelon system with transportation costs." OR spectrum 31(3): 629650.##You, P.S. and Y.C. Hsieh (2007). "An EOQ model with stock and price sensitive demand." Mathematical and Computer Modelling 45(7): 933942.##Zhao, Y., Choi, T.M., Cheng, T.C.E., Sethi, S.P., Wang, S., (2014). “Buyback contracts with pricedependent demands: Effects of demand uncertainty” European Journal of Operational Research, 239(3): 663673.##Zhu, S. X. (2015). "Integration of capacity, pricing, and leadtime decisions in a decentralized supply chain." International Journal of Production Economics 164: 1423.##]
Pricing strategy and return policy of oneechelon green supply chain under both green and hybrid productions
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2
In this paper,we investigate the pricing and return policy issueof oneechelon green supply chain, contain a manufacture who produces two type of products: green and nongreen products. These products have a same functional but in selling price and environmentally issues have different effects. Also we consider return policy for both products that can stimulate the customer valuation. We develop and analysis models of pricing strategy and return policy in both green production and hybrid production modes. System performance in both hybrid and green production mode are studied, and the return policy and its effect on these production modes in supply chain are investigated. The optimal solutions are derived and several numerical examples and sensitivity analysis are performed to demonstrate the applicability of the developed model and solution method.
1

17
29


Ata
Taleizadeh
University of Tehran
University of Tehran
Iran
taleizadeh@ut.ac.ir


Hossein
Heydaryan
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
School of Industrial Engineering, College
Iran
h_heydaryan@ut.ac.ir
Green supply chain
Return policy
pricing
Hybrid production mode
[Balachander, S. (2001). Warranty Signalling and Reputation. Management Science, 47(9), 1282–1289.##Chen, Y. J., & Sheu, J.B. (2009). Environmentalregulation pricing strategies for green supply chain management. Transportation Research Part E: Logistics and Transportation Review, 45(5), 667–677.##Coskun, S., Ozgur, L., Polat, O., & Gungor, A. (2015). A model proposal for green supply chain network design based on consumer segmentation. Journal of Cleaner Production.##Ding, H., Zhao, Q., An, Z., Xu, J., & Liu, Q. (2015). Pricing strategy of environmental sustainable supply chain with internalizing externalities. International Journal of Production Economics.##Ferrer, G., & Swaminathan, J. M. (2006). Managing New and Remanufactured Products. Management Science. Retrieved from##Ghosh, D., & Shah, J. (2012). A comparative analysis of greening policies across supply chain structures. International Journal of Production Economics, 135(2), 568–583.##Hugo, A., & Pistikopoulos, E. N. (2005). Environmentally conscious longrange planning and design of supply chain networks, 13.##Li, B., Zhu, M., Jiang, Y., & Li, Z. (2015). Pricing policies of a competitive dualchannel green supply chain. Journal of Cleaner Production, 1–14.##Li, Y., Xu, L., & Li, D. (2013). Examining relationships between the return policy, product quality, and pricing strategy in online direct selling. International Journal of Production Economics, 144(2), 451–460.##Liu, N., Choi, T. M., Yuen, C. W. M., & Ng, F. (2012). Optimal pricing, modularity, and return policy under mass customization. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 42(3), 604–614.##Mukhopadhyay, S. K., & Setaputra, R. (2007). A dynamic model for optimal design quality and return policies. European Journal of Operational Research, 180(3), 1144–1154.##Panda, S., Modak, N. M., Basu, M., & Goyal, S. K. (2015). Channel coordination and profit distribution in a social responsible threelayer supply chain. International Journal of Production Economics, 168, 224–233.##Ringbom, S., & Shy, O. (2004). Advance booking, cancellations, and partial refunds. Economics Bulletin, 13(1), 1–7.##Wood, S. L. (2001). Remote Purchase Environments: The Influence of Return Policy Leniency on TwoStage Decision Processes. Journal of Marketing Research, 38(2), 157–169.##Xu, L., Li, Y., Govindan, K., & Xu, X. (2015). Consumer returns policies with endogenous deadline and supply chain coordination. European Journal of Operational Research, 242(1), 88–99.##Zhang, C.T., Wang, H.X., & Ren, M.L. (2014). Research on pricing and coordination strategy of green supply chain under hybrid production mode. Computers & Industrial Engineering, 72, 24–31.##]
Benders Decomposition Algorithm for Competitive Supply Chain Network Design under Risk of Disruption and Uncertainty
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2
In this paper, bilevel programming is proposed for designing a competitive supply chain network. A twostage stochastic programming approach has been developed for a multiproduct supply chain comprising a capacitated supplier, several distribution centers, retailers and some resellers in the market. The proposed model considers demand’s uncertainty and disruption in distribution centers and transportation links. Then, Stackelberg game is used to formulate the competition among the component of supply chain. A bilevel mixed integer programming is used for developing a supply chain performed currently, then the impacts of the strategic facility location on the operational decisions such as inventory and shipments, have been investigated. To solve the model, we have used Bender’s decomposition algorithm, which is an exact algorithm for solving mixed integer programming. Finally, the outputs of the model are illustrated for investigating the efficiency of proposed model. Then, some discussions have been done through several numerical examples and some managerial insight has been suggested for the situations similar to the assumed problem.
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30
50


Ahmad
Makui
Iran University of Science and Technology
Iran University of Science and Technology
Iran
amakui@iust.ac.ir


Ali
Ghavamifar
Iran University of Science and Technology
Iran University of Science and Technology
Iran
ali.ghavami2010@gmail.com
competition
Supply chain network design
Disruption
Benders decomposition algorithm
[Azad, N., Davoudpour, H., 2013. Designing a stochastic distribution network model under risk. The International Journal of Advanced Manufacturing Technology 64, 2340.##BenAyed, O., Boyce, D.E., Blair, C.E., 1988. A general bilevel linear programming formulation of the network design problem. Transportation Research Part B: Methodological 22, 311318.##Benders, J.F., 1962. Partitioning procedures for solving mixedvariables programming problems. Numerische mathematik 4, 238252.##Bernstein, F., Federgruen, A., 2004. A general equilibrium model for industries with price and service competition. Operations research 52, 868886.##Bode, C., Wagner, S.M., 2015. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management 36, 215228.##Boyaci, T., Gallego, G., 2004. Supply chain coordination in a market with customer service competition. Production and Operations Management 13, 322.##CardonaValdés, Y., Álvarez, A., Ozdemir, D., 2011. A biobjective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies 19, 821832.##Cardoso, S.R., BarbosaPóvoa, A.P., Relvas, S., Novais, A.Q., 2015. Resilience metrics in the assessment of complex supplychains performance operating under demand uncertainty. Omega 56, 5373.##Drezner, Z., 1987. Heuristic solution methods for two location problems with unreliable facilities. Journal of the Operational Research Society, 509514.##Fallah, H., Eskandari, H., Pishvaee, M.S., 2015. Competitive closedloop supply chain network design under uncertainty. Journal of Manufacturing Systems.##Farahani, R.Z., Rezapour, S., Drezner, T., Esfahani, A.M., AmiriAref, M., 2014a. Locating and capacity planning for retailers of a new supply chain to compete on the plane. Journal of the Operational Research Society.##Farahani, R.Z., Rezapour, S., Drezner, T., Fallah, S., 2014b. Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega 45, 92118.##Giri, B.C., Bardhan, S., 2015. Coordinating a supply chain under uncertain demand and random yield in presence of supply disruption. International Journal of Production Research, 115.##Gurnani, H., Erkoc, M., Luo, Y., 2007. Impact of product pricing and timing of investment decisions on supply chain coopetition. European Journal of Operational Research 180, 228248.##Han, X., Chen, D., Chen, D., Long, H., 2015. Strategy of Production and Ordering in Closedloop Supply Chain under Stochastic Yields and Stochastic Demands. International Journal of U& EService, Science & Technology 8.##Hsu, C.I., Li, H.C., 2011. Reliability evaluation and adjustment of supply chain network design with demand fluctuations. International Journal of Production Economics 132, 131145.##Jabbarzadeh, A., Fahimnia, B., Sheu, J.B., 2015. An enhanced robustness approach for managing supply and demand uncertainties. International Journal of Production Economics.##Jabbarzadeh, A., Jalali Naini, S.G., Davoudpour, H., Azad, N., 2012. Designing a supply chain network under the risk of disruptions. Mathematical Problems in Engineering 2012.##Keyvanshokooh, E., Ryan, S.M., Kabir, E., 2015. Hybrid robust and stochastic optimization for closedloop supply chain network design using accelerated Benders decomposition. European Journal of Operational Research.##Ko, H.J., Evans, G.W., 2007. A genetic algorithmbased heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research 34, 346366.##Pan, F., Nagi, R., 2010. Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research 37, 668683.##Park, S., Lee, T.E., Sung, C.S., 2010. A threelevel supply chain network design model with riskpooling and lead times. Transportation Research Part E: Logistics and Transportation Review 46, 563581.##Peng, P., Snyder, L.V., Lim, A., Liu, Z., 2011. Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological 45, 11901211.##Pishvaee, M., Razmi, J., Torabi, S., 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: Logistics and Transportation Review 67, 1438.##Rezaee, A., Dehghanian, F., Fahimnia, B., Beamon, B., 2015. Green supply chain network design with stochastic demand and carbon price. Annals of Operations Research, 123.##Rezapour, S., Allen, J.K., Mistree, F., 2015. Uncertainty propagation in a supply chain or supply network. Transportation Research Part E: Logistics and Transportation Review 73, 185206.##Rezapour, S., Farahani, R.Z., 2010. Strategic design of competing centralized supply chain networks for markets with deterministic demands. Advances in Engineering Software 41, 810822.##Rezapour, S., Farahani, R.Z., 2014. Supply chain network design under oligopolistic price and service level competition with foresight. Computers & Industrial Engineering 72, 129142.##Romeijn, H.E., Shu, J., Teo, C.P., 2007. Designing twoechelon supply networks. European Journal of Operational Research 178, 449462.##Sadghiani, N.S., Torabi, S., Sahebjamnia, N., 2015. Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review 75, 95114.##Saharidis, G.K., Ierapetritou, M.G., 2009. Resolution method for mixed integer bilevel linear problems based on decomposition technique. Journal of Global Optimization 44, 2951.##Santoso, T., Ahmed, S., Goetschalckx, M., Shapiro, A., 2005. A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research 167, 96115.##Seifert, R.W., Langenberg, K.U., 2011. Managing business dynamics with adaptive supply chain portfolios. European Journal of Operational Research 215, 551562.##Shen, Z.J.M., 2005. A multicommodity supply chain design problem. IIE Transactions 37, 753762.##Shen, Z.J.M., Daskin, M.S., 2005. Tradeoffs between customer service and cost in integrated supply chain design. Manufacturing & service operations management 7, 188207.##Snyder, L.V., Daskin, M.S., 2005. Reliability models for facility location: the expected failure cost case. Transportation Science 39, 400416.##Sun, H., Gao, Z., Wu, J., 2008. A bilevel programming model and solution algorithm for the location of logistics distribution centers. Applied Mathematical Modelling 32, 610616.##Torabi, S., Baghersad, M., Mansouri, S., 2015. Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review 79, 2248.##Üster, H., Agrahari, H., 2011. A Benders decomposition approach for a distribution network design problem with consolidation and capacity considerations. Operations Research Letters 39, 138143.##Vidal, C.J., Goetschalckx, M., 2001. A global supply chain model with transfer pricing and transportation cost allocation. European Journal of Operational Research 129, 134158.##Viswanadham, N., Gaonkar, R.S., 2003. Partner selection and synchronized planning in dynamic manufacturing networks. Robotics and Automation, IEEE Transactions on 19, 117130.##Xiao, T., Yang, D., 2008. Price and service competition of supply chains with riskaverse retailers under demand uncertainty. International Journal of Production Economics 114, 187200.##Xu, J., Liu, Q., Wang, R., 2008. A class of multiobjective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor. Information Sciences 178, 20222043.##Yin, S., Nishi, T., Grossmann, I.E., 2015. Optimal quantity discount coordination for supply chain optimization with one manufacturer and multiple suppliers under demand uncertainty. The International Journal of Advanced Manufacturing Technology 76, 11731184.##You, F., Grossmann, I.E., 2008. Design of responsive supply chains under demand uncertainty. Computers & Chemical Engineering 32, 30903111.##Yu, H., Zeng, A.Z., Zhao, L., 2009. Single or dual sourcing: decisionmaking in the presence of supply chain disruption risks. Omega 37, 788800.##Zhang, D., 2006. A network economic model for supply chain versus supply chain competition. Omega 34, 283295.##Zhang, L., Rushton, G., 2008. Optimizing the size and locations of facilities in competitive multisite service systems. Computers & Operations Research 35, 327338.##]
Blood products supply chain design considering disaster circumstances (Case study: earthquake disaster in Tehran)
2
2
Maintaining the health of people during and after a disaster is one of the most important issues in disaster management. Blood products are among the essential items needed to save the human life and the lack of them may lead to significant losses in human health. In this paper a comprehensive mathematical model of blood products supply chain is presented to respond the need for blood products in disaster situations. The proposed model is a biobjective mixed integer programming and with respect to the unstable conditions during the disaster the uncertain parameters are modelled by fuzzy numbers. An interactive possibilistic programming approach is applied to handle the uncertainty. The developed model is implemented for the earthquake disaster case study in mega city of Tehran using blood transfusion network data. The results show the ability of the proposed model in generating effective solutions under earthquake conditions.
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51
72


Jamal
Kohneh
School of Industrial Engineering, Iran University of science & Technology
School of Industrial Engineering, Iran University
Iran
nahofti@ind.iust.ac.ir


Ebrahim
Teymoury
School of Industrial Engineering, Iran University of science & Technology
School of Industrial Engineering, Iran University
Iran
teimoury@iust.ac.ir


Mir Saman
Pishvaee
School of Industrial Engineering, Iran University of science & Technology
School of Industrial Engineering, Iran University
Iran
pishvaee@iust.ac.ir
Blood products supply chain
Earthquake disaster
Fuzzy mathematical programming
network design
MultiObjective Optimization
[AABB (American Association of Blood Banks). (2008) Disaster operations handbookHospital. Chapter 3, AABB Pub. Co., New York.##Aghezzaf, E.H., Sitompul, C. and Najid, N.M. (2010). Models for robust tactical planning in multistage production systems with uncertain demands. Computers & Operations Research, 37(5), 880889.##Arvan M., TavakkoliMoghaddam R., Abdollahi M. (2015). Designing a biobjective and multiproduct supply chain network for the supply of blood. Uncertain Supply Chain Management, 3(1), 5768.##Beliën, J. and Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 116.##Cetin, E. and Sarul, L.S. (2009). A blood bank location model: A multiobjective approach. European Journal of Pure and Applied Mathematics, 2(1), 112124.##Daskin, M.S., Coullard, C.R. and Shen, Z.J.M. (2002). An inventorylocation model: Formulation, solution algorithm and computational results. Annals of Operations Research, 110(14), 83106.##Ghandforoush, P. and Sen, T.K. (2010). A DSS to manage platelet production supply chain for regional blood centers. Decision Support Systems, 50(1), 3242.##Green, G.B., Modi, S., Lunney, K. and Thomas, T.L. (2003). Generic evaluation methods for disaster drills in developing countries. Annals of emergency medicine, 41(5), 689699.##Gunpinar S., Centeno, G. (2014). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129141.##Hemmelmayr, V., Doerner, K.F., Hartl, R.F. and Savelsbergh, M. W. (2010). Vendor managed inventory for environments with stochastic product usage. European Journal of Operational Research, 202(3), 686695.##IBTO (Iranian Blood Transfusion Organization), http://www.ibto.ir/.##Jabbarzadeh, A., Fahimnia, B. and 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: Logistics and Transportation Review, 70, 225244.##Jacobs, D.A., Silan, M.N. and Clemson, B.A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 4050.##JICA, C. (2000). The study on seismic microzoning of the Greater Tehran Area in the Islamic Republic of Iran. Pacific Consultants International Report, OYO Cooperation, Japan.##Liu, B. and Liu, Y. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445–450.##Mohamadi, A., Yaghoubi, S., & Derikvand, H. (2015). A credibilitybased chanceconstrained 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), 466488.##Mostafa, M.M., Sheaff, R., Morris, M. and Ingham, V. (2004). Strategic preparation for crisis management in hospitals: empirical evidence from Egypt. Disaster Prevention and Management: An International Journal, 13(5), 399408.##Motamedi, N., Shirazi, M.M. and Nouraei, N. (2012). Designing a Rescue System for EarthquakeStricken Area with the Aim of Facilitation and Accelerating Accessibilities (Case Study: City of Tehran). Proceedings of World Academy of Science, Engineering and Technology, World Academy of Science, Engineering and Technology, 69, 380383.##Nagurney, A., Masoumi, A.H., and Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 9(2), 205231.##Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680708.##NateghiA, F. (2001). Earthquake scenario for the megacity of Tehran. Disaster Prevention and Management, 10(2), 95100.##Pelling, M., Maskrey, A., Ruiz, P., Hall, L., Peduzzi, P., Dao, Q. H., Mouton, F., Herold, C. and Kluser, S. (2004). A Global Report: Reducing Disaster Risk a Challenge for Development. United Nations Development Programme, Bureau for Crisis Prevention and Recovery.##Pierskalla, W.P. (2004). Supply chain management of blood banks, In Brandeau, M.L., Sainfort, F., Pierskalla, W.P.(eds), Operations Research and Health Care. A Handbook of Methods and Applications, Kluwer’s International Series, Dordrecht, 103–145.##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: Logistics and Transportation Review, 67, 14–38.##Pishvaee, M.S., Torabi, S.A. and Razmi, J. (2012). Credibilitybased fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624632.##Şahin, G., Süral, H. and Meral, S. (2007). Locational analysis for regionalization of Turkish Red Crescent blood services. Computers & Operations Research, 34(3), 692704.##Sha, Y. and Huang, J. (2012). The multiperiod locationallocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 2128.##Shen, Z.J.M., Coullard, C.R. and Daskin, M.S. (2003). A joint locationinventory model. Transportation Science, 37(1), 4055.##Tehran navigation system (2015). “http://map.tehran.ir.##Torabi, S.A. and Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193214.##Van Zyl, G.J.J. (1963). Inventory control for perishable commodities (Doctoral dissertation, University of North Carolina at Chapel Hill).##Zangi abadi, A. and Tabrizi, N. (2006). Tehran earthquake and evaluating the space of vulnerability in urban areas. Geographical Research Quarterly, 38(1), 115130. (in persian)##Zendehdel M., Bozorgiamiri A., Omrani H.A. (2014). Location Model for Blood Donation Camps with Consideration of Disruption. Journal of Industrial Engineering##]
Cooperative network flow problem with pricing decisions and allocation of benefits: A game theory approach
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2
Several real problems in telecommunication, transportation, and distribution industries can be well analyzed by network flow models. In revenue management, pricing plays a primary role which increases the profit generated from a limited supply of assets. Pricing decision directly affects the amount of service or product demand. Hence, in traditional maximum flow problem, we assume that the demand of sink nodes depends on price of services or products of that nodes. We first develop a mathematical programming model for decision making of pricing by multiple owners in the maximum flow problem. Afterwards, coalitions between owners will be analyzed via different methods of cooperative game theory. A numerical example is given in order to show how these methods suggest appropriate assignments of extra revenue obtained from the cooperation among the owners.
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73
87


Ashkan
Hafezalkotob
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad
Iran
a_hafez@azad.ac.ir


Fateme
Naseri
Industrial Engineering college, Islamic Azad university, South Tehran Branch
Industrial Engineering college, Islamic Azad
Iran
chemicaleng.naseri@gmail.com
Network flow
pricing decision
flow game
cooperative game theory
Coalition
[Ahuja, R. K. (1993). Network Flows: Theory, Algorithms and Applications. Prentice Hall.##Ahuja, R., Magnanti, T., & Orlin, J. (1993). Network Flows: Theory, Algorithms, and Applications. PrenticeHall.##Ahuja, Ravindra K.Magnanti; Thomas L.; Orlin, James B. (1993). Network Flows: Theory, Algorithms and Applications. Prentice Hall.##Altman, E., & Wynter, L. (2004). Equilibrium, games, and pricing in transportation and telecommunication networks. Networks and Spatial Economics, 4(1), 721.##Barron, E. N. (2013). Game Theory: An Introduction (2nd edition ed.). New York: John Wiley & Sons.##Bertsekas, D. (1998). Network Optimization  Continuous and Discrete Models. Athena Scientific.##Branzei, R., Dimitrov, D., & Tijs, S. (2008). Models in cooperative game theory (Vol. 556). Springer Science & Business Media.##Deng, S., & Yano, C. (2006). Joint production and pricing decisions with setup costs and capacity constraints. Manage. Sci., 52, 741756.##Driessen, T. (1985). 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A multiperiod fuzzy mathematical programming model for crude oil supply chain network design considering budget and equipment limitations
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The major oil industry upstream activities include the exploration, drilling, extraction, pipelines installation, and production of crude oil. In this paper, we develop a mathematical model to plan for theseoperations as a crude oil supply chain network design problem.The proposed multiperiod mixed integer linear programming model entails both strategic (e.g., facility location and allocation) and tactical (e.g., project and production planning) decisions. With the objective of maximizing total Net Present Value (NPV) at the end of planning horizon, the decisions to be made comprise the location of the facilities, the flow of commodities and the amount of investment. The uncertain natures of important input parameters such as capital and operational cost, demand and price of crude oil, are taken into account via fuzzy theory. Finally, the performance of the developed model is investigated using the real data of Iranian South Oilfields.
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88
107


Armin
Jabbarzadeh
Iran University of Science and Technology
Iran University of Science and Technology
Iran
arminj@iust.ac.ir


Mirsaman
Pishvaee
Iran University of Science and Technology
Iran University of Science and Technology
Iran
pishvaee@iust.ac.ir


Ali
Papi
Iran University of Science and Technology
Iran University of Science and Technology
Iran
papimath@hotmail.com
Crude oil supply chain
Oilfield development
Multiperiod programming
Time horizon analysis
fuzzy optimization
[ASEERI, A., GORMAN, P. & BAGAJEWICZ, M. J. 2004. Financial Risk Management in Offshore Oil Infrastructure Planning and Scheduling. Industrial & Engineering Chemistry Research, 43, 30633072.##BELLMAN, R. E. & ZADEH, L. A. 1970. DecisionMaking in a Fuzzy Environment. Management Science, 17, B141B164.##CARVALHO, M. C. A. & PINTO, J. M. 2006a. A bilevel decomposition technique for the optimal planning of offshore platforms. Brazilian Journal of Chemical Engineering, 23, 6782.##CARVALHO, M. C. A. & PINTO, J. M. 2006b. An MILP model and solution technique for the planning of infrastructure in offshore oilfields. Journal of Petroleum Science and Engineering, 51, 97110.##DAMGHANI, R. K. V. R. G. K. K. 2015. Optimization of multiproduct, multiperiod closed loop supply chain under uncertainty in product return rate: case study in Kalleh dairy company. Journal of Industrial and Systems Engineering.##DEVINE, M. D. & LESSO, W. G. 1972. Models for the Minimum Cost Development of Offshore Oil Fields. Management Science, 18, B378B387.##GUPTA, V. & GROSSMANN, I. E. 2012. An Efficient Multiperiod MINLP Model for Optimal Planning of Offshore Oil and Gas Field Infrastructure. Industrial & Engineering Chemistry Research, 51, 68236840.##GUPTA, V. & GROSSMANN, I. E. 2014. Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties. Journal of Petroleum Science and Engineering, 124, 180197.##HENNIG, F., NYGREEN, B., CHRISTIANSEN, M., FAGERHOLT, K., FURMAN, K. C., SONG, J., KOCIS, G. R. & WARRICK, P. H. 2012. Maritime crude oil transportation – A split pickup and split delivery problem. European Journal of Operational Research, 218, 764774.##IYER, R. R., GROSSMANN, I. E., VASANTHARAJAN, S. & CULLICK, A. S. 1998. Optimal Planning and Scheduling of Offshore Oil Field Infrastructure Investment and Operations. Industrial & Engineering Chemistry Research, 37, 13801397.##KOSMIDIS, V. D., PERKINS, J. D. & PISTIKOPOULOS, E. N. 2002. A Mixed Integer Optimization Strategy for Integrated Gas/Oil Production. In: JOHAN, G. & JAN VAN, S. (eds.) Computer Aided Chemical Engineering. Elsevier.##KOSMIDIS, V. D., PERKINS, J. D. & PISTIKOPOULOS, E. N. 2004. Optimization of Well Oil Rate Allocations in Petroleum Fields. Industrial & Engineering Chemistry Research, 43, 35133527.##LIU, B. 2010. Uncertainty Theory. Uncertainty Theory. Springer Berlin Heidelberg.##PEIDRO, D., MULA, J., POLER, R. & VERDEGAY, J.L. 2009. Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160, 26402657.##RIBAS, G., LEIRAS, A. & HAMACHER, S. 2011. Tactical planning of the oil supply chain: optimization under uncertainty. PRÉANAIS XLIIISBPO.##SAHEBI & NICKEL 2014. Offshore oil network design with transportation alternatives. European Journal of Industrial Engineering, 8.##SAHEBI, H., NICKEL, S. & ASHAYERI, J. 2014. Strategic and tactical mathematical programming models within the crude oil supply chain context—A review. Computers & Chemical Engineering, 68, 5677.##SHAH, N. K., LI, Z. & IERAPETRITOU, M. G. 2011. Petroleum Refining Operations: Key Issues, Advances, and Opportunities. Industrial & Engineering Chemistry Research, 50, 11611170.##SHAMS, H., MOGOUEE, M. D., JAMALI, F. & HAJI, A. 2012. A Survey on Fuzzy Linear Programming. American Journal of Scientific Research, 117133.##SHEN, Q., CHU, F. & CHEN, H. 2011. A Lagrangian relaxation approach for a multimode inventory routing problem with transshipment in crude oil transportation. Computers & Chemical Engineering, 35, 21132123.##TANAKA†, H., OKUDA, T. & ASAI, K. 1973. On FuzzyMathematical Programming. Journal of Cybernetics, 3, 3746.##TARHAN, B., GROSSMANN, I. E. & GOEL, V. 2009. Stochastic Programming Approach for the Planning of Offshore Oil or Gas Field Infrastructure under DecisionDependent Uncertainty. Industrial & Engineering Chemistry Research, 48, 30783097.##TSARBOPOULOU, C. 2000. Optimization of oil facilities and oil production. Optimisation of Oil Facilities and Oil Production.##VAN DEN HEEVER, S. A. & GROSSMANN, I. E. 2000. An Iterative Aggregation/Disaggregation Approach for the Solution of a MixedInteger Nonlinear Oilfield Infrastructure Planning Model. Industrial & Engineering Chemistry Research, 39, 19551971.##]