2017
10
2
0
0
Considering a Model for Sustainable Energy Planning Under Uncertainty
2
2
In this paper, real options theory is utilized to evaluate the effect of uncertain electricity and CO2 costs on speculation conduct. Methodologically, the allegiance of the newspaper in this appreciation is that uncertainty is not just stopped down as far as stochastic processes and their fluctuation, additionally as far as expected and acknowledged procedures, i.e. the procedures, which are used as a constituent of the progression system, and the processes that the speculator really confronts when picking the choices as per his ideal methodology. We utilize the components of portfolio theory and consolidate them in a vintage setting, keeping in mind the end goal to conquer the lack of it and advantage from that focal point, while as yet having the capacity to think about element portfolios. The idea is to not just discover portfolios that augment returns subject to a predefined level of danger or the other way around keeping in mind the end goal to place the ideal system of innovations at a period in time, yet to decide the ideal means of advancement of such a portfolio after some time, given changing information costs and continuous mechanical advancement and exposure about these processes. In other words, we locate the ideal portfolio over advancements, as well as crosswise over time and quality.
1

1
24


Abdollah
Arasteh
Babol Noshirvani University of Technology
Babol Noshirvani University of Technology
Iran
arasteh@nit.ac.ir
Energy planning
Sustainability
real options theory
portfolio theory
[Azevedo, A.F. and D.A. Paxson, Real options game models: A review. Real Options, 2010. 2010.##Hartman, R., The effects of price and cost uncertainty on investment. Journal of economic theory, 1972. 5(2): p. 258266.##Abel, A.B., Optimal investment under uncertainty. The American Economic Review, 1983. 73(1): p. 228233.##Nickell, S.J., The investment decisions of firms. 1978: Nisbet; Cambridge: Cambridge University Press.##Smit, H.T. and L. Trigeorgis, Strategic investment: Real options and games. 2012: Princeton University Press.##Jorgenson, D.W., Capital theory and investment behavior. The American Economic Review, 1963. 53(2): p. 247259.##Lensink, R., H. Bo, and E. Sterken, Investment, capital market imperfections, and uncertainty: Theory and empirical results. 2001: Edward Elgar Publishing.##Black, F. and M. Scholes, The pricing of options and corporate liabilities. The journal of political economy, 1973: p. 637654.##McDonald, R. and D. Siegel, The Value of Waiting to Invest," The Quarterly Journal of Economics, November 1986. 1986.##Pindyck, R., Irreversibility, Uncertainty and Investment. Journal of Economic Literature, 1991. 29(3): p. 11101148.##Pindyck, R.S., Capital risk and models of investment behaviour. 1988: Springer.##Pindyck, R.S., Investments of uncertain cost. Journal of financial Economics, 1993. 34(1): p. 5376.##Dixit, A.K. and R.S. Pindyck, Investment under uncertainty. 1994: Princeton university press.##Diaz, M., Valuation of Exploration and Production Assets: An Overview of Real Options Models. Journal of Petroleum Science and Engineering, 2004. 44(12): p. 93114.##Kolstad, C.D., Fundamental irreversibilities in stock externalities. Journal of Public Economics, 1996. 60(2): p. 221233.##Kolstad, C.D., Learning and stock effects in environmental regulation: the case of greenhouse gas emissions. Journal of environmental economics and management, 1996. 31(1): p. 118.##Ulph, A. and D. Ulph, GLOBAL WARMING, IRREVERSIBILITY AND LEARNING*. The Economic Journal, 1997. 107(442): p. 636650.##Garvin, M.J. and C.Y. Cheah, Valuation techniques for infrastructure investment decisions. Construction Management and Economics, 2004. 22(4): p. 373383.##Merton, R., The Theory of Rational Option Pricing. Journal of Economic Management Science, 1973. 4: p. 141183.##Pindyck, R.S., Uncertainty and exhaustible resource markets. The Journal of Political Economy, 1980: p. 12031225.##Brennan, M.J. and E.S. Schwartz, Evaluating natural resource investments. Journal of business, 1985: p. 135157.##Majd, S. and R.S. Pindyck, Time to build, option value, and investment decisions. Journal of financial Economics, 1987. 18(1): p. 727.##So, C.K.C.K.T., Game theory and real options: analysis of land value and strategic decisions in real estate development. 2013, Massachusetts Institute of Technology.##Balcer, Y. and S.A. Lippman, Technological expectations and adoption of improved technology. Journal of Economic Theory, 1984. 34(2): p. 292318.##Farzin, Y.H., K.J. Huisman, and P.M. Kort, Optimal timing of technology adoption. Journal of Economic Dynamics and Control, 1998. 22(5): p. 779799.##Grenadier, S.R. and A.M. Weiss, Investment in technological innovations: An option pricing approach. Journal of financial Economics, 1997. 44(3): p. 397416.##Change, I.P.O.C., Climate change 2007: synthesis report. Adopted by Session at IPCC Plenary XXVII, 2007.##Tseng, C.L. and G. Barz, Shortterm generation asset valuation: a real options approach. Operations Research, 2002. 50(2): p. 297310.##Hlouskova, J., et al., Real options and the value of generation capacity in the German electricity market. Review of Financial Economics, 2005. 14(3): p. 297310.##Deng, S.J. and S.S. Oren, Incorporating operational characteristics and startup costs in optionbased valuation of power generation capacity. Probability in the Engineering and Informational Sciences, 2003. 17(02): p. 155181.##Davis, G.A. and B. Owens, Optimizing the level of renewable electric R&D expenditures using real options analysis. Energy Policy, 2003. 31(15): p. 15891608.##Chaton, C. and J.A. Doucet, Uncertainty and Investment in Electricity Generation with an Application to the case of HydroQuebec. Annals of Operations Research, 2003. 120(14): p. 5980.##Keppo, J. and H. Lu, Real options and a large producer: the case of electricity markets. Energy Economics, 2003. 25(5): p. 459472.##Helfat, C.E., Investment choices in industry. 1988: Mit Press.##Seitz, N. and M. Ellison, Capital budgeting and longterm financing decisions. 1995: Harcourt Brace College Publishers.##Bar‐Lev, D. and S. Katz, A portfolio approach to fossil fuel procurement in the electric utility industry. The Journal of Finance, 1976. 31(3): p. 933947.##Awerbuch, S. and M. Berger. Applying portfolio theory to EU electricity planning and policy making. in IAEA/EET Working Paper No. 03, EET. 2003. Citeseer.##Awerbuch, S., Portfoliobased electricity generation planning: policy implications for renewables and energy security. Mitigation and adaptation strategies for Global Change, 2006. 11(3): p. 693710.##Blyth, W. and K. Hamilton, Aligning climate and energy policy. Chatham House. En http://www. chathamhouse. org. uk/pdf/research/sdp/Stern, 2006. 210406.##Daily spot prices for coal. 2016 [cited 2016; Available from: www.infomine.com.##Murto, P., Timing of investment under technological and revenuerelated uncertainties. Journal of Economic Dynamics and Control, 2007. 31(5): p. 14731497.##van Ruijven, B., et al., A global model for residential energy use: Uncertainty in calibration to regional data. Energy, 2010. 35(1): p. 269282.##Mercure, J.F., FTT:Power : A global model of the power sector with induced technological change and natural resource depletion. Energy Policy, 2012. 48: p. 799811.##Kikuchi, Y., et al., A scenario analysis of future energy systems based on an energy flow model represented as functionals of technology options. Applied Energy, 2014. 132: p. 586601.##Lund, P.D., et al., Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renewable and Sustainable Energy Reviews, 2015. 45: p. 785807.##Farzaneh, H., C.N.H. Doll, and J.A. Puppim de Oliveira, An integrated supplydemand model for the optimization of energy flow in the urban system. Journal of Cleaner Production, 2016. 114: p. 269285.##Hach, D. and S. Spinler, Capacity payment impact on gasfired generation investments under rising renewable feedin — A real options analysis. Energy Economics, 2016. 53: p. 270280.##Hull, J.C., Options, futures, and other derivatives. 2006: Pearson Education India.##]
An improved approach to find and rank BCCefficient DMUs in data envelopment analysis (DEA)
2
2
Recently, a mixed integer data envelopment analysis (DEA) model has been proposed to find the most BCCefficient (or the best) decision making unit (DMU) by Toloo (2012). This paper shows that the model may be infeasible in some cases, and when the model is feasible, it may fail to identify the most efficient DMU, correctly. We develop an improved model to find the most BCCefficient DMU that removes the mentioned drawbacks. Also, an algorithm is proposed to find and rank other most BCCefficient DMUs, when there exist more than one BCCefficient DMUs. The capability and usefulness of the proposed model are indicated, using a real data set of nineteen facility layout designs (FLDs) and twelve flexible manufacturing systems (FMSs).
1

25
34


Bohlool
Ebrahimi
Technology Development Institute (ACECR), Sharif University branch
Technology Development Institute (ACECR),
Iran
b.ebrahimi@aut.ac.ir


Morteza
Rahmani
Technology Development Institute (ACECR), Sharif branch, Tehran, Iran
Technology Development Institute (ACECR),
Iran
rahmani.mr@jdsharif.ac.ir
Data Envelopment Analysis (DEA)
most BCCefficient DMU
mixed integer DEA models
Ranking
facility layout design
[Allen, R., Athanassopoulos, A., Dyson, R.G., Thanassoulis, E. (1997). Weights restrictions and value judgments in data envelopment analysis: evolution, development and future directions. Annals of Operations Research, 13–34.##Amin, G.R. (2009). Comments on finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering, 56, 1701–1702.##Amin, G.R., Toloo, M. (2007). Finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering, 52 (2), 71–77.##Andersen, P., Petersen, N.C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261–1294.##Banker, R.D., Charnes, A., Cooper, W.W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30, 1078–1092.##Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–44.##Ertay, T., Ruan, D., Tuzkaya, U.R. (2006). Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems. Information science, 176, 237–262.##Foroughi, A.A. (2013). A revised and generalized model with improved discrimination for finding most efficient DMUs in DEA. Applied Mathematical Modelling, 37, 4067–4074.##Foroughi, A.A. (2011). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers & Industrial Engineering,60, 550–554.##Karsak, E.E., Ahiska, S.S. (2005). Practical common weight multicriteria decisionmaking approach with an improved discriminating power for technology selection. International Journal of Production Research, 43 (8), 1537–1554.##Liu, F.F., Peng, H.H. (2008). Ranking of units on the DEA frontier with common weights. Computers & Operations Research, 35, 1624 – 1637.##Lotfi, F.H., Jahanshahloo, G.R., Khodabakhshi, M., RostamyMalkhalifeh, M., Moghaddas, Z., VaezGhasemi, M. (2013). A review of ranking models in data envelopment analysis. Journal of Applied Mathematics, Article ID 492421, 20 pages.##Sexton, T.R., Silkman, R.H., Hogan, A.J. (1986). Data envelopment analysis: critique and extensions, in: R.H. Silkman (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis, JosseyBass, San Francisco, CA, 73–105.##Toloo, M. (2012). On finding the most BCCefficient DMU: A new integrated MIP–DEA model. Applied Mathematical Modelling, 36, 5515–5520.##Toloo, M., Nalchigar, S. (2009). A new integrated DEA model for finding most BCCefficient DMU. Applied Mathematical Modelling, 33, 597–604.##Toloo, M. (2014a). An epsilonfree approach for finding the most efficient unit in DEA. Applied Mathematical Modelling, 38, 3182–3192.##Toloo, M. (2014b). The role of nonArchimedean epsilon in finding the most efficient unit: With an application of professional tennis players. Applied Mathematical Modelling, 38, 5334–5346.##Toloo, M. (2014c). The most efficient unit without explicit inputs: An extended MILPDEA model. Measurement, 46, 3628–3634.##Toloo, M., Ertayb, T. (2014). The most cost efficient automotive vendor with price uncertainty: A new DEA approach. Measurement, 52, 135–144.##Toloo, M. (2015). Alternative minimax model for finding the most efficient unit in data envelopment analysis. Computers & Industrial Engineering, 81, 186–194.##Wang, Y.M., Jiang, P. (2012). Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis. Computers & Industrial Engineering, 62, 546–553.##]
Vehicle Routing Problem in Competitive Environment: TwoPerson Nonzero Sum Game Approach
2
2
Vehicle routing problem is one of the most important issues in transportation. Among VRP problems, the competitive VRP is more important because there is a tough competition between distributors and retailers. In this study we introduced new method for VRP in competitive environment. In these methods TwoPerson Nonzero Sum games are defined to choose equilibrium solution. Therefore, revenue given in each route is different. In this paper, two distributors has been considered in a city with a set of customers and the best route with maximum revenue has been determined. First we introduced the HawkDove procedure for the VRP problem and then by using Nash bargaining model the equilibrium strategy of the game is calculated. The result of this method is different based on the kind of the strategy that each distributor chooses. In the HawkDove game, if both of distributors choose the Dove procedure, they will get equal but less revenue. In the Nash Bargaining Game, the equilibrium strategy will obtained when distance of revenues of both distributors form its breakdown payoff is maximum.
1

35
52


Ashkan
Hafezalkotob
Industrial Engineering Department, Azad University Tehran South Branch, Tehran
Industrial Engineering Department, Azad University
Iran
a_hafez@azad.ac.ir


Reza
Mahmoudi
Department of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
Department of Industrial Engineering, Islamic
Iran
j.mahmoudi.reza@gmail.com


Mohammad
Shariatmadari
Islamic Azad UniversitySouth Tehran Branch, TehranIran
Islamic Azad UniversitySouth Tehran Branch,
Iran
m.shariat.62@gmail.com
vehicle routing problem
twoperson nonzero sum game
HawkDove game
Nash Bargaining Game
equilibrium solution
[AHMED E., A. S. HEGAZI, A. S. ELGAZZAR. (2002) “ON SPATIAL ASYMMETRIC GAMES”. Advances in Complex Systems A Multidisciplinary Journal, 5(4):433.##Alaei, S., & Setak, M., (1888). “DESIGNING OF SUPPLY CHAIN COORDINATION MECHANISM WITH LEADERSHIP CONSIDERING (RESEARCH NOTE)”. International Journal of EngineeringTransactions C: Aspects, 27(12).##Alexander, J., Skyrms, B. (1999). “Bargaining with neighbors: Is justice contagious?”. The Journal of philosophy, 588598.##Altman, E., ElAzouzi, R., Hayel, Y., Tembine, H. (2008) “An evolutionary game approach for the design of congestion control protocols in wireless networks”. Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks and Workshops, p: 547 – 552.##Asher, D.E. ; Zaldivar, A. ; Barton, B. ; Brewer, A.A (2012) “Reciprocity and Retaliation in Social Games With Adaptive Agents. Autonomous Mental Development”, IEEE Transactions. 4(3): 226 – 238.##Baldacci, R., Mingozzi, A., Roberti, R. (2012). “Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints”. European Journal of Operational Research, 218(1), 16.##Barron, E.N., (2013) “Game Theory: an introduction”, 2nd edition.##Binmore, K., Rubinstein, A., Wolinsky, A., (1986). “The Nash bargaining solution in economic modeling”, The RAND Journal of Economics 17 (2), pp. 176–188.##Bramoullé Y. (2001) Complementarily and Social Networks. French Ministry of Agriculture and the University of Maryland.##Braysy O, Gendreau M. (2005) “Vehicle routing problem with time windows, part I: route construction and local search algorithms”. Transportation Science;39:104–18.##Cantrell R.S. and Cosner C. (2004) “Deriving reaction–diffusion models in ecology from interacting particle systems”. Journal of Mathematical Biology, 48(2): 187217.##Cordeau JF, Desaulniers G, Desrosiers J, Solomon MM, Soumis F, (2002) “The VRP with time windows. In: Toth P, Vigo D, editors. The vehicle routing problem”, SIAM Monographs on Discrete Mathematics and Applications, Vol. 9, Philadelphia, PA; p. 157–194.##CourteneJones, W., & Briffa, M. (2014). “Boldness and asymmetric contests: roleand outcomedependent effects of fighting in hermit crabs”. Behavioral Ecology, aru085.##Crowley P.H. (2000) “Hawks, Doves, and Mixedsymmetry Games”. Journal of Theoretical Biology, 204(4): 543–563.##Dantzig, George Bernard and Ramser, John Hubert (1959), “The Truck Dispatching Problem”. Management Science 6 (1): 80–91.##Errico, F., Desaulniers, G., Gendreau, M., Rei, W., & ROUSSEAU, L. (2013). “The vehicle routing problem with hard time windows and stochastic service times”. Cahier du GERAD, G201345.##Fakhrzada, M. B., & Esfahanib, A. S. (2014). “Modeling the Time Windows Vehicle Routing Problem in Crossdocking Strategy Using Two Metaheuristic Algorithms”, International Journal of EngineeringTRANSACTIONS A: Basics, 27(7), 11131126.##Flisberg, P., Frisk, M., Rönnqvist, M., & Guajardo, M. (2015). “Potential savings and cost allocations for forest fuel transportation in Sweden: A countrywide study”. Energy, 85, 353365.##Geiger MJ., (2003). “Multicriteria und Fuzzy Systeme in Theorie und Praxis. In: A computational study of genetic crossover operators for multiobjective vehicle routing problem with soft time windows”. Deutscher UniversitiesVerlag; p. 191–207.##Golden, B. L., & Assad, A. A. (1988). “Vehicle Routing: Methods and Studies”, volume 16 of Studies in Management Science and Systems.##Kellner, F. (2016). Allocating greenhouse gas emissions to shipments in road freight transportation: Suggestions for a global carbon accounting standard. Energy Policy, 98, 565575.##Ko, Y. D. (2016). An airline's management strategies in a competitive air transport market. Journal of Air Transport Management, 50, 5361.##Hafezalkotob, A., Babaei, M. S., Rasulibaghban, A., Nooridaryan, M. (2014). “Distribution Design of Two Rival Decenteralized Supply Chains: a Twoperson Nonzero Sum Game Theory Approach”. International Journal of EngineeringTransactions B: Applications, 27(8), 12331242.##Helbing D. (2009) “Pattern formation, social forces, and diffusion instability in games with successdriven motion”. The European Physical Journal B, 67(3): 345356.##Hernandez, F., Feillet, D., Giroudeau, R., & Naud, O. (2016). “Branchandprice algorithms for the solution of the multitrip vehicle routing problem with time windows”. European Journal of Operational Research, 249(2), 551559.##Liao, C. H., & Chen, C. W. (2015). “Use of Advanced Traveler Information Systems for Route Choice: Interpretation Based on a Bayesian Model”. Journal of Intelligent Transportation Systems, 19(3), 316325.##Li, X., Tian, P., & Leung, S. C. (2010). “Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm”. International Journal of Production Economics, 125(1), 137145.##LIU Weibing and WANG Xianjia (2007) “Study on evolutionary games based on PSOneural networks”. Systems Engineering and Electronics.##Mahmoudi, R., Hafezalkotob, A., Makui, A. (2014). “Source selection problem of competitive power plants under government intervention: a game theory approach”. Journal of Industrial Engineering International, 10(3), 115.##MeliánBatista, B., De Santiago, A., AngelBello, F., & Alvarez, A. (2014). “A biobjective vehicle routing problem with time windows: a real case in Tenerife”. Applied Soft Computing, 17, 140152.##Nash JF, (1950). “The bargaining problem”. Econometrica; 18 (2), 155162.##Ombuki, B., Ross, B. J., & Hanshar, F. (2006). “Multiobjective genetic algorithms for vehicle routing problem with time windows”. Applied Intelligence,24(1), 1730.##Pedersen P. (2003) “Moral Hazard in Traffic Games”. Journal of Transport Economics and Policy, 37(1): 4768.##Qureshi AG, Taniguchi E, Yamada T. (2009E) “An exact solution approach for vehicle routing and scheduling problems with soft time windows”. Transportation Research;45(9), 60–77.##Sarmiento C. and Wilson W. W. (2005) “Spatial Modeling in Technology Adoption Decisions: The Case of Shuttle Train Elevators”. American Agricultural Economics Association, 87 (4): 10341045.##Schneider, Michael., (2016). "The vehiclerouting problem with time windows and driverspecific times." European Journal of Operational Research 250.1: 101119.##Solomon, M. M. (1986). “On the worst‐case performance of some heuristics for the vehicle routing and scheduling problem with time window constraints”. Networks, 16(2), 161174.##Solomon MM. (1987) “Algorithms for the vehicle routing and scheduling problem with time windows constraints”. Operations Research; 35:254– 65.##Solomon MM, Desrosiers J. (1988) “Time window constrained routing and scheduling problems”. Transportation Science; 22(1):1–13.##Taillard, É., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J. Y. (1997). “A tabu search heuristic for the vehicle routing problem with soft time windows”.Transportation science, 31(2), 170186.##TavakkoliMoghaddam, R., Gazanfari, M., Alinaghian, M., Salamatbakhsh, A., & Norouzi, N. (2011). “A new mathematical model for a competitive vehicle routing problem with time windows solved by simulated annealing”. Journal of manufacturing systems, 30(2), 8392.##TavakkoliMoghaddam, R., Safaei, N., & Shariat, M. “A. (2005). A multicriteria vehicle routing problem with soft time windows by simulated annealing”. Journal of Industrial EngineeringInt, 1(1), 2836.##Tembine, H., Altman, E., ElAzouzi, R., & Hayel, Y. (2011). “Bioinspired delayed evolutionary game dynamics with networking applications”. Telecommunication Systems, 47(12), 137152.##Wang, Z., Li, Y., & Hu, X. (2015). “A heuristic approach and a tabu search for the heterogeneous multitype fleet vehicle routing problem with time windows and an incompatible loading constraint”. Computers & Industrial Engineering, 89, 162176.##William H. Sandholm, Emin Dokumacı, and Ratul Lahkar(2006) “The Projection Dynamic, the Replicator Dynamic, and the Geometry of Population Games”. Conference on Optimization for helpful comments.##Xin Miao, Bo Yu, Bao Xi & Yan hong Tang (2010) “Modeling of bilevel games and incentives for sustainable critical infrastructure system”. Ukio Technologinis ir Ekonominis Vystymas, 16(3): 365379.##Yunrui, G., & Rui, D. (2013). “Evolutionary game of motorized and nonmotorized transport in city”. Journal of Henan Institute of Science and Technology (Natural Sciences Edition), 3, 026.##]
A game Theoretic Approach to Pricing, Advertising and Collection Decisions adjustment in a closedloop supply chain
2
2
This paper considers advertising, collection and pricing decisions simultaneously for a closedloop supplychain(CLSC) with one manufacturer(he) and two retailers(she). A multiplicatively separable new demand function is proposed which influenced by pricing and advertising. In this paper, three wellknown scenarios in the game theory including the Nash, Stackelberg and Cooperative games are exploited to study the effects of pricing, advertising and collection decisions on the CLSC. Using these scenarios, we identify optimal decisions in each case for the manufacture and retailers. Extending the ManufacturerStackelbergscenario, we introduce the manufacturer’s riskaverse behavior in a leader–follower type move under asymmetric information, focusing specifically on how the riskaverse behavior of the manufacturer influences all of the optimal decisions and construct manufacturerStackelberg games in which each retailer has more information regarding the market size than the manufacturer and another retailer. Under the mean–variance decision framework, we develop a closedloop supply chain model and obtain the optimal equilibrium results. In the situation of the stackelberg game, we find that whether utility of the manufacturer is better off or worse off depends on the manufacturer’s return rate and the degree of risk aversion under asymmetric and symmetric information structures. Numerical experiments compare the outcomes of decisions and profits among the mentioned games in order to study the application of the models.
1

53
74


Rashed
Sahraeian
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed
Iran
sahraeian@shahed.ac.ir


Elahe
Mohagheghian
Industrial Engineering Department, Shahed University, Tehran, Iran
Industrial Engineering Department, Shahed
Iran
e.mohaghegh89@yahoo.com
Closedloop supply chain (CLSC)
game theory
advertising
pricing
Asymmetric information
riskaverse behavior
[Basar, T., &Olsder, G. J. (1999). Dynamic noncooperative game theory (Vol. 23). Siam.##Berger, P. D. (1973). Statistical decision analysis of cooperative advertising ventures. Journal of the Operational Research Society, 24(2), 207216.##Chintagunta, P. K., & Jain, D. (1992). A dynamic model of channel member strategies for marketing expenditures. Marketing Science, 11(2), 168188.##Choi, T. M., Li, D., & Yan, H. (2008). Mean–variance analysis of a single supplier and retailer supply chain under a returns policy. European Journal of Operational Research, 184(1), 356376.##Esmaeili, M., & Zeephongsekul, P. (2010). Seller–buyer models of supply chain management with an asymmetric information structure. International Journal of Production Economics, 123(1), 146154.##Facchinei, F., & Kanzow, C. (2007). Generalized Nash equilibrium problems. 4OR, 5(3), 173210.##Feng, Q., Lai, G., & Lu, L. X. (2015). Dynamic Bargaining in a Supply Chain with Asymmetric Demand Information (With Online Appendices).##Harsanyi, J. C. (1968). Games with incomplete information played by “Bayesian” players part II. Bayesian equilibrium points. Management Science, 14(5), 320334.##Hong, X., Xu, L., Du, P., & Wang, W. (2015). Joint advertising, pricing and collection decisions in a closedloop supply chain. International Journal of Production Economics, 167, 1222.##Karray, S. (2015). Modeling brand advertising with heterogeneous consumer response: channel implications. Annals of Operations Research, 233(1), 181199.##Lai, G., Xiao, W., & Yang, J. (2012). Supply chain performance under market valuation: An operational approach to restore efficiency. Management Science, 58(10), 19331951.##Lau, A. H. L., & Lau, H. S. (2005). Some twoechelon supplychain games: Improving from deterministicsymmetricinformation to stochasticasymmetricinformation models. European Journal of Operational Research, 161(1), 203223.##Lau, H. S., & Lau, A. H. L. (1999). Manufacturer's pricing strategy and return policy for a singleperiod commodity. European Journal of Operational Research, 116(2), 291304.##Lee, J. Y., & Ren, L. (2011). Vendormanaged inventory in a global environment with exchange rate uncertainty. International Journal of Production Economics, 130(2), 169174.##Li, Z., Gilbert, S. M., & Lai, G. (2013). Supplier encroachment under asymmetric information. Management Science, 60(2), 449462.##Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments. Cowles Foundation monograph no. 16.##Meca, A., & Timmer, J. (2008). Supply chain collaboration (pp. 118). ITech Education and Publishing.##Savaskan, R. C., Bhattacharya, S., & Van Wassenhove, L. N. (2004). Closedloop supply chain models with product remanufacturing. Management science, 50(2), 239252##Slikker, M., & Van den Nouweland, A. (2012). Social and economic networks in cooperative game theory (Vol. 27). Springer Science & Business Media.##Tayur, S., Ganeshan, R., & Magazine, M. (Eds.). (2012). Quantitative models for supply chain management (Vol. 17). Springer Science & Business Media.##Tsay, A. A. (2002). Managing retail channel overstock: Markdown money and return policies. Journal of retailing, 77(4), 457492.##Wang, S. D., Zhou, Y. W., Min, J., & Zhong, Y. G. (2011). Coordination of cooperative advertising models in a onemanufacturer tworetailer supply chain system. Computers & Industrial Engineering, 61(4), 10531071.##Xie, J., & Neyret, A. (2009). Coop advertising and pricing models in manufacturer–retailer supply chains. Computers & Industrial Engineering, 56(4), 13751385.##Xu, G., Dan, B., Zhang, X., & Liu, C. (2014). Coordinating a dualchannel supply chain with riskaverse under a twoway revenue sharing contract. International Journal of Production Economics, 147, 171179.##Yan, R. (2010). Cooperative advertising, pricing strategy and firm performance in the emarketing age. Journal of the Academy of Marketing Science, 38(4), 510519.##Yue, J., Austin, J., Wang, M. C., & Huang, Z. (2006). Coordination of cooperative advertising in a twolevel supply chain when manufacturer offers discount. European Journal of Operational Research, 168(1), 6585.##Zhang, C. T., & Liu, L. P. (2013). Research on coordination mechanism in threelevel green supply chain under noncooperative game. Applied Mathematical Modelling, 37(5), 33693379.##]
A PFIHBased Heuristic for Green Routing Problem with Hard Time Windows
2
2
Transportation sector generates a considerable part of each nation's gross domestic product and considered among the largest consumers of oil products in the world. This paper proposes a heuristic method for the vehicle routing problem with hard time windows while incorporating the costs of fuel, driver, and vehicle. The proposed heuristic uses a novel speed optimization algorithm to reach its objectives. Performance of the proposed algorithm is validated by comparing its results with the results of the exact method and differential evaluation algorithm for smallscale problems. For largescale problems, the results of the proposed algorithm are compared with those obtained from the differential evaluation algorithm. Overall, results indicate the good performance of the proposed heuristic algorithm.
1

75
86


Mahdi
Alinaghian
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering,
Iran
alinaghian@cc.iut.ac.ir


Zahra
Kaviani Dezaki
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering,
Iran
z.kaviani@in.iut.ac.ir
Microscopic emission models
Vehicle routing with hard time windows
PFIH algorithm
Differential evolution algorithm
[Kirby, H. R., Hutton, B., McQuaid, R. W., Raeside, R., and Zhang, X., "Modelling the effects of transport policy levers on fuel efficiency and national fuel consumption,"##Toro, E., Franco, J., Echeverri, M., Guimarães, F., & Rendón, R. (2017). Green open locationrouting problem considering economic and environmental costs. International Journal of Industrial Engineering Computations, 8(2), 203216.##Prins, C., "A Simple and Effective Evolutionary Algorithm for the Vehicle Routing Problem," Computers & Operations Research, vol.31, pp.1985–2002,2004.##Jeon, G., Leep, H.R. and Shim, J.Y., "A Vehicle Routing Problem Solved by Using a Hybrid Genetic Algorithm, " Computers & Industrial Engineering, vol.53, pp. 680692, 2007.##Berger, J., Barkaoui, M. and Bräysy, O.,"A Routedirected Hybrid Genetic Approach for the Vehicle Routing Problem with Time Windows," Information Systems and Operational Research, vol.41, pp. 179194,2003.##Reimann, M., Doerner, K. and Hartl, R.F.,"DAnts: Savings Based Ants divide and conquer the vehicle routing problem," Computers & Operations Research,vol. 31, pp. 563–591,2004.## Bektaş, T. and Laporte, G., "The pollutionrouting problem," Transportation Research Part B: Methodological, vol. 45, pp. 12321250, 2011.##Demir, E., Bektaş, T., and Laporte, G., "An adaptive large neighborhood search heuristic for the PollutionRouting Problem," European Journal of Operational Research, vol. 223, pp. 346359, 2012.##Demir, E., Bektaş, T., and Laporte, G.,” The biobjective pollution routing problem”, Forthcoming European Journal of Operational Research, 2013.##Franceschetti, A., Honhon, D., VanWoensel, T., Bektas, T. and Laporte, G., “Timedependent pollution routing problem”, Forthcoming to Transportation Research Part B, 2013.##Koc,C., Bektas, T., Jabali, O. and Laporte, G., “The fleet size and mix Pollution routing problem”, Transportation Research Part B: Methodological, vol. 70, pp. 239254, 2014##Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: past and future trends. Expert Systems with Applications, 41(4), 11181138.## Solomon, M., "Algorithms for the vehicle routing and scheduling problems with time window constraints", Operations research, vol 35, pp. 254265, 1987##Thangiah, S.R., Osman, I.H., Sun, T., "Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows", Computer Science Department, Slippery Rock University, Technical Report SRU CpScTR9427, vol 69, 1994## R. Storn. " Differential Evolution, A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces."Journal of Global Optimization11: .341359, (1997).##Lou, Y., Li, J. and Wang, Y., "A BinaryDifferential Evolution algorithm based on Ordering of individuals", Sixth International Conference on Natural Computation, IEEE, vol 5, pp 22072211, 2010##Mingyong, L., & Erbao, C. (2010). An improved differential evolution algorithm for vehicle routing problem with simultaneous pickups and deliveries and time windows. Engineering Applications of Artificial Intelligence, 23(2), 188195.##Erbao, C., & Mingyong, L. (2009). A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands. Journal of computational and applied mathematics, 231(1), 302310.##Marinakis, Y., Marinaki, M., & Spanou, P. (2015). A memetic differential evolution algorithm for the vehicle routing problem with stochastic demands. In Adaptation and hybridization in computational intelligence (pp. 185204). Springer International Publishing.##]
A novel bilevel stochastic programming model for supply chain network design with assembly line balancing under demand uncertainty
2
2
This paper investigates the integration of strategic and tactical decisions in the supply chain network design (SCND) considering assembly line balancing (ALB) under demand uncertainty. Due to the decentralized decisions, a novel bilevel stochastic programming (BLSP) model has been developed in which SCND problem has been considered in the upperlevel model, while the lowerlevel model contains ALB problem as a tactical decision in the assemblers of supply chain network. To deal with demand uncertainty, a scenario generation algorithm has been proposed within the stochastic optimization model that combines time series model, Latin hypercube sampling method and backward scenario reduction technique. In addition based on the special structure of the model, a heuristicbased solution method is proposed to solve the developed BLSP model. Finally, computational experiments on several problem instances are presented to show the performance of the model and its solution method. The comparison between the stochastic and equivalent deterministic model demonstrated that the developed stochastic model mainly performs better than the deterministic model especially in making strategic decisions while the deterministic model works better in making tactical decisions.
1

87
112


Nima
Hamta
Arak University of Technology
Arak, 3818141167, Iran
Arak University of Technology
Arak, 3818141167,
Iran
nima.hamta@gmail.com


Mohsen
Akbarpour Shirazi
Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, 1591634311, Tehran, Iran
Department of Industrial Engineering, Amirkabir
Iran
akbarpour@aut.ac.ir


Sara
Behdad
Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Bell Hall, Amherst, NY, 14260, USA
Department of Industrial and Systems Engineering,
Iran
sarabehd@buffalo.edu


Mohammad
Ehsanifar
Department of Industrial Engineering, Islamic Azad University of Arak, 3836119131, Arak, Iran
Department of Industrial Engineering, Islamic
Iran
mehsanitfar@iauarak.ac.ir
Bilevel stochastic programming
Supply chain network design
scenariobased approach
assembly line balancing
uncertain demand
[Ahmadi Javid, A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597.##Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137–5155.##Babazadeh, R., Razmi, J., & Ghodsi, R. (2012). Supply chain network design problem for a new market opportunity in an agile manufacturing system. Journal of Industrial Engineering International, 8(1), 1–8. Available at: http://dx.doi.org/10.1186/2251712X819.##Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3), 694–715.##BenAyed, O., Boyce, D.E., & Blair III, C.E. (1988). A general bilevel linear programming formulation of the network design problem. Transportation Research Part B: Methodological, 22(4), 311–318.##Birge, J.R., & Louveaux, F. (2011). Introduction to stochastic programming, Springer.##CardonaValdés, Y., Álvarez, A., & Ozdemir, D. (2011). A biobjective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, 19(5), 821–832.##Carle, M.A., Martel, A., & Zufferey, N. (2012). The CAT metaheuristic for the solution of multiperiod activitybased supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.##Chen, T.L., & Lu, H.C. (2012). Stochastic multisite capacity planning of TFTLCD manufacturing using expected shadowprice based decomposition. Applied Mathematical Modelling, 36(12), 5901–5919.##Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation, Springer.##Colson, B., Marcotte, P., & Savard, G. (2005). Bilevel programming: A survey. 4OR, 3(2), 87–107.##Costa, A. et al. (2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers & Industrial Engineering, 59(4), 986–999.##Dupačová, J., GröweKuska, N., & Römisch, W. (2003). Scenario reduction in stochastic programming. Mathematical programming, 95(3), 493–511.##Georgiadis, M.C. et al. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254–272.##Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control, John Wiley & Sons.##Hamta, N. et al. (2013). A hybrid PSO algorithm for a multiobjective assembly line balancing problem with flexible operation times, sequencedependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.##Hamta, N. et al. (2011). Bicriteria assembly line balancing by considering flexible operation times. Applied Mathematical Modelling, 35(12), 5592–5608.##Ahmadi Javid, A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597.##Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137–5155.##Babazadeh, R., Razmi, J., & Ghodsi, R. (2012). Supply chain network design problem for a new market opportunity in an agile manufacturing system. Journal of Industrial Engineering International, 8(1), 1–8. Available at: http://dx.doi.org/10.1186/2251712X819.##Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3), 694–715.##BenAyed, O., Boyce, D.E., & Blair III, C.E. (1988). A general bilevel linear programming formulation of the network design problem. Transportation Research Part B: Methodological, 22(4), 311–318.##Birge, J.R., & Louveaux, F. (2011). Introduction to stochastic programming, Springer.##CardonaValdés, Y., Álvarez, A., & Ozdemir, D. (2011). A biobjective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, 19(5), 821–832.##Carle, M.A., Martel, A., & Zufferey, N. (2012). The CAT metaheuristic for the solution of multiperiod activitybased supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.##Chen, T.L., & Lu, H.C. (2012). Stochastic multisite capacity planning of TFTLCD manufacturing using expected shadowprice based decomposition. Applied Mathematical Modelling, 36(12), 5901–5919.##Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation, Springer.##Colson, B., Marcotte, P., & Savard, G. (2005). Bilevel programming: A survey. 4OR, 3(2), 87–107.##Costa, A. et al. (2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers & Industrial Engineering, 59(4), 986–999.##Dupačová, J., GröweKuska, N., & Römisch, W. (2003). Scenario reduction in stochastic programming. Mathematical programming, 95(3), 493–511.##Georgiadis, M.C. et al. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254–272.##Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control, John Wiley & Sons.##Hamta, N. et al. (2013). A hybrid PSO algorithm for a multiobjective assembly line balancing problem with flexible operation times, sequencedependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.##Hamta, N. et al. (2011). Bicriteria assembly line balancing by considering flexible operation times. Applied Mathematical Modelling, 35(12), 5592–5608.##Hamta, N. et al. (2014). Supply chain network optimization considering assembly line balancing and demand uncertainty. International Journal of Production Research, 53(10), 2970–2994.##Hamta, N., Akbarpour Shirazi, M., & Fatemi Ghomi, S.M.T. (2015). A bilevel programming model for supply chain network optimization with assembly line balancing and push–pull strategy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 117.##Iman, R.L. (2008). Latin hypercube sampling, Wiley Online Library.##KhaliliDamghani, K., Tavana, M., & Amirkhan, M. (2014). A fuzzy biobjective mixedinteger programming method for solving supply chain network design problems under ambiguous and vague conditions. The International Journal of Advanced Manufacturing Technology. Available at: http://link.springer.com/10.1007/s0017001458917.##Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust valuecreating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283–293.##Kristianto, Y. et al. (2014). A model of resilient supply chain network design: A twostage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39–49.##Lin, C.C., & Wang, T.H. (2011). Buildtoorder supply chain network design under supply and demand uncertainties. Transportation Research Part B: Methodological, 45(8), 1162–1176.##Longinidis, P., & Georgiadis, M.C. (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. International journal of production economics, 129(2), 262–276.##MartínezJurado, P.J., & MoyanoFuentes, J. (2013). Lean Management, Supply Chain Management and Sustainability: A Literature Review. Journal of Cleaner Production.##Melo, M.T., Nickel, S., & SaldanhadaGama, F. (2012). A tabu search heuristic for redesigning a multiechelon supply chain network over a planning horizon. International Journal of Production Economics, 136(1), 218–230.##Melo, M.T., Nickel, S., & SaldanhadaGama, F. (2009). Facility location and supply chain management–A review. European Journal of Operational Research, 196(2), 401–412.##Mohammadi Bidhandi, H., & Mohd Yusuff, R. (2011). Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modelling, 35(6), 2618–2630.##Nickel, S., SaldanhadaGama, F., & Ziegler, H.P. (2012). A multistage stochastic supply network design problem with financial decisions and risk management. Omega, 40(5), 511–524.##Olsson, A., Sandberg, G., & Dahlblom, O. (2003). On Latin hypercube sampling for structural reliability analysis. Structural safety, 25(1), 47–68.##Paksoy, T., & Özceylan, E. (2012). Supply chain optimisation with Utype assembly line balancing. International Journal of Production Research, 50(18), 5085–5105.##Paksoy, T., Özceylan, E., & Gökçen, H. (2012). Supply chain optimisation with assembly line balancing. International Journal of Production Research, 50(11), 3115–3136.##Paksoy, T., Pehlivan, N.Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762–2776.##Pishvaee, M.S., Farahani, R.Z., & Dullaert, W. (2010). A memetic algorithm for biobjective integrated forward/reverse logistics network design. Computers & Operations Research, 37(6), 1100–1112.##Pishvaee, M.S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107–114.##Rezapour, S. et al. (2011). Strategic design of competing supply chain networks with foresight. Advances in Engineering Software, 42(4), 130–141.##Roghanian, E., Sadjadi, S.J., & Aryanezhad, M.B. (2007). A probabilistic bilevel linear multiobjective programming problem to supply chain planning. Applied Mathematics and Computation, 188(1), 786–800.##Ahmadi Javid, A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation Research Part E: Logistics and Transportation Review, 46(5), 582–597.##Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137–5155.##Babazadeh, R., Razmi, J., & Ghodsi, R. (2012). Supply chain network design problem for a new market opportunity in an agile manufacturing system. Journal of Industrial Engineering International, 8(1), 1–8. Available at: http://dx.doi.org/10.1186/2251712X819.##Becker, C., & Scholl, A. (2006). A survey on problems and methods in generalized assembly line balancing. European journal of operational research, 168(3), 694–715.##BenAyed, O., Boyce, D.E., & Blair III, C.E. (1988). A general bilevel linear programming formulation of the network design problem. Transportation Research Part B: Methodological, 22(4), 311–318.##Birge, J.R., & Louveaux, F. (2011). Introduction to stochastic programming, Springer.##CardonaValdés, Y., Álvarez, A., & Ozdemir, D. (2011). A biobjective supply chain design problem with uncertainty. Transportation Research Part C: Emerging Technologies, 19(5), 821–832.##Carle, M.A., Martel, A., & Zufferey, N. (2012). The CAT metaheuristic for the solution of multiperiod activitybased supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.##Chen, T.L., & Lu, H.C. (2012). Stochastic multisite capacity planning of TFTLCD manufacturing using expected shadowprice based decomposition. Applied Mathematical Modelling, 36(12), 5901–5919.##Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation, Springer.##Colson, B., Marcotte, P., & Savard, G. (2005). Bilevel programming: A survey. 4OR, 3(2), 87–107.##Costa, A. et al. (2010). A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Computers & Industrial Engineering, 59(4), 986–999.##Dupačová, J., GröweKuska, N., & Römisch, W. (2003). Scenario reduction in stochastic programming. Mathematical programming, 95(3), 493–511.##Georgiadis, M.C. et al. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254–272.##Ghiani, G., Laporte, G., & Musmanno, R. (2004). Introduction to logistics systems planning and control, John Wiley & Sons.##Hamta, N. et al. (2013). A hybrid PSO algorithm for a multiobjective assembly line balancing problem with flexible operation times, sequencedependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.##Hamta, N. et al. (2011). Bicriteria assembly line balancing by considering flexible operation times. Applied Mathematical Modelling, 35(12), 5592–5608.##Hamta, N. et al. (2014). Supply chain network optimization considering assembly line balancing and demand uncertainty. International Journal of Production Research, 53(10), 2970–2994.##Hamta, N., Akbarpour Shirazi, M., & Fatemi Ghomi, S.M.T. (2015). A bilevel programming model for supply chain network optimization with assembly line balancing and push–pull strategy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 117.##Iman, R.L. (2008). Latin hypercube sampling, Wiley Online Library.##KhaliliDamghani, K., Tavana, M., & Amirkhan, M. (2014). A fuzzy biobjective mixedinteger programming method for solving supply chain network design problems under ambiguous and vague conditions. The International Journal of Advanced Manufacturing Technology. Available at: http://link.springer.com/10.1007/s0017001458917.##Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust valuecreating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283–293.##Kristianto, Y. et al. (2014). A model of resilient supply chain network design: A twostage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39–49.##Lin, C.C., & Wang, T.H. (2011). Buildtoorder supply chain network design under supply and demand uncertainties. Transportation Research Part B: Methodological, 45(8), 1162–1176.##Longinidis, P., & Georgiadis, M.C. (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. International journal of production economics, 129(2), 262–276.##MartínezJurado, P.J., & MoyanoFuentes, J. (2013). Lean Management, Supply Chain Management and Sustainability: A Literature Review. Journal of Cleaner Production.##Melo, M.T., Nickel, S., & SaldanhadaGama, F. (2012). A tabu search heuristic for redesigning a multiechelon supply chain network over a planning horizon. International Journal of Production Economics, 136(1), 218–230.##Melo, M.T., Nickel, S., & SaldanhadaGama, F. (2009). Facility location and supply chain management–A review. European Journal of Operational Research, 196(2), 401–412.##Mohammadi Bidhandi, H., & Mohd Yusuff, R. (2011). Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modelling, 35(6), 2618–2630.##Nickel, S., SaldanhadaGama, F., & Ziegler, H.P. (2012). A multistage stochastic supply network design problem with financial decisions and risk management. Omega, 40(5), 511–524.##Olsson, A., Sandberg, G., & Dahlblom, O. (2003). On Latin hypercube sampling for structural reliability analysis. Structural safety, 25(1), 47–68.##Paksoy, T., & Özceylan, E. (2012). Supply chain optimisation with Utype assembly line balancing. International Journal of Production Research, 50(18), 5085–5105.##Paksoy, T., Özceylan, E., & Gökçen, H. (2012). Supply chain optimisation with assembly line balancing. International Journal of Production Research, 50(11), 3115–3136.##Paksoy, T., Pehlivan, N.Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762–2776.##Pishvaee, M.S., Farahani, R.Z., & Dullaert, W. (2010). A memetic algorithm for biobjective integrated forward/reverse logistics network design. Computers & Operations Research, 37(6), 1100–1112.##Pishvaee, M.S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107–114.##Rezapour, S. et al. (2011). Strategic design of competing supply chain networks with foresight. Advances in Engineering Software, 42(4), 130–141.##Roghanian, E., Sadjadi, S.J., & Aryanezhad, M.B. (2007). A probabilistic bilevel linear multiobjective programming problem to supply chain planning. Applied Mathematics and Computation, 188(1), 786–800.##Roni, M.S. et al. (2014). A supply chain network design model for biomass cofiring in coalfired power plants. Transportation Research Part E: Logistics and Transportation Review, 61, 115–134.##Sadjady, H., & Davoudpour, H. (2012). Twoechelon, multicommodity supply chain network design with mode selection, leadtimes and inventory costs. Computers & Operations Research, 39(7), 1345–1354.##Santoso, T. et al. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167(1), 96–115.##Schmidt, G., & Wilhelm, W.E. (2000). Strategic, tactical and operational decisions in multinational logistics networks: a review and discussion of modelling issues. International Journal of Production Research, 38(7), 1501–1523.##Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain design under uncertainty using sample average approximation and dual decomposition. European Journal of Operational Research, 199(2), 409–419.##Shen, Z.J. max (2007). Integrated supply chain design models: a survey and future research directions. Journal of Industrial and Management Optimization, 3(1), 1–27.##SimchiLevi, D., Kaminsky, P., & SimchiLevi, E. (2004). Managing the supply chain: the definitive guide for the business professional, McGrawHill Companies.##Singh, A.R., Jain, R., & Mishra, P.K. (2013). Capacitiesbased supply chain network design considering demand uncertainty using twostage stochastic programming. The International Journal of Advanced Manufacturing Technology, 69(1–4), 555–562. Available at: http://dx.doi.org/10.1007/s0017001350542.##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(4), 610–616.##Sung, C.S., & Song, S.H. (2003). Integrated service network design for a crossdocking supply chain network. Journal of the Operational Research Society, 54(12), 1283–1295.##Tsao, Y.C., & Lu, J.C. (2012). A supply chain network design considering transportation cost discounts. Transportation Research Part E: Logistics and Transportation Review, 48(2), 401–414.##Tsiakis, P., & Papageorgiou, L.G. (2008). Optimal production allocation and distribution supply chain networks. International Journal of Production Economics, 111(2), 468–483.##Vahdani, B. et al. (2013). Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chanceconstrained model. Engineering Optimization, 45(6), 745–765.##Vidal, C.J., & Goetschalckx, M. (1997). Strategic productiondistribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research, 98(1), 1–18.##Wang, F., Lai, X., & Shi, N. (2011). A multiobjective optimization for green supply chain network design. Decision Support Systems, 51(2), 262–269.##Werner, A. (2005). Bilevel stochastic programming problems: Analysis and application to telecommunications.##]
Resilient Supplier Selection in a Supply Chain by a New IntervalValued Fuzzy Group Decision Model Based on Possibilistic Statistical Concepts
2
2
Supplier selection is one the main concern in the context of supply chain networks by considering their global and competitive features. Resilient supplier selection as generally new idea has not been addressed properly in the literature under uncertain conditions. Therefore, in this paper, a new multicriteria group decisionmaking (MCGDM) model is introduced with intervalvalued fuzzy sets (IVFSs) and fuzzy possibilistic statistical concepts. Then, a new weighting method for the supply chain experts or decision makers (DMs) is presented under uncertainty in supply chain networks. Additionally, a modified version of an entropy method is extended for computing the weight of each assessment criterion. Possibilistic mean, standard deviation, and the cuberoot of skewness are proposed within the MCGDM. In addition, a new fuzzy ranking method based on relativecloseness coefficients are proposed to rank the resilient supplier candidates. Finally, a resilient supplier selection problem is solved by the proposed group decision model to demonstrate its validity and is compared with a recent study.
1

113
133


Nazanin
Foroozesh
University of Tehran
University of Tehran
Iran
n.foroozesh@ut.ac.ir


Reza
TavakkoliMoghaddam
School of Industrial Engineering College of Engineering, University of Tehran P.O. Box: 11155/4563, Tehran, IRAN
School of Industrial Engineering College
Iran
tavakoli@ut.ac.ir


Seyed Meysam
Mousavi
Shahed University
Shahed University
Iran
sm.mousavi@shahed.ac.ir
Resilient supplier selection
intervalvalued fuzzy sets
possibilistic statistics
Supply chain management
multicriteria group decision making
[Chen, X., Du, H., & Yang, Y. (2014). The intervalvalued triangular fuzzy soft set and its method of dynamic decision making. Journal of Applied Mathematics, 2014.##Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 114.##Cornelis, C., Deschrijver, G., & Kerre, E. E. (2006). Advances and challenges in intervalvalued fuzzy logic. Fuzzy Sets and Systems, 157(5), 622627.##Deng, X., & Li, R. (2014). Gradually tolerant constraint method for fuzzy portfolio based on possibility theory. Information Sciences, 259, 1624.##Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014). Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 41(1), 156167.##Deschrijver, G. (2007). Arithmetic operators in intervalvalued fuzzy set theory. Information Sciences, 177(14), 29062924.##Dursun, M., & Karsak, E. E. (2013). A QFDbased fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37(8), 58645875.##Fazlollahtabar, H. (2016). An integration between fuzzy PROMETHEE and fuzzy linear program for supplier selection problem: Case study. Journal of Applied Mathematical Modelling and Computing, 1(1).##Guijun, W., & Xiaoping, L. (1998). The applications of intervalvalued fuzzy numbers and intervaldistribution numbers. Fuzzy Sets and Systems, 98(3), 331335.##Haldar, A., Ray, A., Banerjee, D., & Ghosh, S. (2014). Resilient supplier selection under a fuzzy environment. International Journal of Management Science and Engineering Management, 9(2), 147156.##Igoulalene, I., Benyoucef, L., & Tiwari, M. K. (2015). Novel fuzzy hybrid multicriteria group decision making approaches for the strategic supplier selection problem. Expert Systems with Applications, 42(7), 33423356.##Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2016). Supplier selection using fuzzy AHP and TOPSIS: A case study in the Indian automotive industry. 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Classifying inputs and outputs in interval data envelopment analysis
2
2
Data envelopment analysis (DEA) is an approach to measure the relative efficiency of decisionmaking units with multiple inputs and multiple outputs using mathematical programming. In the traditional DEA, it is assumed that we know the input or output role of each performance measure. But in some situations, the type of performance measure is unknown. These performance measures are called flexible measures. In addition, the traditional DEA needs crisp input and output data which may not always be available in real world applications. This paper discusses the input or output role of flexible measures using the DEA in environments with interval inputs and outputs. The application of the proposed DEA models is shown with a real dataset.
1

134
150


Hossein
Azizi
Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran.
Department of Applied Mathematics, Parsabad
Iran
azizhossein@gmail.com


Alireza
Amirteimoori
Department of Applied Mathematics, Rasht Branch, Islamic Azad University, Rasht, Iran
Department of Applied Mathematics, Rasht
Iran
ateimoori@iaurasht.ac.ir
Data Envelopment Analysis
Interval Data
flexible measures
[AMIRTEIMOORI, A. & EMROUZNEJAD, A. 2011. Flexible measures in production process: A DEAbased approach. RAIRO  Operations Research, 45, 6374.##AMIRTEIMOORI, A. & EMROUZNEJAD, A. 2012. Notes on “Classifying inputs and outputs in data envelopment analysis”. Applied Mathematics Letters, 25, 16251628.##AMIRTEIMOORI, A., EMROUZNEJAD, A. & KHOSHANDAM, L. 2013. Classifying flexible measures in data envelopment analysis: A slackbased measure. Measurement, 46, 41004107.##AMIRTEIMOORI, A. & KORDROSTAMI, S. 2005. Multicomponent efficiency measurement with imprecise data. Applied Mathematics and Computation, 162, 12651277.##AMIRTEIMOORI, A. & KORDROSTAMI, S. 2014. Data envelopment analysis with discretevalued inputs and outputs. Expert Systems, 31, 335342.##AMIRTEIMOORI, A., KORDROSTAMI, S. & AZIZI, H. 2016. Additive models for network data envelopment analysis in the presence of shared resources. Transportation Research Part D: Transport and Environment, 48, 411424.##AZIZI, H. 2013a. A note on “A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscriminatory factors”. Annals of Operations Research, 211, 4954.##AZIZI, H. 2013b. A note on data envelopment analysis with missing values: an interval DEA approach. The International Journal of Advanced Manufacturing Technology, 66, 18171823.##AZIZI, H. 2014. A note on “Supplier selection by the new ARIDEA model”. The International Journal of Advanced Manufacturing Technology, 71, 711716.##BALA, K. & COOK, W. D. 2003. Performance measurement with classification information: an enhanced additive DEA model. Omega, 31, 439450.##BANKER, R. D., CHARNES, A. & COOPER, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 10781092.##BEASLEY, J. E. 1990. Comparing university departments. Omega, 18, 171183.##BEASLEY, J. E. 1995. Determining Teaching and Research Efficiencies. Journal of the Operational Research Society, 46, 441452.##CHARNES, A., COOPER, W. W. & RHODES, E. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429444.##COOK, W. D., GREEN, R. & ZHU, J. 2006. Dualrole factors in data envelopment analysis. IIE Transactions, 38, 105115.##COOK, W. D. & HABABOU, M. 2001. Sales performance measurement in bank branches. Omega, 29, 299307.##COOK, W. D., HABABOU, M. & TUENTER, H. H. 2000. Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches. Journal of Productivity Analysis, 14, 209224.##COOK, W. D., HARRISON, J., ROUSE, P. & ZHU, J. 2012. Relative efficiency measurement: The problem of a missing output in a subset of decision making units. European Journal of Operational Research, 220, 7984.##COOK, W. D. & ZHU, J. 2006. Rank order data in DEA: A general framework. European Journal of Operational Research, 174, 10211038.##COOK, W. D. & ZHU, J. 2007. Classifying inputs and outputs in data envelopment analysis. European Journal of Operational Research, 180, 692699.##COOPER, W. W., PARK, K. S. & YU, G. 2001. An Illustrative Application of Idea (Imprecise Data Envelopment Analysis) to a Korean Mobile Telecommunication Company. Operations Research, 49, 807820.##DU, J., LIANG, L., CHEN, Y. & BI, G.B. 2010. DEAbased production planning. Omega, 38, 105112.##DU, K., XIE, C. & OUYANG, X. 2017. A comparison of carbon dioxide (CO2) emission trends among##provinces in China. Renewable and Sustainable Energy Reviews, 73, 1925.##ESKELINEN, J. 2017. Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259, 778788.##FAN, Y., BAI, B., QIAO, Q., KANG, P., ZHANG, Y. & GUO, J. 2017. Study on ecoefficiency of industrial parks in China based on data envelopment analysis. Journal of Environmental Management, 192, 107115.##FARZIPOOR SAEN, R. 2010. A new model for selecting thirdparty reverse logistics providers in the presence of multiple dualrole factors. The International Journal of Advanced Manufacturing Technology, 46, 405410.##FARZIPOOR SAEN, R. 2011. Media selection in the presence of flexible factors and imprecise data. Journal of the Operational Research Society, 62, 16951703.##JAHANSHAHLOO, G. R., AMIRTEIMOORI, A. & KORDROSTAMI, S. 2004. Multicomponent performance, progress and regress measurement and shared inputs and outputs in DEA for panel data: an application in commercial bank branches. Applied Mathematics and Computation, 151, 116.##JAHED, R., AMIRTEIMOORI, A. & AZIZI, H. 2015. Performance measurement of decisionmaking units under uncertainty conditions: An approach based on double frontier analysis. Measurement, 69, 264279.##KAO, C. 2006. Interval efficiency measures in data envelopment analysis with imprecise data. European Journal of Operational Research, 174, 10871099.##KAO, C. & HWANG, S.N. 2008. Efficiency decomposition in twostage data envelopment analysis: An application to nonlife insurance companies in Taiwan. European Journal of Operational Research, 185, 418429.##KAO, C. & LIU, S.T. 2000a. Data envelopment analysis with missing data: An application to University libraries in Taiwan. Journal of the Operational Research Society, 51, 897905.##KAO, C. & LIU, S.T. 2000b. Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets and Systems, 113, 427437.##KAO, C. & LIU, S.T. 2004. Predicting bank performance with financial forecasts: A case of Taiwan commercial banks. Journal of Banking & Finance, 28, 23532368.##KAO, C. & LIU, S.T. 2011. Efficiencies of twostage systems with fuzzy data. Fuzzy Sets and Systems, 176, 2035.##KAO, T.W., SIMPSON, N. C., SHAO, B. B. M. & LIN, W. T. 2017. Relating supply network structure to productive efficiency: A multistage empirical investigation. European Journal of Operational Research, 259, 469485.##KHALILIDAMGHANI, K., TAVANA, M. & HAJISAAMI, E. 2015. A data envelopment analysis model with interval data and undesirable output for combined cycle power plant performance assessment. Expert Systems with Applications, 42, 760773.##KIM, S.H., PARK, C.G. & PARK, K.S. 1999. An application of data envelopment analysis in telephone officesevaluation with partial data. Computers & Operations Research, 26, 5972.##LIU, D.Y., WU, Y.C., LU, W.M. & LIN, C.H. 2017. The Matthew effect in the casino industry: A dynamic performance perspective. Journal of Hospitality and Tourism Management, 31, 2835.##LIU, S.T. 2008. A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers & Industrial Engineering, 54, 6676.##LIU, S.T. 2014. Restricting weight flexibility in fuzzy twostage DEA. Computers & Industrial Engineering, 74, 149160.##LOZANO, S. & VILLA, G. 2006. Data envelopment analysis of integervalued inputs and outputs. Computers & Operations Research, 33, 30043014.##SMIRLIS, Y. G., MARAGOS, E. K. & DESPOTIS, D. K. 2006. Data envelopment analysis with missing values: An interval DEA approach. Applied Mathematics and Computation, 177, 110.##TAVANA, M., KHANJANI SHIRAZ, R., HATAMIMARBINI, A., AGRELL, P. J. & PARYAB, K. ##2013. Chanceconstrained DEA models with random fuzzy inputs and outputs. KnowledgeBased Systems, 52, 3252.##TOLOO, M. 2009. On classifying inputs and outputs in DEA: A revised model. European Journal of Operational Research, 198, 358360.##WANG, K., ZHANG, J. & WEI, Y.M. 2017. Operational and environmental performance in China's thermal power industry: Taking an effectiveness measure as complement to an efficiency measure. Journal of Environmental Management, 192, 254270.##WANG, Y.M., GREATBANKS, R. & YANG, J.B. 2005. Interval efficiency assessment using data envelopment analysis. Fuzzy Sets and Systems, 153, 347370.##WANG, Y.M., LUO, Y. & LIANG, L. 2009. Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises. Expert Systems with Applications, 36, 52055211.##ZHANG, D., LI, X., MENG, W. & LIU, W. 2009. Measuring the performance of nations at the Olympic Games using DEA models with different preferences. Journal of the Operational Research Society, 60, 983990.##]
Coordination of Information Sharing and Cooperative Advertising in a Decentralized Supply Chain with Competing Retailers Considering Free Riding Behavior
2
2
This paper studies a decentralized supply chain in which a manufacturer sells a common generic product through two traditional and online retailers under free riding market. We assume that the traditional retailer provides the value added services but the online retailer does not. Factors such as retail prices, local advertising of the retailers, global advertising of the manufacturer and service level of the traditional retailer simultaneously has effect on market demand. This paper studies the cost information sharing between the manufacturer and traditional retailer and uses the cooperative advertising program as an incentive mechanism for information sharing under free riding. This paper also examines how the free riding phenomenon affects the information sharing between the manufacturer and traditional retailer and also the supply chain coordination. Our analysis shows that, through the cooperative advertising program, information sharing between the manufacturer and traditional retailer is always beneficial for all the supply chain members and therefore, the entire supply chain is coordinated except when the traditional retailer is not efficient and the degree of free riding is relatively small.
1

151
168


Mostafa
Setak
K. N. Toosi University of Technology
K. N. Toosi University of Technology
Iran
mostafa.setak.ie@gmail.com


Hajar
Kafshian Ahar
K. N. Toosi University of Technology
K. N. Toosi University of Technology
Iran
hajar.kafshian@gmail.com


Saeed
Alaei
K. N. Toosi University of Technology
K. N. Toosi University of Technology
Iran
s.alaei@khatam.ac.ir
supply chain coordination
information sharing
vertical cooperative advertising
online shopping
free riding
game theory
[Alaei, S., Alaei, R. & Salimi, P. (2014). A game theoretical study of cooperative advertising in a singlemanufacturertworetailers supply chain. The International Journal of Advanced Manufacturing Technology, 74, 101111.##Alaei, S. & Setak, M. (2015). Multi objective coordination of a supply chain with routing and service level consideration. International Journal of Production Economics, 167, 271281.##Arshinder, Kanda, A. & Deshmukh, S. G. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115, 316335.##Aust, G. & Buscher, U. (2014a). Cooperative advertising models in supply chain management: A review. European Journal of Operational Research, 234, 114.##Aust, G. & Buscher, U. (2014b). Game theoretic analysis of pricing and vertical cooperative advertising of a retailerduopoly with a common manufacturer. Central European Journal of Operations Research, 121.##Aust, G. & Buscher, U. (2014c). Vertical cooperative advertising in a retailer duopoly. Computers & Industrial Engineering, 72, 247254.##Cachon, G. P. & Lariviere, M. A. (2005). Supply chain coordination with revenuesharing contracts: strengths and limitations. Management science, 51, 3044.##Chen, J. & Bell, P. (2011). The impact of customer returns on decisions in a newsvendor problem with and without buyback policies. International Transactions in Operational Research, 18, 473491.##Chiang, W. & Feng, Y. (2007). The value of information sharing in the presence of supply uncertainty and demand volatility. International Journal of Production Research, 45, 14291447.##Choi, H.c. P. (2010). Information sharing in supply chain management: A literature review on analytical research. California Journal, 8, 110116.##Dukes, A., GalOr, E. & Geylani, T. (2017). Bilateral Information Sharing and Pricing Incentives in a Retail Channel. Handbook of Information Exchange in Supply Chain Management. Springer.##Ghadimi, S., Szidarovszky, F., Farahani, R. Z. & Yousefzadeh Khiabani, A. (2011). Coordination of advertising in supply chain management with cooperating manufacturer and retailers. IMA Journal of Management Mathematics, 24, 119.##Hall, D. C. & Saygin, C. (2012). Impact of information sharing on supply chain performance. The International Journal of Advanced Manufacturing Technology, 58, 397409.##Hsiao, J. & Shieh, C. (2006). Evaluating the value of information sharing in a supply chain using an ARIMA model. The International Journal of Advanced Manufacturing Technology, 27, 604609.##Iyer, G. (1998). Coordinating channels under price and nonprice competition. Marketing Science, 17, 338355.##Jørgensen, S. & Zaccour, G. (2014). A survey of gametheoretic models of cooperative advertising. European Journal of Operational Research, 237, 114.##Karray, S. & Hassanzadeh Amin, S. (2015). Cooperative advertising in a supply chain with retail competition. International Journal of Production Research, 53, 88105.##Kunter, M. (2012). Coordination via cost and revenue sharing in manufacturer–retailer channels. European Journal of Operational Research, 216, 477486.##Li, B., Hou, P.W. & Li, Q.H. (2015). Cooperative advertising in a dualchannel supply chain with a fairness concern of the manufacturer. IMA Journal of Management Mathematics.##Liu, H. & Özer, Ö. (2010). Channel incentives in sharing new product demand information and robust contracts. European Journal of Operational Research, 207, 13411349.##Liu, H., Sun, S., Lei, M., Leong, G. K. & Deng, H. (2016). Research on Cost Information Sharing and Channel Choice in a DualChannel Supply Chain. Mathematical Problems in Engineering, 2016.##Liu, Y., Ding, C., Fan, C. & Chen, X. (2014). Pricing decision under dualchannel structure considering fairness and freeriding behavior. Discrete Dynamics in Nature and Society, 2014, 10 pages.##Mahajan, S. & Venugopal, V. (2011). Value of information sharing and lead time reduction in a supply chain with autocorrelated demand. Technology Operation Management, 2, 3949.##Partha Sarathi, G., Sarmah, S. P. & Jenamani, M. (2014). An integrated revenue sharing and quantity discounts contract for coordinating a supply chain dealing with short lifecycle products. Applied Mathematical Modelling, 38, 41204136.##Pasternack, B. A. (2008). Optimal pricing and return policies for perishable commodities. Marketing Science, 27, 133140.##Qian, Y., Chen, J., Miao, L. & Zhang, J. (2012). Information sharing in a competitive supply chain with capacity constraint. Flexible Services and Manufacturing Journal, 24, 549574.##Qin, Z. (2008). Towards integration: a revenuesharing contract in a supply chain. IMA Journal of Management Mathematics, 19, 315.##Raju, J. S., Sethuraman, R. & Dhar, S. K. (1995). The introduction and performance of store brands. Management science, 41, 957978.##SeyedEsfahani, M. M., Biazaran, M. & Gharakhani, M. (2011). A game theoretic approach to coordinate pricing and vertical coop advertising in manufacturer–retailer supply chains. European Journal of Operational Research, 211, 263273.##Shamir, N. (2012). Strategic information sharing between competing retailers in a supply chain with endogenous wholesale price. International Journal of Production Economics, 136, 352365.##Shin, J. (2007). How does free riding on customer service affect competition? Marketing Science, 26, 488503.##Tsay, A. A. (1999). The quantity flexibility contract and suppliercustomer incentives. Management science, 45, 13391358.##Tsay, A. A. & Agrawal, N. (2000). Channel dynamics under price and service competition. Manufacturing & Service Operations Management, 2, 372391.##Umit Kucuk, S. & Maddux, R. C. (2010). The role of the Internet on freeriding: An exploratory study of the wallpaper industry. Journal of Retailing and Consumer Services, 17, 313320.##Van Baal, S. & Dach, C. (2005). Free riding and customer retention across retailers' channels. Journal of Interactive Marketing, 19, 7585.##Wu, D., Ray, G., Geng, X. & Whinston, A. (2004). Implications of reduced search cost and free riding in ecommerce. Marketing Science, 23, 255262.##Wu, J., Zhai, X. & Huang, Z. (2008). Incentives for information sharing in duopoly with capacity constraints. Omega, 36, 963975.##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.##Xie, J. & Wei, J. C. (2009). Coordinating advertising and pricing in a manufacturer–retailer channel. European Journal of Operational Research, 197, 785791.##Yao, D.Q., Yue, X. & Liu, J. (2008). Vertical cost information sharing in a supply chain with valueadding retailers. Omega, 36, 838851.##Zhang, J. & Chen, J. (2013). Coordination of information sharing in a supply chain. International Journal of Production Economics, 143, 178187.##Zhang, J., Gou, Q., Liang, L. & Huang, Z. (2013). Supply chain coordination through cooperative advertising with reference price effect. Omega, 41, 345353.##]