A novel bi-level stochastic programming model for supply chain network design with assembly line balancing under demand uncertainty

Document Type : Research Paper

Authors

1 Arak University of Technology Arak, 38181-41167, Iran

2 Department of Industrial Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, 1591634311, Tehran, Iran

3 Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Bell Hall, Amherst, NY, 14260, USA

4 Department of Industrial Engineering, Islamic Azad University of Arak, 3836119131, Arak, Iran

Abstract

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 bi-level stochastic programming (BLSP) model has been developed in which SCND problem has been considered in the upper-level model, while the lower-level 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 heuristic-based 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.

Keywords

Main Subjects

References

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/10.1186/2251-712X-8-19.
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.
Ben-Ayed, 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.
Cardona-Valdés, Y., Álvarez, A., & Ozdemir, D. (2011). A bi-objective 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 multi-period activity-based supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.
Chen, T.-L., & Lu, H.-C. (2012). Stochastic multi-site capacity planning of TFT-LCD manufacturing using expected shadow-price 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., Gröwe-Kuska, 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 multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.
Hamta, N. et al. (2011). Bi-criteria 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/10.1186/2251-712X-8-19.
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.
Ben-Ayed, 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.
Cardona-Valdés, Y., Álvarez, A., & Ozdemir, D. (2011). A bi-objective 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 multi-period activity-based supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.
Chen, T.-L., & Lu, H.-C. (2012). Stochastic multi-site capacity planning of TFT-LCD manufacturing using expected shadow-price 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., Gröwe-Kuska, 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 multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.
Hamta, N. et al. (2011). Bi-criteria 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 bi-level 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, 1-17.
Iman, R.L. (2008). Latin hypercube sampling, Wiley Online Library.
Khalili-Damghani, K., Tavana, M., & Amirkhan, M. (2014). A fuzzy bi-objective mixed-integer 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/s00170-014-5891-7.
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating 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 two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39–49.
Lin, C.-C., & Wang, T.-H. (2011). Build-to-order 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.
Martínez-Jurado, P.J., & Moyano-Fuentes, J. (2013). Lean Management, Supply Chain Management and Sustainability: A Literature Review. Journal of Cleaner Production.
Melo, M.T., Nickel, S., & Saldanha-da-Gama, F. (2012). A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. International Journal of Production Economics, 136(1), 218–230.
Melo, M.T., Nickel, S., & Saldanha-da-Gama, 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., Saldanha-da-Gama, F., & Ziegler, H.-P. (2012). A multi-stage 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 U-type 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 bi-objective 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 bi-level linear multi-objective 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/10.1186/2251-712X-8-19.
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.
Ben-Ayed, 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.
Cardona-Valdés, Y., Álvarez, A., & Ozdemir, D. (2011). A bi-objective 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 multi-period activity-based supply chain network design problems. International Journal of Production Economics, 139(2), 664–677.
Chen, T.-L., & Lu, H.-C. (2012). Stochastic multi-site capacity planning of TFT-LCD manufacturing using expected shadow-price 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., Gröwe-Kuska, 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 multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99–111.
Hamta, N. et al. (2011). Bi-criteria 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 bi-level 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, 1-17.
Iman, R.L. (2008). Latin hypercube sampling, Wiley Online Library.
Khalili-Damghani, K., Tavana, M., & Amirkhan, M. (2014). A fuzzy bi-objective mixed-integer 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/s00170-014-5891-7.
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating 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 two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41(1), 39–49.
Lin, C.-C., & Wang, T.-H. (2011). Build-to-order 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.
Martínez-Jurado, P.J., & Moyano-Fuentes, J. (2013). Lean Management, Supply Chain Management and Sustainability: A Literature Review. Journal of Cleaner Production.
Melo, M.T., Nickel, S., & Saldanha-da-Gama, F. (2012). A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. International Journal of Production Economics, 136(1), 218–230.
Melo, M.T., Nickel, S., & Saldanha-da-Gama, 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., Saldanha-da-Gama, F., & Ziegler, H.-P. (2012). A multi-stage 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 U-type 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 bi-objective 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 bi-level linear multi-objective 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 co-firing in coal-fired power plants. Transportation Research Part E: Logistics and Transportation Review, 61, 115–134.
Sadjady, H., & Davoudpour, H. (2012). Two-echelon, multi-commodity supply chain network design with mode selection, lead-times 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 multi-national 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.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2004). Managing the supply chain: the definitive guide for the business professional, McGraw-Hill Companies.
Singh, A.R., Jain, R., & Mishra, P.K. (2013). Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming. The International Journal of Advanced Manufacturing Technology, 69(1–4), 555–562. Available at: http://dx.doi.org/10.1007/s00170-013-5054-2.
Sun, H., Gao, Z., & Wu, J. (2008). A bi-level 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 cross-docking 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 chance-constrained model. Engineering Optimization, 45(6), 745–765.
Vidal, C.J., & Goetschalckx, M. (1997). Strategic production-distribution 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 multi-objective 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.
Journal of Industrial and Systems Engineering
Volume 10, Issue 2
April 2017
Pages 87-112

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History

  • Receive Date: 28 September 2016
  • Revise Date: 10 December 2016
  • Accept Date: 21 February 2017

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