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


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Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: a literature review. International Journal of Production Research, 48(17), 5137–5155.

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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.

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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.

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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.

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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.

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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.

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