Annals of Operations Research

, Volume 251, Issue 1–2, pp 351–365 | Cite as

Supply chain management through the stochastic goal programming model

  • Alireza Azimian
  • Belaid AouniEmail author


Supply chain (SC) design problems are often characterized with uncertainty related to the decision-making parameters. The stochastic goal programming (SGP) was one of the aggregating procedures proposed to solve the SC problems. However, the SGP does not integrate explicitly the Manager’s preferences. The aim of this paper is to utilize the chance constrained programming and the satisfaction function concept to formulate strategic and tactical decisions within the SC while demand, supply and total cost are random variables.


Supply chain Stochastic goal programming Chance constrained programming Manager’s preferences Satisfaction functions 


  1. Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427–1433.CrossRefGoogle Scholar
  2. Aouni, B., & Kettani, O. (2001). Goal programming model: A glorious history and promising future. European Journal of Operational Research, 133(2), 38–46.CrossRefGoogle Scholar
  3. Aouni, B., Abdelaziz, F. B., & Martel, J. M. (2005). Decision-maker’s preferences modeling in the stochastic goal programming. European Journal of Operational Research, 162(3), 610–618.CrossRefGoogle Scholar
  4. Azadeh, A., Ghaderi, S. F., Dehghanbaghi, M., & Dabbaghi, A. (2010). Integration of simulation, design of experiment and goal programming for minimization of make span and tardiness. The International Journal of Advanced Manufacturing Technology, 46(5–8), 431–444.CrossRefGoogle Scholar
  5. Azaron, A., Furmans, K., & Modarres, M. (2010). Multi-objective stochastic programming approaches for supply chain management. In New developments in multiple objective and goal programming, (pp. 1–14). Berlin: Springer.Google Scholar
  6. Ben Abdelaziz, F., & Sameh, M. (2001). Application of goal programming in a multi-objective reservoir operation model in Tunisia. European Journal of Operational Research, 133(2), 352–361.CrossRefGoogle Scholar
  7. Bhattacharya, U. K. (2009). A chance constraints goal programming model for the advertising planning problem. European Journal of Operational Research, 192(2), 382–395.CrossRefGoogle Scholar
  8. Bravo, M., & Gonzalez, I. (2009). Applying stochastic goal programming: A case study on water use planning. European Journal of Operational Research, 196(3), 1123–1129.CrossRefGoogle Scholar
  9. Charnes, A., & Cooper, W. W. (1952). Chance conctraints and normal deviates. Journal of American Statistics Association, 57, 134–148.CrossRefGoogle Scholar
  10. Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management Sciences, 6, 73–78.CrossRefGoogle Scholar
  11. Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisfying under chance constraints. Operations Research, 11, 18–39.CrossRefGoogle Scholar
  12. Charnes, A., Cooper, W. W., & Ferguson, R. (1955). Optimal estimation of executive compensation by linear programming. Management Sciences, 1, 138–351.CrossRefGoogle Scholar
  13. Cherif, M. S., Chabchoub, H., & Aouni, B. (2008). Quality control system design through the goal programming model and the satisfaction functions. European Journal of Operational Research, 186, 1084–1098.CrossRefGoogle Scholar
  14. Chopra, S., & Meindl, P. (2001). Supply chain management: Strategy, planning and operation. ISBN 0-13-026465-2, pp. 1–7.Google Scholar
  15. Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586.CrossRefGoogle Scholar
  16. Forza, C., Salvador, F., & Rungtusanatham, M. (2005). Coordinating product design, process design, and supply chain design decisions: Part B. Coordinating approaches, tradeoffs, and future research directions. Journal of Operations Management, 23(3–4), 319–324.CrossRefGoogle Scholar
  17. Ho, W., Lee, C. K. M., & Ho, G. T. S. (2008). Optimization of the facility location-allocation problem in a customer-driven supply chain. Operations Management Research, 1(1), 69–79.CrossRefGoogle Scholar
  18. Hung, S. J. (2011). Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach. Expert Systems with Applications, 38(8), 9053–9062.CrossRefGoogle Scholar
  19. Ignizio, J. P. (1982). On the (re)discovery of fuzzy goal programming. Decision Sciences, 13, 331–336.CrossRefGoogle Scholar
  20. Jolai, F., Razmi, J., & Rostami, N. K. M. (2011). A fuzzy goal programming and meta heuristic algorithms for solving integrated production: Distribution planning problem. Central European Journal of Operations Research, 19(4), 547–569.CrossRefGoogle Scholar
  21. Jung, H. (2011). A fuzzy AHP–GP approach for integrated production-planning considering manufacturing partners. Expert Systems with Applications, 38(5), 5833–5840.CrossRefGoogle Scholar
  22. Ku, C. Y., Chang, C. T., & Ho, H. P. (2010). Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming. Quality and Quantity, 44(4), 623–640.CrossRefGoogle Scholar
  23. Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection in supply chain. Computers & Industrial Engineering, 46(1), 69–85.CrossRefGoogle Scholar
  24. Lee, A. H., Kang, H. Y., & Chang, C. T. (2009). Fuzzy multiple goal programming applied to TFT-LCD supplier selection by downstream manufacturers. Expert Systems with Applications, 36(3), 6318–6325.CrossRefGoogle Scholar
  25. Leung, S. C. H., & Ng, Wan-lung. (2007). A goal programming model for production planning of perishable products with postponement. Computers & Industrial Engineering, 53(3), 531–541.CrossRefGoogle Scholar
  26. Leung, S. C. H., & Chan, S. S. W. (2009). A goal programming model for aggregate production planning with resource utilization constraint. Computers & Industrial Engineering, 56(3), 1053–1064.CrossRefGoogle Scholar
  27. Li, L., Fonseca, D. J., & Chen, Der-San. (2006). Earliness-tardiness production planning for just-in-time manufacturing: A unifying approach by goal programming. European Journal of Operational Research, 175(1), 508–515.CrossRefGoogle Scholar
  28. Liang, T. F. (2009). Fuzzy multi-objective project management decisions using two-phase fuzzy goal programming approach. Computers & Industrial Engineering, 57(4), 1407–1416.CrossRefGoogle Scholar
  29. Liao, C. N., & Kao, H. P. (2011). An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Systems with Applications, 38(9), 10803–10811.CrossRefGoogle Scholar
  30. Liu, B. (1996). Dependent-chance goal programming and its genetic algorithm based approach. Mathematical and Computer Modeling, 24(7), 43–52.CrossRefGoogle Scholar
  31. Lotfi, M. M., & Torabi, S. A. (2011). A fuzzy goal programming approach for mid-term assortment planning in supermarkets. European Journal of Operational Research, 213(2), 430–441.CrossRefGoogle Scholar
  32. Martel, J. M., & Aouni, B. (1990). Incorporating the decision-maker’s preferences in the goal programming model. Journal of Operational Research Society, 41(1), 1121–1132.CrossRefGoogle Scholar
  33. Martel, J. M., & Aouni, B. (1996). Incorporating the decision-maker’s preferences in the goal programming model with fuzzy goals values, a new formulation lecture notes in economics and mathematical systems. Berlin: Springer.Google Scholar
  34. Martel, J. M., & Aouni, B. (1998). Diverse imprecise goal programming model formulations. Journal of Global Optimization, 12, 127–138.CrossRefGoogle Scholar
  35. Min, H., & Melachrinoudis, E. (1996). Dynamic location and entry mode selection of multinational manufacturing facilities under uncertainty: A chance-constrained goal programming approach. International Transactions in Operational Research, 3(1), 65–76.CrossRefGoogle Scholar
  36. Mula, J., Peidro, D., Diaz-Madronero, M., & Vicens, E. (2009). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204, 377–390.CrossRefGoogle Scholar
  37. Özcan, U., & Toklu, B. (2009). Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models. Computers & Operations Research, 36(6), 1955–1965.CrossRefGoogle Scholar
  38. Rostami NKi, M., Razmi, J., & Jolai, F. (2010). Designing a genetic algorithm to solve an integrated model in supply chain management using fuzzy goal programming approach. Balanced Automation Systems for Future Manufacturing Networks, 168–176.Google Scholar
  39. Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28, 581–598.CrossRefGoogle Scholar
  40. Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational research, 167, 96–115.CrossRefGoogle Scholar
  41. Selim, H., & Ozkarahan, I. (2008). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3–4), 401–418.CrossRefGoogle Scholar
  42. Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production-distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396–419.CrossRefGoogle Scholar
  43. Sinha, S. B., Rao, K. A., & Mangaraj, B. K. (1988). Fuzzy goal programming in multi-criteria decision systems: A case study in agriculture planning. Socio-Economic Planning Sciences, 22(2), 93–101.CrossRefGoogle Scholar
  44. Stevens, G. C. (1989). Integrating the supply chains. International Journal of Physical Distribution and Materials Management, 8(8), 3–8.CrossRefGoogle Scholar
  45. Syntetos, A. A., Babai, M. Z., Lengu, D., & Altay, N. (2011). Distributional assumptions for parametric forecasting of intermittent demand, in service parts management. London: Springer.Google Scholar
  46. Taylor, B. W., & Anderson, P. F. (1979). Goal programming approach to marketing/ production planning. Industrial Marketing Management, 8(2), 136–144.CrossRefGoogle Scholar
  47. Wang, G., Huang, S. H., & Dismukes, J. P. (2005). Manufacturing supply chain design and evaluation. The International Journal of Advanced Manufacturing Technology, 25(1–2), 93–100.CrossRefGoogle Scholar
  48. Wong, J. T. (2012). DSS for 3PL provider selection in global supply chain: Combining the multi-objective optimization model with experts’ opinions. Journal of Intelligent Manufacturing, 23(3), 599–614.CrossRefGoogle Scholar
  49. Yang, L., & Feng, Y. (2007). A bicriteria solid transportation problem with fixed charge under stochastic environment. Applied Mathematical Modeling, 31(12), 2668–2683.CrossRefGoogle Scholar
  50. Zarandi, M. H. F., Sisakht, A. H., & Davari, S. (2011). Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming. The International Journal of Advanced Manufacturing Technology, 56(5–8), 809–821.CrossRefGoogle Scholar
  51. Zhou, S. Y., & Chen, R. Q. (2001). A decision model for selecting participants in supply chain. Journal of Shanghai University (English Edition), 5(4), 341–344.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Business and EconomicsWilfrid Laurier UniversityWaterlooCanada
  2. 2.Management and Marketing Department, College of Business and EconomicsQatar UniversityDohaQatar

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