Supply chain management through the stochastic goal programming model
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.
KeywordsSupply chain Stochastic goal programming Chance constrained programming Manager’s preferences Satisfaction functions
- 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
- Chopra, S., & Meindl, P. (2001). Supply chain management: Strategy, planning and operation. ISBN 0-13-026465-2, pp. 1–7.Google Scholar
- 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
- 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
- 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