Abstract:
Nowadays, the profit is more concern in industry due to high business competition. Therefore, reducing the cost is needed to survive competition. Stochastic optimization had been interesting method to reduce opportunity and over-demand loss in the supply chain under uncertainties. Chance constrained optimization is one of the approaches to stochastic optimization. The uncertainties are included in chance constraint of event under level of confidence. This method is used to design the realistic supply chain giving more stabilities than deterministic optimization. The two case studies have been used to show the effectiveness of chance constrained optimization: the simple supply chain network and the biodiesel supply chain network. The objective of the optimization is to maximize the profit under the different levels of confidence. After obtaining the optimized supply chain, the networks from chance constrained optimization and deterministic optimization have been investigated and compared on the validation and violation of constraints. The results show that the chance constrained optimization can provide higher profit than the deterministic optimization under appropriate level of confidence. The violation of constraints results show that the stability of the network has been improved at higher levels of confidence. Finally, the sensitivity analysis shows that the opportunity loss played the major effect in the system.
