A Multiobjective Multiperiod Mixed-Integer Programming Optimization Model for Integrated Scheduling of Supply Chain Under Demand Uncertainty

Abstract

The problem of integrated scheduling of supply chain has a huge impact on operational efficiency and cost effectiveness. The increasing number of nodes, different time window constraints for customers, and a variety of uncertain scenarios make supply chain scheduling complicated. This research develops a multi-objective multi-period mixed-integer programming optimization model. We consider comprehensively the effect of demand uncertainty, time window constraints, the constraints of node capability, multi-period and sub-period factors. As the conflicting benefit factors, the cost and service level are two optimization objectives. The first objective function aims to minimize the total cost in all periods. The second objective function considers service level through minimizing the material flow of out-of-stock items in all periods to maximize the service level. And the suppliers’ capacity, the selection of suppliers, manufacturers’ productivity, the transaction relationship, the sub-period time, the inventory capacity and lead time for delivery are also considered. Then total costs and service levels are normalized, whose sum is the objective function. And the problem is transformed into a multi-period non-linear optimization problem. An improved Mixed Genetic Algorithm is designed to solve the model. Finally, the practicability of the proposed model and algorithm is demonstrated through its application in an electronics supply chain case study. The results indicate that the proposed model and algorithm can provide a promising approach to fulfill a multi-objective multi-period integrated scheduling plan under uncertain demand scenarios.