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

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