Multiobjective direction driven local search for constrained supply chain configuration problem

Abstract

Supply chain configuration (SCC) is to make optimal choices for the members in supply chain management. The configurations of members with different choices can influence the total cost (totalCost) and the total time (totalTime) for the productions of the supply chain. This paper focuses on a constrained SCC (CSCC) problem to minimize the totalCost and to meet the totalTime constraint (i.e., the deadline limitation). To handle the constrained problem, existing methods may use constraints handing techniques like penalty or repair to help obtain feasible solutions. Differently, this paper considers the constraint of totalTime as another objective, and proposes a multiobjective direction driven local search (MDDLS) algorithm to solve the CSCC problem. The MDDLS explores the search path from a solution with smaller totalCost (or totalTime) to another solution with smaller totalTime (or totalCost). This way, MDDLS can obtain a set of solutions with different totalCost and totalTime values. The solution with best (smallest) totalCost value among all the feasible solutions (whose totalTime values satisfy the time constraint) is regarded as the final solution. The experiments show that MDDLS outperforms some compared algorithms for the CSCC problem.