Multi-objective cooperative co-evolutionary algorithm for negotiated scheduling of distribution supply chain

It is investigated that multiple distributors simultaneously negotiate with a manufacturer to improve themselves schedules on a distribution supply chain in which manufacturer has stronger power than distributors and does scheduling decision prior to distributors. A compensation based negotiation scheduling model is built. A novel multi-objective cooperative co-evolutionary algorithm (GLCCEC) that concurrently implements local evolutionary computing of distributors and global evolutionary computing of manufacturer is proposed. Global elite solution combination strategy with gradually gene skipping change and real time updating of global non-dominated solution set are designed for manufacturer. A dynamic programming algorithm with constraint of retaining sequence of local schedule is designed in order to get global solution from a local solution of distributor. Computational experiments show that GLCCEC algorithm can effectively improve schedule of each distributor with no deterioration of manufacturer's schedule. Moreover, the non-dominated solutions of GLCCEC not only are better than that of other best cooperative co-evolutionary algorithms: MOCCGA, NSCCGA, GBCCGA, but also has good spread in solution space. © 2013 ISCAS.