Abstract
This paper addresses a green transportation scheduling problem with realistic constraints widely existing in make-to-order manufacturing supply chains, such as pickup time and transport mode selections. The mathematical model of this problem is presented, which is formulated as a bi-objective mixed integer nonlinear program. The problem is simplified first by converting this program to a bi-objective integer nonlinear program. A novel evolution-strategy-based memetic Pareto optimization (ESMPO) approach is then developed to handle this new program, in which a multi-objective local search process is proposed to seek promising neighboring individuals and the faster nondominated sorting procedure is introduced into the memetic algorithm to perform multi-objective sorting. The performance of the proposed ESMPO approach is evaluated by numerical experiments based on industrial data and industrial-sized problems. Experimental results demonstrate that the proposed approach can effectively solve the investigated problem by generating much better solutions than 3 other metaheuristic-based Pareto optimization approaches and the industrial method do.
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