Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming
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Order acceptance and scheduling (OAS) is an important issue in make-to-order production systems that decides the set of orders to accept and the sequence in which these accepted orders are processed to increase total revenue and improve customer satisfaction. This paper aims to explore the Pareto fronts of trade-off solutions for a multi-objective OAS problem. Due to its complexity, solving this problem is challenging. A two-stage learning/optimising (2SLO) system is proposed in this paper to solve the problem. The novelty of this system is the use of genetic programming to evolve a set of scheduling rules that can be reused to initialise populations of an evolutionary multi-objective optimisation (EMO) method. The computational results show that 2SLO is more effective than the pure EMO method. Regarding maximising the total revenue, 2SLO is also competitive as compared to other optimisation methods in the literature.