A matheuristic for the generalized order acceptance and scheduling problem

Abstract

In make-to-order production systems, manufacturer can have limited capacity and due to the order delivery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem with release times and sequence dependent setup times that determines which orders to accept and how to schedule them simultaneously to maximize the revenue (GOAS). The aim of this study is to develop an effective and efficient solution methodology for the GOAS problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model, and a matheuristic algorithm that consists of a time-bucket based mixed integer linear programming model, a variable neighborhood search algorithm and a tabu search algorithm. Computational results show that the proposed matheuristic outperforms both the proposed exact models and previous state-of-the-art algorithms developed for the GOAS problem. The boundary of optimally solved instance size is pushed further and near optimal solutions are obtained in reasonable time for instances falling beyond this boundary.