A matheuristic for the generalized order acceptance and scheduling problem

• Published in: European journal of operational research Vol. 299; no. 1; pp. 87 - 103
• Main Authors: Tarhan, İstenç, Oğuz, Ceyda
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16.05.2022
• Subjects: Matheuristic; Metaheuristics; Order acceptance; Scheduling; Time-indexed models; Time-indexed models; Scheduling; Matheuristic; Order acceptance; Metaheuristics
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2021.08.024

•Generalized order acceptance and scheduling problem is addressed.•Mathematical and constraint programming models for small and medium size instances.•Matheuristic algorithm based on decomposing planning horizon into smaller segments.•New relaxed time-bucket formulation to cope with excessive CPU times.•State-of-the-art algorithms are outcompeted and new optimal solutions are identified. 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.