A hybrid algorithm for order acceptance and scheduling problem in make-to-stock/make-to-order industries. (January 2019)

• Record Type: Journal Article
• Authors: Wang, Zhen; Qi, Yuanqing; Cui, Hairong; Zhang, Jihui
• Is Part Of: Computers & industrial engineering. Volume 127(2019)
• Page Start: 841
• Page End: 852
• Publication Date: 2019-01
• Subjects: Hybrid MTS/MTO production environment -- Order acceptance and scheduling -- Variable dimensional particle swarm optimization -- The Giffler-Thompson algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.11.021
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: An integrated production planning and scheduling decision framework is established. A mixed integer programming model is established to maximize total profit of the accepted MTO orders. Variable dimensional particle swarm optimization algorithm is adopted for order selection. The Giffler-Thompson algorithm is modified to schedule the selected orders. Abstract: Production systems' optimization is a complex and important problem in production and operations management. Integrated optimization from a whole system perspective of complex systems such as production systems is an interesting and important issue during recent years. First, an integrated decision framework is established. Then order acceptance and scheduling (OAS) as an important joint decision problem is studied in a job shop under hybrid make-to-stock/make-to-order (MTS/MTO) production environment. The OAS problem is formulated as a mixed integer programming model to maximize total profit of the accepted MTO orders. A fixed schedule for MTS orders are made in advance wherein the MTO orders are inserted into the idle time slots of schedule of MTS tasks. A hybrid algorithm is developed to solve the OAS problem. On the one hand, MTO orders are selected by a variable dimension particle swarm optimization (VDPSO). A new particle movement method is applied to cope with variable dimensions. On the other hand, the Giffler-Thompson algorithm is modified to schedule the selected orders. In order to verify and validate the proposed algorithm, a number of test problems are randomly generated. By comparing the computational results obtained by this algorithm and by fixed dimension particle swarm optimization (FDPSO), it can be clearly seen that VDPSO is more effective than FDPSO when the problem size increases. The research carried out in this paper opens up opportunities to study new methodology for solving OAS problem in a hybrid MTS/MTO environment.