Abstract
We deal with a very complex and hard scheduling problem. Two types of products are processed by a heterogeneous resource set, where resources have different operating capabilities and setup times are considered. The processing of the products follows different workflows, allowing also assembly lines. The complexity of the problem arises from having a huge number of products from both types. The goal is to process all products in minimum time, i.e., the makespan is to be minimized. We consider a special case, where there are two job types on four different tasks, and four types of machines. Some of the machines are multi-purpose and some operations can be processed by different machine types. The processing time of an operation may depend also on the machine that processes it. The problem is very difficult to solve even in this special setting. Because of the complexity of the problem an exact solver would require too much running time. We propose a compound method where a heuristic is combined with an exact solver. Our proposed heuristic is composed of several phases applying different smart strategies. In order to reduce the computational complexity of the exact approach, we exploit the makespan determined by the heuristic as an upper bound for the time horizon, which has a direct influence on the instance size used in the exact approach. We demonstrate the efficiency of our combined method on multiple problem classes. With the help of the heuristic the exact solver is able to obtain an optimal solution in a much shorter amount of time.
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 1079-1113 |
Seitenumfang | 35 |
Fachzeitschrift | Central European Journal of Operations Research |
Jahrgang | 29 |
Ausgabenummer | 3 |
DOIs | |
Publikationsstatus | Veröffentlicht - Sept. 2021 |
Bibliographische Notiz
Funding Information:Open access funding provided by University of Pannonia (PE). Tibor Dulai, Gy?rgy D?sa and ?gnes Werner-Stark acknowledge the financial support of Sz?chenyi 2020 under the EFOP-3.6.1-16-2016-00015, Armin F?genschuh acknowledges the financial support of DFG Grant FU860/1-1 and D?sa was also supported by the National Research, Development and Innovation Office ? NKFIH under the Grant SNN 129364. Peter Auer, Gy?rgy D?sa, Tibor Dulai, Ronald Ortner and ?gnes Werner-Stark are supported by Stiftung Aktion ?sterreich-Ungarn 99?u1. Moreover, we would like to thank the valuable comments and proposals of the two anonymous referees which helped us in the presentation of our paper.
Funding Information:
Open access funding provided by University of Pannonia (PE). Tibor Dulai, György Dósa and Ágnes Werner-Stark acknowledge the financial support of Széchenyi 2020 under the EFOP-3.6.1-16-2016-00015, Armin Fügenschuh acknowledges the financial support of DFG Grant FU860/1-1 and Dósa was also supported by the National Research, Development and Innovation Office – NKFIH under the Grant SNN 129364. Peter Auer, György Dósa, Tibor Dulai, Ronald Ortner and Ágnes Werner-Stark are supported by Stiftung Aktion Österreich-Ungarn 99öu1. Moreover, we would like to thank the valuable comments and proposals of the two anonymous referees which helped us in the presentation of our paper.
Publisher Copyright:
© 2020, The Author(s).