An adaptive TS approach to JIT sequencing with variable processing times and sequence-dependent setups |
| |
| Institution: | 1. Department of Industrial & Management Systems Engineering, Dong-A University, Busan, South Korea;2. Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA;3. Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA;1. University of Groningen, Department of Operations, Faculty of Economics and Business, 9700 AV Groningen, The Netherlands;2. Lancaster University, Department of Management Science, Lancaster University Management School, LA1 4YX, UK;3. Jinan University, No 601, Huangpu Road, 510632 Guangzhou, PR China;1. University of Bremen, Bibliothekstr. 1, 28359 Bremen, Germany;2. BIBA – Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Hochschulring 20, 28359 Bremen, Germany;1. School of Business, Konkuk University, Seoul 05029, Republic of Korea;2. Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706, USA;3. School of Engineering, Master of Engineering Management Program, Nazarbayev University, Astana 010000, Kazakhstan;1. Jinan University, Huangpu Road, No 601, 510632 Guangzhou, PR China;2. Lancaster University, Department of Management Science, Lancaster University Management School, LA1 4YX, UK;3. University of Groningen, Department of Operations, Faculty of Economics and Business, 9700 AV Groningen, The Netherlands |
| |
| Abstract: | This paper addresses a single machine sequencing problem with variable processing times and sequence-dependent setups. The objective is to find the best trade-off between the JIT goal and the processing time compression and extension costs by simultaneously determining the job sequence and processing times for concerned jobs. Due to the combinatorial nature of the problem, it cannot be optimally solved in polynomial time. A tabu search approach is used to provide good and quick solutions. To improve the computational efficiency, an adaptive neighbourhood generation method is proposed and used in the tabu search algorithm. A total of 100 problems of different sizes have been solved to test the proposed approach. Our computational experience shows that the adaptive approach outperforms several other neighbourhood generation methods in terms of both convergence rate and solution quality. The effects of the search parameters are also discussed. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
