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Research on permutation flow shop scheduling problems with general position-dependent learning effects |
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| Authors: | Lin-Hui Sun Kai Cui Ju-Hong Chen Jun Wang Xian-Chen He |
| Affiliation: | 1. The School of Management, Xi’an University of Science and Technology, Xi’an, 710054, People’s Republic of China 2. The State Key Laboratory for Manufacturing Systems Engineering, Xi’an, 710053, People’s Republic of China 3. System Efficiency and Human Safety Ergonomics Institute of Xi’an University of Science and Technology, Xi’an, 710600, People’s Republic of China 4. Knowledge Management & Innovation Research Centre of Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China 5. School of Management Engineering, Xi’an University of Posts & Telecommunications, Xi’an, 710061, People’s Republic of China 6. The School of Economics and Management, Xi’an University of Technology, Xi’an, 710054, People’s Republic of China |
| Abstract: | Machine learning exists in many realistic scheduling situations. This study focuses on permutation flow shop scheduling problems, where the actual processing time of a job is defined by a general non-increasing function of its scheduled position, i.e., general position-dependent learning effects. The objective functions are to minimize the total completion time, the makespan, the total weighted completion time, and the total weighted discounted completion time, respectively. To solve these problems, we present approximation algorithms based on the optimal permutations for the corresponding single machine scheduling problems and analyze their worst-case error bound. |
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