Name
Abstract | ||
|---|---|---|
Due to the complexity of a real-practice manufacturing process, various complex constraints should be considered to make the conventional model more suitable for the realistic production. This paper proposes a distributed assembly no-idle flow shop scheduling problem (DANIFSP) with the objective of minimizing the makespan at the assembly stage. The DANIFSP consists of two stages, i.e., production and assembly. The production stage contains several identical flow shops working in parallel, in which all jobs with series of operations that should be allocated to one of these factories and all operations of jobs should be performed in the allocated factories. To satisfy the no-idle constraint, each machine must process jobs without any interruption from the start of processing the job to the completion of processing the last job. In the second assembly stage, the processed jobs are assembled by a single machine. For addressing the DANIFSP, this paper extends three constructive heuristics based on a new job assignment rule and proposes two simple meta-heuristics including iterated local search (ILS) and variable neighborhood search (VNS). A comprehensive calibration and analysis for the proposed algorithms through a design of experiments are carried out. The comparison with recently published algorithms demonstrates the high effectiveness of the proposed ILS and VNS. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/jsyst.2018.2825337 | IEEE Systems Journal |
Keywords | Field | DocType |
Production facilities,Job shop scheduling,Processor scheduling,Search methods,Manufacturing | Mathematical optimization,Job shop scheduling,Variable neighborhood search,Idle,Computer science,Flow shop scheduling,Job assignment,Real-time computing,Local search (optimization),Iterated local search,Design of experiments | Journal |
Volume | Issue | ISSN |
13 | 2 | 1932-8184 |
Citations | PageRank | References |
3 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weishi Shao | 1 | 70 | 5.06 |
| De-Chang Pi | 2 | 177 | 39.40 |
| Zhongshi Shao | 3 | 92 | 7.91 |