Optimization of the distributed permutation flowshop scheduling problem
| dc.contributor.author | Ali, Arshad | |
| dc.contributor.examiningcommittee | Ping, Qingjin (Mechanical Engineering) Appadoo, Srimantoorao S. (Supply Chain Management) | en_US |
| dc.contributor.supervisor | ElMekkawy, Tarek (Mechanical Engineering) Gajpal, Yuvraj (Supply Chain Management) | en_US |
| dc.date.accessioned | 2020-04-01T13:23:25Z | |
| dc.date.available | 2020-04-01T13:23:25Z | |
| dc.date.copyright | 2020-03-31 | |
| dc.date.issued | 2020-03 | en_US |
| dc.date.submitted | 2020-04-01T03:58:06Z | en_US |
| dc.degree.discipline | Mechanical Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | There are (n!)m possible solutions for scheduling jobs in a flowshop. Keeping the same schedule for all machines of a factory, brings possible solutions to n!. That’s the reason of knowing this problem as permutation flowshop scheduling problem. In case of distributed permutation flowshop, F possible permutation flowshops are considered for scheduling simultaneously, making it a distributed permutation flowshop scheduling problem (DPFSP). Distributed permutation flowshop scheduling problem consists of solving two problems simultaneously, allocation and sequencing of jobs for each flowshop. In this thesis, distributed permutation flowshop scheduling problem is studied for total flow time and makespan objectives. Additional constraints of no-wait and heterogenous nature of the factories are also considered while solving DPFSP for more realistic problems. The problems are solved by using mathematical model, construction heuristic and tabu search (TS) metaheuristic. Addition of insertion cost matrix and improvement scheme helped achieve improved results for the problems. Extensive numerical experiments are conducted to illustrate the efficiency and validity of proposed algorithms. The solutions of problems are useful to the decentralized, geographically scattered plants. It may help reduce manufacturing cost, organizational risk and can help improve quality of products. Current research improved results by 0.167% for homogeneous DPFSP problem with total flow time objective. Similarly, improved results by 4.77% for no-wait heterogenous DPFSP problem with makespan objective. | en_US |
| dc.description.note | May 2020 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34618 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | parallel flowshop, distributed permutation flowshop, tabu search, total completion time, makespan, no-wait heterogenous DPFSP | en_US |
| dc.title | Optimization of the distributed permutation flowshop scheduling problem | en_US |
| dc.title.alternative | parallel flowshop, distributed permutation flowshop, tabu search, total completion time, makespan, no-wait heterogenous DPFSP | en_US |
| dc.type | master thesis | en_US |