Abstract
Identical parallel machine scheduling problem with controllable processing times is investigated in this research. In such an area, our focus is mostly motivated by the adoption of just-in-time (JIT) philosophy with the objective of minimizing total weighted tardiness and earliness as well as job compressions/expansion cost simultaneously. Also the optimal set amounts of job compressions/expansion plus the job sequence are determined on each machine. It is assumed that the jobs processing times can vary within a given interval, i.e., it is permitted to compress or expand in return for compression/expansion cost. A mixed integer linear programming (MILP) model for the considered problem is firstly proposed and thereafter the optimal jobs set amounts of compression and expansion processing times in a known sequence are determined via parallel net benefit compression–net benefit expansion called PNBC–NBE heuristic. An intelligent water drop (IWD) algorithm, as a new swarm-based nature-inspired optimization one, is also adopted to solve this multi-criteria problem. A heuristic method besides three meta-heuristic algorithms is then employed to solve small- and medium- to large-size sample-generated instances. Computational results reveal that the proposed IWDNN outperforms the other techniques and is a trustable one which can solve such complicated problems with satisfactory consequences.
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Communicated by José Mario Martínez.
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Kayvanfar, V., Zandieh, M. & Teymourian, E. An intelligent water drop algorithm to identical parallel machine scheduling with controllable processing times: a just-in-time approach. Comp. Appl. Math. 36, 159–184 (2017). https://doi.org/10.1007/s40314-015-0218-3
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DOI: https://doi.org/10.1007/s40314-015-0218-3
Keywords
- Controllable processing times
- Earliness and tardiness
- Intelligent water drops algorithm
- Identical parallel machines