Abstract
A novel differential evolution (DE) algorithm, namely DE_TWET, is presented to deal with the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs). The criterion is to minimize a total weighted earliness/tardiness (TWET) cost function. The presented algorithm is a hybrid of DE, problem’s properties, and a special designed local search. In DE_TWET, DE is adopted to execute global search in the solution space, and the problem’s properties are utilized to give a speed-up evaluation method and construct the local search, and the special local search is designed to enhance the local search ability of DE. Experimental results and comparisons demonstrate the effectiveness and robustness of the presented algorithm.
Highlights
With the development of just-in-time (JIT) manufacturing systems, the study on the scheduling problems with both earliness and tardiness (E/T) costs is of greater significance
∑ classified as Fm / no − wait, STsd, rj / (w′j E j + w′j′T j ), which can be identified as total weighted earliness/tardiness (TWET)-no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs)
Because 1// ∑T j is NP-hard and it reduces to Fm / ∑ ∑ ∑ no − wait, STsd, rj / (w′j E j + w′j′T j ) (i.e., 1// Tj ∝ Fm / no − wait, STsd, rj / (w′j E j + w′j′T j ) ), ∑ it can be concluded that Fm / no − wait, STsd, rj / (w′j E j + w′j′T j ) is NP-hard [3]
Summary
With the development of just-in-time (JIT) manufacturing systems, the study on the scheduling problems with both earliness and tardiness (E/T) costs is of greater significance. A typical production scheduling problem with strong engineering background [1,2], the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs), is considered, whose criterion is to minimize a total weighted earliness/tardiness (TWET) cost function. In such a case, each job j must be processed through all machines without any interruption, and both the setup times and the release dates need to be explicitly treated, and an optimal schedule is the one that all jobs finish exactly on their due dates. It is meaningful and practical to develop an effective algorithm for the considered problem
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