Abstract
The distributed assembly permutation flowshop scheduling problem (DAPFSP) is a kind of typical NP-hard combinatorial optimization problem, and represents important area in operational research. This paper presents a new type of DAPFSP with four realistic extensions, i.e., random nature of processing times and assembly times, stochastic sequence-dependent setup times (SDST) on processing machines, stochastic job release times, as well as the no-wait constraint in the processing stage. To address the above mentioned stochastic version of DAPFSP with complex constraints, our previously proposed optimization framework, labeled as PSOSAHT which is characterized by PSO-based exploration, SA-based local search and HT method for evaluating and comparing the stochastic makespan, is adapted and investigated. Computational tests are conducted on benchmark problems, demonstrating the effectiveness and efficiency of the PSOSAHT method. To the best of knowledge, it is the first attempt to study the DAPFSP with job release time and sequence-dependent setup time both of which are stochastic.
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