Abstract
In the path of coordinating regional development, if different enterprises cannot coordinate and progress together, it will inevitably lead to the phenomenon of “bad money drives out good money”. Therefore, this research addresses a distributed blocking flow shop scheduling problem with sequence-dependent setup time and aims to balance the energy consumption costs among factories while satisfying the makespan upper-bound criterion. This optimization problem is denoted as DFm|block,stsd|ɛECC/Cmax. We propose an accelerated discrete artificial bee colony (A-DABC) algorithm to address our established mathematical model. In the A-DABC algorithm, various advanced neighborhood operators are integrated to achieve a relative balance in the cost of energy consumption among distributed factories. To address the computational cost associated with the designed operators, we developed three accelerated evaluation mechanisms by integrating the energy cost adjustments resulting from insertions or swaps into the original energy cost, effectively reducing the overall computational complexity. The comprehensive statistical experiments demonstrate that the computational effort of energy consumption costs is significantly decreased, and A-DABC has prominent advantages in addressing DFm|block,stsd|ɛECC/Cmax by comparing to four existing algorithms in all test cases.
Published Version
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