An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion.

With the increase in production levels, a pattern of industrial production has shifted from a single factory to multiple factories, resulting in a distributed production model. The distributed flowshop scheduling problem (DPFSP) is of great research significance as a frequent pattern in real production activities. In this paper, according to real-world scenarios, we have added blocking constraints and sequence-dependent setup times (SDST) to the DFSP and proposed a distributed blocking flowshop scheduling problem with sequence-dependent setup times (DBFSP_SDST). In a distributed environment, the allocation of resources and utilization have become an urgent problem to be solved. In addition, scheduling problems related to resource conservation have also attracted increasing attention. Therefore, we study DBFSP_SDST and consider minimizing the energy consumption cost of the critical factory (critical factory is the factory with maximum energy consumption cost) under resource balance. To tackle this problem, an effective iterated greedy algorithm based on a learning-based variable neighborhood search algorithm (VNIG) is proposed. In VNIG, an efficient construction heuristic is well designed. Two different local searches based on the characteristics of the proposed problem are developed to enhance the local exploitation by neighborhood searching. A learning-based selection variable neighborhood search strategy is designed to avoid the solution trapping in local optima. By conducting extensive simulation experiments, the proposed VNIG shows superior performance compared with artificial chemical reaction optimization (CRO, 2017), the discrete artificial bee colony algorithm (DABC, 2018), the iterative greedy algorithm with a variable neighborhood search scheme (IGR, 2021), and the evolution strategy approach (ES, 2022). • A mathematical model of an energy-balance DBFSP_SDST is formulated. • An efficient heuristic is presented to generate a high-quality initial solution. • Two different local searches based on the characteristics of the proposed problem are developed. • A learning-based selection variable neighborhood search is designed. • The VNIG shows superior performance compared with four algorithms on 90 instances. [ABSTRACT FROM AUTHOR]