A Knowledge-Driven Cooperative Meta-Heuristic Algorithm for Multiplex Industrial Chain Networks Design with Multiple Supply Cycles

The current industrial chain structure is gradually transitioning from a single industrial chain  network to multiplex industrial chain networks. The design of multiplex industrial chain networks is crucial  for resource allocation within the industrial chain. In this paper, the design problem of multiplex industrial  chain networks with multiple supply cycles (MICND-MSC) is studied, and the impact of cross-chain supply  of multiple raw materials by raw material manufacturing enterprises is considered in the problem. The  mixed-integer linear programming (MILP) model of MICND-MSC is constructed, and a knowledge-driven  cooperative meta-heuristic algorithm (KDCMA) that integrates the framework of the artificial bee colony  algorithm (ABC) and reinforcement learning mechanism is proposed to address the MICND-MSC.  Multiple heuristic methods are designed to generate potential solutions for the initial population. The  neighborhood structures for different production stages are utilized in the KDCMA to explore the solution  space of MICND-MSC. The reinforcement learning mechanism is employed to learn empirical knowledge  of neighborhood structures to guide the search process. The experimental results indicate that the  KDCMA is a potential algorithm to address the MICND-MSC.