Cost-minimized and Multi-plant Scheduling in Distributed Industrial Systems

As a new paradigm, industrial Internet provides information sharing of various elements and resources in a whole industrial production process. It makes industrial production processes intelligent and provides low-cost and efficient scheduling. Manufacturing planning for multi-plant enterprises in industrial Internet brings many big challenges due to numerous optimization variables and limits of manufacturing capacities of plants, production resources, etc. Current studies fail to jointly consider the cost of different products in multiple heterogeneous plants, and ignore machine-level scheduling of manufacturing tasks. This work designs an improved framework for multi-plant enterprises, based on which a constrained non-linear integer program for reducing the total cost including production cost and transportation one is formulated. It jointly considers many complex nonlinear constraints, e.g., limits of replacement times, storage space, substitution and pairing production. It investigates machine-level task scheduling where different machines have heterogeneous manufacturing capacities. To solve it, this work proposes an algorithm named Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO). Realistic data-based experiments demonstrate GSPSO reduces the cost of a multi-plant system by at least 23% than its typical peers.