Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

The characteristic of multi-shop scheduling is that the processing equipment is scattered in multiple workshops in different geographical locations. To solve this problem, a multi-shop comprehensive scheduling algorithm considering migration dual-objective is proposed of migration of a single complex product in multi-shop equipment, and the workpiece is migrated from one device to another during the processing. The algorithm first uses the leaf nodes of the process tree as a set of schedulable operations; second, the pre-scheduled operation set is determined according to the long path first strategy and the short time strategy; third, the actual scheduling set of the same idle equipment is determined according to the same equipment procedure selection strategy in the workshop; finally, considering the migration time and cost of the process to be processed, a two-objective optimization strategy for process shop selection is proposed. The example results show that the algorithm in this paper consumes less time and cost during the migration process, and can obtain a relatively compromised solution of the total time and total cost of product scheduling.

This paper studies the flexible integrated scheduling problem, which is an extension of job-shop scheduling considering the assembly and the machining stages at the same time, with networked devices (FISND). The completion time of the entire product may be impacted by the uncertainty of the process constraint structure and flexible equipment, so we take machining structure evaluation (MSE) into account. We proposed an improved artificial bee colony algorithm considering MSE (ABC[Formula: see text]) with two new strategies: one is a dynamic perturbation step strategy and the other is double-chain similarity and migration time factor strategy to evaluate the product processing structure, and then we computed the selection probability of the followers by it. Finally, the experimental results show that ABC[Formula: see text] has a better performance and faster convergence than the algorithm ABC, hyABC, GA. It can not only solve the flexible integrated scheduling problem with networked devices, but also yield better solutions to the typical flexible integrated scheduling problem than the flexible equipment integrated scheduling algorithm based on device-driven.