KMOEA: A Knowledge-Based Multiobjective Algorithm for Distributed Hybrid Flow Shop in a Prefabricated System

In this article, a distributed hybrid flow shop scheduling problem with variable speed constraints is considered. To solve it, a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) is developed. In the proposed algorithm, each solution is represented with a 3-D vector, where the factory assignment, machine assignment, operation scheduling, and speed setting are encoded. Then, four problem-specific lemmas are proposed, which are used as the knowledge to guide the main components of the algorithm, including the initialization, global, and local search procedures. Next, an efficient initialization approach is presented, which is embedded with several problem-related initialization rules. Furthermore, a novel Pareto-based crossover heuristic is designed to learn from more promising solutions. To enhance the local search abilities, a speed adjustment local search method is investigated. Finally, a set of instances generated based on the realistic prefabricated production system is tested to verify the efficiency and effectiveness of the proposed algorithm.

Automated summary

This article discusses a distributed hybrid flow shop scheduling problem with variable speed constraints and proposes a knowledge-based adaptive reference points multiobjective algorithm (KMOEA) to solve it. Each solution is represented by a 3-D vector encoding the factory assignment, machine assignment, operation scheduling, and speed setting. Several problem-specific lemmas are proposed to guide the algorithm's main components, including initialization, global, and local search procedures, resulting in an efficient and effective algorithm.