Q-learning guided algorithms for bi-criteria minimization of total flow time and makespan in no-wait permutation flowshops

Abstract

Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly a deterministic mixed-integer linear programming (MILP) model is provided. Then Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BC-IG<inf>QL</inf>). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIH<inf>QL</inf>). Moreover the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS) Bi-Criteria Iterated Greedy Algorithm (BC-IG) Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IG<inf>ALL</inf>) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment performed on 480 problem instances of Vallada et al. (2015) which is known as the VRF benchmark set indicates that the BC-BIH<inf>QL</inf> and the BC-IG<inf>QL</inf> algorithms outperform the BC-GLS BC-IG BC-IG<inf>ALL</inf> and BC-VBIH algorithms in comparative performance metrics. More specifically the proposed BC-BIH<inf>QL</inf> and BC-IG<inf>QL</inf> algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time both are competitive with each other on the benchmark problems. Moreover the BC-IG<inf>QL</inf> algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG BC-IG<inf>ALL</inf> and BC-VBIH algorithms in small and large datasets. Similarly The BC-BIH<inf>QL</inf> algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG BC-IG<inf>ALL</inf> and BC-VBIH algorithms in small and large datasets respectively. This means that among all the features that have been compared the Q-learning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives such as makespan & the number of early jobs to overcome various industries' problems. © 2024 Elsevier B.V. All rights reserved.