A Two-Stage Optimization Algorithm for Multi-objective Job-Shop Scheduling Problem Considering Job Transport

Abstract

This paper solves the job-shop scheduling problem (JSP) considering job transport, with the aim to minimize the maximum makespan, tardiness, and energy consumption. In the first stage, the improved fast elitist nondominated sorting genetic algorithm II (INSGA-II) was combined with N5 neighborhood structure and the local search strategy of nondominant relationship to generate new neighborhood solutions by exchanging the operations on the key paths. In the second stage, the ant colony algorithm based on reinforcement learning (RL-ACA) was designed to optimize the job transport task, abstract the task into polar coordinates, and further optimizes the task. The proposed two-stage algorithm was tested on small, medium, and large-scale examples. The results show that our algorithm is superior to other algorithms in solving similar problems.

Production-Distribution of Perishable Food considering Customer Time Window

“…For more results on this topic, we refer readers to [4][5][6]. In recent years, many scholars have conducted related research on the production and distribution of perishable products, namely, the integrated scheduling of production and delivery problem (ISPDP), the main research results are concentrated in the work of [7,8]. Chen [7] studied the consideration of customer time.…”

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Order Management and Completion Date Prediction of Manufacturing Job-Shop Based on Deep Learning

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A Model for an Order-Picking Problem with a One-Directional Conveyor and Buffer

“…The quantity of consignments rises as a result [9]. Very stringent requirements for the corporate capacity of production and on-time completion are put forth by the increasingly market-centric order completion technique [12,13].…”