scholarly journals Improved artificial immune algorithm for the flexible job shop problem with transportation time

2020 ◽  
pp. 002029402096213
Author(s):  
Xiao-long Chen ◽  
Jun-qing Li ◽  
Yu-yan Han ◽  
Hong-yan Sang
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Heuristic Algorithms ◽  
Immune Algorithm ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Transportation Time ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP), as one branch of the job shop scheduling, has been studied during recent years. However, several realistic constraints including the transportation time between machines and energy consumptions are generally ignored. To fill this gap, this study investigated a FJSP considering energy consumption and transportation time constraints. A sequence-based mixed integer linear programming (MILP) model based on the problem is established, and the weighted sum of maximum completion time and energy consumption is optimized. Then, we present a combinational meta-heuristic algorithm based on a simulated annealing (SA) algorithm and an artificial immune algorithm (AIA) for this problem. In the proposed algorithm, the AIA with an information entropy strategy is utilized for global optimization. In addition, the SA algorithm is embedded to enhance the local search abilities. Eventually, the Taguchi method is used to evaluate various parameters. Computational comparison with the other meta-heuristic algorithms shows that the improved artificial immune algorithm (IAIA) is more efficient for solving FJSP with different problem scales.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

An Immune Algorithm for Least Cost Advanced Tolerance Design Problem

2006 ◽  
Vol 505-507 ◽  
pp. 511-516
Author(s):  
Ta Cheng Chen ◽  
Tung-Chou Hsu
Keyword(s):  
Immune Algorithm ◽  
Allocation Problem ◽  
Tolerance Allocation ◽  
Mixed Integer ◽  
Manufacturing Cost ◽  
Process Selection ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Allocation Problems ◽  
Artificial Immune Algorithms

This paper considers nonlinearly mixed integer tolerance allocation problems in which both tolerance and process selection are to be decided simultaneously so as to minimize the manufacturing cost. The tolerance allocation problem has been studied in the literature for decades, usually using mathematical programming or heuristic/metaheuristic optimization approaches. The difficulties encountered for both methodologies are the number of constraints and the difficulty of satisfying the constraints. A penalty-guided artificial immune algorithm is presented for solving such mixed integer tolerance allocation problems. Numerical examples indicate that the proposed artificial immune algorithms perform well for the tolerance allocation problem considered in this paper. In particular, as reported, solutions obtained by artificial immune algorithm are as well as or better than the previously best-known solutions.

A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

2010 ◽  
Vol 121-122 ◽  
pp. 266-270
Author(s):  
Lu Hong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

scholarly journals Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Andy Ham ◽  
Myoung-Ju Park ◽  
Kyung Min Kim
Keyword(s):  
Energy Consumption ◽  
Constraint Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Study ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Reducing Energy

Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.

scholarly journals Mathematical Modeling and Optimization Of Flexible Job Shop Problem with Sequence-Dependent Setup and Transportation Energy Consumptions

2021 ◽  
Author(s):  
Leilei Meng
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Job Shop ◽  
Mixed Integer ◽  
Flexible Job Shop ◽  
Total Energy Consumption ◽  
Sequence Dependent ◽  
Sequence Dependent Setup ◽  
Milp Model ◽  
Searching Ability

Abstract As environmental awareness grows, energy-aware scheduling is attracting increasing attention. This paper investigates the flexible job shop scheduling problem with sequence-dependent setup times and transportation times (FJSP-SDST-T) and the objective is to minimize total energy consumption. To begin with, the total energy consumption of the workshop is analyzed and a novel mixed integer linear programming (MILP) model is formulated. Due to that FJSP-SDST-T is NP-hard, an effective hybrid algorithm (HGA) that hybridizes the genetic algorithm (GA) and variable neighborhood search (VNS) algorithm is proposed to solve the problem specifically for that with large size. HGA takes advantage of the good global searching ability of GA and the powerful local searching ability of VNS, and it can have a good balance of intensification and diversification. Then, four energy-conscious decoding methods are designed, in which two energy-saving strategies namely postponing strategy and Turn Off/On strategy are specially designed according to the characteristics of FJSP-SDST-T. Finally, experiments are carried out and the results show the effectiveness of the MILP model, the energy-conscious decoding methods and HGA.

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