Multi-Objective Optimization Algorithm for Job Shop Scheduling Problem in Discrete Manufacturing Enterprise

2015 ◽  
Vol 741 ◽  
pp. 860-864
Author(s):  
Li Lan Liu ◽  
Xue Wei Liu ◽  
Sen Wang ◽  
Wei Zhou ◽  
Gai Ping Zhao
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Manufacturing Enterprise ◽  
Shop Scheduling ◽  
Discrete Manufacturing ◽  
Scheduling Plan

Job Shop scheduling should satisfy the constraints of time, order and resource. To solve this NP-Hard problem, multi-optimization for job shop scheduling problem (JSSP) in discrete manufacturing plant is researched. Objective of JSSP in discrete manufacturing enterprise was analyzed, and production scheduling optimization model was constructed with the optimization goal of minimizing the bottleneck machines’ make-span and the total products’ tardiness; Then, Particle Swarm Optimization (PSO) algorithm was used to solve this model by the process-based encoding mode; To solve the premature convergence problem of PSO, advantages of Simulated Annealing (SA) algorithm, such as better global optimization performance, was integrated into PSO algorithm and a Hybrid PSO-SA Algorithm (HPSA) was proposed and the flowchart was presented; Then, this hybrid algorithm was applied in actual production scheduling of a discrete manufacturing enterprise. Finally, comparative analysis of HPSA/SA/PSO optimal methods and actual scheduling plan was carried out, which verify the result that the HPSA is effective and superiority.

The Optimization Model of in Job-Shop Scheduling Problem with Alternative Machines Based on Improved Genetic Algorithm

2014 ◽  
Vol 607 ◽  
pp. 569-572 ◽  
Author(s):  
Qing Chi ◽  
Xiu Li Fu ◽  
Ya Nan Pan ◽  
Zeng Hui An
Keyword(s):  
Optimization Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Processing Technology ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Manufacturing Enterprise ◽  
Shop Scheduling ◽  
Discrete Manufacturing ◽  
Alternative Machines

The job-shop scheduling problem with alternative machines is very complicated and hard to simplify during product management system for discrete manufacturing enterprise. According to the integrated constraint condition of the processing technology and equipment resources, an optimization model for the dispatch plan of processing technology for the gear shaft assembly is analyzed and established in this paper. Furthermore, the optimization results for the process sequence planning of the gear shaft assembly are obtained by iterative algorithm and improved genetic algorithms approach. The calculating program of optimization layout is developed by Matlab. The optimization results show that the production cycle time and operating cost is reduced remarkably and the efficiency is also improved. Through analysis and verification, it is optimal and feasible for discrete manufacturing enterprise in engineering applications.

The Effect of Crossover and Mutation Operators on Genetic Algorithm for Job Shop Scheduling Problem

2012 ◽  
Vol 542-543 ◽  
pp. 1251-1259
Author(s):  
Long Xu ◽  
Wen Bin Hu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Scheduling Plan ◽  
Crossover And Mutation

Job Shop Scheduling Problem (JSSP) is a famous NP-hard problem in scheduling field. The concentration of JSSP is to find a feasible scheduling plan to figure out the earliest completion time under machine and processing sequence constraints. At present, genetic algorithm has been widely adopted in varies of operation research problems including JSSP, and good performance have been achieved. However, few work have stress the selection of varies operators when implemented for JSSP. Using benchmark problems, this paper compares the effect of crossover and mutation operators on genetic algorithm for JSSP.

Minimizing Makespan in Job Shop Scheduling Problem Using Genetic Algorithm

2015 ◽  
Vol 813-814 ◽  
pp. 1183-1187 ◽  
Author(s):  
Aathi Muthiah ◽  
R. Rajkumar ◽  
B. Muthukumar
Keyword(s):  
Genetic Algorithm ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Setup Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Knowing That ◽  
Batch Sizes

- Scheduling is an important tool for manufacturing and engineering, where it can have a major impact on the productivity of a process. In manufacturing, the purpose of scheduling is to minimize the production time and costs. Production scheduling aims to maximize the efficiency of the operation and reduce costs. We keep all of our machines well-maintained to prevent any problems, but there is on way to completely prevent down-time. With redundant machines we have the security of knowing that we are not going to be in trouble meeting our deadlines if a machine has any unexpected down-times. Finally we can work to get our batch sizes as small as is reasonably possible while also reducing the setup time of each batch. This allows us to eliminate a sizable portion of each part waiting while the rest of the parts in the batch are being machined.

scholarly journals A Sequence List Algorithm For The Job Shop Scheduling Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 55-61
Author(s):  
Shuli Zhang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Manufacturing Enterprises ◽  
Machine Sequence ◽  
Discrete Manufacturing ◽  
Processing Order ◽  
Process Constraints

For the discrete manufacturing enterprises, the job shop scheduling problem is an important class of actual combinatorial optimization problem with resources and sequence constraints. According to the needs of the job shop scheduling problem, a sequence list algorithm for the job shop scheduling problem was designed in this paper. In order to make all jobs being finished as soon as possible, the goal of the sequence list algorithm is minimizing the maximal the finish time of all operations. In the sequence list algorithm, two types of sequence lists were built. They are the job sequence lists and the machine sequence lists. A job sequence list was used to store all operations of a job on the basis of its process constraints. A machine sequence list which is null initially was used to store all operations on a machine in accordance with the actual processing order. The important tasks of the sequence list algorithm are inserting all operations of the job sequence lists into the machine sequence lists and adjusting the processing order of the operations in the machine sequence lists. The sequence list algorithm could always achieve a good job shop schedule which ensures the select performance indicators. The feasibility and efficiency of the algorithm was verified through examples.

scholarly journals Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems

2019 ◽  
Vol 1 (22) ◽  
pp. 61-74
Author(s):  
Tadeusz Witkowski
Keyword(s):  
Particle Swarm Optimization ◽  
Production Scheduling ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Bee Colony

This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.

Study on Job Shop Scheduling Optimization with Multi-Objective

2011 ◽  
Vol 217-218 ◽  
pp. 326-329
Author(s):  
Tao Ze ◽  
Di Liang ◽  
Zhou Qun
Keyword(s):  
Mathematical Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Maximum Load ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Scheduling Plan ◽  
The Mathematical Model

A new multi-objective scheduling method based on the GA is proposed to the job-shop scheduling problem (JSP) constrained by machines, workers. Function objectives of the proposed method are to minimize the completion time, the maximum load of machines and the total expense of machines and workers. Firstly, the mathematical model is constructed. Then, on the basis of the mathematical model, the genetic algorithm (GA) based on Pareto is applied, and an optimal or suboptimal scheduling plan can be obtained. The optimal solutions are not unique due to the multi-objective of JSP. Finally, a scheduling example is employed to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.

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