Research on Dynamic Scheduling of Job-Shop Production with the Ant Colony Optimal Algorithm

2007 ◽  
Vol 10-12 ◽  
pp. 109-113
Author(s):  
Yong Xian Liu ◽  
J. Xiong ◽  
B.M. Sun
Keyword(s):  
Production Management ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Job Shop Scheduling ◽  
Optimal Algorithm ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Multi Agent ◽  
High Computational Complexity

Job-shop dynamic scheduling is an important subject in the fields of production management and combinatorial optimization. It is usually hard to achieve the optimal solution with classical methods due to the high computational complexity of the problem. A solution of job-shop scheduling problem based on multi-agent is presented for the comparability between the dynamic scheduling problem of job-shop production and the TSP problem. The dynamic scheduling of job-shop production is designed according to the pattern of TSP problem which can be applied with ACO. By the application case, the ACO is the new method to solve the dynamic scheduling of job-shop production.

scholarly journals Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammed Al-Salem ◽  
Leonardo Bedoya-Valencia ◽  
Ghaith Rabadi
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Exact Algorithms ◽  
Just In Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.

scholarly journals Firefly algorithm applied to the job-shop scheduling problem

2021 ◽  
Author(s):  
Piotr Świtalski ◽  
Arkadiusz Bolesta
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Parameter ◽  
Optimal Solution ◽  
Experimental Program ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The job shop scheduling problem (JSSP) is one of the most researched scheduling problems. This problem belongs to the NP-hard class. An optimal solution for this category of problems is rarely possible. We try to find suboptimal solutions using heuristics or metaheuristics. The firefly algorithm is a great example of a metaheuristic. In this paper, this algorithm is used to solve JSSP. We used some benchmarking JSSP datasets for experiments. The experimental program was implemented in the aitoa library. We investigated the optimal parameter settings of this algorithm in terms of JSSP. Analysis of the experimental results shows that the algorithm is useful to solve scheduling problems.

Hybrid Genetic Algorithm for Flexible Job Shop Scheduling with Overlapping in Operations

2012 ◽  
Vol 433-440 ◽  
pp. 1499-1505 ◽  
Author(s):  
Ali Rahimi Fard ◽  
Babak Yousefi Yegane ◽  
Narges Khanlarzade
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Large Size

Flexible job shop scheduling problem )FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. However, it is quite difficult to achieve an optimal solution to this problem in medium and large size problems with traditional optimization approaches. In this paper a memetic algorithm )MA) for flexible job shop scheduling with overlapping in operation is proposed that solves the FJSP to minimize makespan time. The experimental results show that the proposed algorithm is capable to achieve the optimal solution for small size problems and near optimal solutions for medium and large size problems

An Improved Artificial Bee Colony Algorithm for Job Shop Problem

2010 ◽  
Vol 26-28 ◽  
pp. 657-660 ◽  
Author(s):  
Bao Zhen Yao ◽  
Cheng Yong Yang ◽  
Juan Juan Hu ◽  
Guo Dong Yin ◽  
Bo Yu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Production Management ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony

Job shop scheduling problem (JSP) plays a significant role for production management and combinatorial optimization. An improved artificial bee colony (IABC) algorithm with mutation operation is presented to solve JSP in this paper. The results for some benchmark problems reveal that IABC is effective and efficient compared to those of other approaches. IABC seems to be a powerful tool for optimizing job shop scheduling problem.

scholarly journals Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

2014 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
JarosÅ‚aw Rudy ◽  
Dominik Żelazny
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Pareto Frontier ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Real World Applications ◽  
Multi Agent ◽  
Disjoint Sets

In this paper the job shop scheduling problem (JSP) with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP) were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO) and genetic algorithm (GA) for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

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