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.

An Improved Genetic Algorithm For Just-In-Time Job-Shop Scheduling Problem

2012 ◽  
Vol 472-475 ◽  
pp. 2462-2467 ◽  
Author(s):  
Hong An Yang ◽  
Jin Yuan Li ◽  
Liang Liang Qi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Idle Time ◽  
Just In Time ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Insertion Mechanism ◽  
Early Operations

This paper studies a just-in-time job-shop scheduling problem (JITJSSP) in which each operation has an earliness cost or a tardiness cost if it is completed before or after its due date and the objective function is to minimize the total earliness and tardiness costs of all operations. In order to solve this problem, an improved genetic algorithm (IGA) is introduced in this paper. IGA utilizes an operation-based scheme to represent schedules as chromosomes. Then, each chromosome is processed through a three-stage mechanism. Firstly, the semi-active decoding process is employed to expand the search space of solutions and guarantee comprehensive solutions. Secondly, the greedy insertion mechanism for tardy operations is executed to move the tardy operations left to the appropriate idle time to reduce the tardiness costs. Finally, the greedy insertion mechanism for early operations is proposed to shift the early operations right to the suitable idle time to decrease the earliness costs. After the maximum number of generations is reached, IGA continues with selection, crossover and mutation. The experimental results finally show that most of solutions on the benchmarks are improved by our algorithm.

Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms

2021 ◽  
Vol 101 ◽  
pp. 104207
Author(s):  
K. Dehghan-Sanej ◽  
M. Eghbali-Zarch ◽  
R. Tavakkoli-Moghaddam ◽  
S.M. Sajadi ◽  
S.J. Sadjadi
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

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

Application of Ant Colony Algorithm to Job-Shop Scheduling Problem

2011 ◽  
Vol 411 ◽  
pp. 407-410
Author(s):  
Yan Cao ◽  
Lei Lei ◽  
Ya Dong Fang
Keyword(s):  
Ant Colony Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Graph Model ◽  
Optimal Solution ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Set Up

Production sequence of workpieces on machines, also called job-shop scheduling problem (JSP), is a focus both in academics and in practices. The research on the problem can promote theoretical progress, shorten the production cycles, improve efficiency in using resources, and strengthen market response in actual production. Ant colony optimization (ACO) is very suitable for the solving of the problem. In the paper, a disjunctive graph model of JSP is set up, which transforms the problem into a natural expression that is suitable for ACO. Then, realization steps of ACO for JSP are discussed. Finally, a 3×3 JSP problem is solved in Jbuilder X. The obtained optimal solution verifies the feasibility and effectiveness of ACO in solving JSP.

scholarly journals Job Shop Scheduling Problem Optimization by Means of Graph-Based Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1921
Author(s):  
Jiri Stastny ◽  
Vladislav Skorpil ◽  
Zoltan Balogh ◽  
Richard Klein
Keyword(s):  
Genetic Algorithm ◽  
Operations Research ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
A Algorithm ◽  
Shop Scheduling

In this paper we introduce the draft of a new graph-based algorithm for optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was tested on the Job Shop Scheduling Problem against a genetic algorithm’s classic implementation. The acquired results of these experiments were compared by each algorithm’s required time (to find the best solution) as well as makespan. The comparison of these results showed that the proposed algorithm exhibited a promising convergence rate toward an optimal solution. Job shop scheduling (or the job shop problem) is an optimization problem in informatics and operations research in which jobs are assigned to resources at particular times. The makespan is the total length of the schedule (when all jobs have finished processing). In most of the tested cases, our proposed algorithm managed to find a solution faster than the genetic algorithm; in five cases, the graph-based algorithm found a solution at the same time as the genetic algorithm. Our results also showed that the manner of priority calculation had a non-negligible impact on solutions, and that an appropriately chosen priority calculation could improve them.

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