PRUNING SEARCH SPACE BY DOMINANCE RULES IN BEST FIRST SEARCH FOR THE JOB SHOP SCHEDULING PROBLEM

Multi-objective decision making and search space for the evaluation of production process schedulingOver the years, various approaches have been proposed in order to solve the multi-objective job-shop scheduling problem - particularly a hard combinatorial optimization problem. The paper presents an evaluation of job shop scheduling problem under multiple objectives (mean flow time, max lateness, mean tardiness, mean weighted tardiness, mean earliness, mean weighted earliness, number of tardy tasks). The formulation of the scheduling problem has been presented as well as the evaluation schedules for various optimality criteria. The paper describes the basic mataheuristics used for optimization schedules and the approaches that use domination method, fuzzy method, and analytic hierarchy proccess (AHP) for comparing schedules in accordance with multiple objectives. The effectiveness of the algorithms has been tested on several examples and the results have been shown. New search space for evaluation and generation of schedules has been created. The three-dimensional space can be used for the analysis and control of the production processes.

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Ant colony optimisation with parameterised search space for the job shop scheduling problem

International Journal of Production Research ◽

10.1080/00207540802538021 ◽

2009 ◽

Vol 48 (4) ◽

pp. 1143-1154 ◽

Cited By ~ 29

Author(s):

Minseok Seo ◽

Daecheol Kim

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Search Space ◽

Ant Colony ◽

Scheduling Problem ◽

Ant Colony Optimisation ◽

Job Shop Scheduling Problem ◽

Shop Scheduling

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A Job Shop Scheduling Heuristic Algorithm Based on Probabilistic Model of the Search Space

Materials Science Forum ◽

10.4028/www.scientific.net/msf.532-533.1084 ◽

2006 ◽

Vol 532-533 ◽

pp. 1084-1087

Author(s):

Hong An Yang ◽

Ya Ping Xu ◽

Shu Dong Sun ◽

Jian Jun Yu

Keyword(s):

Probabilistic Model ◽

Large Scale ◽

Job Shop ◽

Job Shop Scheduling ◽

Constraint Satisfaction Problem ◽

Search Space ◽

Time Interval ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Shop Scheduling

The job shop scheduling problem is an NP-hard problem and conveniently formulated as Constraint Satisfaction Problem (CSP). Research in CSP has produced variable and value ordering heuristics techniques that can help improve the efficiency of the basic backtrack search procedure. However, the popular variable and value ordering heuristics play poor in solving the large-scale job shop scheduling problem. In this paper, a new probabilistic model of the search space was introduced which allows to estimate the reliance of an operation on the availability of a reservation, and the degree of contention among unscheduled operations for the possession of a resource over some time interval. Based on this probabilistic model, new operation and reservation ordering heuristics were defined. new operation ordering heuristic selects the operation that relies most on the most contended resource/time interval, and new reservation ordering heuristic assigns to that operation the reservation which is expected to be compatible with the largest number of survivable job schedules. Computer simulations indicate that this new algorithm yields a optimal result of FT10 benchmark job shop scheduling problem under small time cost.

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A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

Revista Facultad de Ingeniería ◽

10.19053/01211129.v26.n44.2017.5776 ◽

2017 ◽

Vol 26 (44) ◽

pp. 111 ◽

Cited By ~ 4

Author(s):

Henry Lamos-Díaz ◽

Karin Aguilar-Imitola ◽

Yuleiny Tatiana Pérez-Díaz ◽

Silvia Galván-Núñez

Keyword(s):

Optimization Problem ◽

Job Shop ◽

Job Shop Scheduling ◽

Memetic Algorithm ◽

Search Space ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Neighborhood Structure ◽

Selection Probability ◽

Shop Scheduling

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature.

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