Wyniki wyszukiwania - BazTech

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A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

Del Gallo Mateo, Ciarapica Filippo Emanuele, Mazzuto Giovanni, Bevilacqua Maurizio

Management and Production Engineering Review

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2023

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Vol. 14, No. 4

56--70

EN

Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.

2

Meta-heuristic approach based on genetic and greedy algorithms to solve flexible job-shop scheduling problem

Rezaeipanah Amin, Sarhangnia Fariba, Abdollahi Mohammad Javad

Computer Science

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2021

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T. 22 (4)

463--488

EN

Job-shop scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in their various aspects of construction and design. Additionally, these systems are identified as cellular manufacturing systems (CMS). In this paper, a meta-heuristic method that is based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of the flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated by the greedy algorithm, and several elitist operators are used to improve the solutions. The greedy algorithm that is used to improve the generation of the initial population prioritizes the cells and the job in each cell and, thus, offers quality solutions. The proposed algorithm is tested over the P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality, and run-time criteria were used in a multi-objective function. The results of the simulation indicate the better performance of the proposed method as compared to the NRGA and NSGA-II methods.

3

Towards job-shop scheduling with the use of virtual manufacturing cells

Wojakowski P.

Research in Logistics & Production

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2014

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Vol. 4, No. 4

331--339

EN

The article presents the possibility to modify finding solutions when job-shop scheduling is conducted based on the idea of virtual cellular manufacturing. It is demonstrated that creation of virtual manufacturing cells for defined production orders and exploiting them in the process of job-shop scheduling allows to reduce makespan. In a virtual manufacturing cell, machines are dedicated to produce for selected production orders as in a regular manufacturing cell, but machines are not physically allocated in designated area. Virtual cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. In this research, an example of job-shop scheduling problem with embedded virtual cellular manufacturing is presented. The conditions of application of virtual manufacturing cells in terms of production flow modification are described.

4

Zarządzanie opóźnieniami w ruchu kolejowym z zastosowaniem programowania ewolucyjnego

Palikowska K.

Logistyka

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2012

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nr 4

PL

Zarządzanie opóźnieniami w ruchu kolejowym zostało potraktowane jako wielokryterialny problem optymalizacyjny, który obejmuje między innymi zapewnienie skomunikowania pociągów na stacjach węzłowych oraz dążenie do uzyskania biegu pociągów najbardziej zgodnego z pierwotnym rozkładem jazdy. Uwzględniono priorytety poszczególnych pociągów uzależnione od sytuacji ruchowej i kategorii pociągów. W przeprowadzonym procesie optymalizacji wykorzystano algorytm genetyczny z operatorami genetycznymi dopasowanymi do specyfiki problemu traktowanego jako ogólny problem (job-shop) szeregowania zadań.

EN

Railway delay management problem was treated as multi-objective optimization problem dealing with ensuring train connections at hub stations and trying to re-schedule delayed trains to obtain a new schedule as far as possible correspondent to the base timetable. Different train priorities dependent of the current state of the railway network and train class has been taken into account. The optimization process has been carried out using the genetic algorithm with the genetic operators adjusted to the specific character of the railway re-scheduling modeled as job-shop scheduling task problem.

5

Evolutionary algorithms for job-shop scheduling

Mesghouni K., Hammadi S., Borne P.

International Journal of Applied Mathematics and Computer Science

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2004

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Vol. 14, no 1

91-103

EN

This paper explains how to use Evolutionary Algorithms (EA) to deal with a flexible job shop scheduling problem, especially minimizing the makespan. The Job-shop Scheduling Problem (JSP) is one of the most difficult problems, as it is classified as an NP-complete one (Carlier and Chretienne, 1988; Garey and Johnson, 1979). In many cases, the combination of goals and resources exponentially increases the search space, and thus the generation of consistently good scheduling is particularly difficult because we have a very large combinatorial search space and precedence constraints between operations. Exact methods such as the branch and bound method and dynamic programming take considerable computing time if an optimum solution exists. In order to overcome this difficulty, it is more sensible to obtain a good solution near the optimal one. Stochastic search techniques such as evolutionary algorithms can be used to find a good solution. They have been successfully used in combinatorial optimization, e.g. in wire routing, transportation problems, scheduling problems, etc. (Banzhaf et al., 1998; Dasgupta and Michalewicz, 1997). Our objective is to establish a practical relationship between the development in the EA area and the reality of a production JSP by developing, on the one hand, two effective genetic encodings, such as parallel job and parallel machine representations of the chromosome, and on the other, genetic operators associated with these representations. In this article we deal with the problem of flexible job-shop scheduling which presents two difficulties: the first is the assignment of each operation to a machine, and the other is the scheduling of this set of operations in order to minimize our criterion (e.g. the makespan).