An efficient genetic algorithm for job shop scheduling problems

Janeš, Gordan; Perinić, Mladen; Jurković, Zoran

doi:10.17559/tv-20150527133957

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Production scheduling plays a vital role in planning and operation of a manufacturing system [23]. The Job Shop Scheduling Problem (JSSP) is one of the most general and difficult of all traditional scheduling combinatorial problems with considerable importance in industry [24]. Today, we can observe much pressure on enterprises from the market, the competition and the customer [25,26].…”

Section: Discussionmentioning

confidence: 99%

Determination of Optimal Production Process Using Scheduling and Simulation Software

Dupláková¹,

Telišková²,

Duplák³

et al. 2018

Int. j. simul. model.

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The objective of the article is to present the linking of simulation and planning software. The paper begins with a review of recent literature as well as description of the problem under investigation. Following from practical requirements, five decision-making rules were implemented to the production process. In the final part, the overall results to ensure time and economic efficiency of the production process are presented. The obtained results show that the best option is to apply the rule during which the first part to be machined is the one that first enters the production process to ensure minimum production time, maximum machine load and limit machine costs. By application of this rule, the total production time accounts for 524 seconds at the total cost of 7914.60 €. The overall benefits of the research are being described in details in the final part of the article.

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Section: Discussionmentioning

confidence: 99%

Determination of Optimal Production Process Using Scheduling and Simulation Software

Dupláková¹,

Telišková²,

Duplák³

et al. 2018

Int. j. simul. model.

View full text Add to dashboard Cite

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“…One of the applications of GA is the resourceconstrained project scheduling problems. Many researchers studied on this subject [8][9][10][11][12][13][14][15]. Davis and Patterson [16] solved a project consisting of 2 dummy activities and a total of 27 activities with GA and they compared the solution with the results of other heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms

Erdal

Kanit

2021

Teh. vjesn.

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The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized.

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“…The paper is a continuation research of already published papers by the same author [3][4][5][6] and follows the concept of presenting 3D truss latticed portals with L-beam elements. The basis for the development of the described GA method are general papers [7,8,9], papers about usage of GA in electrical application [10][11][12] and papers in the field of optimization of steel structures [13,14]. The program GAUSAB3D is developed in MATLAB, using Genetic Algorithm and Direct Search Toolbox.…”

Section: Introductionmentioning

confidence: 99%

Optimum Wire Busbar Design by Genetic Algorithm

2019

Teh. vjesn.

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This work shows genetic algorithm approach to the optimum wire busbar design. Parameters of the wire busbar are mapped into a chromosome like a string. This includes insulator string type, wire conductor type and number, bundle wire conductor distance, rigid spacer number, phase distance, wire conductor nominal stress, 3D truss L elements types and foundation type. GA uses natural selection on a population of such strings. The fitness function is the wire busbar price. GA performance is presented on one example with variation of input data -nominal current, short-circuit current and short-circuit duration.

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An efficient genetic algorithm for job shop scheduling problems

Cited by 7 publications

References 7 publications

Determination of Optimal Production Process Using Scheduling and Simulation Software

Determination of Optimal Production Process Using Scheduling and Simulation Software

Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms

Optimum Wire Busbar Design by Genetic Algorithm

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