An Improved Genetic Algorithm for Job-Shop Scheduling Problem with Process Sequence Flexibility

Huang, Xiabao; Zhao, Xinchao; L., X.

doi:10.2507/ijsimm13(4)co20

Cited by 24 publications

(12 citation statements)

References 30 publications

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“…A dynamic approach for conforming the customers' satisfactions was constructed. Huang et al [13] developed an improved genetic algorithm for job-shop scheduling problem with process sequence flexibility. Haider et.al [14] proposed an iterative system improvement strategy to explore the potential of tool room.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation Model for Job Shop Production Process Improvement in Machine Parts Manufacturing

Supsomboon¹,

Vajasuvimon²

2016

Int. j. simul. model.

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Job shop production system is characterized by manufacturing feature with low volume and high variety of product designs. It is a process with high flexibility which produces a wide variety of products in low quantities and high divergence. Generally, a job shop production line follows sequence of orders with specific process flow for each particular job. In each work station, special skills of workers are required to perform explicit tasks. Due to these distinctive process characteristics, effective production planning is crucial for job shop manufacturing process. Ineffective production planning may cause several types of waste in production line. This study proposed simulation modeling for job shop production process in machine parts manufacturing. Simulation models present the great benefits to assist in performance improving, problems solving, including a great help in decision making. The experimental results of the study showed that group technology, plant layout, job enlargement, and capacity expansion accomplished the definite value in reducing operating cost and increasing average worker utilization, thereby increasing the efficiency of the system.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation Model for Job Shop Production Process Improvement in Machine Parts Manufacturing

Supsomboon¹,

Vajasuvimon²

2016

Int. j. simul. model.

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“…In each iteration (generation), a certain number of operators is applied to the current population of the specimens so that the next generation of specimens is created. Commonly used are the crossover operator, which combines two or more specimens to gain a new specimen, and the mutation, which causes the self-adaptation of specimens [14,15].…”

Section:  Departure Of the Ordersmentioning

confidence: 99%

Hybrid Algorithm Based on Priority Rules for Simulation of Workshop Production

Zupan¹,

Herakovič²,

Starbek³

2016

Int. j. simul. model.

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The proposed hybrid algorithm is a combination of heuristic algorithm extended with priority rules, discrete event simulation and genetics algorithm. It takes into account 11 different priority rules and scenarios, is based on the assumption that for a realistic workshop scheduling of orders, it is necessary to consider real throughput times of the operations, otherwise the obtained scheduling of orders is not suitable for the industrial environment. The simulation result for the proposed model is the optimal sequence of selected orders for the selected time interval while taking into account three criteria: the minimum flow time of all orders, the maximum average utilization of workplaces, and the minimum waiting time of the orders. Because the mentioned criteria are usually mutually exclusive the advantage of the proposed model is that we can find the optimum with respect to all three criteria. In the paper, an example of the application of the proposed model is shown.

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“…Scheduling is concerned with the timing aspect of a job, whereas process planning lays emphasis on the technical processing requirements. As the process planner does not have any control or view of the actual shop floor status, the process planning function comes in conflict with the scheduling function [3][4][5]. Hence, in today's competitive environment productivity can be significantly improved by employing efficient methods by integrating the scheduling and process planning functions.…”

Section: Introductionmentioning

confidence: 99%

Integrated process planning and scheduling using genetic algorithms

Chaudhry

Usman

2017

Teh. vjesn.

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INTEGRATED PROCESS PLANNING AND SCHEDULING USING GENETIC ALGORITHMS Imran Ali Chaudhry, Muhammad UsmanOriginal scientific paper Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine. Keywords: integrated process planning and scheduling (IPPS); genetic algorithms; job shop Integracija projektiranja tehnoloških procesa i planiranja primjenom genetičkih algoritamaIzvorni znanstveni članak Projektiranje tehnoloških procesa i planiranje predstavljaju dvije najvažnije funkcije svakog proizvodnog procesa. Tradicionalno se one smatraju dvjema odvojenim funkcijama. U ovom se radu predlaže Genetički Algoritam (GA) za integraciju ovih aktivnosti, gdje se simultano odvija izbor najboljeg tehnološkog procesa i vremenski plan poslova u pogonu. U radu se za rješavanje te vrste problema predstavlja pristup zasnovan na proračunskoj tablici neovisnog područja. U modelu se razmatraju odnosi prvenstva u izvođenju poslova na temelju kojih se donosi implicitno predstavljanje mogućih planova za izvršenje svakog posla. Zbog provjere izvršenja i ostvarivosti predstavljenog pristupa, predloženi se algoritam provjeravao na nizu referentnih problema prilagođenih iz ranije objavljene literature. Eksperimentalni rezultati pokazuju da se predloženim pristupom mogu učinkoviti postići optimalna ili njima blizu rješenja za probleme prilagođene iz literature. Također je pokazano da predloženi algoritam ima opću namjenu i može se primijeniti za optimizaciju bilo koje objektivne funkcije bez promjene modela ili osnovne GA rutine. Ključne riječi: integracija projektiranja tehnoloških proces i planiranja (IPPS); genetički algoritami; radna mjesta

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An Improved Genetic Algorithm for Job-Shop Scheduling Problem with Process Sequence Flexibility

Cited by 24 publications

References 30 publications

Simulation Model for Job Shop Production Process Improvement in Machine Parts Manufacturing

Simulation Model for Job Shop Production Process Improvement in Machine Parts Manufacturing

Hybrid Algorithm Based on Priority Rules for Simulation of Workshop Production

Integrated process planning and scheduling using genetic algorithms

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