Minimizing flow time variance in a single machine system using genetic algorithms

Gupta, Mahesh; Gupta, Yash P.; Kumar, Anup

doi:10.1016/0377-2217(93)90240-n

Cited by 72 publications

(16 citation statements)

References 15 publications

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“…The empirical analysis presented in section 6 indicates that crossover rate often plays an insignificant role in the performance of the GA, that conforms to the results by Haida and Akimoto [47] and Gupta et al [48]. The results also highlight that there is a certain degree of insensitivity to the mutation rate as long as the mutation values lie between a fairly broad 'good' range rather than coming from more precise point.…”

Section: Discussionsupporting

confidence: 82%

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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Section: Discussionsupporting

confidence: 82%

Integrated process planning and scheduling using genetic algorithms

Chaudhry

Usman

2017

Teh. vjesn.

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“…In the resulting schedule, we ensure that the longest and second longest are in the first and last schedule positions, respectively. For example, in a 5-job problem, the initial schedule would be p [1] p [3] p [5] p [4] p [2] , assuming that p [i ] represents the ith longest job. Once an initial solution has been obtained, we need to devise a procedure for generating the neighboring solutions.…”

Section: M21mentioning

confidence: 99%

“…The CTV problem was shown to be NP-hard by Kubiak [8]. The weighted version of the CTV problem was also introduced by Merten and Muller [9] and studied by Schrage [10] and Gupta, Gupta, and Kumar [5].…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Minimizing weighted mean absolute deviation of flow times in single machine systems

Aneja

Kabadi

Nagar

1998

Naval Research Logistics

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“…The GAs have been used by several researchers to solve the sequencing and scheduling problem. The GAs were applied to single machine problem by Liepins et al (1987), Gupta, Gupta, and Kumar (1993), Lee and Choi (1995), Rubin and Ragatz (1995), and David et al (1999). Cleveland and Smith (1989), Biegel and Davern (1990), Vempati, Chen, and Bullington (1993), Neppalli (1994), Chen, Vempati, and Aljaber (1995), Reeves (1995), and Sridhar and Rajendran (1995) used the GA approach to solve the flow shop problem.…”

Section: A Brief Introduction To Gasmentioning

confidence: 99%

Merging optimality conditions with genetic algorithm operators to solve single machine total weighted tardiness problem

Al-Harkan

2005

J. Syst. Sci. Syst. Eng.

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In this paper, a constrained genetic algorithm (CGA) is proposed to solve the single machine total weighted tardiness problem. The proposed CGA incorporates dominance rules for the problem under consideration into the GA operators. This incorporation should enable the proposed CGA to obtain close to optimal solutions with much less deviation and much less computational effort than the conventional GA (UGA). Several experiments were performed to compare the quality of solutions obtained by the three versions of both the CGA and the UGA with the results obtained by a dynamic programming approach. The computational results showed that the CGA was better than the UGA in both quality of solutions obtained and the CPU time needed to obtain the close to optimal solutions. The three versions of the CGA reduced the percentage deviation by 15.6%, 61.95%, and 25% respectively and obtained close to optimal solutions with 59% lower CPU time than what the three versions of the UGA demanded. The CGA performed better than the UGA in terms of quality of solutions and computational effort when the population size and the number of generations are smaller.

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Minimizing flow time variance in a single machine system using genetic algorithms

Cited by 72 publications

References 15 publications

Integrated process planning and scheduling using genetic algorithms

Integrated process planning and scheduling using genetic algorithms

Minimizing weighted mean absolute deviation of flow times in single machine systems

Merging optimality conditions with genetic algorithm operators to solve single machine total weighted tardiness problem

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