A meta-heuristic approach for ipps problem

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dc.authorscopusid37123384000
dc.authorscopusid57195873041
dc.authorscopusid24075527600
dc.contributor.authorAlcan, Pelin
dc.contributor.authorUslu, Mehmet Fatih
dc.contributor.authorBasligil, Huseyin
dc.date.accessioned2024-09-11T19:59:05Z
dc.date.available2024-09-11T19:59:05Z
dc.date.issued2016
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.descriptionCity Council of Roubaix; Metropole Europeenne de Lilleen_US
dc.descriptionUncertainty Modelling in Knowledge Engineering and Decision Making - 12th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2016 -- 24 August 2016 through 26 August 2016 -- Roubaix -- 131626en_US
dc.description.abstractIn this paper, we focused on the Integrated Process Planning and Scheduling (IPPS) which was an example of job-shop scheduling problem. Several approaches were proposed to solve this problem and Ant Colony Optimization (ACO) was one of the widely used approaches. Examining the articles in which ACO algorithm was described and applied to the IPPS problem gave us an insight of current performance of optimization algorithms to this problem. We then proposed a Genetic Algorithm (GA) for the problem and implemented both algorithms, ACO and GA, in Javascript. According to the results, increasing the running time of GA leaded to more optimal results than ACO. In addition, GA found better results compared to ACO in small-scale problems. On the other hand, ACO performed better than GA in limited time or in bigger problems. In this paper, we proposed a GA approach for IPPS problems. Our chromosome model had 2 parts; first part represented machines of processes and second part showed the orders of the jobs. We applied different mutation/crossover types to these parts and then determined better parameters with numerous experiences. Also, we created an iOS application for visually comparing this GA approach with an ACO algorithm previously proposed. Our GA approach gave better results in some problem types. Our application could be downloaded in the following link (iPad was recommended): https://itunes.apple.com/co/app/ipps-solver/id876097527?l=en&mt=8. © 2016 by World Scientific Publishing Co. Pte. Ltd.en_US
dc.identifier.doi10.1142/9789813146976_0121
dc.identifier.endpage784en_US
dc.identifier.isbn978-981314696-9en_US
dc.identifier.scopus2-s2.0-85037349019en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage778en_US
dc.identifier.urihttps://doi.org/10.1142/9789813146976_0121
dc.identifier.urihttps://hdl.handle.net/11363/8629
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.relation.ispartofUncertainty Modelling in Knowledge Engineering and Decision Making - Proceedings of the 12th International FLINS Conference, FLINS 2016en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectAnt colony optimization; Artificial intelligence; Decision making; Fuzzy logic; Genetic algorithms; Heuristic methods; Job shop scheduling; Knowledge engineering; Optimization; Scheduling; Uncertainty analysis; ACO algorithms; Ant Colony Optimization (ACO); Current performance; Integrated process planning and scheduling; Job shop scheduling problems; Meta-heuristic approach; Optimal results; Optimization algorithms; Problem solvingen_US
dc.titleA meta-heuristic approach for ipps problemen_US
dc.typeConference Objecten_US

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