A Multi-objective Optimization Model for Sustainable-Robust Aggregate Production Planning Problem

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dc.contributor.authorTirkolaee, E.B.
dc.contributor.authorAydın, N.S.
dc.contributor.authorMahdavi, I.
dc.contributor.authorÇelik, B.
dc.date.accessioned2024-05-19T14:34:08Z
dc.date.available2024-05-19T14:34:08Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.descriptionScience, Engineering Management and Information Technology First International Conference, SEMIT 2022 -- 2 February 2022 through 3 February 2022 -- -- 299619en_US
dc.description.abstractAggregate production planning (APP) is known as a demand-driven production planning activity using aggregate plans for manufacturing processes. It tries to match supply and demand within a medium-term time horizon. In this work, a sustainable-robust APP problem is modeled through a multi-objective mixed-integer linear programming (MOMILP) model. The objective functions are formulated in a way to simultaneously minimize total cost, minimize total environmental impacts and maximize service level. Then, robust optimization (RO) technique is used to treat the demand uncertainty within the problem. To treat the multi-objectiveness and find the optimal solution, an improved multi-choice goal programing (IMCGP) method is introduced as an extension to the classical goal programming approach (GP). Next, several numerical examples are generated in different scales to assess the validity and applicability of the suggested methodology under deterministic as well as uncertain conditions. Eventually, a set of sensitivity analyses are implemented to assess the behavior of objective functions against real-world uncertainty in model parameters. It is demonstrated that the proposed methodology is capable of modeling, solving and analyzing the sustainable-robust APP problem efficiently. As one of the main findings, although the 1st and 2nd objective functions are sensitive to conservatism level, the 3rd objective function remains neutral. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.en_US
dc.identifier.doi10.1007/978-3-031-40395-8_2
dc.identifier.endpage34en_US
dc.identifier.isbn9783031403941
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85172726236en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage18en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-40395-8_2
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4418
dc.identifier.volume1808 CCISen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectImproved Multi-Choice Goal Programingen_US
dc.subjectRobust Optimizationen_US
dc.subjectSensitivity Analysisen_US
dc.subjectService Levelen_US
dc.subjectSustainable Aggregate Production Planningen_US
dc.titleA Multi-objective Optimization Model for Sustainable-Robust Aggregate Production Planning Problemen_US
dc.typeConference Objecten_US

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