Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation

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dc.authoridBoru Ipek, Asli/0000-0001-6403-5307
dc.authoridGOCKEN, Mustafa/0000-0002-1256-2305
dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorSahin, Yeliz Buruk
dc.contributor.authorGocken, Mustafa
dc.contributor.authorIpek, Asli Boru
dc.date.accessioned2025-01-06T17:38:18Z
dc.date.available2025-01-06T17:38:18Z
dc.date.issued2024
dc.description.abstractPurpose This study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance. Design/methodology/approach Response surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables. Findings The high R-2 values of 97.38% for CO(2 )and 97.28% for service level, along with adjusted R-2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval. Originality/value This study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.
dc.identifier.doi10.1108/K-08-2023-1488
dc.identifier.issn0368-492X
dc.identifier.issn1758-7883
dc.identifier.scopus2-s2.0-85192985288
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1108/K-08-2023-1488
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2521
dc.identifier.wosWOS:001222341100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofKybernetes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGreen supply chain
dc.subjectCO2 emissions
dc.subjectDiscrete event simulation
dc.subjectResponse surface methodology
dc.titleDetermining the factor levels for a green supply chain using response surface methodology based discrete event simulation
dc.typeArticle

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