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dc.contributor.authorFuad Rahman, Humyun
dc.contributor.authorServranckx, Tom
dc.contributor.authorChakrabortty, Ripon K.
dc.contributor.authorVanhoucke, Mario
dc.contributor.authorEl Sawah, Sondoss
dc.date.accessioned2023-01-16T07:36:07Z
dc.date.available2023-01-16T07:36:07Z
dc.date.issued2022en_US
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2022.109764
dc.identifier.urihttp://hdl.handle.net/20.500.12127/7157
dc.description.abstractMake-to-order (MTO) or engineer-to-order (ETO) systems produce complex and highly customized products and, therefore, there is a need for advanced project scheduling approaches for production planning in these systems. An important aspect of production scheduling is the assignment of operators with specific human factors to activities in a manufacturing project. This assignment impacts the duration of the activities, the total wage cost of the project and even the energy consumption during production. With increasing concern regarding low-carbon production in manufacturing, the human factors of operators thus cannot be ignored in the decision-making process in production project scheduling. In this context, our study considers an extension of the well-known resource-constrained project scheduling problem for manufacturing. This problem is represented as a bi-objective optimization problem with the conjoint objectives of minimizing the total cost of the project and its carbon footprint. Two variants of a genetic algorithm-based memetic algorithm (MA) are proposed to solve this problem and a set of artificial, realistic project instances are generated to evaluate the proposed solution procedure. Experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithms (NSGA-II and NSGA-III) and its enhanced approach (ENSGA-II) in terms of both solution quality and computational efficiency. The experiments are conducted on both real-life case study data from an MTO project in the furniture industry and a large set of artificial data instances. Our research allows project managers to select appropriate operators to execute activities based on human factors, wage and power consumption with the objectives of minimum total cost and carbon footprint.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectProject Schedulingen_US
dc.subjectManufacturingen_US
dc.subjectCarbon footprintsen_US
dc.subjectHuman Factorsen_US
dc.subjectMulti-objectiveen_US
dc.titleManufacturing project scheduling considering human factors to minimize total cost and carbon footprintsen_US
dc.identifier.journalApplied Soft Computingen_US
dc.source.volume131en_US
dc.source.issueDecemberen_US
dc.contributor.departmentCardiff School of Management, Cardiff Metropolitan University, Western Avenue, Cardiff, CF5 2YB, UKen_US
dc.contributor.departmentCapability Systems Centre, School of Eng. & IT, UNSW Canberra at ADFA, ACT 2610, Australiaen_US
dc.contributor.departmentFaculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgiumen_US
dc.contributor.departmentUCL School of Management, University College London, 1 Canada Square, London E14 5AA, UKen_US
dc.identifier.eissn1872-9681
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid58614en_US


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