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    Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints

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    Publication type
    Journal article with impact factor
    Author
    Fuad Rahman, Humyun
    Servranckx, Tom
    Chakrabortty, Ripon K.
    Vanhoucke, Mario
    El Sawah, Sondoss
    Publication Year
    2022
    Journal
    Applied Soft Computing
    Publication Volume
    131
    Publication Issue
    December
    
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    Abstract
    Make-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.
    Keyword
    Project Scheduling, Manufacturing, Carbon footprints, Human Factors, Multi-objective
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.asoc.2022.109764
    URI
    http://hdl.handle.net/20.500.12127/7157
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.asoc.2022.109764
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