MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH
Keywords:Multiobjective production planning, Robust optimization, Workforce efficiency, Non-dominated Sorting Genetic Algorithm-II, Strength Pareto Evolutionary Algorithm 2, Pareto Envelope-based Selection Algorithm II
Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same.
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