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    A Multi-objective Optimization Model for Sustainable-Robust Aggregate Production Planning Problem
    (Springer Science and Business Media Deutschland GmbH, 2023) Tirkolaee, E.B.; Aydın, N.S.; Mahdavi, I.; Çelik, B.
    Aggregate 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.