Multi-objective Aggregate Production Planning Model Considering Overtime and Outsourcing Options Under Fuzzy Seasonal Demand

This paper investigates a novel fuzzy multi-objective multi-period Aggregate Production Planning (APP) problem under seasonal demand. As two of the main real-world assumptions, the options of workforce overtime and outsourcing are studied in the proposed Mixed-Integer Linear Programming (MILP) model. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/ unemployment costs, and maximize the customers' satisfaction level. To deal with demand uncertainty, triangular fuzzy numbers are considered for demand parameters. Then the proposed model is validated by solving an illustrative example using a Weighted Goal Programming (WGP) method and CPLEX solver. Finally, it is demonstrated that uncertain conditions and considering real-world assumptions can yield different results in developing a practical aggregate production plan. Moreover, a sensitivity analysis is then performed to provide qualitative managerial insights and decision aids

Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization

Cell formation (CF) problem is one of the most important decision problems in designing a cellular manufacturing system includes grouping machines into machine cells and parts into part families. Several factors should be considered in a cell formation problem. In this work, robust optimization of a mathematical model of a dynamic cell formation problem integrating CF, production planning and worker assignment is implemented with uncertain scenario-based data. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all possible future scenarios. In this research, miscellaneous cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer nonlinear programming model is developed to formulate the related robust dynamic cell formation problem. The objective function seeks to minimize total costs including machine constant, machine procurement, machine relocation, machine operation, intercell and intra-cell movement, overtime, shifting labors between cells and inventory holding. Finally, a case study is carried out to display the robustness and effectiveness of the proposed model. The tradeoff between solution robustness and model robustness is also analyzed in the obtained results