In existence of interval-valued parameters and managerial limitations, multicriteria decision-making (MCDM) problems become more complex than their original situation. In such complex cases, the classical MCDM solution methods mainly cannot handle the managerial constraints of the problem and, therefore, become useless. This paper focuses on an MCDM problem such as establishment of the branches of a glass production company in all districts (provinces) of Iran in existence of interval-valued parameters and some managerial limitations like total available budget, number of selected branches for establishment, etc. To overcome the aforementioned difficulties, a novel modified version of the simple additive weighting method, which is of the well-known MCDM approaches of the literature, is formulated as a mixed integer linear formulation. The novelty of this formulation can be summarized by the following points: 1) to cope with the interval values of the criteria; 2) to keep the possibility of selecting a set of best locations among all possible locations; and 3) to handle the managerial limitations of the problem. The proposed formulation is validated by the real data of the case study, and its sensitivity is studied in detail. Finally, the performance of the proposed approach is compared to an alternative proposed solution approach.

This paper develops a multi-objective mathematical formulation for supplier-material selection in cardboard box manufacturing. The aim is to minimize three different scaled objectives: wastage of raw material, raw material cost and product surplus. To model industry reality more closely, the formulation is extended to an environment that includes uncertain costs and demands. In this sense, the model is reformulated as a robust optimization problem. To solve this problem, a weighted global criterion approach is developed and applied to find Pareto optimal solutions. A case study from a cardboard box manufacturer is used to test the efficiency of the proposed robust formulation and the proposed solution approach. The sensitivity of the Pareto optimal solutions to different levels of uncertainty in the parameters and different weights of the objective functions is also studied.