In today’s industrial and service sectors the role of transportation is unavoidable. Due to this importance, an optimized transportation plan with minimum transportation costs can be a favour for the managers. In this study a multi-item fixed-charge transportation problem with capacitated multiple transportation mode is considered. As such problem is of high degree of complexity, we focus on the Benders decomposition approach to solve it. For this aim, first the classical Benders decomposition approach is developed for the problem. This is the first time in the literature that the Benders decomposition algorithm is developed for this problem. In continue, as another novelty, an accelerated benders decomposition algorithm is developed for the problem by adding some valid inequalities to the classical Benders decomposition algorithm. These valid inequalities can effectively influence the performance of the classical Benders decomposition algorithm. Several test problems with various sizes are generated to test the proposed solution approaches. The test problems are solved by the classical branch and bound algorithm and the proposed classical and accelerated Benders decomposition algorithms. According to the obtained results the accelerated Benders decomposition algorithm performs better than others in terms of reporting optimal solution and CPU running time.

Renewable energy resources have got much attention in recent years. Because of climate change in the world, the countries try to develop renewable energy resources. Wind farms are renewable energy resources that can produce electricity with no negative effect on climate change. In this study, as an important topic, the location selection problem of wind farms is considered as a real case study. For the first time, a multi-criteria wind farm location selection problem with neutrosophic fuzzy and uncertain criteria at the same time is developed. As the set of criteria consists of both input and output criteria, we develop a novel hybrid neutrosophic-fuzzy-uncertain scheme of BCC DEA model for the first time to solve the problem. As solution approach, a chance-constrained programming approach based on possibility measure of the neutrosophic fuzzy constraints of the model also for the first time is proposed in this study. An extensive computational study on the case study by the proposed approach is performed. The candidate locations of the case study are prioritized where the location of Birjand city with score of 1.1656 is selected as the best location. A sensitivity analysis on the confidence levels of the chance-constrained programming approach is performed and also the obtained results are compared to the approaches of the literature.

This study addresses the critical challenge faced by organizations in selecting an optimal subgroup of decision-making units (DMUs). Such a selection procedure can significantly influence efficiency, profitability, and strategic development. Recognizing the limitations of existing methods in handling inexact data and incorporating managerial preferences, this study proposes a novel framework that integrates data envelopment analysis (DEA) with binary linear programming models. The model applies belief-degree–based representations of uncertainty to capture imprecise inputs and outputs. For this model, two solution approaches—namely, chance-constrained programming and expected value approaches—were developed. These approaches are suitable for real-world applications using standard optimization software. The effectiveness of the proposed method was validated through a case study in Iran’s petrochemical industry, where it successfully identified the optimal technology for a new refinery unit while balancing efficiency and profitability under uncertainty. This work is the first study in the literature to combine DEA and binary linear programming under belief-degree–based uncertainty for DMU selection, offering a systematic, practical, and computationally efficient solution, with recommendations for future research to explore alternative uncertainty modeling techniques and broader industrial applications.