Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best-worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates productio

Providing New Multi-Component Data Envelopment Analysis to Evaluate Efficiency of Bank Branches

Looking at the economic definition of efficiency as optimal use of resources to produce possible maximum output, it can be understood the importance of this concept in management systems. Basically, Managers are trying to meet the satisfaction of all their stakeholders by optimally utilize of their resources to produce outputs. Due to high cost of holding money this point has more importance to the banking industry in Iran. In this paper, by looking at the structure of bank activities in Iran, a model with five different parts is provided that depicts the flow of affairs in banks. A mathematical model based on data envelopment analysis is presented to evaluate the efficiency of proposed structure and by using fuzzy approach, a method has been proposed to solve it. The results of applying the proposed model to 210 branches of one bank show that despite relative acceptable efficiency in resource attracting, and management, the efficiency of service, resource allocation and profitability parts are facing with important proble