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

Investigating the impact of behavioral factors on supply network efficiency:insights from banking’s corporate bond networks

This paper highlights the role of behavioral factors for efficiency measurement in supply networks. To this aim, behavioral issues are investigated among interrelations between decision makers involved in corporate bond service networks. The corporate bond network was considered in three consecutive stages, where each stage represents the relations between two members of the network: issuer-underwriter, underwriter-bank, and bank-investor. Adopting a multi-method approach, we collected behavioral data by conducting semi-structured interviews and applying the critical incident technique. Financial and behavioral data, collected from each stage in 20 corporate bond networks, were analyzed using fuzzy network data envelopment analysis to obtain overall and stage-wise efficiency scores for each network. Sensitivity analyzes of the findings revealed inefficiencies in the relations between underwriters-issuers, banks-underwriters, and banks-investors stemming from certain behavioral factors. The results show that incorporating behavioral factors provides a better means of efficiency measurement in supply networks

DEA with common set of weights based on a multi objective fractional programming problem

Data envelopment analysis operates as a tool to appraise the relative efficiency of a set of homogenous decision making units. DEA allows each DMU to take its optimal weight in comparison to other DMUs while a similar condition is considered for other units. This feature threats the comparability of different units because different weighting schemes are used for different DMUs. In this paper, a model is presented to determine a common set of weights to calculate DMUs efficiency. This model is developed based on a multi objective fractional linear programming model that considers the original DEA's results as ideal solution and seeks a set of common weights to evaluate DMUs and increases the model's discrimination power. A numerical example is solved and the proposed method's results are compared to some previous methods. This Comparison has shown the proposed method's advantages in ranking DMUs

Game theoretic approach for coordinating unlimited multi echelon supply chains

In order to achieve the overall objectives of the supply chain (SC), there have been seen many contradictions between the components and different levels, and these disorders may result in decreased strength and competitiveness The main contradictions that are considered in this paper comprise inventory, pricing and marketing costs in an unlimited three echelon supply chain. The basics of the game theory make it a suitable and reliable tool for solving contradiction situations by considering all the levels and players’ goals. Initially, an unlimited three echelon supply chain, including S suppliers, M manufacturers, and K retailers, is considered in order to solve the aforementioned problem. Further on, a nonlinear mathematical cooperative model based on specific assumptions, game theory approach, Nash equilibrium definition, Pareto efficiency, and revenue sharing contract is proposed. Subsequently, the proposed model is employed in a numerical example, and the results are illustrated according to the genetic algorithm. Furthermore, the sensitivity of the proposed model is analysed using the design of experiment. Ultimately, the validation of the proposed cooperative model is assessed by the simulatio

A grey mathematical programming model to time-cost trade-offs in project management under uncertainty

Time and cost are two salient elements indicative of success in project management. This importance obliges the project managers to seek for the best feasible amalgamation of time and cost regarding project's activities. This condition engenders a trade-off problem in terms of creating a required balance between time and cost considerations to execute all activities in a project efficiently. Such problem relates to time and cost trade-offs issue. Time and cost trade-offs model is based on estimated values of time and cost required for a given activity to be complete in a normal or crashed form. Current models of time and cost trade-offs have made use of crisp values for these estimations. In this paper, we extend a model for time and cost trade-offs based on grey numbers to deal with the uncertain nature of time and cost estimation. The proposed method has also been applied in an example and interpretations pertaining to offered solutions have been examined

Multi‐objective linear programming with interval coefficients

Purpose The purpose of this paper is to extend a methodology for solving multi‐objective linear programming (MOLP) problems, when the objective functions and constraints coefficients are stated as interval numbers. Design/methodology/approach The approach proposed in this paper for the considered problem is based on the maximization of the sum of membership degrees which are defined for each objective of multi objective problem. These membership degrees are constructed based on the deviation from optimal solutions of individual objectives. Then, the final model based on membership degrees is itself an interval linear programming which can be solved by current methods. Findings The efficiency of the solutions obtained by the proposed method is proved. It is shown that the obtained solution by the proposed method for an interval multi objective problem is Pareto optimal. Research limitations/implications The proposed method can be used in modeling and analyzing of uncertain systems which are modeled in the context of multi objective problems and in which required information is ill defined. Originality/value The paper proposed a novel and well‐defined algorithm to solve the considered problem

An Integer Grey Goal Programming For Project Time, Cost and Quality Trade-Off

Project management (PM) is one of the prominent fields in business and industry. Every task of an organization can be imagined as a project, being a coordinated set of activities toward a common goal. One important aspect of PM is analysing the information related to the optimum balance among the project’s objectives. Each project is a combination of different activities, being connected to each other and having several success criteria, among which the time, cost and quality of the project completion are more significant, due to their significant effect on obtained results. Accordingly, the time might lead to delay and penalty which means more cost; and cost may be underestimated than real required funds. They both will lead to failure in project management. On the other hand, quality is the final key which confirms the success. The aim of a time-cost-quality trade-off problem (TCQTP) is to select a set of activities and an appropriate execution mode for each activity; the cost and time of the project is minimized while the project quality is maximized. The purpose of this paper is to present a model for TCQTP in which these parameters are approximated by grey numbers. Since there are various modes to accomplish each activity, the trade-off problem is formulated based upon a multi-objective integer grey programming model. Afterwards, a goal programming- based approach is designed to solve this model. The model's results provide a framework for the project manager to manage his/ her project successfully, in acceptable time, with the lowest cost and the highest quality. The main originality of the proposed model is the approximation of time, cost and quality parameters of activities mode with grey numbers and the development of a two phase goal programming- based approach to solve this problem. Ultimately, the proposed model is applied in two different cases and results are illustrated to clarify the outstanding capabilities of the mode

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

Effective factors of implementing efficient supply chain strategy on supply chain performance

Nowadays, the importance of supply chain management and its effect on business performance is undeniable. Boosting competitive environment makes every single firm adopt an assignable supply chain strategy. This study is one of the rare practical researches that recognize key factors related to the application of a successful and efficient supply chain strategy. So far, many researchers have conducted studies on responsive supply chain strategy; but in this study, it is sought to focus on efficient supply chain strategies due to increasing need for organizations to enhance efficiency and reduce costs. Structural equation modelling using SmartPLS software is used to examine the research assumptions. Analysis of the structural model showed that there is a positive relationship between implementation of efficient supply chain strategy with supply chain performance; therefore the main research hypothesis is confirmed. Research revealed internal integration, top management support and information technology as efficient supply chain characteristics that have positive effects on supply chain performance. To reduce costs of implementation of efficient supply chain strategy, it is necessary to invest in factors that influence supply chain performance positively

A complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment

Multi-criteria decision making is an implicational field that concerns with selecting or designing the best scenarios among a finite set of scenarios based on a finite set of criteria. Different methods and techniques for handling this issue have been proposed. Complex proportional assessment is an analytical tool for solving multi-criteria decision making problems. Originally, the COPRAS method has been developed for decision making under a deterministic environment. Since uncertainty is an unavoidable property of decision making due to a lack of knowledge, this paper suggests an extended form of the COPRAS method used for group decision making problems in an uncertain environment where such uncertainty is captured through a generalized form of fuzzy sets - the so called interval valued intuitionistic fuzzy sets. An algorithmic scheme for the COPRAS-IVIF method has been introduced thus examining its application with reference to two numerical examples. It seems that the recommended framework of COPRAS-IVIF can be satisfactorily implemented in decision making problems under ambiguous and ill-defined conditions