Mostafa Hajiaghaei-Keshteli’s research while affiliated with Tecnológico de Monterrey and other places

To address the challenges faced by global operations, top management must react urgently and rapidly. To stay competitive and advance the supply and logistics processes, a management system must be able to respond to the operational requirements and market. One of the mistakes that can be made when it comes to achieving successful operations and supply is the ignorance of the risk and uncertainty that is an inevitable part of leading logistic operations. Sometimes, it is essential to expand the infrastructure and the processes to commit stakeholders and meet customer expectations, in addition to the routine managerial responsibilities. This research aims to model a logistic hub location selection problem to aid logistic managers, enabling them to respond to the market as quickly as possible. We propose a new approach to extended Z-numbers (ZE-numbers) in multi-criteria decision analysis to uncover the reliability and uncertainty in the decision problem. In the developed group decision-making model, we consider the experts' votes and the decision-makers' opinions, assisting logistics managers in locating the most appropriate location for establishing a logistics hub. The ZE-numbers are integrated with the Multi-Attribute Border Approximation Area Comparison (MABAC) and Best-Worst Method (BWM). The outcomes of this study show that cost and infrastructure are the most important indexes for choosing the logistic hub locations for this research. Also, security is determined as the least important index. The findings of this research show that Santander is the top location, and Almeria and Murcia have been deemed less crucial to improving the hub location problem.

The lack of information and hybrid uncertainties in Supply Chain (SC) parameters affect managerial decisions. It is inevitable to consider random uncertainty based on fuzzy scenarios and cognitive uncertainty to model a Sustainable Closed-Loop SC (SCLSC) problem. Using Pentagonal Fuzzy Numbers (PFNs) has higher comprehensiveness and accuracy than triangular and trapezoidal fuzzy numbers due to taking into account higher uncertainty, less lack of information, and taking into account maximum subjectivity Decision-Makers (DMs). There is a gap in the literature regarding the use of PFNs in SCLSC problems. This research presents a new model using PFNs to solve deficiencies in stochastic-possibilistic programming. Developing a Robust Stochastic-Possibilistic (RSP) based on PFNs under fuzzy scenarios, presenting measures of necessity, possibility, and credibility for making decisions founded on different levels of DMs’ risk, and proposing global solutions through providing linear programming models are the main innovations and contributions of the present research. An actual case study evaluates the presented approach to reduce the cost and carbon pollution in the stone paper SC. In the suggested method, trade-offs could be formed between the mean of objective functions and risk by modifying the robustness coefficients. According to the proposed approach, an optimal value of confidence is specified. Additionally, robustness deviations are controlled in the model, which results in more accurate and reliable results. Numerical simulations confirmed the efficacy of the robust approach proposed.

The purpose of this paper is to design a sustainable supply chain network in the presence of uncertainty. This network includes suppliers, manufacturers, distributors, and customers. In the network, some features, such as different levels of capacity, environmental effects of establishing distribution centers, and number of job opportunities created in the supply chain, are considered. Furthermore, in the case of sustainability, various production technologies and their effects on production speed and emission rates are studied. Various types of vehicles are considered for transportation of the products between the chain members. To model the problem, a bi-level mathematical model with a leader and a follower is introduced. In this chain, the government plays the role of leader and the private sector or industry owner plays the role of follower. The demand is from the parameters whose determination always creates a wide range of uncertainty in the input parameters. Determining the exact value of this parameter has a high computational complexity due to the wide range of customers. To cope with this uncertainty, a robust optimization scheme is adopted. As a result of the inherent uncertainty of the parameters, a realistic approach is developed to deal with the situation. A fuzzy-goal interactive approach is used to solve the proposed model, and its performance is tested using a case study in Iran. The calculation results indicate the appropriateness of the solution method used as well as the necessity to use appropriate uncertainty methods, such as robust planning. Graphical abstract

Evidence shows that the issue of transportation has been one of the most decisive factors in optimizing the supply chain in the new global economy. The transportation aspect is also identified as a critical factor in achieving customer satisfaction and increasing supply chain efficiency. In response to the dynamic nature of customer demands, organizations are increasingly diversifying their products to remain competitive. To address this issue, a multi-product mathematical model is formulated considering transportation modes and fixed costs. Considering the complexity of the model, meta-heuristic algorithms including genetic algorithm, harmony search, simulation annealing and combining these algorithms with the restart mechanism have been proposed and evaluated. Then, the best operators and algorithm parameter values are obtained using the Taguchi method and the proposed algorithms are compared. The results showed that the hybrid simulated annealing algorithm performs better than other algorithms. This study contributes to the field of supply chain management by providing insights into transportation optimization in multi-product supply chains.