mohammad mousazadeh

The meat supply chain is crucial in meeting household nutritional needs, public health, and food security. The meat supply chain's inherent dynamic and uncertain nature, along with existing risks and disruptions, has made optimizing the supply chain for resilience unavoidable. For various reasons, suppliers may experience partial disruptions and be unable to service their customers promptly. In this paper, we propose an integrated design of a fresh meat supply chain network that considers resilience strategies such as capacity expansion through contracts with reliable suppliers under demand uncertainty using a robust approach. The objectives of this research are to minimize total transportation costs and fixed costs and maximize the service level of the supply chain. Key decisions in the proposed supply chain network include selecting farms from available farms, allocating locations to slaughterhouses, selecting retailers for selling meat products and processed meat products, determining the flow of selected materials between facilities at different levels of the proposed supply chain network, identifying meat supply chain network risks, and determining the level of supply chain service. The problem is modeled based on a bi-objective mixed-integer programming approach. Finally, the augmented ε-constraint method is used to solve the problem, and the performance and efficiency of the model are evaluated and analyzed using numerical examples and compared to the base model in the research literature.

Population growth has led to more food demand, especially meat. Designing a supply chain, especially a meat one, is complicated due to the uncertainty of food demand and the perishability of meat. To this aim, we develop a multi-objective mixed-integer linear programming model. The developed model contains four echelons, i.e., farms, slaughterhouses, retailers, and customers. The first objective function minimizes the total costs, the second objective minimizes the distribution time, and the third objective minimizes the network's non-resiliency simultaneously. An enhanced version of the augmented ε-constraint method is employed to solve the suggested model, and a set of Pareto–optimal solutions is found. This study also explores the impact of using the robust possibilistic approach in modeling a supply chain network under uncertainty. Numerical experiments demonstrate that the robust optimization approach brings significantly superior outcomes in comparison to the conventional deterministic approach, and the model provides a practical and valuable tool for real-world supply chain challenges.

The war environment is a conflict situation and operations research is one of the used techniques to make decisions in different battle conditions. In this article, game theory is used to model the conflict situation, which is one of the widely used methods in the military field. In this article, after modeling the problem as a matrix game, a hierarchical analysis method is proposed to determine the payoffs and prioritize the outputs of the game. On the other hand, due to the fact that in the real world information is presented in an imprecise and ambiguous way, Neutrosaphic collections have been used to express this information. A method of solving the hierarchical analysis problem with Neutrosaphic data is proposed and then solving the game problem with the outputs of the hierarchical analysis method is discussed. To solve the matrix game problem with neutrosaphic payoffs, the concept of nearest interval approximation of the generalized triangular neutrosaphic fuzzy number is used and the game model is written as a problem with interval payoffs. In the following, a method is proposed to solve this game problem. Finally, the solution of a practical example in military problems has been investigated with the proposed method. To solve this problem, two optimistic and pessimistic problems have been proposed for each player, and by solving these problems, the optimal solutions of the players are obtained in two optimistic and pessimistic states. Efficiency and simplicity in the problem solving method as well as the ability to develop the proposed method to game problems with interval-type payoffs are among the results of this research.

In this paper, the problem of designing a resilience closed-loop shrimp supply chain network is studied through developing a mixed integer linear programming (MILP) model. This research considers the shrimp supply chain as a set of suppliers (fishing and aquaculture centers), shrimp processing factories, distribution centers, wholesalers, markets, shrimp waste powder factory and shrimp waste powder market. The three objective functions introduced in this mathematical model are aimed at 1) minimizing the total costs of the network, including establishment, transportation and traceability costs, 2) maximizing sustainability by improving the employment rate, and 3) maximizing the resilience of the supply chain. Finally, the model has been analyzed considering the uncertainty in the demand parameter. The model is solved using the improved version of the augmented constraint method (AUGMECON) using GAMS software and Pareto efficient solutions are found. In order to find the most suitable solution among the Pareto solutions, TOPSIS method is utilized.

Today, the high importance of achieving food security for communities has made it necessary to provide a new perspective on the design of the grain supply chain distribution network to better adapt to real-world uncertainties and also to take into account disruptions. To this end, in this study, a mixed-integer linear programming model has been developed for the distribution network design problem in the grain supply chain, which has three objectives such as minimizing costs and maximizing job opportunities, both of which are examples of sustainability. In addition, considering the importance of grain in the food basket of households and the importance of food security, the third objective function has been developed focusing on the issue of resilience to deal with any disruption in the design of the distribution network. The robust-possibilistic programming approach is used to deal with uncertain demand and the multi-objective problem is solved using the improved epsilon constraint method. Finally, a compromised solution is selected using the TOPSIS method.</em>

We developed an equation of state (EOS) by Ihm, Song, and Mason (ISM) for polar fluids. The model consists of four parameters, namely, the second virial coefficient, an effective van der Waals co-volume, a scaling factor, and the reduced dipole moment. The second virial coefficient is calculated from a correlation that uses the heat of vaporization, and the liquid density at the normal boiling point. The reduced dipole moment is calculated from experimental dipole moments data and other parameters were obtained by regressing against saturated liquid density data. In this work, we generalized this equation to calculate the saturated liquid density of n-alkanol, water, and ammonia. The calculated results are also compared with SAFT equation of state and show that the saturated liquid density can be predicted within about 1.2%.

In this paper, a bi-objective mixed-integer linear optimization model for Closed-loop Supply Chain Network Design Problem (CLSCND) is developed. The proposed model includes both the forward and reverse directions and includes different types of facilities, namely, manufacturing/remanufacturing centers, warehouses, and disassembly centers. The first objective function tried to minimize the total cost of the supply chain, while the second one was aimed at maximizing the responsiveness of the network in both forward and reverse directions, simultaneously. To solve the proposed bi-objective model, an augmented ε-constraint method was implemented by which a set of Pareto-optimal solutions for the problem were generated. An illustrative numerical example is given in the study to show the applicability and efficiency of the presented optimization model.