فهرست مطالب احسان نیکبخش

The world is facing with the increase in natural disasters, wars and terrorism. Proper logistic planning and timely delivery of relief items before and after disasters can save lives and property of affected people. This study aims to review the literature on the concepts of resilience and fairness and their modeling in humanitarian logistics. 

The data in this study are qualitative, collected from books and articles published from 2010 to 2023 and available in online databases such as Google Scholar and Scopus. Then, using the meta-analysis method, they were analyzed using Microsoft Excel software.

Fairness and resilience are challenging issues for emergency and relief organizations. Despite the practical importance of these two concepts in humanitarian logistics, limited attention has been paid to them in literature. Some research areas included simultaneous consideration of fairness and resilience, the use of indicators to create efficient resilience in the distribution of relief items and the adoption of risk-averse approaches to estimate uncertainty, such as robust optimization.

This study highlights the importance of fairness and resilience and their measurement in humanitarian logistics. Despite their importance in humanitarian logistics, there are still research gap. Further studies can greatly contribute to improving the planning ability of emergency and relief organizations.

Loading and haulage operation in open pit mines is the last stage of the mining process. truck- shovel system, due to its many advantages  including high flexibility, is preferred for this operation. Due to high operating costs, proper fleet management and optimization can significantly affect the project economics. Truck allocation and dispatching issue is a very complex problem, especially in large mines with numerous loading and dumping points. Because of the problem size and complexity, employing mathematical methods is not justified due to very high solution time which leads to employing super computers. To overcome the aforesaid shortcoming, heuristic algorithms can be applied. In this paper, in MATLAB environment, a heuristic algorithm was developed to solve allocation and dispatching problem of transportation fleet of a real mine. According to the obtained results, a running time of 39 seconds was computed for the heuristic algorithm. Finally, the same problem was solved with an available mathematical model with a running time of 24 hours which shows the superiority of the proposed algorithm over the mathematical modeling.

The order-picking problem is important as one of the warehouse's logistics activities. This problem is defined as collecting orders from different warehouse locations to respond to customers' orders quickly. This paper aims to provide a multi-objective mathematical programming model for integrating the decisions of batching, routing, and scheduling of selectors with the packaging problem in a multi-warehouse environment. The objective functions include depreciation of the delivery times and total order picking costs.

In this research, first, by reviewing the literature in the field of order picking, the research gaps of the problem have been identified. Then, taking into account the main constraints of the problem, a multi-objective mathematical model has been formulated for the multi-warehouse order-picking problem. The classic Benders decomposition algorithm and the accelerated Benders decomposition algorithm have been used to solve the problem. The data related to the warehouses of a company producing sanitary products in Iran was used as a case study to validate the applicability of the proposed model, and its results were reported in the article.

The proposed model's results indicate that CPLEX can solve these problems up to small sizes in an acceptable time. Also, the numerical results show the performance of the Benders decomposition algorithm and the accelerated Benders algorithm as suitable alternatives for solving the model in large-sized problems. The calculation results obtained from the implementation of the solution methods for the proposed model showed that in terms of the number of iterations and the calculation time, the accelerated Benders algorithm had better results than the classic Benders algorithm.

Originality/Value:

 In this research, the order-picking problem with the integrity of operational decisions has been formulated as a multi-objective mathematical model for a multi-warehouse environment for the first time. Also, in this article regarding the solution method, exact solution approaches have been used for the first time considering the structure of the problem. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

The iron and steel industry is one of the most resource-intensive and pollutant industries that creates the highest value across all mining and metal industries. While the recent studies provide recommendations to improve sustainable development in this industry, the complexity of the socio-environmental impacts of activities in this industry due to its multi-tier and multi-supply chain structures has given rise to the problem of sustainable steel supply chain network design. This work proposes a new multi-objective mixed-integer linear programming model to integrate sustainability factors with managerial and technical restrictions. The total economic profitability is maximized, while environmental pollution is minimized. There is also a focus on the social and environmental compliance. Accordingly, a novel sustainability assessment system is proposed. The managerial restrictions are also satisfied by maximizing the demand fulfillment priority using a new method. The augmented ε-constraint method is applied to tackle the mathematical problem under study. Finally, a real case study is used. The results obtained 35% and 41% reductions in CO2 and particulate matter emissions, respectively, while the total profit decreases up to 15%. A sensitivity analysis is also performed. In addition, several managerial insights are discussed based on the results.

Inventory control of blood and its products is the most important challenge in efficiently managing a blood supply chain as blood products are perishable. In addition, the replenishment of blood, which donors supply, and the demand of patients for blood products are uncertain. In particular, platelets with the shortest lifespan, up to five days, are the most expensive blood products. Therefore, this study investigates the problem of platelet inventory control in a hospital network using integer programming. Due to the uncertainty of the demand of patients in hospitals, a scenario-based robust optimization approach is applied to minimize the worst-case performance of the system. The proposed model is coded and solved using GAMS software. The results demonstrate that using a robust optimization approach reduces hospital costs by 7.6% on average compared to a solution that ignores uncertainty. Moreover, the efficiency of hospitals' inventory control depends on the capacity of the Blood Transfusion Organization to deliver fresh platelets.

In the past few decades, due to negative environmental and social impacts of companies, there has been more pressure from the government and non-governmental organizations on companies to observe these issues in their operations, including their supply chain operations. To achieve an environment-friendly supply chain, the role of suppliers as the initial point of the supply chain is very crucial. So in addition to traditional criteria for evaluating and selecting suppliers such as price, delivery, and quality, it is essential to use new criteria, that while encompassing environmental issues, are fit to the characteristics of the industry under study. In this study, taking into account a set of economic and environmental criteria, a multi-objective integer mathematical programming model is proposed for solving the green supplier selection and order allocation problem under the multi-product, multi-sourcing, and single period assumptions. The case of this study is part of the IRAN KHODRO supply chain and focuses on the supplier selection and order allocation processes for 31 components of four types of the final products provided by 45 suppliers. The proposed mathematical model for the supply chain under study has 507 constraints and 449 decision variables. Then proposed model is solved via the ε–constraint method and Total Value of Green Purchasing (TVGP) is calculated. Results of the solution and sensitive analysis of the proposed model demonstrate that with increasing the importance of environmental issues in the supply chain, TVGP will increases.

Hub networks are among the important variations of transportation networks that have applications in air transportation, post, and telecommunication. An important feature of these networks is the possibility of disruption in the network performance due to failure of hub facilities or access links. In this study, the hub arc covering location problem is studied under disruption of hub arcs. First a new mathematical model is introduced based on the chance-constrained programming technique in stochastic programming. Then computational results of solving the proposed model for problem instance (based on Iran Aviation Data and Australian Post) are presented. The computational results confirmed the importance of disruption consideration before designing hub networks and changes in the optimal configuration of the network under disruption.

Hub location problems are among the extensions of classical location problems that have numerous applications in designing transportation, postal, and telecommunication networks. Despite the evident attention of researchers to these problems, there are few studies on designing hub networks under uncertainty and disruption. In this study, the capacitated single allocation hub location problem under demand uncertainty and disruption is investigated. First, a mathematical model based on budget of uncertainty concept is proposed, then a hybrid solution method based on genetic algorithm and variable neighborhood search is proposed. Computational experiments demonstrate the accuracy of the proposed solution method in solving the testinstances. In addition, the results obtained from conducting Monte-Carlo simulation experiments to analyze the effects of uncertainty on the performance of different hub network configurations show the necessity of considering demand uncertainty and disruption while designing hub networks.

During the last three decades, the integrated optimization approach to logistics systems has become one of the most important aspects of the supply chain optimization. This approach simultaneously considers the interdependence of the location of the facilities, suppliers/customers allocation to the facilities, the structure of transportation routes, and inventory planning and control. Location-routing problem is one of the most important classes of location problems for considering this approach. In this problem, the number and location of facilities, size of the transportation fleet, and the route structures are to be found with respect to the location and characteristics of suppliers and customers. In this study, a mathematical model, an efficient and fast heuristic algorithm, an effective metaheuristic algorithm based on simulated annealing, and a new lower bound for the two-echelon location-routing problem with vehicle fleet capacity and maximum route length constraints are presented. At the end, the computational results show the efficiency of the proposed algorithms using the proposed obtained lower bound.