Increased demand for transportation is inevitable along with the growth of social welfare and economic development. Furthermore, providing cheap transportation services has brought about a bulk of benefits in the ever-expanding development of countries. Therefore, the optimization of fuel consumption in the transportation sector is essential for the continued low cost of transportation and reduced amount of pollution caused by the traffic. A major strategy for optimizing fuel consumption and transportation costs in the transportation of goods and passengers is the proper design of transportation networks. Accordingly, in this study, hub covering location problem is modeled taking into account the queuing system in order to minimize the cost of the whole network as well as to minimize emissions of greenhouse gases. The model presented in this study includes uncertain parameters of demand and transportation costs. To control the parameters, a robust-box optimization method is used. As the hub covering network model is multi-objective and NP-hard, three meta-heuristic algorithms; namely MOPSO, NSGA II and MOALO are presented. Computational results show the high efficiency of the MOALO algorithm for obtaining efficient problem-solving in large sizes.

In this paper, the authors proffer a novel mathematical model for the simultaneous optimization of facility location and network design in the presence of uncertainty, with the aim of minimizing operational and transportation costs. The proposed model constitutes a departure from conventional methods in its consideration of probable events in the real world and the incorporation of uncertainty assumptions into the mathematical framework. An algorithm based on simulated annealing is then advanced for the solution of the problem, and the performance of the algorithm is evaluated through comparison with exact methods for problems of modest size, as well as with a basic simulated annealing algorithm for larger problems. The results of these comparisons demonstrate the superiority of the proposed meta-heuristic algorithm. Finally, the robust approach is compared with four other approaches in the presence of uncertainty, with a thorough analysis of the results obtained from each of the methods conducted in a suite of sample problems.

The growing environmental and economic concerns oblige logistics operations managers to look for simple solutions to optimize their processes and to corporate sustainability in logistics networks. Logistics collaboration is one of the management practices to foster the sustainability of freight transport. This paper presents an ‘ex ante’ decision support tool to evaluate the economic and ecologic impacts of shippers’ horizontal collaboration in urban freight delivery. Optimization model as a two-echelon location routing problem (2E-LRP) is exploited to demonstrate the benefits of joining facility location and vehicle routing decisions under multi-objective optimization approach. Numerical instances reproducing the real urban network are regenerated to test the proposed mechanism. Scenario analysis is conducted to analyze and discuss the effect of parameters’ changes in generated gains.

Tehran, as one of the most populated capital cities worldwide, is categorized in the group of highly polluted cities in terms of the geographical location as well as increased number of industries, vehicles, domestic fuel consumption, intra-city trips, increased manufacturing units, and in general excessive increase in the consumption of fossil energies. City logistics models can be effectively helpful for solving the complicated problems of this city. In the present study, a queuing theory-based bi-objective mathematical model is presented, which aims to optimize the environmental and economic costs in city logistics operations. It also tries to reduce the response time in the network. The first objective is associated with all beneficiaries and the second one is applicable for perishable and necessary goods. The proposed model makes decisions on urban distribution centers location problem. Subsequently, as a case study, the fruit and vegetable distribution network of Tehran city is investigated and redesigned via the proposed modelling. The results of the implementation of the model through traditional and augmented ε-constraint methods indicate the efficiency of the proposed model in redesigning the given network.

Today, urban areas have been considerably expanded, which has caused numerous problems due to the large population in metropolitan areas. City logistics models can be effective in solving complicated problems such as traffic congestion, air pollution, accidents, and high energy consumption rate. The present paper proposes a bi-objective mathematical model for designing a city logistics distribution network in order to minimize the economic and environmental costs as well as the response time. The proposed model would make decisions on the location and allocation problems in the city logistics distribution network. In fact, the objective of the problem is to select some fixed sites in order to construct the city distribution centers. The demand for commodities is considered probabilistic and the network is modeled based on the queuing theory. Furthermore, in the proposed model, the policy of putting tax on carbon and applying the low-carbon emission resources are used for establishment at the city distribution centers. Afterwards, in order to validate the proposed model, a numerical example is generated and, then, the results obtained from solving the model via epsilon constraint method are presented. Analysis of the results in the present study indicates that the proposed model was highly efficient in achieving its objectives. Finally, there are some suggestions for future studies that can be taken into account.

Research Project Supply Chain is one of the most complex supply networks. The management of this complex system has created new challenges for industry managers and engineers. The optimization of supply chain networks is, therefore, essential within the context of the research projects. The aims of this paper is to design the network of Supply Chain Management for research projects in an Iranian project-based organization. This model integrates strategic decisions in the whole life cycle for research projects using a mathematical approach. This model is a guideline for managers in research projects that will enable them to identify the strategic decisions of PSCM in their own organizations in order to resolve and improve them. It also serves as a useful base for researchers to further expand research concerning PSCM in research projects.

Although supply chains disruptions rarely occur, their negative effects are prolonged and severe. In this paper, we propose a reliable capacitated supply chain network design (RSCND) model by considering random disruptions in both distribution centers and suppliers. The proposed model determines the optimal location of distribution centers (DC) with the highest reliability, the best plan to assign customers to opened DCs and assigns opened DCs to suitable suppliers with lowest transportation cost. In this study, random disruption occurs at the location, capacity of the distribution centers (DCs) and suppliers. It is assumed that a disrupted DC and a disrupted supplier may lose a portion of their capacities, and the rest of the disrupted DC & apos; s demand can be supplied by other DCs. In addition, we consider shortage in DCs, which can occur in either normal or disruption conditions and DCs, can support each other in such circumstances. Unlike other studies in the extent of literature, we use new approach to model the reliability of DCs; we consider a range of reliability instead of using binary variables. In order to solve the proposed model for real-world instances, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied. Preliminary results of testing the proposed model of this paper on several problems with different sizes provide seem to be promising.

During the past few years, operations research applications in health care operation management have grown quickly. On the other hand blood as a perishable, valuable and lifesaving product is one important asset of any healthcare center. Therefore, designing a blood supply network comes to importance. It also should be noted that a blood supply chain comprises specific modifications. This study intends to locate blood bank components in a network, and to determine the allocations among the network components. The supply chain components considered in this study are donation sites, testing and processing labs, blood banks, and demand points. It is known that demand centers such as hospitals and clinics highly depend on blood products and any deficiency in procurement can even result in a person’s death. Thus, in the last layer of the considered network a transshipment sub-network is considered between demand points. Most of the intricacies in problem formulation of blood supply chain are regarded in this study; cases such as blood wastage, blood product decomposition in lab facilities, and transshipments between demand points. Due to the fact that for such an important and lifesaving supply chain the aim would go beyond minimizing cost, another objective function is presented for the problem. Hence, to obtain a Pareto solution for both objective functions ?-constraint method is utilized. Finally, to demonstrate the applicability of the problem, the model is implemented on a number of problem sets.

Graphical evaluation and review techniques (GERT) is a technique to study the stochastic nature of networks consists of different branches. In GERT, all branches are explained in terms of the probability that the branch is traversed and the tile to traverse the branch in case it is realized. This paper presents recent advances of the implementation of GERT in various industries. The study presents a comprehensive description of GERT and recent advances on the implementation of GERT in various industries over the period 2002-2017.

One of the primary concerns on many traditional capacitated facility location/network problems is to consider transportation and setup facilities in one single objective function. This simple assumption may lead to misleading solutions since the cost of transportation is normally considered for a short period time and, obviously, the higher cost of setting up the facilities may reduce the importance of the transportation cost/network. In this paper, we introduce capacitated facility location/network design problem (CFLNDP) with two separate objective functions in forms of multi-objective with limited capacity. The proposed model is solved using a new hybrid algorithm where there are two stages. In the first stage, locations of facilities and design of fundamental network are determined and in the second stage demands are allocated to the facilities. The resulted multi-objective problem is solved using Lexicography method for a well-known example from the literature with 21 node instances. We study the behaviour of the resulted problem under different scenarios in order to gain insight into the behaviour of the model in response to changes in key problem parameters.