nader ghaffarinasab

In this research, a modular hub location problem has been investigated where the objective is to reduce the transportation costs in the hub network. The proposed model determines the location of hubs, allocation of the non-hub nodes to the hubs, and the optimal vehicle traffic, i.e., the number of flights or the number of trucks traveling in the network, considering the appropriate capacity for each vehicle. Also, decisions regarding the percentage of the traffic volume sent via multiple network routes are made by the presented model.

The mathematical model, including the objective function and constraints, is constructed and solved by GAMS software. The effect of different parameters on the results is investigated. Due to long solution times for the MIP model, a heuristic solution method based on LP relaxation of the integer variables is developed for the proposed problem, which is able to obtain near-optimal solutions in less time.

The developed mathematical model is implemented on the air passenger transportation data for the airports of the United States of America, which is known as the CAB data set. The results give the optimal number of hubs, as well as the optimal number of transportation units on each arc of the network, which depend on the capacity of the means of transportation.

Originality/Value:

 In this research, a mixed integer programming model is developed for the multiple allocation modular hub location problem. Numerical experiments are conducted with the use of GAMS software and the results are discussed.

Hubs are special facilities used as switching, transferring, and sorting points in many distribution systems. Instead of serving each origin-destination pair directly, the hub facility concentrates flow to take advantage of the resulting economic savings. Flows from the same source combine with different destinations on their path to a hub and combine with flows that have different sources but have the same destination. The accumulation of flows takes place in the path from the origin to the hub and from the hub to the destination, as well as between the hubs. These types of systems, commonly known as hub-and-spoke, are studied in the form of hub location problems. In this paper, we develop the single allocation hub location problem with uncertain zero-one demands, in which the amount of demand between each origin-destination pair is considered as a Bernoulli random variable with a definite probability p. Due to the fact that this problem has not been studied in the literature so far, a new mathematical model of mixed integer programming type is developed for the problem and is solved using GAMS software. Also, the results of solving different test problems from the CAB data set are examined and the effect of different parameters on the optimal solution of the problem is examined.

Regarding the growing population and widespread consumption growth around the world, supply chain networks have changed to extensive networks, but the components of these networks have made severe environmental problems. Sustainable supply chain management is one of the emerging concepts in production and operations which improves environmental and social performance as well as economic performance. Hence, manufacturers should strive to employ it. Considering the reverse logistics and closed-loop supply chain can be practicable to reduce waste or to exploit corrupted items. This issue is less discussed in the agricultural supply chain. The present paper develops and solves a multi-objective and multi-period linear integer programming model for optimizing aggregate forward and reverse apple logistics network. It consists of three levels (gardens, distribution centers, and internal and external customer areas) in forward flow and two levels (composting centers and the compost markets) in reverse flow. The model also considers all aspects of carbon dioxide emissions throughout the entire supply chain network and proposes a fair and reasonable balance between economic, social, and environmental goals.

Hub location problem (HLP) is one of the strategic planning problems in logistics with numerous applications in passenger/cargo transportation, postal services, telecommunications, etc. This paper addresses the competitive single allocation HLP where the market is assumed to be a duopoly. Two firms (decision makers) sequentially decide on the configuration of their hub networks trying to maximize their own market shares and the customers choose one firm based on the service level (cost, distance, and etc.) provided by these firms. Mathematical formulations are provided for the problems of the first and second firms (the leader and the follower, respectively) and a tabu search (TS) based solution algorithm is proposed for solving the leader's and the follower's problems. Standard data sets, which exist in the literature of HLP, have been used to validate the solution methods and mathematical models. Computational experiments show that considering the competition in deciding on the location of hubs can positively affect the captured market share from the international shipping markets.

The hub location problems (HLP) constitute an important class of facility location problems that have been addressed by numerous operations researchers in recent years. HLP is a strategic problem frequently encountered in designing logistics and transportation networks. Here, we address the competitive multiple allocation HLP in a duopoly market. It is assumed that an incumbent firm (the leader) is operating an existing hub network in a market and an entrant firm (the follower) tries to enter the market by locating its own hubs aiming at capturing as much flow as possible from the leader. The customers choose one firm based on the service level (cost, time, distance, etc.) provided by the firm. We formulate the problem from the entrant firm’s point of view and propose an efficient tabu search based solution algorithm to solve it. Computational experiments show the capability of the proposed solution algorithm to obtain the optimal solutions in short computing times.