Modeling and solving the uncapacitated r-allocation p-hub median problem under congestion

The hub location problems deal with determining the optimal location of hub facilities and allocating the demand nodes to these hubs in such a way that the traffic between any origin–destination pair is routed effectively. This paper proposes the uncapacitated r-allocation p-hub median problem under congestion. The problem is formulated as a second-order cone programming and an efficient simulated annealing heuristic algorithm is proposed to solve the large instances of the problem. Extensive computational experiments are conducted based on three well-known data sets to demonstrate the efficiency of the proposed algorithm and also to study the effect of different input parameters on the optimal solutions. Some managerial insights are derived based on the obtained numerical results

A heuristic approach to the stochastic capacitated single allocation hub location problem with Bernoulli demands

Hubs are critical components of transportation and distribution systems, and hub networks play a special role in freight and passenger transportation services worldwide. This paper studies a capacitated single allocation hub location problem with Bernoulli demands. Since the origin-destination (OD) demands are stochastic in nature, and the nodes are allocated to hubs before knowing their realized values, the actual total demand allocated to each hub is uncertain. Therefore, demand can exceed the capacity of hubs, rendering a need for outsourcing. The problem is studied under two distinct outsourcing policies, namely the facility and customer outsourcing. Mathematical models are developed for each case as two-stage stochastic programs. Deterministic equivalent formulations are obtained for problems, assuming a homogeneous demand distribution for all OD pairs. A Tabu Search-based algorithm is presented as a solution approach to deal with large problem instances. Extensive computational tests demonstrate the outstanding performance of the developed models and the metaheuristic procedure in terms of solution quality and computational time. The relevance of using stochastic programming approach for the problems is demonstrated via computational results. This study also reports solutions for problems with 200 nodes for the first time

An adaptive large neighborhood search heuristic for solving the reliable multiple allocation hub location problem under hub disruptions

The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method

A continuous approximation approach to the planar hub location-routing problem: Modeling and solution algorithms

The design of many-to-many parcel delivery networks is an important problem in freight transportation. To exploit economies of scale and provide a better service level, these networks usually have a hub-and-spoke architecture. We address a planar hub location-routing problem (HLRP) where the market demand is modeled as a uniform density function over a convex polygon service region. The continuous approximation (CA) technique is used for modeling the HLRP in such a way that it jointly decides on the location of hubs and the allocation of a service region to the hubs. The objective is to minimize the approximate total transportation cost, including local pickup and delivery costs, as well as line-haul transportation costs. Two solution algorithms are developed for the problem: an iterative Weiszfeld-type algorithm (IWA) and a particle swarm optimization (PSO) metaheuristic. The performance and solution quality of the proposed algorithms are compared with an adapted algorithm from the literature. Furthermore, extensive computational experiments are performed to study the effect of different input parameters such as the discount factor value, demand points density, and vehicle capacity on the total system cost and the final configuration of the network

Single allocation hub location problem considering zero-one uncertain demands

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

Robust single allocation p-hub median problem under hose and hybrid demand uncertainties: models and algorithms

Hub location problem is one of the extensively studied and important problems in the field of facility location and network design with numerous applications in transportation, postal services, and telecommunications. In this paper, we address the robust single allocation p-hub median problem under polyhedral demand uncertainty. The origin-destination (O/D) flow values are assumed to be uncertain and are modeled as two different polyhedral uncertainty sets, called the hose and the hybrid uncertainty sets. The problems are formulated as linear MIP models and matheuristic solution algorithms based on Tabu Search (TS) are developed for solving them. Computational experiments show the capability of the proposed algorithms to solve the real-world instances of the problem in small computational times. Furthermore, the effect of different input parameters on the final solutions are studied

A multi-objective mixed-integer linear model for sustainable fruit closed-loop supply chain network

Purpose: This paper aims to present a closed-loop supply chain (CLSC) optimization problem for a perishable agricultural product to achieve three pillars of sustainability, including minimizing total network costs and carbon dioxide emissions from different network activities and maximizing responsiveness to demands simultaneously. Design/methodology/approach: The research problem is formulated as a multi-objective mixed-integer linear programming model, and classical approaches, including the LP-Metric and weighted Tchebycheff method, have been applied to solve the optimization model. A set of test problems has been proposed to validate the model, and the results are presented. Findings: Computational time to find Pareto optimal solutions by using the weighted Tchebycheff method was twice as much as that of the LP-Metric method. Also, the result of the study is a mathematical model that can be applied to other products that are close to the fruit, such as vegetables. Research limitations/implications: The present study is limited to fruits supply chains and the inventory is considered at the distribution centers only. The study also considers only one type of transport. Practical implications: The paper can assist supply chain managers to define strategies to achieve a sustainable CLSC network configuration for the fruits. Originality/value: This study is one of the early studies to consider environmental indicators in fruits supply chain design along with two other indicators of sustainability, namely, economic and social indicators. Therefore, this can help supply chain managers to achieve sustainability by optimizing location decisions, inventory quantities and flow between facilities

A multi-objective linear optimization model for designing sustainable closed-loop agricultural supply chain

Demand for agricultural products will grow by nearly 70 percent in 2050 and high volume of chemical pesticides and agricultural fertilizers along with considerable waste in this sector induces serious environmental concerns. Hence it is not a priority but a necessity to modify unsustainable procedures to make them sustainable. The aim of this study is developing and analyzing a multi-objective (MO) linear mathematical model for sustainable close-loop agricultural supply chain (CLASC) with a deteriorating product to determine (1) the optimal flow to every echelon and (2) the optimal location of some facilities to achieve three objectives: reducing costs and carbon dioxide (CO2) emissions throughout the proposed supply chain (SC) network, and increasing the responsiveness. Finally, a numerical example is used to evaluate the optimization model.N/