An electromagnetism-like metaheuristic for the uncapacitated multiple allocation p-hub median problem

This paper deals with the uncapacitated multiple allocation p-hub median problem (UMApHMP). An electromagnetism-like (EM) method is proposed for solving this NP-hard problem. Our new scaling technique, combined with the movement based on the attraction–repulsion mechanism, directs the EM towards promising search regions. Numerical results on a battery of benchmark instances known from the literature are reported. They show that the EM reaches all previously known optimal solutions, and gives excellent results on large-scale instances. The present approach is also extended to solve the capacitated version of the problem. As it was the case in the uncapacitated version, EM also reached all previously known optimal solutions.     

Path relinking approach for multiple allocation hub maximal covering problem

We consider the multiple allocation hub maximal covering problem (MAHMCP): Considering a serviced O–D flow was required to reach the destination optionally passing through one or two hubs in a limited time, cost or distance, what is the optimal way to locate p hubs to maximize the serviced flows? By designing a new model for the MAHMCP, we provide an evolutionary approach based on path relinking. The Computational experience of an AP data set was presented. And a special application on hub airports location of Chinese aerial freight flows between 82 cities in 2002 was introduced.     

A scatter search-based heuristic to locate capacitated transshipment points

We consider a fixed charge two-stage location problem in which a given number of intermediate transshipment points are to be located between the supply plants and the customer locations. Both plants and transshipment points are capacitated. Scatter search is a population-based heuristic that has been applied to several combinatorial optimization problems. We develop an efficient scatter search-based heuristic approach with hybrid improvements including local search and path-relinking routines. Computational results demonstrate the effectiveness of the heuristic even for realistic problems with larger instances and tighter capacities.     

New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs.     

A traffic grooming problem considering hub location for synchronous optical network-wavelength division multiplexing networks

In this paper, we deal with a traffic demand clustering problem for designing SONET-WDM rings. The objective is to minimize the total cost of optical add-drop multiplexers (OADMs) and inter-ring hub equipments, while satisfying intra-ring and inter-ring capacities. Also, the minimum number of nodes, for example three, for each ring should be satisfied. We develop an integer programming (IP) formulation for the problem and develop some valid inequalities that provide a tight lower bound for the problem. Dealing with the inherent computational complexity of the problem, we also devise an effective tabu search procedure for finding a feasible solution of good quality within reasonable computing time. Computational results are provided to demonstrate the efficacy of the lower and upper bound procedures for solving the problem.     

A graph coloring heuristic using partial solutions and a reactive tabu scheme

Most of the recent heuristics for the graph coloring problem start from an infeasible k-coloring (adjacent vertices may have the same color) and try to make the solution feasible through a sequence of color exchanges. In contrast, our approach (called FOO-PARTIALCOL), which is based on tabu search, considers feasible but partial solutions and tries to increase the size of the current partial solution. A solution consists of k disjoint stable sets (and, therefore, is a feasible, partial k-coloring) and a set of uncolored vertices. We introduce a reactive tabu tenure which substantially enhances the performance of both our heuristic as well as the classical tabu algorithm (called TABUCOL) proposed by Hertz and de Werra [Using tabu search techniques for graph coloring, Computing 1987;39:345–51]. We will report numerical results on different benchmark graphs and we will observe that FOO-PARTIALCOL, though very simple, outperforms TABUCOL on some instances, provides very competitive results on a set of benchmark graphs which are known to be difficult, and outperforms the best-known methods on the graph flat300_28_0. For this graph, FOO-PARTIALCOL finds an optimal coloring with 28 colors. The best coloring achieved to date uses 31 colors. Algorithms very close to TABUCOL are still used as intensification procedures in the best coloring methods, which are evolutionary heuristics. FOO-PARTIALCOL could then be a powerful alternative. In conclusion FOO-PARTIALCOL is one of the most efficient simple local search coloring methods yet available.     

Solving the hub location problem with modular link capacities

This paper deals with a capacitated hub location problem arising in the design of telecommunications networks. The problem is different from the classical hub location problem in two ways: the cost of using an edge is not linear but stepwise and the capacity of a hub restricts the amount of traffic transiting through the hub rather than the incoming traffic. In this paper both an exact and a heuristic method are presented. They are compared and combined in a heuristic concentration approach to investigate whether it is possible to improve the results within limited computational times.     

A 2-phase algorithm for solving the single allocation p-hub center problem

The single allocation p-hub center problem is an NP-hard location–allocation problem which consists of locating hub facilities in a network and allocating non-hub nodes to hub nodes such that the maximum distance/cost between origin–destination pairs is minimized. In this paper we present an exact 2-phase algorithm where in the first phase we compute a set of potential optimal hub combinations using a shortest path based branch and bound. This is followed by an allocation phase using a reduced sized formulation which returns the optimal solution. In order to get a good upper bound for the branch and bound we developed a heuristic for the single allocation p-hub center problem based on an ant colony optimization approach. Numerical results on benchmark instances show that the new solution approach is superior over traditional MIP-solver like CPLEX. As a result we are able to provide new optimal solutions for larger problems than those reported previously in literature. We are able to solve problems consisting of up to 400 nodes in reasonable time. To the best of our knowledge these are the largest problems solved in the literature to date.     

Hub location for time definite transportation

Time definite motor carriers provide very reliable scheduled truck transportation service between specified terminals. They provide service competitive with airfreight carriers over continental-scale distances at a much lower cost. This paper provides time definite models for multiple allocation p-hub median problems and hub arc location problems. Service levels are imposed by limiting the maximum travel distance via the hub network for each origin–destination pair. Computational results are presented to demonstrate the effects of the time definite service levels on practical network design for truck transportation in North America.     

一种求解TSP问题的ACO&SS算法设计

提出一种求解旅行商(TSP)问题的新型分散搜索算法.将蚁群算法(ACO)的构解方法引入分散搜索(SS)算法,在搜索过程中既考虑解的质量,又考虑解的分散性.采用一种将蚁群算法的信息素更新技术与分散搜索的组合机制相结合的新型子集组合成新解的构解机制,同时采用动态更新参考集与临界准则策略来加快收敛速度.实验结果表明,该算法优于其他现有的方法,获得了较好的结果.     

A bi-objective reverse logistics network analysis for post-sale service

Reverse logistics, induced by various forms of return, has received growing attention throughout this decade. Reverse logistics network design is a major strategic issue. This paper addresses the analysis of reverse logistic networks that deal with the returns requiring repair service. A problem involving a manufacturer outsourcing to a third-party logistics (3PLs) provider for its post-sale services is proposed. First, a bi-objective optimization model is developed. Two objectives, minimization of the overall costs and minimization of the total tardiness of cycle time, are addressed. The facility capacity option at each potential location is treated as a discrete parameter. The purpose is to find a set of non-dominated solutions to the facility capacity arrangement among the potential facility locations, as well as the associated transportation flows between customer areas and service facilities. Then, a solution approach is designed for solving this bi-objective optimization model. The solution approach consists of a combination of three algorithms: scatter search, the dual simplex method and the constraint method. Finally, computational analyses are performed on trial examples. Numerical results present the trade-off relationship between the two objectives. The numerical results also show that the optimization for the first objective function leads to a centralized network structure; the optimization for the second objective function results in a decentralized network structure.     

Tabu search-based metaheuristic algorithm for software system reliability problems

This paper presents a new metaheuristic-based algorithm for complex reliability problems. The algorithm effectively uses features of the Tabu Search paradigm, with special emphasis on the exploitation of memory-based mechanisms. It balances intensification with diversification via the use of short-term and long-term memory. The algorithm has been thoroughly tested on benchmark problems from the literature as well as on a pool of random generated instances of very large scale software systems. The proposed algorithm proves to be robust with respect to its parameters and it is especially suited for very large scale instances of the reliability problem, when exact approaches are doomed to fail.     

A tabu-search based heuristic for the hub covering problem over incomplete hub networks

Hub location problems deal with finding the location of hub facilities and with the allocation of demand nodes to these located hub facilities. In this paper, we study the single allocation hub covering problem over incomplete hub networks and propose an integer programming formulation to this end. The aim of our model is to find the location of hubs, the hub links to be established between the located hubs, and the allocation of non-hub nodes to the located hub nodes such that the travel time between any origin–destination pair is within a given time bound. We present an efficient heuristic based on tabu search and test the performance of our heuristic on the CAB data set and on the Turkish network.     

Applying two efficient hybrid heuristics for hub location problem with fully interconnected backbone and access networks

This paper considers the design of two-layered networks with fully interconnected backbone and access networks. The problem, a specific application of hub location to network design, is known as fully interconnected network design problem (FINDP). A novel mathematical programming formulation advantageous over an earlier formulation is presented to model the problem. Two hybrid heuristics are proposed to solve the problem, namely SA_VNS and TS_VNS which incorporate a variable neighborhood search (VNS) algorithm into the framework of simulated annealing (SA) and tabu search (TS). The proposed algorithms are able to easily obtain the optimal solutions for 24 small instances existing in the literature in addition to efficiently solve new generated medium and large instances. Results indicate that the proposed algorithms generate high quality solutions in a quite short CPU time.     

Robust optimization models for project scheduling with resource availability cost

We address a project scheduling problem with resource availability cost for which the activity durations are uncertain. The problem is formulated within the robust optimization framework, where uncertainty is modeled via a set of scenarios. The proposed solution method is based on the scatter search methodology and employs advanced strategies, such as dynamic updating of the reference set, a frequency-based memory mechanism, and path relinking. A multistart heuristic was also developed and comparative results are reported. The tradeoffs for risk-averse decision makers are discussed.     

A conditional p-hub location problem with attraction functions

We formulate the competitive hub location problem in which customers have gravity-like utility functions. In the resulting probabilistic model, customers choose an airline depending on a combination of functions of flying time and fare. The (conditional) follower's hub location problem is solved by means of a heuristic concentration method. Computational experience is obtained using the Australian data frequently used in the literature. The results demonstrate that the proposed method is viable even for problems of realistic size, and the results appear quite robust with respect to the leader's hub locations.