一种改进的禁忌搜索算法及其在选址问题中的应用

本文研究了选址问题中无容量限制的p-中值问题,在Rolland等人提出的有效禁忌搜索算法基础上,提出了一种以目标函数变化量作为评价函数的改进禁忌搜索算法,并进行了理论分析,然后将其与有效禁忌搜索算法作了性能比较.通过比较三个公共测试数据集的计算结果,验证了本文提出的禁忌搜索算法的可行性和有效性.     

Tabu search for large location–allocation problems

Recently it has been demonstrated that the use of simulated annealing is a good alternative for solving the minisum location–allocation problem with rectilinear distances compared with other popular methods. In this study it is shown that the same solution quality and a great saving of computational time can be achieved by using tabu search. It is also possible to transfer this method to location–allocation problems with euclidean distances.     

Massively parallel analog tabu search using neural networks applied to simple plant location problems

Neural networks and tabu search are two very significant techniques which have emerged recently for the solution of discrete optimization problems. Neural networks possess the desirable quality of implementability in massively parallel hardware while the tabu search metaheuristic shows great promise as a powerful global search method. Tabu Neural Network (TANN) integrates an analog version of the short term memory component of tabu search with neural networks to generate a massively parallel, analog global search strategy that is hardware implementable. In TANN, both the choice of the element to enter the tabu list as well as the maintenance of the decision elements in tabu status is accomplished via neuronal activities. In this paper we apply TANN to the simple plant location problem. Comparisons with the Hopfield-Tank network show an average improvement of about 85% in the quality of solutions obtained.     

Avoiding local optima in thep-hub location problem using tabu search and GRASP

In the discretep-hub location problem, various nodes interact with each other by sending and receiving given levels of traffic (such as telecommunications traffic, data transmissions, airline passengers, packages, etc.). It is necessary to choosep of the given nodes to act as hubs, which are fully interconnected; it is also necessary to connect each other node to one of these hubs so that traffic can be sent between any pair of nodes by using the hubs as switching points. The objective is to minimize the sum of the costs for sending traffic along the links connecting the various nodes. Like many combinatorial problems, thep-hub location problem has many local optima. Heuristics, such as exchange methods, can terminate once such a local optimum is encountered. In this paper, we describe new heuristics for thep-hub location problem, based on tabu search and on a greedy randomized adaptive search procedure (GRASP). These recently developed approaches to combinatorial optimization are capable of examining several local optima, so that, overall, superior solutions are found. Computational experience is reported in which both tabu search and GRASP found optimal hub locations (subject to the assumption that nodes must be assigned to the nearest hub) in over 90% of test problems. For problems for which such optima are not known, tabu search and GRASP generated new best-known solutions.     

The multi-depot vehicle routing problem with inter-depot routes

This article addresses an extension of the multi-depot vehicle routing problem in which vehicles may be replenished at intermediate depots along their route. It proposes a heuristic combining the adaptative memory principle, a tabu search method for the solution of subproblems, and integer programming. Tests are conducted on randomly generated instances.     

A tabu search approach for solving a difficult forest harvesting machine location problem

This paper deals with two main problems in forest harvesting. The first is that of selecting the locations for the machinery to haul logs from the points where they are felled to the roadside. The second consists in designing the access road network connecting the existing road network with the points where machinery is installed. Their combination induces a very important and difficult problem to solve in forest harvesting. It can be formulated as a combination of two difficult optimization problems: a plant location problem and a fixed charge network flow problem. In this paper, we propose a solution approach based on tabu search. The proposed heuristic includes several enhancements of the basic tabu search framework. The main difficulty lies in evaluating neighboring solutions, which involves decisions related to location of machinery and to road network arcs. Hence, the neighborhood is more complex than in typical applications of metaheuristics. Minimum spanning tree algorithms and Steiner tree heuristics are used to deal with this problem. Numerical results indicate that the heuristic approach is very attractive and leads to better solutions than those provided by state-of-the-art integer programming codes in limited computation times, with solution times significantly smaller. The numerical results do not vary too much when typical parameters such as the tabu tenure are modified, except for the dimension of neighborhood.     

A Tabu Search with Slope Scaling for the Multicommodity Capacitated Location Problem with Balancing Requirements

In this paper, a tabu search heuristic is combined with slope scaling to solve a discrete depot location problem, known as the multicommodity location problem with balancing requirements. Although the uncapacitated version of this problem has already been addressed in the literature, this is not the case for the more challenging capacitated version, where each depot has a fixed and finite capacity. The slope scaling approach is used during the initialization phase to provide the tabu search with good starting solutions. Numerical results are reported on various types of large-scale randomly generated instances. The quality of the heuristic is assessed by comparing the solutions obtained with those of a commercial mixed-integer programming code.     

An Application of Tabu Search Heuristic for the Maximum Edge-Weighted Subgraph Problem

The purpose of this article is to describe an efficient search heuristic for the Maximum Edge-weighted Subgraph (MEwS) problem. This problem requires to find a subgraph such that the sum of the weights associated with the edges of the subgraph is maximized subject to a cardinality constraint. In this study a tabu search heuristic for the MEwS problem is proposed. Different algorithms to obtain an initial solution are presented. One neighborhood search strategy is also proposed. Preliminary computational results are reported for randomly generated test problems of MEwS problem with different densities and sizes. For most of test problems, the tabu search heuristic found good solutions. In addition, for large size test problems, the tabu search outperformed the local search heuristic appearing in the literature.     

Tabu search for graph partitioning

In this paper, we develop a tabu search procedure for solving the uniform graph partitioning problem. Tabu search, an abstract heuristic search method, has been shown to have promise in solving several NP-hard problems, such as job shop and flow shop scheduling, vehicle routing, quadratic assignment, and maximum satisfiability. We compare tabu search to other heuristic procedures for graph partitioning, and demonstrate that tabu search is superior to other solution approaches for the uniform graph partitioning problem both with respect to solution quality and computational requirements.     

A tabu search heuristic procedure for the capacitated facility location problem

A tabu search heuristic procedure is developed, implemented and computationally tested for the capacitated facility location problem. The procedure uses different memory structures. Visited solutions are stored in a primogenitary linked quad tree. For each facility, the recent move at which the facility changed its status and the frequency it has been open are also stored. These memory structures are used to guide the main search process as well as the diversification and intensification processes. Lower bounds on the decreases of total cost are used to measure the attractiveness of the moves and to select moves in the search process. A specialized network algorithm is developed to exploit the problem structure in solving transportation problems. Criterion altering, solution reconciling and path relinking are used to perform intensification functions. The performance of the procedure is tested through computational experiments using test problems from the literature and new test problems randomly generated. It found optimal solutions for almost all test problems from the literature. As compared to the heuristic method of Lagrangean relaxation with improved subgradient scheme, the tabu search heuristic procedure found much better solutions using much less CPU time.     

A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal

The scheduling problem in a container terminal is characterized by the coordination of different types of equipment. In this paper, we present an integrated model to schedule the equipment. The objective is to minimize the makespan, or the time it takes to serve a given set of ships. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence and Blocking constraints (HFSS-B). A tabu search algorithm is proposed to solve this problem. Certain mechanisms are developed and introduced into the algorithm to assure its quality and efficiency. The performance of the tabu search algorithm is analyzed from the computational point of view.     

Investigation of practical,robust and flexible decisions for facility location problems using tabu search and simulation

We investigate how robust and flexible solutions of stochastic capacitated facility location problems (CFLPs) can be obtained by combining metaheuristic optimization with Monte Carlo sampling techniques. To this end, we develop a tabu search procedure for the CFLP, and use this to solve an extensive set of stochastic versions of this problem.     

Strategic oscillation for the capacitated hub location problem with modular links

The capacitated single assignment hub location problem with modular link capacities is a variant of the classical hub location problem in which the cost of using edges is not linear but stepwise, and the hubs are restricted in terms of transit capacity rather than in the incoming traffic. We propose a metaheuristic algorithm based on strategic oscillation, a methodology originally introduced in the context of tabu search. Our method incorporates several designs for constructive and destructive algorithms, together with associated local search procedures, to balance diversification and intensification for an efficient search. Computational results on a large set of instances show that, in contrast to exact methods that can only solve small instances optimally, our metaheuristic is able to find high-quality solutions on larger instances in short computing times. In addition, the new method, which joins tabu search strategies with strategic oscillation, outperforms the previous tabu search implementation.     

Heuristic Search Algorithms for the Minimum Volume Ellipsoid

Abstract

A method of robust estimation of multivariate location and shape that has attracted a lot of attention recently is Rousseeuw's minimum volume ellipsoid estimator (MVE). This estimator has a high breakdown point but is difficult to compute successfully. In this article, we apply methods of heuristic search to this problem, including simulated annealing, genetic algorithms, and tabu search, and compare the results to the undirected random search algorithm that is often cited. Heuristic search provides several effective algorithms that are far more computationally efficient than random search. Furthermore, random search, as currently implemented, is shown to be ineffective for larger problems.     

Port space allocation with a time dimension

In the Port of Singapore, as in many other ports, space has to be allocated in yards for inbound and transit cargo. Requests for container space occur at different times during the planning period, and are made for different quantities and sizes of containers. In this paper, we study space allocation under these conditions. We reduce the problem to a two-dimensional packing problem with a time dimension. Since the problem is NP-hard, we develop heuristic algorithms, using tabu search, simulated annealing, a genetic algorithm and ‘squeaky wheel’ optimization, as solution approaches. Extensive computational experiments compare the algorithms, which are shown to be effective for the problem.     

Routing and scheduling in a flexible job shop by tabu search

A hierarchical algorithm for the flexible job shop scheduling problem is described, based on the tabu search metaheuristic. Hierarchical strategies have been proposed in the literature for complex scheduling problems, and the tabu search metaheuristic, being able to cope with different memory levels, provides a natural background for the development of a hierarchical algorithm. For the case considered, a two level approach has been devised, based on the decomposition in a routing and a job shop scheduling subproblem, which is obtained by assigning each operation of each job to one among the equivalent machines. Both problems are tackled by tabu search. Coordination issues between the two hierarchical levels are considered. Unlike other hierarchical schemes, which are based on a one-way information flow, the one proposed here is based on a two-way information flow. This characteristic, together with the flexibility of local search strategies like tabu search, allows to adapt the same basic algorithm to different objective functions. Preliminary computational experience is reported.     

Protein Conformation of a Lattice Model Using Tabu Search

We apply tabu search techniques to the problem of determining the optimal configuration of a chain of protein sequences on a cubic lattice. The problem under study is difficult to solve because of the large number of possible conformations and enormous amount of computations required. Tabu search is an iterative heuristic procedure which has been shown to be a remarkably effective method for solving combinatorial optimization problems. In this paper, an algorithm is designed for the cubic lattice model using tabu search. The algorithm has been tested on a chain of 27 monomers. Computational results show that our method outperforms previously reported approaches for the same model.     

Lower and upper bounds for a two-stage capacitated facility location problem with handling costs

We study in this paper multi-product facility location problem in a two-stage supply chain in which plants have production limitation, potential depots have limited storage capacity and customer demands must be satisfied by plants via depots. In the paper, handling cost for batch process in depots is considered in a realistic way by a set of capacitated handling modules. Each module can be regards as alliance of equipment and manpower. The problem is to locate depots, choose appropriate handling modules and to determine the product flows from the plants, opened depots to customers with the objective to minimize total location, handling and transportation costs. For the problem, we developed a hybrid method. The initial lower and upper bounds are provided by applying a Lagrangean based on local search heuristic. Then a weighted Dantzig–Wolfe decomposition and path-relinking combined method are proposed to improve obtained bounds. Numerical experiments on 350 randomly generated instances demonstrate our method can provide high quality solution with gaps below 2%.     

An incremental tabu search heuristic for the generalized vehicle routing problem with time windows

This paper describes an incremental neighbourhood tabu search heuristic for the generalized vehicle routing problem with time windows. The purpose of this work is to offer a general tool that can be successfully applied to a wide variety of specific problems. The algorithm builds upon a previously developed tabu search heuristic by replacing its neighbourhood structure. The new neighbourhood is exponential in size, but the proposed evaluation procedure has polynomial complexity. Computational results are presented and demonstrate the effectiveness of the approach.     

A Tabu Search Approach for the Resource Constrained Project Scheduling Problem

An appropriate tabu search implementation is designed to solve the resource constrained project scheduling problem. This approach uses well defined move strategies and a structured neighbourhood, defines appropriate tabu status and tenure and takes account of objective function approximation to speed up the search process. A sound understanding of the problem has helped in many ways in designing and enhancing the tabu search methodology. The method uses diversification, intensification and handles infeasibility via strategic oscillation.The above methodology is tested on existing problems from the literature and also on parametrically generated problems with encouraging results. For comparison of results, optimal solutions are used in the former and lower bounds obtained by Lagrangian heuristics are used in the latter.