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.     

An improved ant colony optimisation heuristic for graph colouring

The focus of this paper is an ant colony optimisation heuristic for the graph colouring problem. We start by showing how a series of improvements enhance the performance of an existing ant colony approach to the problem and then go on to demonstrate that a further strengthening of the construction phase, combined with a tabu search improvement phase, raise the performance to the point where it is able to compete with some of the best-known approaches on a series of benchmark problems.     

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 unified tabu search heuristic for vehicle routing problems with time windows

This paper presents a unified tabu search heuristic for the vehicle routing problem with time windows and for two important generalizations: the periodic and the multi-depot vehicle routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the vehicle routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.     

A biased random-key genetic algorithm for routing and wavelength assignment

The problem of routing and wavelength assignment in wavelength division multiplexing optical networks consists in routing a set of lightpaths and assigning a wavelength to each of them, such that lightpaths whose routes share a common fiber are assigned different wavelengths. This problem was shown to be NP-hard when the objective is to minimize the total number of wavelengths used. We propose a genetic algorithm with random keys for routing and wavelength assignment with the goal of minimizing the number of different wavelengths used in the assignment. This algorithm extends the best heuristic in the literature by embedding it into an evolutionary framework. Computational results show that the new heuristic improves the state-of-the-art algorithms in the literature.     

Path relinking for the vehicle routing problem

This paper describes a tabu search heuristic with path relinking for the vehicle routing problem. Tabu search is a local search method that explores the solution space more thoroughly than other local search based methods by overcoming local optima. Path relinking is a method to integrate intensification and diversification in the search. It explores paths that connect previously found elite solutions. Computational results show that tabu search with path relinking is superior to pure tabu search on the vehicle routing problem.     

A Neural-Tabu Search Heuristic for the Real Time Vehicle Routing Problem

The Real Time Vehicle Routing Problem RTVRP is a dynamic routing problem where requests are generated dynamically during the operation horizon without any previous knowledge. Received requests need to be answered as fast as possible and then assigned to a vehicle to be served. Due to timing constraints of the RTVRP, a solving approach should give the best compromise between the cost of the provided solution and the computation time needed to find it. In this paper, we present a neural-tabu search solving scheme for the RTVRP. The developed approach is composed by two phases; The first part consists of learning and reproducing previous routing decisions using a feed forward neural network with a particular structure. The second phase is based on a tabu search heuristic that takes its initial solution from the assignment provided by the neural module. If the reaction time is still available, the tabu search module will continue ameliorating the final solution. To evaluate the proposed approach a set of problems are simulated and solved. The obtained results are compared to those given by the First Come First Served FCFS and Nearest Neighbor NN policies and also to the optimal solutions provided by the GNU Linear Programming Kit GLPK.      

A tabu search heuristic for the generalized minimum spanning tree problem

This paper describes an attribute based tabu search heuristic for the generalized minimum spanning tree problem (GMSTP) known to be NP-hard. Given a graph whose vertex set is partitioned into clusters, the GMSTP consists of designing a minimum cost tree spanning all clusters. An attribute based tabu search heuristic employing new neighborhoods is proposed. An extended set of TSPLIB test instances for the GMSTP is generated and the heuristic is compared with recently proposed genetic algorithms. The proposed heuristic yields the best results for all instances. Moreover, an adaptation of the tabu search algorithm is proposed for a variation of the GMSTP in which each cluster must be spanned at least once.     

Transportation planning in freight forwarding companies: Tabu search algorithm for the integrated operational transportation planning problem

The integrated operational transportation planning problem extends the traditional vehicle routing and scheduling problem by the possibility of outsourcing a part of the requests by involving subcontractors. The purpose of this paper is to present the integrated planning problem and to propose an approach for solving it by a tabu search heuristic. Existing approaches from literature which discuss vehicle routing combined with outsourcing regard only one specific type of subcontracting. This paper describes and explores the complex situation where an own fleet and several types of subcontracting are used for request fulfillment. As the approach contains new aspects, unknown to the literature so far, tabu search is extended to special types of moves. On the basis of computational results the cost structure is analyzed in order to investigate the long-term planning question whether and to what extend it is profitable to maintain an own fleet.     

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.     

A tabu search heuristic for the vehicle routing problem with private fleet and common carrier

This paper describes a tabu search heuristic for a vehicle routing problem where the owner of a private fleet can either visit a customer with one of his vehicles or assign the customer to a common carrier. The owner’s objective is to minimize the variable and fixed costs for operating his fleet plus the total costs charged by the common carrier. The proposed tabu search is shown to outperform the best approach reported in the literature on 34 benchmark instances with a homogeneous fleet.     

A New Heuristic for the Quadratic Assignment Problem

We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems.     

On minimum congestion routing in rearrangeable multihop lightwave networks

In this article we consider the problem of minimizing the congestion in logically rearrangeable multihop lightwave networks. Namely, we consider a network in which each node is equipped with a small number of transmitters and receivers, and tuning a transmitter at nodei and a receiver at nodej to the same wavelength creates a logical link (i, j) through which traffic could be sent. For a given traffic matrix-the matrix of flows between nodes—the objective is to find the best connectivity diagram and the corresponding flow assignment so that the maximal flow on any link is minimized. We develop a tabu search heuristic that yields a suboptimal connectivity diagram and an optimal flow assignment on it. Computational experiments are conducted on some benchmark data sets, on a real-world traffic matrix, and on some randomly generated problems of larger dimension. The results are compared with known results from the literature and with a known greedy approach. The results suggest that a tabu search—based heuristic is a promising approach for handling this NP-hard combinatorial problem. In addition, we discuss the performance of the method in view of different patterns of input data.     

Solving the maximum clique problem using a tabu search approach

We describe two variants of a tabu search heuristic, a deterministic one and a probabilistic one, for the maximum clique problem. This heuristic may be viewed as a natural alternative implementation of tabu search for this problem when compared to existing ones. We also present a new random graph generator, the -generator, which produces graphs with larger clique sizes than comparable ones obtained by classical random graph generating techniques. Computational results on a large set of test problems randomly generated with this new generator are reported and compared with those of other approximate methods.The authors are grateful to the Quebec Government (Fonds F.C.A.R.) and to the Canadian Natural Sciences and Engineering Research Council (grant 0GP0038816) for financial support.     

A simple and efficient tabu search heuristic for solving the open vehicle routing problem

The open vehicle routing problem (VRP) is an immediate variant of the standard vehicle routing problem where the vehicle need not return to the depot after servicing its last customer. In this paper, we present results on an implementation of the attribute-based hill climber heuristic to the open VRP. The attribute-based hill climber heuristic is a parameter-free variant of the tabu search principle and has shown to be highly effective for the standard vehicle routing problem.     

Optimizing Bernoulli Routing Policies for Balancing Loads on Call Centers and Minimizing Transmission Costs

We address the problem of assigning probabilities at discrete time instants for routing toll-free calls to a given set of call centers to minimize a weighted sum of transmission costs and load variability at the call centers during the next time interval.We model the problem as a tripartite graph and decompose the finding of an optimal probability assignment in the graph into the following problems: (i) estimating the true arrival rates at the nodes for the last time period; (ii) computing routing probabilities assuming that the estimates are correct. We use a simple approach for arrival rate estimation and solve the routing probability assignment by formulating it as a convex quadratic program and using the affine scaling algorithm to obtain an optimal solution.We further address a practical variant of the problem that involves changing routing probabilities associated with k nodes in the graph, where k is a prespecified number, to minimize the objective function. This involves deciding which k nodes to select for changing probabilities and determining the optimal value of the probabilities. We solve this problem using a heuristic that ranks all subsets of k nodes using gradient information around a given probability assignment.The routing model and the heuristic are evaluated for speed of computation of optimal probabilities and load balancing performance using a Monte Carlo simulation. Empirical results for load balancing are presented for a tripartite graph with 99 nodes and 17 call center gates.     

A new tabu search heuristic for the open vehicle routing problem

In this paper, another version of the vehicle routing problem (VRP)—the open vehicle routing problem (OVRP) is studied, in which the vehicles are not required to return to the depot, but if they do, it must be by revisiting the customers assigned to them in the reverse order. By exploiting the special structure of this type of problem, we present a new tabu search heuristic for finding the routes that minimize two objectives while satisfying three constraints. The computational results are provided and compared with two other methods in the literature.     

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.     

On a parallel genetic–tabu search based algorithm for solving the graph colouring problem

In this paper, we are interested in a particular combinatorial optimisation problem (COP), namely the graph colouring problem (GCP). To solve the GCP, we present a parallel approach adopting an efficient strategy. A brief survey on known methods for solving the GCP enables us to justify our approach which is based on a hybrid method, starting from a set of solutions initialized by the so-called RLF colouring method and combining both a genetic algorithm and the tabu search. A parallelising strategy is then applied. The performances of our method were evaluated through a series of experimentations achieved on an IBM SP2 multiprocessor. The processed graphs were chosen from two benchmark sets. The first, taken from the Internet, involves graphs whose chromatic numbers are known and the second involves random generated graphs. The analysis of the results proves the interest of our approach.     

A Tabu Search Algorithm for the Resource-Constrained Assignment Problem

Efficient algorithms are availabe to solve the unconstrained assignment problem. However, when resource or budgetary restrictions are imposed, the problem becomes difficult to solve. We consider such a resource-constrained assignment problem and present a tabu search heuristic to solve it. Extensive computational results are presented which establish the superiority of the proposed algorithm over the existing algorithms. Our adaptation of tabu search uses strategic oscillation, randomized short-term memory and multiple start as a means of search diversification.