A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problem

Neural Computing and Applications - The generalized vertex cover problem, an extension of classic minimum vertex cover problem, is an important NP-hard combinatorial optimization problem with a...     

A tabu search heuristic for the concentrator location problem

The concentrator location problem (CLP) is a classical problem in the network design literature. Given a set of candidate locations and the concentrator capacities, the problem is to answer the following related questions. How many concentrators should be used? Where should they be located? Which users are to be assigned to each concentrator? A Lagrangian relaxation is used to obtain lower bounds for this problem. The Lagrangian relaxation is complemented by a tabu search (TS) metaheuristic. Computational results are given for a set of randomly generated problems and for test problems available in the literature. The tabu search heuristic (TSH) is shown to be competitive with other solution procedures available for the problem.     

A tabu search approach for the livestock collection problem

We present a problem dealing with transportation of live animals to slaughterhouses. The problem is taken from the Norwegian meat industry, and may be viewed as a vehicle routing problem extended with inventory constraints to ensure a smooth production flow at the slaughterhouse. In addition, several constraints to ensure animal welfare have to be met. These include duration limits for how long animals can stay on the vehicle as well as rules for mixing different animal categories. In this paper, we show that this real-world problem can be solved heuristically, even if it is large both in size and complexity. A tabu search based solution method for the problem is presented, and results from computational testing are given, including comparisons with manual solutions from today's planning system.     

A tabu search approach for the minimum sum-of-squares clustering problem

In this paper, a tabu search based clustering approach called TS-Clustering is proposed to deal with the minimum sum-of-squares clustering problem. In the TS-Clustering algorithm, five improvement operations and three neighborhood modes are given. The improvement operation is used to enhance the clustering solution obtained in the process of iterations, and the neighborhood mode is used to create the neighborhood of tabu search. The superiority of the proposed method over some known clustering techniques is demonstrated for artificial and real life data sets.     

A tabu search heuristic for the quay crane scheduling problem

This paper proposes a tabu search heuristic for the Quay Crane Scheduling Problem (QCSP), the problem of scheduling a fixed number of quay cranes in order to load and unload containers into and from a ship. The optimality criterion considered is the minimum completion time. Precedence and non-simultaneity constraints between tasks are taken into account. The former originate from the different kind of operations that each crane has to perform; the latter are needed in order to avoid interferences between the cranes. The QCSP is decomposed into a routing problem and a scheduling problem. The routing problem is solved by a tabu search heuristic, while a local search technique is used to generate the solution of the scheduling problem. This is done by minimizing the longest path length in a disjunctive graph. The effectiveness of our algorithm is assessed by comparing it to a branch-and-cut algorithm and to a Greedy Randomized Adaptive Search Procedure (GRASP).     

A tabu search algorithm for the multi-period inspector scheduling problem

This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP, each inspector is scheduled to perform a route in a given multi-period planning horizon. At the end of each period, each inspector is not required to return to the depot but has to stay at one of the vertices for recuperation. If the remaining time of the current period is insufficient for an inspector to travel from his/her current vertex A to a certain vertex B, he/she can choose either waiting at vertex A until the start of the next period or traveling to a vertex C that is closer to vertex B. Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time. We first describe an approach of computing the shortest transit time between any pair of vertices with an arbitrary departure time. To solve the MPISP, we then propose several local search operators adapted from classical operators for the VRPTW and integrate them into a tabu search framework. In addition, we present a constrained knapsack model that is able to produce an upper bound for the problem. Finally, we evaluate the effectiveness of our algorithm with extensive experiments based on a set of test instances. Our computational results indicate that our approach generates high-quality solutions.     

Frequency-driven tabu search for the maximum s-plex problem

The maximum s-plex problem is an important model for social network analysis and other studies. In this study, we present an effective frequency-driven multi-neighborhood tabu search algorithm (FD-TS) to solve the problem on very large networks. The proposed FD-TS algorithm relies on two transformation operators (Add and Swap) to locate high-quality solutions, and a frequency-driven perturbation operator (Press) to escape and search beyond the identified local optimum traps. We report computational results for 47 massive real-life (sparse) graphs from the SNAP Collection and the 10th DIMACS Challenge, as well as 52 (dense) graphs from the 2nd DIMACS Challenge (results for 48 more graphs are also provided in the Appendix). We demonstrate the effectiveness of our approach by presenting comparisons with the current best-performing algorithms.     

A tabu search heuristic for the truck and trailer routing problem

Two new construction heuristics and a tabu search heuristic are presented for the truck and trailer routing problem, a variant of the vehicle routing problem. Computational results indicate that the heuristics are competitive to the existing approaches. The tabu search algorithm obtained better solutions for each of 21 benchmark problems.     

一种适用于求解TSP问题的改进的禁忌算法

利用传统的禁忌算法的基本思想,针对TSP问题,提出了一种改进的禁忌算法（MTS）。该算法在初始解的生成,邻域结构及禁忌策略方面进行了大的改进,充分地利用了问题本身的启发式信息与禁忌算法的优点。算法首先通过对城市分区,然后对区域连接,生成初始解;同时生成每个城市的k邻居列表,利用k邻居列表和改进的禁忌策略来突破局部最优。通过对CHN144问题及若干TSPLIB中问题的求解,结果表明所提算法能够以较快速度求得较好的满意解。     

A tabu search algorithm for parallel machine total tardiness problem

In this study, we consider the problem of scheduling a set of independent jobs with sequence dependent setups on a set of uniform parallel machines such that total tardiness is minimized. Jobs have non-identical due dates and arrival times. A tabu search (TS) approach is employed to attack this complex problem. In order to obtain a robust search mechanism, several key components of TS such as candidate list strategies, tabu classifications, tabu tenure and intensification/diversification strategies are investigated. Alternative approaches to each of these issues are developed and extensively tested on a set of problems obtained from the literature. The results obtained are considerably better than those reported previously and constitute the best solutions known for the benchmark problems as to date. Scope and purposeSeveral surveys on parallel machine scheduling with due date related objectives (Oper. Res. 38(1) (1990) 22; EJOR 38 (1989) 156; Oper. Res. 42 (1994) 1025) reveal that the NP-hard nature of the problem renders it a challenging area for many researchers who studied various versions. However, most of these studies make the assumption that jobs are available at the beginning of the scheduling period, which is an important deviation form reality. In this study, as well as distinct due dates and ready times, features such as sequence dependent setup times and different processing rates for machines are incorporated into the classical model. These enhancements approach the model to the actual practice at the expense of complicating the problem further. For this complex problem, we present a tabu search (TS) algorithm to minimize total tardiness and provide the best solutions known for a set of benchmark problems.     

A tabu search based memetic algorithm for the max-mean dispersion problem

Given a set V of n elements and a distance matrix ${[d_{\mathit{ij}}]}_{n\times n}$ among elements, the max-mean dispersion problem (MaxMeanDP) consists in selecting a subset M from V such that the mean dispersion (or distance) among the selected elements is maximized. Being a useful model to formulate several relevant applications, MaxMeanDP is known to be NP-hard and thus computationally difficult. In this paper, we present a tabu search based memetic algorithm for MaxMeanDP which relies on solution recombination and local optimization to find high quality solutions. One key contribution is the identification of the fast neighborhood induced by the one-flip operator which takes linear time. Computational experiments on the set of 160 benchmark instances with up to 1000 elements commonly used in the literature show that the proposed algorithm improves or matches the published best known results for all instances in a short computing time, with only one exception, while achieving a high success rate of 100%. In particular, we improve 53 previous best results (new lower bounds) out of the 60 most challenging instances. Results on a set of 40 new large instances with 3000 and 5000 elements are also presented. The key ingredients of the proposed algorithm are investigated to shed light on how they affect the performance of the algorithm.     

A tabu search based hybrid evolutionary algorithm for the max-cut problem

This paper presents a tabu search based hybrid evolutionary algorithm (TSHEA) for solving the max-cut problem. The proposed algorithm integrates a distance-and-quality based solution combination operator and a tabu search procedure based on neighborhood combination of one-flip and constrained exchange moves. Comparisons with leading reference algorithms from the literature disclose that the proposed algorithm discovers new best solutions for 15 out of 91 instances, while matching the best known solutions on all but 4 instances. Analysis indicates that the neighborhood combination and the solution combination operator play key roles to the effectiveness of the proposed algorithm.     

A deterministic tabu search algorithm for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult optimisation problem in vehicle routing with applications where a service must be provided by a set of vehicles on specified roads. A heuristic algorithm based on tabu search is proposed and tested on various sets of benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

A tabu search approach to an urban transport problem in northern Spain

In this work we analyze an urban transport problem that the City Council of Burgos, a city in northern Spain, has posed to the authors. Given a fleet of buses and drivers, the problem consists in designing routes and assigning buses to the routes such that the service level is optimized. The optimality of the service level is measured in terms of the waiting time at the bus stops and the duration of the trip. Thus, the problem comprises two decision levels (route design and bus assignment) and differs from other urban transport models found in the literature. In order to solve the problem, we propose two algorithms: one with a local search strategy and another with a tabu search strategy. In both cases, the solutions of the two decision levels are modified in alternating steps. The proposed algorithms obtained significantly better results than the tools currently applied by the transport authorities. In addition, the solutions obtained are very robust with respect to variations on demand, as shown by the experiments.     

A tabu search algorithm for the job shop problem with sequence dependent setup times

This paper addresses the classic job shop scheduling problem where sequence dependent setup times are present. Based on a modified disjunctive graph, we further investigate and generalize structural properties for the problem under study. A tabu search algorithm with a sophisticated neighbourhood structure is then developed. Compared to most studies in this research area, we are interested in moving internal critical operations rather than merely focusing on non-internal ones. Moreover, neighbourhood functions are defined using insertion techniques instead of simple swaps. Test results show that our algorithm outperforms a simulated annealing algorithm which is recently published. We have also conducted experiments considering the efficiency of developed propositions.     

A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem

In the heterogeneous fixed fleet vehicle routing problem there are different types of vehicles and a given number of vehicles of each type. The resolution of this problem consists of assigning the customers to the existing vehicles and, in relation to each vehicle, defining the order of visiting each customer for the delivery or collection of goods. The objective is to minimize the total costs, satisfying customers’ requirements and visiting each customer exactly once. In this paper a tabu search algorithm is proposed and tested on several benchmark problems. The computational experiments show that the proposed algorithm produces high quality solutions within an acceptable computation time. Four new best solutions are reported for a set of test problems used in the literature.     

A fast hybrid tabu search algorithm for the no-wait job shop problem

This paper describes a hybrid tabu search algorithm dedicated to a job shop problem with a no-wait constraint with a makespan criterion. The proposed here algorithm complexity is that the superior algorithm based on the tabu search technique selects parameters controlling the work of a certain constructional algorithm. This approach limits the checked solutions only to a group of solutions being able to be generated by the structural algorithm in question. It bears serious consequences both positive, for example it limits the research scope for a small fraction of relatively extremely well quality of acceptable solutions, and negative that is the lack of possibility of finding the optimal solution. In this paper numerical researches of the proposed algorithm are conducted as well as a comparative analysis with reference to the literature algorithms of the algorithm in question is made.     

Genetic tabu search for the fuzzy flexible job shop problem

This paper tackles the flexible job-shop scheduling problem with uncertain processing times. The uncertainty in processing times is represented by means of fuzzy numbers, hence the name fuzzy flexible job-shop scheduling. We propose an effective genetic algorithm hybridised with tabu search and heuristic seeding to minimise the total time needed to complete all jobs, known as makespan. To build a high-quality and diverse set of initial solutions we introduce a heuristic method which benefits from the flexible nature of the problem. This initial population will be the starting point for the genetic algorithm, which then applies tabu search to every generated chromosome. The tabu search algorithm relies on a neighbourhood structure that is proposed and analysed in this paper; in particular, some interesting properties are proved, such as feasibility and connectivity. Additionally, we incorporate a filtering mechanism to reduce the neighbourhood size and a method that allows to speed-up the evaluation of new chromosomes. To assess the performance of the resulting method and compare it with the state-of-the-art, we present an extensive computational study on a benchmark with 205 instances, considering both deterministic and fuzzy instances to enhance the significance of the study. The results of these experiments clearly show that not only does the hybrid algorithm benefit from the synergy among its components but it is also quite competitive with the state-of-the-art when solving both crisp and fuzzy instances, providing new best-known solutions for a number of these test instances.     

An adaptive tabu search algorithm for the capacitated clustering problem

In the Capacitated Clustering Problem, a given set of customers with distinct demands must be partitioned into p clusters with limited capacities. The objective is to find p customers, called medians, from which the sum of the distances to all other customers in the cluster is minimized. In this article, a new adaptive tabu search approach is applied to solve the problem. Initial solutions are obtained by four constructive heuristics that use weights and distances as optimization criteria. Two neighborhood generation mechanisms are used by the local search heuristic: pairwise interchange and insertion . Computational results from 20 instances found in the literature indicate that the proposed method outperforms alternative metaheuristics developed for solving this problem.