Efficient Tabu Search Algorithm Research Articles

A tabu search algorithm for the unrelated parallel machine scheduling problem with varied carbon emission constraints in different time intervals

The carbon emissions generated during the rapid development of the manufacturing industry have caused severe environmental pollution. Research on parallel machine scheduling problems considering carbon emission constraints has started to emerge in recent years to promote sustainable development and achieve low carbon emissions in production. In this paper, we consider an unrelated parallel machine scheduling problem with various carbon emission constraints in different time intervals. A mixed integer linear programming model with the objective of minimising the makespan is formulated to describe the problem accurately. Since the high complexity of our problem, we propose an efficient tabu search algorithm with a dedicated linked list structure to address this problem. Firstly, we generate four initial sequences according to the proposed heuristic rules and apply a greedy insertion decoding method to obtain the scheduling scheme. Then, we employ the 2-swap neighbourhood search strategy to exploit the promising solution space. The proposed model and algorithm are tested on extensive instances generated randomly. Computational results validate the correctness of the model and the effectiveness of the tabu search algorithm.

Food inspector scheduling with outcome and daily-schedule effects

Food-safety inspection is regularly executed by the government for quality assessment. Evidence from recent research demonstrates that inspection accuracy and consistency are affected by inspection biases that result from an operational decision: inspector scheduling. More precisely, an inspector's stringency in an inspection is affected by the inspection results at the previous-inspected establishment (outcome effects) and when this inspection occurs within a workday (daily-schedule effects). To our best knowledge, the impact of these effects on scheduling decisions has not been studied in the scheduling literature. In this paper, we study a novel food inspector scheduling problem with these effects, where the inspector should scrutinise establishments with different locations. The problem is viewed as a single-machine scheduling problem with a complex objective function including (i) inspection accuracy, (ii) inspection consistency and (iii) workload of the inspector. To facilitate quantitative analyses of these effects, we model them by sequence-dependent functions and formulate a mixed integer linear programming model. To overcome the computational difficulty in large-scale problems, an efficient Tabu Search algorithm is developed. Experiment results on 135 randomly generated instances with up to 50 establishments and 10 workdays validate the efficiency of the solution method. Besides, managerial insights are drawn.

An efficient tabu search algorithm for the linear ordering problem

Given a directed graph with n vertices, m edges and costs on the edges, the linear ordering problem (LOP) consists of finding a permutation of the vertices so that the total cost of the reverse edges is minimized, where an edge is called a reverse edge if its head vertex is at a position before the tail in the permutation. Known as an NP-hard problem, the LOP has a number of real-world applications such as the analysis of data called industry transaction table or input-output matrix (I/O table) in the field of economy. In this paper, we propose an algorithm based on tabu search for the LOP. We generalize an algorithm called TREE, which was proposed to search for a best solution in the neighborhood efficiently, so that the new algorithm can be incorporated in the tabu search algorithm and can search for a non-tabu best solution in the neighborhood efficiently. Our algorithm also features an adaptive control mechanism of the tabu tenure by using a cycling-detection mechanism based on a probabilistic analysis. Finally, we compare the experimental results of the proposed algorithm with those of an existing iterated local search algorithm (ILS). The proposed algorithm obtained better results than the ILS for many instances.

Alternative-fuel station network design under impact of station failures

In this paper, we have formulated a mixed-integer non-linear programming model for alternative-fuel station location problem in which each station can fail with a site-specific probability. The model aims to maximise the total expected traffic volume that can be refuelled by the unreliable alternative-fuel stations. Based on the linearisation techniques, i.e., probability chains and piecewise-linear functions, we linearise the non-linearity of compound probability terms in the non-linear model to solve this problem efficiently. An efficient Tabu search algorithm is also developed to solve the large-size instances. In addition, we extend the model to deal with reliable multi-period alternative-fuel station network design. Computational experiments, carried out on the well-known benchmark instances where the probability of station failures is uniformly generated, show that the proposed models and algorithm can obtain the optimal solutions within a reasonable computation time. Compared to a standard station location model that disregards the potential for station failures, our model designs more reliable alternative-fuel station network under risk of station failures. A sensitivity analysis of failure probabilities in the station network design is investigated to demonstrate the robustness of our model and study how variability in the probability of station failure affects solution robustness.

Improving post-disaster road network accessibility by strengthening links against failures

We study a network improvement problem to increase the resilience of a transportation network against disasters. This involves optimizing pre-disaster investment decisions to strengthen the links of the network structurally. The goal is to improve the expected post-disaster accessibility. We first propose a new dependency model for random link failures to predict the post-disaster status of the network. We show that the probability of any network realization can be computed using a Bayesian network representation of the dependency model. As the computational effort grows with the network size, we use our proposed dependency model in a network sampling algorithm. We then estimate an accessibility measure, namely, the expected weighted average distance between supply and demand points by checking pre-generated short and dissimilar paths in the sample. We minimize this measure and decide on the links that should be strengthened in a two-stage stochastic programming framework. As the failure probability of a strengthened link decreases, the discrete scenario probabilities depend on the first-stage decisions. To tackle this challenge, we develop an efficient tabu search algorithm. We apply our methods to a case study of Istanbul under the risk of an earthquake, both to illustrate the use of the methods and to derive insights for decision makers.

MILP formulations and a TS algorithm for reliable last train timetabling with uncertain transfer flows

This paper aims to develop reliable last train timetabling models for increasing number of successful transfer passengers and reducing total running time for metro corporations. The model development is based on an observation that real-world transfer flows capture the characteristics of randomness in a subway network. For systematically modelling uncertainty, a sample-based representation and two types of non-expected evaluation criteria, namely max–min reliability criterion and percentile reliability criterion are proposed to generate reliable timetables for last trains. The equivalent mixed integer linear programming formulations are deduced for the respective evaluation strategies by introducing auxiliary variables. Based upon the linearised models, an efficient tabu search (TS) algorithm incorporating solution generation method is presented. Finally, a number of small problem instances are solved using CPLEX for the linear models. The obtained results are also used as a platform for assessing the performance of proposed TS approach which is then tested on large Beijing Subway instances with promising results.

A capacitated vehicle routing problem with order available time in e-commerce industry

ABSTRACTIn this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.

A petrol station replenishment problem: new variant and formulation

One of the most important problems in the petroleum industry is the well-known petrol station replenishment problem with time windows, which calls for the determination of optimal routes by using a fleet of tank trucks to serve a set of petrol stations over a given planning horizon. In this paper, we introduce a model and solve a specific problem that originates from a real-life application arising in the fuel distribution where specific attention is paid to tank trucks with compartments and customers with different types of products and time windows. Literally, we call the resulting problem the multi-compartment vehicle routing problem with time windows (MCVRPTW). To solve the MCVRPTW, we begin by describing the problem, providing its mathematical formulation and discussing the sense of its constraints. As the problem is NP-hard, we propose an efficient tabu search algorithm for its solution. We introduce the Kolmogorov–Smirnov statistic into the framework of the tabu search to manage the neighbourhood size. We evaluate the performance of the algorithm on a set of vehicle routing problems with time windows instances as well as other realistic instances. Our results are compared to CPLEX, to the heuristics reported in the literature and also to those extracted from the company plans.

Cell formation using tabu search for design of cellular manufacturing system

A large number of non–traditional search algorithms are available for function optimisation. The cell formation problem is the important step in the design of a cellular manufacturing system. The objective is to identify part families and machine groups and consequently to form manufacturing cells with respect to minimising the number of exceptional elements. An efficient tabu search (TS) algorithm is proposed to solve cell formation problem because it perform considerable search before terminating to provide a good solution to the problem. In this work the implementation of tabu search for the design of cell formation problem and minimise the number of exceptional elements has been done by this method and it is compared with other existing methods.

An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems

Cellular manufacturing systems (CMS) are used to improve production flexibility and efficiency. They involve the identification of part families and machine cells so that intercellular movement is minimized and the utilization of the machines within a cell is maximized. Previous research has focused mainly on cell formation problems and their variants; however, only few articles have focused on more practical and complicated problems that simultaneously consider the three critical issues in the CMS-design process, i.e., cell formation, cell layout, and intracellular machine sequence. In this study, a two-stage mathematical programming model is formulated to integrate the three critical issues with the consideration of alternative process routings, operation sequences, and production volume. Next, because of the combinatorial nature of the above model, an efficient tabu search algorithm based on a generalized similarity coefficient is proposed. Computational results from test problems show that our proposed model and solution approach are both effective and efficient. When compared to the mathematical programming approach, which takes more than 112h (LINGO) and 1139s (CPLEX) to solve a set of ten test instances, the proposed algorithm can produce optimal solutions for the same set of test instances in less than 12s.

An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem

Distributed permutation flow shop scheduling problem (DPFSP) is a newly proposed scheduling problem, which is a generalisation of classical permutation flowshop scheduling problem. Studies on algorithms for solving this problem are in the early stage. In this paper, we propose a new tabu algorithm for solving this problem, which exploits a novel tabu strategy. A method that swaps sub-sequences of jobs is presented to generate neighbourhood. Moreover, an enhanced local search method is proposed and also combined into the tabu algorithm. We also use the well-known benchmark of Taillard (extended to the distributed permutation flowshop problems) to test the algorithm. From the intensive experiments we carried out, we conclude that the proposed tabu algorithm outperforms all the existing algorithms including heuristics algorithms (i.e. NEH1, NEH2, VND(a) and VND(b)) and a hybrid genetic algorithm, so the best-known solutions for 472 instances are updated. Moreover, it is worth mentioning that the efficiency of the tabu algorithm is also better than that of the genetic algorithm.

An efficient tabu search algorithm for flexible flow shop sequence-dependent group scheduling problems

In this paper, the flexible flow shop sequence-dependent group scheduling problem (FFSDGS) with minimisation of makespan as the criterion (FFm | fmls, Splk | C max) is investigated. For the first time a mathematical model for the proposed research problem is developed. Since the problem is shown to be NP-hard, six metaheuristic algorithms based on tabu search (TS) are developed to efficiently solve the problem. The proposed metaheuristics are different to the only available metaheuristic algorithm in the literature based on TS. By applying randomised complete block design and using available test problems in the literature, the best of the proposed TS algorithms in this research is identified. The performance of the best developed metaheuristic algorithm is then compared with the existing algorithm in the literature by solving the test problems, also available in the literature, ranging in size from small, medium, to large. A comparison based on paired t-test at 95% confidence interval, shows that the best proposed algorithm in this research has a better performance than the existing algorithm in the literature with an average percentage deviation of around 1.0% for medium and large size problems.

An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations

Cell formation is the first step in the design of cellular manufacturing systems. In this study, an efficient tabu search algorithm based on a similarity coefficient is proposed to solve the cell formation problem with alternative process routings and machine reliability considerations. In the proposed algorithm, good initial solutions are first generated and later on improved by a tabu search algorithm combining the mutation operator and an effective neighborhood solution searching mechanism. Computational experiences from test problems show that the proposed approach is extremely effective and efficient. When compared with the mathematical programming approach which took three hours to solve problems, the proposed algorithm is able to produce optimal solutions in less than 2 s.

Selecting profitable custom instructions for reconfigurable processors

Custom-instruction selection is an essential phase in instruction set extension for reconfigurable processors. It determines the most profitable custom-instruction candidates for implementing in the reconfigurable fabric of a reconfigurable processor. In this paper, a practical computing model is proposed for the custom-instruction selection problem that takes into account the area constraint of the reconfigurable fabric. Based on the new computing model, two heuristic algorithms and an exact algorithm are proposed. The first heuristic algorithm, denoted as HEA, dynamically assigns priorities to the custom instruction candidates and incorporates efficient strategies to select custom instructions with the highest priority. The second heuristic algorithm, denoted as TSA, employs an efficient tabu search algorithm to refine the results of HEA to near-optimal ones. Also, a branch-and-bound algorithm (BnB) is proposed to produce exact solutions for relatively small-sized problems or problems with stringent area-constraints. Experimental results show that HEA can produce more specific approximate solutions with a difference of only about 3% when compared to the optimal solutions produced by BnB. This difference is further reduced to about 0.6% by TSA. In addition, for large-sized problems where the exact algorithm becomes prohibitive, HEA and TSA can still produce solutions within reasonable time.

A tabu search heuristic for ship routing and scheduling

The purpose of this paper is to solve a planning problem faced by many shipping companies dealing with the transport of bulk products. These shipping companies are committed to carrying some contract cargoes and will try to derive additional revenue from optional spot cargoes. An efficient tabu search algorithm has been developed to ensure quick decision support for the planners. The solutions generated by the tabu search heuristic are compared with those produced by a previously published multi-start local search heuristic. Computational results show that the tabu search heuristic yields optimal or near-optimal solutions to real-life instances within reasonable time. For large and tigthly constrained cases, the tabu search heuristic provides much better solutions than the multi-start local search heuristic. A version of the tabu search heuristic will be integrated as an improved solver in a prototype decision support system used by several shipping companies.

Efficient heuristic algorithms for path-based hardware/software partitioning

Hardware/software (HW/SW) partitioning is one of the crucial steps of co-design systems. It determines which components of the systems are implemented in hardware and which ones are in software. In this paper the computing model is extended to cater for the path-based HW/SW partitioning with the fine granularity in which communication penalties between system components must be considered. On the new computing model an efficient heuristic algorithm is developed, in which both speedup in hardware and communication penalty are taken into account. In addition, an efficient tabu search algorithm is also customized in this paper to refine the approximate solutions produced by the heuristic algorithm. Simulation results show that the heuristic algorithm runs fast and is able to produce high-quality approximate solutions. Moreover, the tabu search algorithm can further refine them to nearly optimal solutions within an acceptable runtime. The difference between the approximate solutions and the optimal ones is bounded by 0.5%, and it hardly increases with the increase of the problem size.

A tabu search algorithm for the training of neural networks

The most widely used training algorithm of neural networks (NNs) is back propagation (BP), a gradient-based technique that requires significant computational effort. Metaheuristic search techniques such as genetic algorithms, tabu search (TS) and simulated annealing have been recently used to cope with major shortcomings of BP such as the tendency to converge to a local optimal and a slow convergence rate. In this paper, an efficient TS algorithm employing different strategies to provide a balance between intensification and diversification is proposed for the training of NNs. The proposed algorithm is compared with other metaheuristic techniques found in literature using published test problems, and found to outperform them in the majority of the test cases.

ADSS: An approach to determining semantic similarity

Determining the semantic similarity is an important issue in the development of semantic search technology. In this paper, we propose an approach to determining the semantic similarity. This approach takes into consideration the similarity between two entities and their similarity reflected in context. Furthermore, the approach provides an efficient Tabu Search algorithm combined with multi-objective programming algorithm to improve the precision.

A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations

AbstractVariable selection is an extensively studied problem in chemometrics and in the area of quantitative structure–activity relationships (QSARs). Many search algorithms have been compared so far. Less well studied is the influence of different objective functions on the prediction quality of the selected models. This paper investigates the performance of different cross‐validation techniques as objective function for variable selection in latent variable regression. The results are compared in terms of predictive ability, model size (number of variables) and model complexity (number of latent variables). It will be shown that leave‐multiple‐out cross‐validation with a large percentage of data left out performs best. Since leave‐multiple‐out cross‐validation is computationally expensive, a very efficient tabu search algorithm is introduced to lower the computational burden. The tabu search algorithm needs no user‐defined operational parameters and optimizes the variable subset and the number of latent variables simultaneously. Copyright © 2002 John Wiley & Sons, Ltd.

Machine cell formation in generalized group technology

In this paper, we study the configuration of machine cells in the presence of alternative routings for part types. The objective function is the minimization of transportation costs. Limits on cell sizes as well as separation constraints (i.e. machines that are not allowed to be placed in the same cell) and co-location constraints (i.e. machines that must be placed in the same cell) may be imposed. An efficient Tabu Search (TS) algorithm is proposed to solve this problem. Extensive computational experiences with large-size problems show that this method outperforms some existing Simulated Annealing (SA) approaches.