Tabu Search Heuristics Research Articles

Scheduling parallel extrusion lines

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

Integration of machine learning prediction and heuristic optimization for mask delivery in COVID-19.

The novel coronavirus pneumonia (COVID-19) has created huge demands for medical masks that need to be delivered to a lot of demand points to protect citizens. The efficiency of delivery is critical to the prevention and control of the epidemic. However, the huge demands for masks and massive number of demand points scattered make the problem highly complex. Moreover, the actual demands are often obtained late, and hence the time duration for solution calculation and mask delivery is often very limited. Based on our practical experience of medical mask delivery in response to COVID-19 in China, we present a hybrid machine learning and heuristic optimization method, which uses a deep learning model to predict the demand of each region, schedules first-echelon vehicles to pre-distribute the predicted number of masks from depot(s) to regional facilities in advance, reassigns demand points among different regions to balance the deviations of predicted demands from actual demands, and finally routes second-echelon vehicles to efficiently deliver masks to the demand points in each region. For the subproblems of demand point reassignment and two-batch routing whose complexities are significantly lower, we propose variable neighborhood tabu search heuristics to efficiently solve them. Application of the proposed method in emergency mask delivery in three megacities in China during the peak of COVID-19 demonstrated its significant performance advantages over other methods without pre-distribution or reassignment. We also discuss key success factors and lessons learned to facilitate the extension of our method to a wider range of problems.

A Model for Blood Bank Facility-Location Problem at Post Disaster Area

Effective handling of post-disaster victims is essential for the safety of humanity, both direct victims and those affected by the disaster. Especially the effectiveness, Including a reliable network design for the distribution of essential commodities in the form of blood units, where a disaster can suddenly cause the demand for blood units to be high, but the supply in the hospital is not enough. The main objective of this research is to model and solve the problem of determining the location of post-disaster temporary blood bank facilities that are expected to serve hospitals that must be located in such a way with a minimum response time to be able to help hospital requests in the nearest area. Completing the model is done by making the distance to the hospital minimal and also expected to maximize the number of blood donors that can be reached. Heuristic Tabu Search (HTS) is proposed as a solution to solving the problem by taking the case of multi-facility-location with the mini-max model by calculating the distance using the euclidean distance. The number of temporary blood banks can be optimized by calculating the minimum total cost by considering various cost elements such as transportation costs, fixed costs, and maintenance costs. To demonstrate the proposed solution, a case study on hospitals and blood banks in Medan City was presented.

Self-adaptive randomized constructive heuristics for the multi-item capacitated lot sizing problem

The Capacitated Lot-Sizing Problem (CLSP) and its variants are important and challenging optimization problems. Constructive heuristics are known to be the most intuitive and fastest methods for finding good feasible solutions for the CLSPs and therefore are often used as a subroutine in building more sophisticated exact or metaheuristic approaches. Classical constructive heuristics, such as period-by-period heuristics and lot elimination heuristics, are widely used by researchers. This paper introduces four perturbation strategies to the period-by-period and lot elimination heuristics to further improve the solution quality. We propose a new procedure to automatically adjust the parameters of the randomized period-by-period (RPP) heuristics. The procedure is proved to offer better solutions with reduced computation times by improving time-consuming parameter tuning phase. Combinations of the self-adaptive RPP heuristics with Tabu search and lot elimination heuristics are tested to be effective. Computational experiments provided high-quality solutions with a 0.88% average optimality gap on benchmark instances of 12 periods and 12 items, and an optimality gap within 1.2% for the instances with 24 periods and 24 items.

Dynamic Rolling Horizon Scheduling of Waterborne AGVs for Inter Terminal Transportation: Mathematical Modeling and Heuristic Solution

The demand for transport between terminals within port areas, known as inter terminal transportation (ITT), is increasing. This paper proposes a dynamic rolling horizon scheduling strategy for ITT using a fleet of waterborne Autonomous Guided Vessels (waterborne AGVs). The strategy is dynamic in that it can handle dynamically arriving ITT requests. Every certain period of time, transport schedules are updated according to the current vessel states, dynamic waterway transport network, and ITT requests over a future time horizon. Specifically, the dynamic scheduling problem is mathematically modeled in a rolling horizon fashion considering time windows of ITT requests, capacity limits of waterborne AGVs and load/unload service times at terminals. Considering the computational complexity for possible large scale ITT scenarios, we further propose an efficient solution approach based on improved insertion, tabu search and restart heuristics. Initial routes are first constructed by inserting new ITT requests into the previously computed routes in the rolling horizon framework. Tabu search with two types of neighborhoods are then designed to improve the initial routes. Moreover, a select-remove-insert restart procedure is activated to diversify the search space whenever necessary. A waterborne ITT network in the port of Rotterdam is considered. Comprehensive simulations based on realistic ITT dataset are run to demonstrate the effectiveness of the proposed dynamic scheduling strategy. This work could be readily used to build towards a fully autonomous waterborne ITT system. Insights that support long term strategical decisions, such as the fleet size, could also be gained from the simulations.

Tabu search heuristic for the network design model with lead time and safety stock considerations

This research proposes an effective and efficient tabu search heuristic for an integrated location-inventory problem. Integrated location-inventory models extend classical facility location problems by incorporating operational and tactical aspects such as lead times and safety stock. Owing to these additional considerations, integrated problems are not only NP-hard but also include non-linear terms, unlike classical facility location problems. To address this added complexity, we develop a tabu search by introducing a novel concept, the indirect cost, a ratio used to evaluate candidate facilities, along with direct cost, changes in the objective function value. For an integrated non-linear problem, the proposed tabu search heuristic with indirect cost yields near-optimal solutions. It outperforms an existing Lagrangian-based heuristic and a genetic algorithm in computational efficiency and classical tabu search heuristics without indirect costs, in terms of solution quality and robustness. We conclude that indirect costs play a key role in identifying good candidate facilities, and that the proposed tabu search is an effective and efficient solution methodology for integrated location-inventory models with non-linear terms that are neither convex nor concave.

The multi-route location-routing problem and zone price decision-making using a tabu and variable neighborhood search algorithm

Zone pricing is one of the most important pricing policies that are prevalently used by very companies. This paper considers integrating zone pricing and location-routing problems for profit maximization. Hence, a node-based model is proposed as mixed integer nonlinear programming to solve the problem. In order to handle the nonlinearity elements and change the model to a mixed integer linear programming, a piecewise linearization method is employed to approximate the problem. Moreover, we introduce a new heuristic variable neighborhood search (VNS) and tabu search algorithm for large-scale problems. The proposed algorithms consist of four intra-tour and seven inter-tour operators in its local search. Finally, some test instances with 20, 200, 400, and 600 nodes are solved and the results of them confirm the efficiency of the proposed algorithms and comparative superiority of the VNS algorithm over the tabu search algorithm.

Exact and heuristic methods to solve the parallel machine scheduling problem with multi-processor tasks

This paper studies a special parallel machine scheduling problem where some tasks require more than one machine to process, known as the Parallel Machine Scheduling Problem with Multi-processor Tasks. Two mathematical models and several theoretical properties are proposed for the studied problem. To solve this problem, this paper develops an exact branch and bound algorithm and a heuristic tabu search algorithm. A series of numerical experiments are conducted to test the performance of these solution methods. The computational results show that the solution methods are effective and efficient in solving the problem with different sizes.

An Optimization Heuristic Based on Non-Dominated Sorting and Tabu Search for the Fixed Spectrum Frequency Assignment Problem

Frequency assignment for minimum interference is a fundamental problem in telecommunications networks. Combinatorial optimization heuristics are among the successful techniques employed to solve this problem. The heuristic proposed in this paper is a hybrid of the non-dominated sorting genetic algorithm-II and tabu search (TS) heuristics. Although several hybrid heuristics exist, in addition to the parameters of the metaheuristics, they also contain problem-specific parameters. The proposed heuristic does not have any problem-specific parameter. In each iteration, we apply TS to each element of the population to replace it with the best solution in its neighborhood. We select the elements for the population of the next generation using the principles of the non-dominated sorting. The non-dominated sorting considers two attributes of a solution: 1) interference (i.e., a measure of the violation of constraints) and 2) entropy (i.e., a measure of the diversity of the solutions in a population). Experimental results show that the proposed heuristic can produce solutions of quality better than four existing known heuristics. The gain of the proposed heuristic over a recently proposed hybrid heuristic is verified using the Wilcoxon statistical test. The convergence time of the proposed heuristic is also comparable with the existing heuristics.

Hybrid neural network and bi-criteria tabu-machine: comparison of new approaches to maximum clique problem

This paper presents two new approaches to solving a classical NP-hard problem of maximum clique problem (MCP), which frequently arises in the domain of information management, including design of database structures and big data processing. In our research, we are focusing on solving that problem using the paradigm of artificial neural networks. The first approach combines the artificial neuro-network paradigm and genetic programming. For boosting the convergence of the Hopfield neural network (HNN), we propose a specific design of the genetic algorithm as the selection mechanism for terms of the HNN energy function. The second approach incorporates and extends the tabu-search heuristics improving performance of network dynamics of so-called tabu machine. Introduction of a special penalty function in tabu machine facilitates better evaluation of the search space. As a result, we demonstrate the proposed approaches on well-known experimental graphs and formulate two hypotheses for further research.

Hybrid neural network and bi-criteria tabu-machine: comparison of new approaches to maximum clique problem

This paper presents two new approaches to solving a classical NP-hard problem of maximum clique problem (MCP), which frequently arises in the domain of information management, including design of database structures and big data processing. In our research, we are focusing on solving that problem using the paradigm of artificial neural networks. The first approach combines the artificial neuro-network paradigm and genetic programming. For boosting the convergence of the Hopfield neural network (HNN), we propose a specific design of the genetic algorithm as the selection mechanism for terms of the HNN energy function. The second approach incorporates and extends the tabu-search heuristics improving performance of network dynamics of so-called tabu machine. Introduction of a special penalty function in tabu machine facilitates better evaluation of the search space. As a result, we demonstrate the proposed approaches on well-known experimental graphs and formulate two hypotheses for further research.

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

The aggravating trend of the aging population, the miniaturization of the family structure, and the increase of families with empty nesters greatly affect the sustainable development of the national economy and social old-age security system of China. The emergence of home health care or home care (HHC/HC) service mode provides an alternative for elderly care. How to develop and apply this new mobile service mode is crucial for the government. Therefore, the pertinent optimization problems regarding HHC/HC have constantly attracted the attention of researchers. Unexpected events, such as new requests of customers, cancellations of customers’ services, and changes of customers’ time windows, may occur during the process of executing an a priori visiting plan. These events may sometimes make the original plan non-optimal or even infeasible. To cope with this situation, we introduce disruption management to the real-time home caregiver scheduling and routing problem. The deviation measurements on customers, caregivers, and companies are first defined. A mathematical model that minimizes the weighted sum of deviation measurements is then constructed. Next, a tabu search (TS) heuristic is developed to efficiently solve the problem, and a cost recorded mechanism is used to strengthen the performance. Finally, by performing computational experiments on three real-life instances, the effectiveness of the TS heuristic is tested, and the advantages of disruption management are analyzed.

Two-layer simulated annealing and tabu search heuristics for a vehicle routing problem with cross docks and split deliveries

Cross docking plays an increasingly important role in improving the efficiency of large-scale distribution networks. Unlike traditional warehouses, cross docks hold little or no inventory. Instead, goods from incoming trucks are unloaded and immediately transferred through the cross dock to outgoing trucks. Thus, cross docks serve to reduce inventory holding costs and shorten lead times from suppliers to retailers. However, to fully realize these benefits, trucks must be effectively coordinated at each cross dock. Such coordination brings a challenging extension to vehicle routing problems. In this paper a new vehicle routing problem with cross docks and split deliveries is proposed. A mixed-integer linear programming formulation is established for this problem, along with solution methodologies combining a constructive heuristic with two-layer simulated annealing and tabu search. The constructive heuristic creates a solution which is further improved by two-layer variants of simulated annealing or tabu search. The first layer optimizes the allocation of trucks to cross docks while the second layer optimizes the visitation order to suppliers and retailers for trucks assigned to each cross dock. Experimental results demonstrate that the proposed approach effectively solves large-size problems within a reasonable computational time.

A tabu search heuristic for the uncapacitated single allocation p-hub maximal covering problem

This paper describes a tabu search (TS) heuristic for the uncapacitated single allocation p-hub maximal covering problem. The objective is to determine the best location for p hubs and the assignment of each of the spokes to a single hub such that the total demand between pairs of nodes within a given coverage distance is maximized. We consider all nodes as possible candidates for establishing hub facilities, what increases the complexity of the problem. Based on the mathematical programming formulation proposed by Peker and Kara (2015) we also report, for the first time, the optimal solutions for instances with up to 50 nodes from the AP (Australian Post) benchmark dataset, as well as the complete set of results for the CAB (Civil Aeronautics Board) dataset, including some heretofore yet unpublished results. The computational experiments have also demonstrated that our TS heuristic is efficient, leading to improved solutions in shorter CPU times when compared to previously published results, as well as for new derived instances with tigher coverage. It was also able to solve, for the first time, all instances of the AP data set, with up to 200 nodes, as well as new instances with tighter coverage parameters, thus evidencing it capacity to solve effectively large, realistic-sized instances of the problem.

Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

The aim of this study was to compare the performance of the well-known genetic algorithms and tabu search heuristics with the financial problem of the partial tracking of a stock market index. Although the weights of each stock in a tracking portfolio can be efficiently determined by means of quadratic programming, identifying the appropriate stocks to include in the portfolio is an NP-hard problem which can only be addressed by heuristics. Seven real-world indexes were used to compare the above techniques, and results were obtained for different tracking portfolio cardinalities. The results show that tabu search performs more efficiently with both real and artificial indexes. In general, the tracking portfolios obtained performed well in both in-sample and out-of-sample periods, so that these heuristics can be considered as appropriate solutions to the problem of tracking an index by means of a small subset of stocks.

Optimal sensor deployment to increase the security of the maximal breach path in border surveillance

Wireless Sensor Networks (WSN) are based on the collaborative effort of a large number of sensors which are low-cost, low-power, multi-functional small electronic devices. They provide a distributed sensing and monitoring environment for the area of interest and hence are used for applications such as environmental monitoring, border surveillance, and target tracking. In this work we study optimal deployment of WSNs for border surveillance using a static Stackelberg game frame and propose a bilevel optimization model for the optimal deployment of a heterogenous WSN so that the security of the area under consideration is increased as much as possible. There are two players in this game: defender and intruder. The defender is the leader and tries to determine the best sensor locations so as to maximize the security measured in terms of coverage intensity at discretized points in the area. The well-informed intruder assuming the role of the follower is capable of destroying some of the sensors so as to identify the maximal breach path, which represents the safest path from his perspective and thus increases the chance of being undetected by the sensors. This new approach results in a mixed-integer linear bilevel programming formulation that is difficult to solve exactly. Therefore, we propose three Tabu search heuristics and realize computational experiments on a large set of test instances in order to assess their performances.

Optimizing Intermediate Crossovers and Ramp Closures for Hurricane Evacuation

Current hurricane evacuation plans of coastal states in the United States do not actively coordinate the provision of contraflow access points and ramp closure locations on evacuation routes. In this paper, contraflow and ramp closure techniques are integrated and optimized to achieve higher evacuation efficiency. A simulation-based optimization framework for identifying the intermediate crossover between regular and contraflow lanes and ramp closure locations is proposed. The framework is applied to a case study of Hampton Roads, Virginia; using models developed based on evacuee behavioral data and transportation network and traffic control data. Two heuristic solution approaches, Genetic algorithm and Tabu search, are applied to determine optimal intermediate crossover and ramp closure locations for the case study network. The importance of each crossover and ramp closure location included in the optimal solution was also determined. Keywords: hurricane evacuation, intermediate crossovers, contraflow, heuristic algorithm, dynamic traffic assignment Copyright c 2016 SERSC Language: en

Tabu search for solving the black-and-white travelling salesman problem

The black-and-white travelling salesman problem (BWTSP) is an extension to the well-known TSP by partitioning the set of vertices into black and white vertices, and imposing cardinality and length constraints between two consecutive black vertices in a Hamiltonian tour. BWTSP has various applications in aircraft routing, telecommunication network design and logistics. In this paper, we develop several tabu search (TS) heuristics for solving the BWTSP. Our TS is built upon a new efficient neighbourhood structure, which exploits both the permutation and knapsack features of BWTSP. We also embed our TS as a heuristic procedure to improve the upper bound in a mixed-integer linear programming method. Extensive computational experiment on both benchmark and randomly generated instances shows effectiveness and efficiency of our algorithms. Our algorithms are able to obtain optimal and near optimal solutions to small instances in seconds, and find feasible solutions to large instances that have not been solved by the existing methods in the literature.

New Heuristic Algorithm for Dynamic Traffic in WDM Optical Networks

<p class="Abstractandkeywordscontent">The results and comparison of the simulation of a new heuristic algorithm called Snake One are presented. The comparison is made with three heuristic algorithms, Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation was made on the WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking, but this causes a relative growth of network utilization. There are also load intervals at which its performance improves, decreasing the number of blocked requests.</p>

Tabu Search enhances network robustness under targeted attacks

We focus on the optimization of network robustness with respect to intentional attacks on high-degree nodes. Given an existing network, this problem can be considered as a typical single-objective combinatorial optimization problem. Based on the heuristic Tabu Search optimization algorithm, a link-rewiring method is applied to reconstruct the network while keeping the degree of every node unchanged. Through numerical simulations, BA scale-free network and two real-world networks are investigated to verify the effectiveness of the proposed optimization method. Meanwhile, we analyze how the optimization affects other topological properties of the networks, including natural connectivity, clustering coefficient and degree–degree correlation. The current results can help to improve the robustness of existing complex real-world systems, as well as to provide some insights into the design of robust networks.