Variable Neighborhood Tabu Search Research Articles

A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

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.

Evaluation and Analysis of Intelligent Logistics Distribution Using the Expectation-Maximization Algorithm Calculation Model

The purpose of this article is to solve the problem that the accuracy of logistics distribution path planning is affected by the lack of data in the process of traditional logistics distribution planning and management. This exploration innovatively applies an effective data addition algorithm expectation-maximization (EM) algorithm to the intelligent logistics distribution system to improve logistics distribution’s overall efficiency and management quality. First, the concept of intelligent logistics and the composition and main functions of the intelligent logistics system are introduced. Then, the core idea of the EM algorithm and its applications in intelligent logistics are described. The logistics distribution of a chain company is taken as an example. Finally, the advantages and disadvantages of the intelligent logistics system based on the EM algorithm are compared with those of the traditional intelligent logistics systems based on variable neighborhood search (VNS), Tabu search (TS), and ant colony optimization (ACO). The performance test results show that the EM algorithm’s optimal solution times are 7 times. Its convergence speed is slightly lower than that of the ACO, but there is no obvious difference. The intelligent logistics distribution system based on the EM algorithm has faster order processing speed and higher efficiency in the actual case application. The average processing time of each order is 1.78 min, which is 0.237 min less than that of VNS and only 0.022 min more than that of ACO. It reveals that the intelligent logistics distribution system based on the EM algorithm is more efficient. The study provides a new idea for the efficient distribution of enterprise logistics.

Two-echelon vehicle routing problem with time windows and simultaneous pickup and delivery

This paper proposes a tabu search algorithm for the two-echelon vehicle routing problem with time windows and simultaneous pickup and delivery (2E-VRPTWSPD), which is a new variant of the two-echelon vehicle routing problem. 2E-VRPTWSPD involves three stages of routing: (1) the first-echelon vehicles start from the depot to deliver cargoes to satellites; (2) the second-echelon vehicles start from satellites to serve customers within time windows in a simultaneously pickup and delivery manner and finally return to their satellites with pickup cargoes; (3) the first-echelon vehicles start from the depot to collect cargoes on satellites. To solve this problem, we formulate it with a mathematical model. Then, we implement a variable neighborhood tabu search algorithm with a tailored solution representation to solve large-scale instances. Dummy satellites time windows are applied in our algorithm to speed up the search. Finally, we generate two benchmark instance sets to analyze the performance of our algorithm. Computational results show that our algorithm can obtain the optimal results for 10 out of 11 small-scale instances and produce promising solutions for 200-customer instances within a few minutes. Additional experiments indicate that the usage of dummy satellite time windows can save 19% computation time on average.

Optimal Route Planning for Truck–Drone Delivery Using Variable Neighborhood Tabu Search Algorithm

The optimal delivery route problem for truck–drone delivery is defined as a traveling salesman problem with drone (TSP-D), which has been studied in a wide range of previous literature. However, most of the existing studies ignore truck waiting time at rendezvous points. To fill this gap, this paper builds a mixed integer nonlinear programming model subject to time constraints and route constraints, aiming to minimize the total delivery time. Since the TSP-D is non-deterministic polynomial-time hard (NP-hard), the proposed model is solved by the variable neighborhood tabu search algorithm, where the neighborhood structure is changed by point exchange and link exchange to expand the tabu search range. A delivery network with 1 warehouse and 23 customer points are employed as a case study to verify the effectiveness of the model and algorithm. The 23 customer points are visited by three truck–drones. The results indicate that truck–drone delivery can effectively reduce the total delivery time by 20.1% compared with traditional pure-truck delivery. Sensitivity analysis of different parameters shows that increasing the number of truck–drones can effectively save the total delivery time, but gradually reduce the marginal benefits. Only increasing either the truck speed or drone speed can reduce the total delivery time, but not to the greatest extent. Bilateral increase of truck speed and drone speed can minimize the delivery time. It can clearly be seen that the proposed method can effectively optimize the truck–drone delivery route and improve the delivery efficiency.

Optimization of LPG Distribution Route Using Variable Neighborhood Tabu Search Algorithm

Optimization of LPG Distribution Route Using Variable Neighborhood Tabu Search Algorithm

Optimizing Medical Enterprise’s Operations Management considering Corporate Social Responsibility under Industry 5.0

Corporate social responsibility (CSR) is the concrete practice of sustainable development at the enterprise level, emphasizing the human value in the production process. The proposal of Industry 5.0 begins to take the coordinated development between human and various production factors as one of the key points of corporate sustainable management. Therefore, this paper studies the optimization of medical enterprise’s operations management considering enterprise CSR under Industry 5.0. A mixed-integer programming (MIP) model is developed to maximize the CSR with the consideration of the impact of precision medical technologies such as surgical robots (SRs) and 3D bone printing on employee social welfare, corporate profits, social environment, and customer surplus value. An improved variable neighborhood tabu search (IVNTS) algorithm which combines the variable neighborhood tabu search (VNTS) algorithm and simulated annealing (SA) algorithm is designed to solve the model, and numerical experiments are analyzed to verify the effectiveness of the proposed IVNTS. The research aids medical enterprise to make reasonable operations management decisions, while providing a reference for the government to draft and implement related policies, thereby achieving sustainable social development.

Free-floating bike-sharing green relocation problem considering greenhouse gas emissions

Abstract This paper introduces the problem of green bike relocation considering greenhouse gas emissions in free-floating bike-sharing systems (FFBSSs) and establishes a mathematical model of the problem. This model minimizes the total imbalance degree of bikes in the FFBSS and the greenhouse gas emissions generated by relocation in the FFBSS. Before the relocation phase, the FFBSS is divided into multiple relocation areas using a two-layer clustering method to reduce the scale of the relocation problem. In the relocation phase, the relocation route problem is converted into a pickup and delivery vehicle-routing problem. Then, an adaptive variable neighbourhood tabu search algorithm with a three-dimensional tabu list is proposed, which can simultaneously solve the relocation problem and the routing problem. A computational study based on the actual FFBSS used in Shanghai shows that this method can effectively solve the green relocation problem of FFBSSs.

A Multi-Start Granular Skewed Variable Neighborhood Tabu Search for the Roaming Salesman Problem

This paper presents a novel hybrid metaheuristic algorithm for the Roaming Salesman Problem (RSP), called Multi-Start Granular Skewed Variable Neighborhood Tabu Search (MS-GSVNTS). The objective in RSP is to design daily tours for a traveling campaigner who collects rewards from activities in cities during a fixed planning horizon. RSP exhibits a number of exclusive features: It is selective which implies that not every node needs a visit. The rewards of cities are time-dependent. Daily tours can be either an open or a closed tour which implies the absence of a fixed depot. Instead, there is a campaign base that is to be attended frequently. Multiple visits are allowed for certain cities. The proposed method MS-GSVNTS is tested on 45 real-life instances from Turkey which are built with actual travel distances and times and on 10 large scale instances. Computational results suggest that MS-GSVNTS is superior to the existing solution methods developed for RSP. It produces 50 best known solutions including 18 ties and 32 new ones. The performance of MS-GSVNTS can be attributed to its multi-start feature, rich neighborhood structures, skewed moves, and granular neighborhoods.

An ACS-based memetic algorithm for the heterogeneous vehicle routing problem with time windows

This paper presents a solution methodology to solve the heterogeneous vehicle routing problem with time windows (HVRPTW). This problem appears when a limited fleet of vehicles, characterized by different capacities, fixed costs and variable costs, is available for serving a set of customers which have to be visited within a predefined time window. The objective is to perform the route design minimizing the total fixed vehicle costs and distribution costs and satisfying all problem constraints. The problem is solved using an Ant Colony System (ACS) algorithm which has been successfully applied to combinatorial optimization problems. Moreover, to improve the performance of the ACS on the HVRPTW, a hybridized ACS with local search, called memetic ACS algorithm is proposed where the local search is performed by a variable neighborhood Tabu Search algorithm. Experiments are conducted on sets of benchmark instances from the scientific literature to evaluate the performance of the proposed algorithm. The results show that the algorithm has a good performance on the HVRPTW. In particular, out of the 80 instances, it obtained 65 new best solutions and matched 6 within reasonable computational times.

Tabu Variable Neighborhood Search for Designing DNA Barcodes.

Massively parallel sequencing, a popular and efficient sequencing method, produces a tremendous number of sequences from multiple individual samples. Labeling sequences with barcodes (tags) prevents them from being unrecoverable or confused during sequencing, replication, and oligonucleotide synthesis. In view of DNA barcode set design, we propose a tabu variable neighborhood search (TVNS) to design DNA barcode sets. We propose using sequences with the maximum sum of edit distances as the basis for building the neighborhood structures of TVNS. We adopt an exhaustive search to complete local searches built from these structures. The algorithm switches between neighborhoods built from different seed data. Compared with previous designs, our proposed algorithm is effective for designing DNA barcode sets and improving the lower bound of DNA barcode sets.

Studying the influence of algorithmic parameters and instance characteristics on the performance of a multiobjective algorithm using the Promethee method

A method is proposed to analyze the effect of the algorithmic parameters and instance characteristics on the quality of a Pareto front produced by a multiobjective algorithm. This method is applied to a variable neighborhood tabu search that is used to solve a multiobjective microzone-based vehicle routing problem. Our method can accommodate many different performance indices for Pareto fronts and uses the Promethee multicriteria decision analysis method to select the best configuration based on the characteristics of the instance being solved.

Reducing pollutant emissions in a waste collection vehicle routing problem using a variable neighborhood tabu search algorithm: a case study

This paper focuses on designing waste collection routes with a single landfill using eco-efficiency as a performance indicator. In this problem, there are a limited number of heterogeneous vehicles based at a single depot. Empty vehicles leave the depot, collect waste from a set of locations and drop off the collected waste at a specific landfill. Then, vehicles leave the landfill and may collect more waste from other locations or return empty to the depot. Traditional performance indicators in vehicle routing problems are mainly focused on economic objectives, not explicitly considering environmental issues. In this paper, a mathematical model is presented with an eco-efficient objective function that takes into account external costs (climate change and air pollution). The COPERT model is used for estimating fuel consumption, carbon dioxide and pollutant emissions. The problem is first heuristically solved using a semi-parallel construction algorithm. Then, solutions are improved by a variable neighborhood tabu search algorithm developed for this problem. The algorithm is validated for a real problem in the municipality of Alcala de Guadaira, within the metropolitan area of Seville (Spain). Results obtained on a set of case studies improve the solution that is currently implemented in the municipality, in terms of total distance traveled, carbon dioxide emissions and pollutant emissions.

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.

A Game Theoretic Approach for the Real-Life Multiple-Criterion Vehicle Routing Problem With Multiple Time Windows

This paper presents a new framework for the optimization of real-life multiple-objective vehicle routing problems with multiple time windows. This framework uses a hybrid variable neighborhood tabu search heuristic that chooses Pareto nondominated solutions from the search space of solutions that satisfy a set of Nash equilibrium conditions for a multiple-agent game theory model. Even though the framework is general and can tackle different classes of vehicle routing problems, it is herein tested on three objectives: minimizing the total travel cost (expressed in time units), maximizing the minimal customers’ utility, and maximizing the minimal drivers’ utility. The results on real-life instances provided by a Canadian transportation company highlight the benefits of the multiple-criteria model; an important motivation to the transportation industry for its real-life implementation.

Variable Neighborhood Search for Vehicle Routing Problem with Multiple Time Windows

The Vehicle Routing Problem (VRP) with Multiple Time Windows is a generalization of VRP, where the customers have one or more time windows in which they can be visited. The best heuristic in the literature is a Hybrid Variable Neighborhood Tabu Search (HVNTS) that mostly deals with infeasible solutions, because it is assumed that one may not reach some regions of the search space without passing through infeasible solutions. In this short paper, we propose a simpler Variable Neighborhood Search heuristic where all the computational effort is spent on searching for feasible solutions. Computational experiments showed that the proposed heuristic is competitive with the best heuristic in the literature.

Metaheuristic Algorithms for Proactive and Reactive Project Scheduling to Minimize Contractor’s Cash Flow Gap under Random Activity Duration

This paper investigates the project scheduling problem to minimize the maximum cash flow gap of contractors under random activity duration. A comprehensive optimization model with time buffers added to the baseline schedule by project proactive scheduling and the schedule adjustment cost determined by project reactive scheduling is constructed to manage disruptions caused by the randomness of activity durations. Due to the problem’s intractability, two hybrid metaheuristic algorithms, namely, tabu simulated annealing (tabu-SA) and variable neighborhood tabu search (VNTS), are developed, and several improvement measures are proposed to enhance the algorithms’ performance. Based on a randomly generated data set, a computational experiment is performed to evaluate the algorithms, with the effects on results of certain key parameters also analyzed. The conclusions are as follows: tabu-SA outperforms VNTS for large-scale problems, however, the reverse holds for small-scale problems. The key parameters, including the project deadline, the cost per time buffer, the activity instability weight, the activity duration variability, the number of payments, and the payment proportion can exert important effects on the contractor’s maximum cash flow gap.

A hybridised variable neighbourhood tabu search heuristic to increase security in a utility network

We propose a decision model aimed at increasing security in a utility network (e.g., electricity, gas, water or communication network). The network is modelled as a graph, the edges of which are unreliable. We assume that all edges (e.g., pipes, cables) have a certain, not necessarily equal, probability of failure, which can be reduced by selecting edge-specific security strategies. We develop a mathematical programming model and a metaheuristic approach that uses a greedy random adaptive search procedure to find an initial solution and uses tabu search hybridised with iterated local search and a variable neighbourhood descend heuristic to improve this solution. The main goal is to reduce the risk of service failure between an origin and a destination node by selecting the right combination of security measures for each network edge given a limited security budget.

A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows

This paper presents a new hybrid variable neighborhood-tabu search heuristic for the Vehicle Routing Problem with Multiple Time windows. It also proposes a minimum backward time slack algorithm applicable to a multiple time windows environment. This algorithm records the minimum waiting time and the minimum delay during route generation and adjusts the arrival and departure times backward. The implementation of the proposed heuristic is compared to an ant colony heuristic on benchmark instances involving multiple time windows. Computational results on newly generated instances are provided.

Variable Neighborhood Search and Tabu Search for the Web Service Selection Problem

Abstract When a composition of web services is needed to achieve some desired functionality it is necessary to choose a particular service for each of the components. There may be a large number of web services offering similar functionality and when choosing a service, the decision should be based on the relevant Quality of Service (QoS) attributes. Therefore, the main goal of the web service selection problem is to choose the composition with the best performance which also satisfies certain constraints regarding the required QoS levels. Since for large problems the exact solution may be impossible to reach in an acceptable timeframe, due to the exponentially growing number of possible combinations, for the first time Variable Neighborhood Search (VNS) is introduced for solving this problem. The greedy-add algorithm with back-tracking is introduced for obtaining the initial solution and a better solution is sought through VNS as well as Tabu Search. The experimental results demonstrate the efficiency of the proposed approaches.