A developed Tabu Search algorithm for heterogeneous fleet vehicle routing problem

Meliani, Youssef; Hani, Yasmina; Elhaq, Saâd Lissane; Mhamedi, Abderrahman El

doi:10.1016/j.ifacol.2019.11.334

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…In the succeeding search, the information in the tabu table is either selectively searched or not searched to jump out of the local optimum and achieve global optimization. Thus far, tabu search algorithms have achieved remarkable success in combinatorial optimization, production scheduling, machine learning, and neural networks [27,28].…”

Section: Proposed Sensor Selection Optimization Methodsmentioning

confidence: 99%

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

Chen

Yang

2020

Sensors

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This paper proposes a time difference of arrival (TDOA) passive positioning sensor selection method based on tabu search to balance the relationship between the positioning accuracy of the sensor network and system consumption. First, the passive time difference positioning model, taking into account the sensor position errors, is considered. Then, an approximate closed-form constrained total least-squares (CTLS) solution and a covariance matrix of the positioning error are provided. By introducing a Boolean selection vector, the sensor selection problem is transformed into an optimization problem that minimizes the trace of the positioning error covariance matrix. Thereafter, the tabu search method is employed to solve the transformed sensor selection problem. The simulation results show that the performance of the proposed sensor optimization method considerably approximates that of the exhaustive search method. Moreover, it can significantly reduce the running time and improve the timeliness of the algorithm.

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Section: Proposed Sensor Selection Optimization Methodsmentioning

confidence: 99%

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

Chen

Yang

2020

Sensors

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“…Path planning is an NP-hard problem. Hence, many heuristic algorithms such as particle swarm optimization [34]- [36], genetic algorithm [37], [38], artificial bee colony algorithm [39], [40], fruit fly optimization algorithm [41], [42], Tabu search algorithm [43], [44], ant colony algorithm are used to solve the problem. Among all of them, the ant colony algorithm was first proposed by Dorigo [45], and its characteristics can be defined as positive feedback and a heuristic random search.…”

Section: Route Optimizationmentioning

confidence: 99%

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

Zhao

Gui

et al. 2020

IEEE Access

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In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible. INDEX TERMS Cold chain logistics, path optimization, multi-objective optimization, carbon constraints, customer satisfaction.

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“…Choi and Tcha [14] developed a column generation algorithm and found that it was superior to others both in solution quality and in run time. Meliani, Hani, Elhaq, et al [15] proposed a Tabu Search procedure to HFVRP in urban logistics with minimization of costs. Another study by Li, Zhi, and Li [16] presented a novel algorithm, which could obtain satisfactory results of route allocation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Parking Lot and Shelter Heterogeneous Vehicle Routing Problem With Split Pickup Under Emergencies

Wang

Chen

et al. 2022

IEEE Access

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The vehicle rescue process for individuals in residential areas in disaster scenarios is a typical vehicle routing problem (VRP). However, most studies do not consider the factor of individual mobility. In residential areas, there are two types of individuals: individuals with high mobility and individuals with low mobility, such as the elderly. To improve the evacuation efficiency, besides ordinary vehicles, special vehicles equipped with wheelchairs and volunteers are also in great need. Thus, evacuation vehicles should consist of a heterogeneous fleet. Vehicles depart from parking lots, arrive at residential areas to pick up individuals, and then transport them to shelters. In other words, the origin and destination are different, but they are viewed as the same in classical VRP. Each residential area can be served directly by vehicles departing from parking lots or by vehicles that have already served others, which means demands can be split. All these make the VRP in emergency rescue more complicated than classical VRP. Therefore, we propose an integer liner program modelmulti-parking lot and shelter heterogeneous vehicle routing problem with split pickup (MPSHVRPSP) model, which includes matching constraints of individuals and vehicles to satisfy the demands of different types of individuals, and considers the selectivity of parking lots and shelters too. We provide a Tabu Search (TS) algorithm with diversification strategy to solve the model and ensure the high quality of solution. A lot of experiments are carried out on various instances. Our results show that MPSHVRPSP can be applied to efficient evacuation of complicated scenarios that satisfies the demands of all individuals in residential areas. Besides, it is more reasonable compared with classical VRP, and TS can also obtain a satisfactory solution in less time. Furthermore, sensitivity analysis is conducted on factors that may affect the result of objective function.INDEX TERMS Vehicle routing problem, multi-parking lot and shelter, heterogeneous fleet, split pickup, Tabu Search.

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A developed Tabu Search algorithm for heterogeneous fleet vehicle routing problem

Cited by 22 publications

References 23 publications

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

Time Difference of Arrival Passive Localization Sensor Selection Method Based on Tabu Search

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

Multi-Parking Lot and Shelter Heterogeneous Vehicle Routing Problem With Split Pickup Under Emergencies

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