Vehicle routing with time window for regional network services &amp;#x2014; Practical modelling approach

Niroomand, Iman; Khataie, Amir H.; Galankashi, Masoud Rahiminezhad

doi:10.1109/ieem.2014.7058769

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…When studying the VRPTW problem, Susilawati et al believed that the goal of the problem was to minimize the total travel cost and operating cost, and proposed a heterogeneous VRP (HVRP) to solve the problem, used a feasible domain method to solve the problem [14]. Niroomand et al successfully applied the VRPTW to the postal network, and proved the effectiveness of the ant colony optimization algorithm (ACO) to solve the VRPTW service provided by regional post offices in their own research [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Optimization of Electric Vehicle Routing Problem With Time Window

Cao

et al. 2020

IEEE Access

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As the urban population and scale gradually increase, the per capita income level of urban residents is also constantly increasing, more people have put forward higher requirements for material life. The degree of congestion of urban roads has a strong positive correlation with the development level of the national economy. The crowded lanes directly affect the way people travel, especially in the field of logistics distribution. Electric vehicle distribution is one of the existing distribution methods that is less affected by traffic, but it is subject to mileage, cargo capacity and number of vehicles. In order to find an optimal urban distribution route that satisfies both the electric vehicle limitation and the customer time window limitation, a mixed planning model is established to conduct in-depth research on the routing problem. In the process of verifying the correctness of the model, a mixed algorithm with lower time complexity, which was calculated by the highest order in the model, is established, and two sets of instance data are used for calculation. The results show that the mixed algorithm not only has a faster calculation speed, but also can calculate the vehicle route in a large-scale situation.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Optimization of Electric Vehicle Routing Problem With Time Window

Cao

et al. 2020

IEEE Access

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“…that uses a minimum number of vehicles along the optimal route to serve the customer at the time window that the customer needs, and if the vehicle arriving later than the required time will suffer penalty costs. Existing works in this area mostly leverage the ant colony optimization (ACO) algorithm [1]- [4], particle swarm optimization [5], and so on [6]. However, the traditional ACO algorithm may easily be trapped in local optimum due to a limited solution space [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Brainstorming-Based Ant Colony Optimization for Vehicle Routing With Soft Time Windows

Chen

et al. 2019

IEEE Access

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In this paper, we propose a novel ant colony optimization algorithm based on improved brainstorm optimization (IBSO-ACO) to solve the vehicle routing problem with soft time windows. Compared with the traditional ant colony algorithm, the proposed IBSO-ACO can better address the local optimum problem, since we have carefully designed an improved brainstorming optimization algorithm to update the solutions obtained by the ant colony algorithm, which enhance the solution diversity and the global search ability. Furthermore, we use the classification method to accelerate the convergence of the proposed algorithm. The extensive experimental results have confirmed that the proposed IBSO-ACO algorithm can achieve a lower routing cost at a high convergence rate than the traditional ant colony algorithm and the simulated annealing ant colony algorithm.INDEX TERMS Vehicle routing problem with soft time window, improved brainstorm optimization.

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