Multi-Objective Distribution Model and Algorithm for Online Shopping Express Logistics

Yang, Jianjie; Jin, Li; Chen, Yun; Liu, Xintong

doi:10.4304/jcp.8.10.2558-2564

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…There are finally several references with weighted or multiple objectives (for instance Attanasio et al , Chen et al , Haghani et al , Psaraftis , Respen et al , Wohlgemuth , Yang et al ), and even one that only checks the feasibility of a DVRP route (Berbeglia ).…”

Section: The Taxonomymentioning

confidence: 99%

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Dynamic vehicle routing problems: Three decades and counting

2015

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Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.

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Section: The Taxonomymentioning

confidence: 99%

“…Yang et al studied the multiobjective distribution problem with time windows for online shopping express logistics as an extension of the VRP with time windows. To solve this problem, they designed a modified particle swarm PSO which can enhance the quality of the particle evolution and the speed of the original algorithm.…”

Section: The Taxonomymentioning

confidence: 99%

Dynamic vehicle routing problems: Three decades and counting

2015

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“…Calculation. According to formula (13), the degrees of traffic congestion of the th shortest path ∏ −1 = ( , +1 ), = 1, . .…”

Section: Degree Of Path Traffic Congestionmentioning

confidence: 99%

“…Ghannadpour et al [12] proposed a solving strategy based on the genetic algorithm and three basic modules to solve the multiobjective dynamic vehicle routing problem with fuzzy time windows, aiming at minimizing the total required fleet size, overall total traveling distance, and waiting time imposed on vehicles. Yang et al [13] designed a modified particle swarm optimization algorithm (PSO) to solve the multiobjective dynamic vehicle routing problem with time window by considering the customers' satisfaction degree, the cost, and both integrated conditions. With the objective of minimizing a weighted sum of operating, service cancellation, and route disruption costs, Li et al [14] developed a Lagrangian relaxation based-heuristic to solve the dynamic vehicle routing problem with a time window.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

Zhao

Guo

Duan

2017

Journal of Advanced Transportation

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The dynamic paths planning problem of emergency vehicles is usually constrained by the factors including time efficiency, resources requirement, and reliability of the road network. Therefore, a two-stage model of dynamic paths planning of emergency vehicles is built with the goal of the shortest travel time and the minimum degree of traffic congestion. Firstly, according to the dynamic characteristics of road network traffic, a polyline-shaped speed function is constructed. And then, based on the real-time and historical data of travel speed, a new kernel clustering algorithm based on shuffled frog leaping algorithm is designed to predict the travel time. Secondly, combined with the expected travel time, the traffic congestion index is defined to measure the reliability of the route. Thirdly, aimed at the problem of solving two-stage target model, a two-stage shortest path algorithm is proposed, which is composed of K-paths algorithm and shuffled frog leaping algorithm. Finally, based on the data of floating vehicles of expressway in Beijing, a simulation case is used to verify the above methods. The results show that the optimization path algorithm meets the needs of the multiple constraints.

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“…Ref. [18] designed a modified PSO which can enhance the quality and speed of the particle evolution.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Particle Swarm Optimization Algorithm for Archipelago Berth Allocation Problem

Peng

Li²

2014

JCP

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According to the special circumstances of the archipelago berth allocation, this paper proposes a model of Archipelago Berth Allocation Problem(ABAP), which considers numerous constraints such as the adaptability of berths, the depth of channels and the characteristics of ships. The objective of the problem is to minimize the sum of the handling time, waiting time and the sailing time in the port area of every ship. We introduce a formulation for solving this problem. Next, we present a Discrete Particle Swarm Optimization (DPSO) to find an approximate solution for the problem. Some reasonable parameters of DPSO are determined through several tests. Finally, we set the Zhoushan Islands, China as an example. Computational experiments show that the algorithm is effective, and has achieved great results in terms of stability, convergence and running time.

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Multi-Objective Distribution Model and Algorithm for Online Shopping Express Logistics

Cited by 6 publications

References 8 publications

Dynamic vehicle routing problems: Three decades and counting

Dynamic vehicle routing problems: Three decades and counting

Dynamic Path Planning of Emergency Vehicles Based on Travel Time Prediction

A Discrete Particle Swarm Optimization Algorithm for Archipelago Berth Allocation Problem

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