A new mathematical model for a competitive vehicle routing problem with time windows solved by simulated annealing

Tavakkoli–Moghaddam, Reza; Gazanfari, M.; Alinaghian, Mahdi; Salamatbakhsh, A.; Norouzi, Narges

doi:10.1016/j.jmsy.2011.04.005

Cited by 50 publications

(28 citation statements)

References 29 publications

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“…(3) In a set of RC, the average rate of 100 iteration times is nearly 13% for each problem, the average rate of 200 iteration times is nearly 6% and almost all problems of C coverage to the best known results by 500 iteration times, except RC101, RC102, RC105, RC107, RC201, RC202, RC203 and RC207. We can see that HPSO has a better performance on problem set RC than TavakkoliMoghaddam et al [24]. …”

Section: Hpso Performance Comparison By Different Problem Setsmentioning

confidence: 70%

“…(2) In a set of R, the average rate of 100 iteration times is nearly 15% for each problem, the average rate of 200 iteration times is nearly 4% and almost all problems of C coverage to the best known results by 500 iteration times. We can see that HPSO has a better performance on problem set R than Tavakkoli-Moghaddam et al [24]. (3) In a set of RC, the average rate of 100 iteration times is nearly 13% for each problem, the average rate of 200 iteration times is nearly 6% and almost all problems of C coverage to the best known results by 500 iteration times, except RC101, RC102, RC105, RC107, RC201, RC202, RC203 and RC207.…”

Section: Hpso Performance Comparison By Different Problem Setsmentioning

confidence: 91%

“…Tavakkoli-Moghaddam et al [23] proposed a liner-integer model based on SA, which constructs VRPTW with the independent route length in order to minimize the heterogeneous fleet cost and the capacity utilization. TTavakkoliMoghaddam et al [24] also proposed a new mathematical model based on SA to find the short routes with the minimum fleet travel cost, the minimum travel distances and the maximum sale.…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Liang

Peng

et al. 2013

Entropy

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State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW) usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO) is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data's real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.

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Section: Hpso Performance Comparison By Different Problem Setsmentioning

confidence: 70%

Section: Hpso Performance Comparison By Different Problem Setsmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Liang

Peng

et al. 2013

Entropy

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“…This is the dilemma in running the BRISS model to employing a probability ratio precisely between crossover and mutation within a limited time. When using the GA application in the BRISS model to determine superior route planning for solving the carbon-routing problem, previous other studies have considered applying weight values and a time window for advancing evolutions in each application [37][38][39][40]. However, the GA application was intended to obtain a partial optimal solution in some areas, but not to be considered an optimal solution of the entire applied region [41].…”

Section: Model Formationmentioning

confidence: 99%

“…To mitigate these shortcomings, more advanced studies for diverse path problems should involve lower overall distances and emphasize saving computational time by improving model qualities with hybrid GA/Tabu [45], GA/particle swarm optimization [46], GA/fuzzy [47], GA/ time windows [48,49], SA/fuzzy [50], SA/time windows [37][38][39][40], SA/Tabu [51][52][53], and advanced evolutionary algorithms [44].…”

Section: Model Formationmentioning

confidence: 99%

Low Carbon Footprint Routes for Bird Watching

Fang¹,

Huang

Chou

et al. 2015

Sustainability

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Abstract:Bird watching is one of many recreational activities popular in ecotourism. Its popularity, therefore, prompts the need for studies on energy conservation. One such environmentally friendly approach toward minimizing bird watching's ecological impact is ensuring a reduced carbon footprint by using an economic travel itinerary comprising a series of connected routes between tourist attractions that minimizes transit time. This study used a travel-route planning approach using geographic information systems to detect the shortest path, thereby solving the problems associated with time-consuming transport. Based on the results of road network analyses, optimal travel-route planning can be determined. These methods include simulated annealing (SA) and genetic algorithms (GA). We applied two algorithms in our simulation research to detect which one is an appropriate algorithm for running carbon-routing algorithms at the regional scale. SA, which is superior to GA, is considered an excellent approach to search for the optimal path to reduce carbon dioxide and high gasoline fees, thereby controlling travel time by using the shortest travel routes. OPEN ACCESSSustainability 2015, 7 3291

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Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

et al. 2020

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This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on customers' requests, and the subsequent installation by a number of technicians. The main objective is to reduce the overall costs of hired resources, and the total transportation costs of trucks/technicians. The problem was the topic of the fourth edition of the VeRoLog Solver Challenge in cooperation with the ORTEC company. Our contribution to research is the development of a mathematical model for this problem and a novel hyper-heuristic algorithm to solve the problem based on a population of solutions. Experimental results on two datasets of small and real-world size revealed the success of the hyper-heuristic approach in finding optimal solutions in a shorter computational time, when compared to our exact model. The results of the large size dataset were also compared to the results of the eight finalists in the competition and were found to be competitive, proving the potential of our developed hyper-heuristic framework.

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A new mathematical model for a competitive vehicle routing problem with time windows solved by simulated annealing

Cited by 50 publications

References 29 publications

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

A Hybrid Chaos-Particle Swarm Optimization Algorithm for the Vehicle Routing Problem with Time Window

Low Carbon Footprint Routes for Bird Watching

Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge

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