A simulated annealing for multi-criteria network path problems

Liu, Linzhong; Mu, Haibo; Luo, Haipeng; Li, Xiaojing

doi:10.1016/j.cor.2012.03.013

Cited by 22 publications

(13 citation statements)

References 39 publications

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“…And based on this processing, all the deterministic algorithms for the SSP problem can be used to solve the MSP problem. However, it is unreasonable as pointed by Mooney and Liu [5,6]. In the MSP problem, the statistical unit of all the criteria should be the route, and not the arc.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Urban Multicriteria Shortest Path Stochastic Optimization Algorithm

Gong

Lin

2015

Mathematical Problems in Engineering

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Drivers’ route choice behavior is usually personalized and multicriteria in practice. Therefore, the urban shortest path problem is the personalized urban multicriteria shortest path (PUMSP) problem. However, the solutions of the PUMSP problem are difficult to meet the drivers’ travel habits in the state of the art. To solve this problem, first, a new stochastic optimization algorithm based on the iterative calculation of a valid route set is proposed in this paper. The effective and reasonable path searching mechanism is designed based on drivers’ route choice habits. Then, the evaluation method of calculation results is given. The comparative experimental results with the genetic algorithm show that the proposed algorithm has reached better results in the evaluation parameters and computing time. The experimental results also demonstrate that it is meaningful to consider drivers’ travel law in the personalized urban multicriteria shortest path algorithm design for avoiding obtaining impractical routes solutions.

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Section: Introductionmentioning

confidence: 99%

A Personalized Urban Multicriteria Shortest Path Stochastic Optimization Algorithm

Gong

Lin

2015

Mathematical Problems in Engineering

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“…Numerical results from Fan and Machemehl [28] have demonstrated that the proposed SA outperformed the GA in most cases in their models. Liu et al [44] also argued that the GA runtime is typically much longer.…”

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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“…This is a critical point in SA algorithm that causes probability of accepting inferior solutions decreased with model repetition and helps to algorithm stability. Another parameter in above equation is β(0,1), called cooling rate and give a control on cooling speed (Liu et al, 2012) and also its value must be tuned.…”

Section: Simulated Annealing (Sa) Algorithmmentioning

confidence: 99%

“…In this research, they integrated GIS and GA utilization. (Liu, Mu, Luo, & Li, 2012) used an oriented spanning tree based on simulated annealing (SA) for solving shortest path problem in a single mode transportation network. The most important reason of using oriented spanning tree in their research was improving in local and global search of the designed SA algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

Ghaderi

Pahlavani

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user's bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.

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A simulated annealing for multi-criteria network path problems

Cited by 22 publications

References 39 publications

A Personalized Urban Multicriteria Shortest Path Stochastic Optimization Algorithm

A Personalized Urban Multicriteria Shortest Path Stochastic Optimization Algorithm

Low Carbon Footprint Routes for Bird Watching

A New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

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