Empirical-type simulated annealing for solving the capacitated vehicle routing problem

Rabbouch, Bochra; Saâdaoui, Foued; Mraïhi, Rafaa

doi:10.1080/0952813x.2019.1652356

Cited by 32 publications

(20 citation statements)

References 23 publications

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“…e experimental results demonstrate that it can obtain the best algorithm solution when the temperature cooling rate is 0.95 and the final temperature is 0.1 [36].…”

Section: Algorithmmentioning

confidence: 95%

Signal Timing Optimization Model for Intersections in Traffic Incidents

Wang

Hang

Zhou

2020

Journal of Advanced Transportation

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The intersection control and management can alleviate the traffic congestion caused by traffic incidents. Therefore, it becomes essential to develop a signal optimization method for intersections influenced by traffic incidents, which will be beneficial to prevent congestion spreading. In this paper, the proposed model is capable of maximizing the intersection throughput by comprehensively considering the queue length as the penalty value. The headway of leaving vehicles is assumed to follow the Cowan’s M3 headway distribution, where formulas for queue length can be derived based on gap acceptance theory. To satisfy the conditions for efficiently identifying feasible solutions in a short time, a heuristic algorithm (simulated annealing algorithm) is employed to solve the model. The numerical results can validate that the proposed method can solve the problem more efficiently and alleviate the intersection congestion caused by the incidents more desirably. When the incident occurs away from the intersection stop line, the impacts on intersection throughput will be gradually weakened. The proposed method is capable of improving the signalized intersection throughput while preventing the congestion from spreading to the upstream intersection.

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“…e experimental results demonstrate that it can obtain the best algorithm solution when the temperature cooling rate is 0.95 and the final temperature is 0.1 [36].…”

Section: Algorithmmentioning

confidence: 95%

Signal Timing Optimization Model for Intersections in Traffic Incidents

Wang

Hang

Zhou

2020

Journal of Advanced Transportation

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“…The probability of accepting a worse solution is determined by a formula that considers the gap of solution quality with the earlier solution and the parameter denoted as temperature. Several other studies have proven that SA successfully solved the problem of VRP and its variants [28][29][30].…”

Section: Solving 2evrp-df With Simulated Annealingmentioning

confidence: 99%

Solving the Two Echelon Vehicle Routing Problem Using Simulated Annealing Algorithm Considering Drop Box Facilities and Emission Cost: A Case Study of Reverse Logistics Application in Indonesia

et al. 2021

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A two echelon distribution system is often used to solve logistics problems. This study considers a two-echelon distribution system in reverse logistics context with the use of drop box facility as an intermediary facility. An optimization model of integer linear programming is proposed, representing a two-echelon vehicle routing problem with a drop box facility (2EVRP-DF). The aim is to find the minimum total costs consisting of vehicle transportation costs and the costs to compensate customers who have to travel to access these intermediary facilities. The results are then compared to those of common practice in reverse logistics. In common practice, customers are assumed to go directly to the depot to drop their goods. In addition, this study analyzes the environmental impact by adding a component of carbon emissions emitted by the vehicles. A set of comprehensive computational experiments is conducted. The results indicate that the 2EVRP-DF model can provide optimal costs and lower carbon emissions than the common practice.

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“…Constraint (11), (12), (13) guarantee that after D max = 10 km of flight, the drone must come back to truck position for change battery. Constraint (14) defines the type of decision variable.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…To avoid the local minimum, the SA uses a probability to accept a solution as the best even if it is worse than the interior. SA has been adapted to solve a wide variety of hard optimization problems, as the berth allocation problem [1], the vehicle routing problem ( [9] and room scheduling problem [14]. In the following subsections we present the general procedure of the proposed SA.…”

Section: Decision Variablementioning

confidence: 99%

WITHDRAWN: The Drone delivery problem in disaster and pandemics

Ie¹,

Lajjam²,

Me³

2020

Preprint

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Recently, the use of drone is growing in importance in several fields. Their capacity toavoid traffic jam and operate without a human pilot, make it the perfect method tohome delivery in natural disasters and pandemics. This paper addresses the dronedelivery problem inside Covid-19 outbreak area where quarantine status has beenapplied. This problem aims to minimize the time spent by a drone to complete thehome delivery of pharmaceutical products and other indispensable goods to a set ofresidents inside the quarantine area. We propose a new mathematical formulation forthe problem, and we develop a simulated annealing algorithm to solve large sizedinstances. To show the performance of our metaheuristic; extensive computationalexperience inspired by a real case of study in Morocco (Nador) are realized. Ourresults confirm that the proposed metaheuristic is a better alternative to solve the dronedelivery problem in disaster and pandemics than the commercial solver (CPLEX 12.7)

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Empirical-type simulated annealing for solving the capacitated vehicle routing problem

Cited by 32 publications

References 23 publications

Signal Timing Optimization Model for Intersections in Traffic Incidents

Signal Timing Optimization Model for Intersections in Traffic Incidents

Solving the Two Echelon Vehicle Routing Problem Using Simulated Annealing Algorithm Considering Drop Box Facilities and Emission Cost: A Case Study of Reverse Logistics Application in Indonesia

WITHDRAWN: The Drone delivery problem in disaster and pandemics

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