On different formulations of green vehicle routing problem

Abdoli, Behroz; MirHassani, S. A.; Hooshmand, F.

doi:10.1080/02522667.2018.1460137

“…A solution algorithm and test results were developed using real data. Abdoli et al (2019) discussed a modern aspect of the green environment and using clean energy sources integrated with DARP research. They addressed the green vehicle routing problem with the alternative-fuel vehicles (AFVs) fleet, where refueling decisions are also considered during route planning to allow all vehicles to use clean fuel at the right time.…”

Section: Request Destinationmentioning

confidence: 99%

Optimization of Multimodal Dial-A-Ride Systems with Fixed-Line Flexible Time Schedules

Elgazzar,

Fors,

Ali

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

0

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This paper investigates a multimodal system that integrates the general dial-a-ride problem with a set of fixed-line vehicles having a flexible schedule. The passenger trips are made in a three-stage journey using a dial-a-ride vehicle and a fixed-line bus. Passengers are picked up from an origin point and transferred to a collecting point via a dial-aride vehicle. Then, fixed-route buses transfer passengers to a second collecting point where another dial-a-ride vehicle will pick up and transfer them to their requested destination. The main objective of this integration is to reduce the total trip cost by carrying part of it through cheaper fixed-route service while maximizing the utilization of seats in the fixed-route vehicle. The model provides each fixed line with a suggested start time. A mixed-integer linear programming model was developed for the problem, and computational testing on small-size instances has been carried out to demonstrate the viability of the proposed system.

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“…Ozcan and Kaya (2018) investigated the importance of the TSP for speeding up sightseeing in urban areas. Several formulations of the VRP, generally referred to as green VRP aimed at minimizing the degradation of the urban environment due to freight transportation, have been proposed (Gajanand and Narendran 2013;Abdoli et al 2019).…”

Section: Routing Problems In Smart Citiesmentioning

confidence: 99%

Optimization Problems Under Uncertainty in Smart Cities

Fadda¹,

Tiotsop²,

Manerba³

et al. 2020

Handbook of Smart Cities

2

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Combinatorial optimization has proven to be a valuable tool in several real case applications. With the advent of smart cities, it has naturally been leveraged by both researchers and industries to deal with problems such as optimal allocation of urban services, effective management of scarce resources, planning of logistic operations over congested networks, and pollution reduction. In practice, these problems are turned into optimization models, and their solution reveals the best tactical or operational policy able to maximize a particular utility or minimize a particular cost. Typically, several parameters involved in these models (e.g., people's behavior and market fluctuations) are influenced by many uncertain variables that are only partially or not known by the decision-maker. This lack of knowledge can be dealt with by using specific optimization paradigms such as the Robust Optimization or the Stochastic Programming. Nevertheless, the complexity of these models precludes, in most cases, to find an optimal solution. Thus, approximation methods have to be considered. This chapter presents several applications leveraging optimization models and algorithms for effective management of urban areas and their operations and a novel and efficient Extreme Values Theory-based deterministic approximation framework to cope with uncertainty in smart cities' optimization and planning.

show abstract

Optimization Problems Under Uncertainty in Smart Cities

Fadda¹,

Tiotsop²,

Manerba³

et al. 2021

Handbook of Smart Cities

0

View full text Add to dashboard Cite

Combinatorial optimization has proven to be a valuable tool in several real case applications. With the advent of smart cities, it has naturally been leveraged by both researchers and industries to deal with problems such as optimal allocation of urban services, effective management of scarce resources, planning of logistic operations over congested networks, and pollution reduction. In practice, these problems are turned into optimization models, and their solution reveals the best tactical or operational policy able to maximize a particular utility or minimize a particular cost. Typically, several parameters involved in these models (e.g., people's behavior and market fluctuations) are influenced by many uncertain variables that are only partially or not known by the decision-maker. This lack of knowledge can be dealt with by using specific optimization paradigms such as the Robust Optimization or the Stochastic Programming. Nevertheless, the complexity of these models precludes, in most cases, to find an optimal solution. Thus, approximation methods have to be considered. This chapter presents several applications leveraging optimization models and algorithms for effective management of urban areas and their operations and a novel and efficient Extreme Values Theory-based deterministic approximation framework to cope with uncertainty in smart cities' optimization and planning.

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On different formulations of green vehicle routing problem

Cited by 6 publications

References 10 publications

Optimization of Multimodal Dial-A-Ride Systems with Fixed-Line Flexible Time Schedules

Optimization of Multimodal Dial-A-Ride Systems with Fixed-Line Flexible Time Schedules

Optimization Problems Under Uncertainty in Smart Cities

Optimization Problems Under Uncertainty in Smart Cities

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