Time-dependent fleet size and mix multi-depot vehicle routing problem

Schmidt, Carise Elisane; Silva, Arinei C.L.; Darvish, Maryam; Coelho, Leandro C.

doi:10.1016/j.ijpe.2022.108653

Cited by 19 publications

(8 citation statements)

References 54 publications

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“…Simultaneously, [34] introduced advancements in Ant Colony Optimization (ACO) techniques for MDVRP by incorporating node clustering to enhance algorithm performance and reduce complexity. Furthermore, [35] addressed the time-dependent fleet size and mixed MDVRP, introducing a mathematical model with generic and problem-specific valid inequalities along with a powerful meta-heuristic for solving large instances of the problem. Finally, [36] focused on hazardous material transportation, introducing multi-objective optimization models and algorithms such as the Hybrid Multi-Objective Evolutionary Algorithm (HMOEA) and Two-Stage Algorithm (TSA) to optimize vehicle routing while considering various factors such as time windows, actual load, and depot stock.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Iksan,

Tufail,

Anna Apong

et al. 2024

IEEE Access

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Ship routing is a fundamental component of maritime passenger logistics, involving the careful planning of sea routes to facilitate the efficient and comfortable transportation of passengers. However, the ship routing problem presents unique challenges in the transit network design, encompassing various Vehicle Routing Problem (VRP) variants. These challenges arise from complex characteristics including multiple depots, ship types, and asymmetric distances between ports. This study aims to minimize passenger transfers in ship routing by incorporating constraints on maximum distance and travel time.To address these challenges, we propose a two-step approach: a hybrid Genetic Algorithm (GA) that incorporates the Fixed Radius Near Neighbor (FRNN) heuristic method alongside GA optimization. Moreover, a comparison was conducted between our proposed hybrid GA and both the standalone FRNN and conventional GA. The comparative analysis demonstrated that the hybrid GA outperformed both the FRNN and the standard GA in the reduction of passenger transfers and attainment of optimal fitness values. This highlights the significance of incorporating various optimization methodologies to enhance performance. Subsequent studies should investigate the mechanisms underlying the success and suitability of hybrid GA in practical maritime logistics scenarios.

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Section: Related Workmentioning

confidence: 99%

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Iksan,

Tufail,

Anna Apong

et al. 2024

IEEE Access

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“…Tey develop a semiclosed queuing network that evaluates the efectiveness of three diferent relocation strategies: no relocations, customer relocations, and vehicle relocations. In a departure from the standard practice of assuming deterministic travel times between locations, Schmidt et al [27] consider a timedependent feet size and multidepot vehicle routing problem for logistics distribution in an urban area, where trafc congestion varies by the time of day.…”

Section: Multi-island Rentals Allowedmentioning

confidence: 99%

Optimal Fleet Policy of Rental Vehicles with Relocation: A Simulation Study

Favier,

Ganguly,

Shalpegin

2024

Journal of Advanced Transportation

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Popularity of one-way car rentals poses a challenge to rental car fleet management and brings to focus the importance of a strategic decision for rental car operators: whether to implement a single-fleet or a multifleet model. The single-fleet model allows movement of vehicles between regions, whereas the multifleet model does not. It is not obvious whether a single-fleet model is optimal due to its pooling effect, or a multifleet model due to shorter car relocation times. In practice, different rental car operators use different models. To answer this conundrum, we develop two simulation models and compare them in terms of fleet utilisation, branch service level, relocations, and operating profit. We have taken the New Zealand rental car industry as an example as the country consists of two well-defined regions: the North Island and the South Island. The results indicate that a multifleet model has a higher service level at key centres and higher utilisation. At the same time, the single-fleet model is relatively more profitable at the expense of a lower service level in key centres due to vehicles accumulating in the South Island due to a significant volume of one-way southbound travel. Overall, the implementation of either model should depend on the strategic goals of the rental car operator. Our work will be useful for practitioners considering whether or not to pool their fleet when allowing for one-way rentals with subsequent relocation.

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“…For example, the reorganization of business processes may lead companies to look for new facilities to have more space available [11]. In addition, fleet size, which is one of the most important critical success factors for an LSP, should also be considered in terms of the vehicle mix (both small vans and trucks) [12], as it can become a source of tactical and operational advantages [13]. Finally, the impact on the size of the LSP's workforce, in terms of the number of drivers, warehouse operators, and office staff, should be considered as a proxy for the estimation of both resource availability and business volume [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…These works cover different topics: supply chain risk management [8,9], LSP resilience [4,10], economic and financial performance [8], how to ensure a sustainable competitive advantage [5,11], and innovation adoption [12]. The situations in different countries have also been analyzed: USA [2]; Thailand [13,14]; Malaysia, Indonesia, and India [15]; China [16]; South Africa [17]; UK [18]; Poland; and Germany and Sweden [12]. However, to the best of the authors' knowledge, very few studies have investigated the impact of COVID-19 on LSPs in Italy, despite the fact that it was one of the countries most affected by the pandemic in the world, especially during the first wave.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of COVID-19 on Logistics Service Providers’ Operations: An Italian Empirical Study

Zenezini,

Cagliano,

Mangano

et al. 2023

Sustainability

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The lockdowns caused by the COVID-19 pandemic between 2020 and 2021 resulted in a substantial increase in e-commerce purchases, with the consequent growth of logistics services. Thus, this paper is aimed at studying the effects of the pandemic on the operational processes of logistics service providers. To this end, a survey questionnaire was developed and administered to a sample of identified respondents. The collected data were quantitatively analyzed via the Kruskal–Wallis test. The outcomes point out that logistics operators faced an increase in the distances traveled to carry out pick-up and delivery activities, and larger companies added more light vehicles to their fleets, proving that the company size was a relevant aspect of ensuring a quick response to the pandemic. These results show an increased business-to-consumer market share that is leading to a redesign toward more sustainable operational strategies.

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Time-dependent fleet size and mix multi-depot vehicle routing problem

Cited by 19 publications

References 54 publications

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Optimizing Maritime Passenger Transfer in Rich Vehicle Routing Problem Using a Hybrid Genetic Algorithm

Optimal Fleet Policy of Rental Vehicles with Relocation: A Simulation Study

Impacts of COVID-19 on Logistics Service Providers’ Operations: An Italian Empirical Study

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