Location of bus garages

Waters, Nigel; Wirasinghe, S. C.; Babalola, Afeez Mayowa; Marion, K. E. D.

doi:10.1002/atr.5670200204

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…The applications of market area models go far beyond the basic calculation of facility sizes, which can be quite complex for some service systems [91,96]. In the field of operations research, such models have been used to interpret and guide the use of large integer programming location models [36,37].…”

Section: Introductionmentioning

confidence: 99%

The General Optimal Market Area Model

Erlenkotter¹

1988

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for their helpful comments. AbstractMarket area models determine the optimal size of market for a facility. These models are grounded in classical location theory, and express the fundamental tradeoff between economies-of-scale from larger facilities and the higher costs of transport to more distant markets. The simpler market area models have been discovered and rediscovered, and applied and reapplied, in a number of different settings. We review the development and use of market area models, and formulate a General Optimal Market Area model that accommodates both economies-of-scale in facilities costs and economies-of-distance in transport costs as well as different market shapes and distance norms. Simple expressions are derived for both optimal market size and optimal average cost, and also the sensitivity of average cost to a non-optimal choice of size. The market area model is used to explore the implications of some recently proposed distance measures and to approximate a large discrete location model, and an extension to price-sensitive demands is provided. i ' 1 "Accession Fo, NTIS GRA&I DTIC TAB

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Section: Introductionmentioning

confidence: 99%

The General Optimal Market Area Model

Erlenkotter¹

1988

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“…The model then determined the optimal number of facilities using a CA method, information that was then that integrated in a discrete heuristic, location-allocation model. Waters et al (1986) used the same method, a CA model combined with a discrete location-allocation model, to show how the optimal locations of bus garages changed depending on whether the authors included local air quality in the CA objective function or not. Wijeratne and Wirasinghe (1986) proposed a different second stage, as the authors used the optimal number of facilities per analytical zone to group communities together heuristically to form catchment areas for fire stations.…”

Section: Continuum Approximation Location-allocation Modellingmentioning

confidence: 99%

Planning delivery-by-drone micro-fulfilment centres

Lamb

Wirasinghe

Waters

2022

Transportmetrica A: Transport Science

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Delivery drones are a disruptive technology that is spurring logistics system change, such as the adoption of urban micro-fulfilment centres (MFCs). In this paper, we develop and implement a two-stage continuum approximation (CA) model of this disruptive system in a geographic information system (GIS). The model includes common CA techniques at a local level to minimise cost, and then these local solutions are used in a second stage regional location-allocation multiple knapsack problem. We then compare the drone MFC system to a traditional delivery-by-truck system and investigate potential cost or emissions savings by adjusting time-window demand, logistical sprawl, electric truck alternatives, and MFC emissions. Furthermore, we conduct a sensitivity analysis to show that uncertainty in demand and effective storage density both significantly influence the number of MFCs selected and benchmark our model against commercial solvers. This methodology may also be further developed and applied to other new delivery vehicle modes.

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“…At approximately the same time, Ball et al (1984) also discussed a model for the bus garage location-allocation problem but with the significant difference that it disaggregated the demand into three parts: base requirement, morning and afternoon incremental requirements. Waters et al (1986) analysed a complementary approach compared to the models proposed by Kutsal (1982, 1983) and Ball et al (1984). In their article, the number of bus garages is first obtained using the basic facility location model.…”

Section: Literature Reviewmentioning

confidence: 99%

A queuing–location–allocation model for designing a capacitated bus garage system

Chen

Yao

Chen

et al. 2021

Engineering Optimization

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The spatial distribution of bus garages determines the total vehicular dead mileage of the transit system because buses must travel between bus garages and terminals at the start or conclusion of a day. By contrast, the size of the garages determines the queuing status when buses enter or leave the garages. Thus, a bus garage system with reasonable distribution and size is required to address these problems. In this article, a queuing-location-allocation model for optimizing a bus garage system is developed. Since a nonlinear objective function is involved, a linearization technique is introduced to convert the proposed model into an equivalent linear form. Next, a Lagrangian relaxation algorithm is designed to solve the linear form model. To validate the proposed algorithm, two groups of randomly generated test instances and a real-life case, the Dalian transit system in China, are applied. The results show that the proposed Lagrangian heuristic is efficient and stable.

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Location of bus garages

Cited by 8 publications

References 3 publications

The General Optimal Market Area Model

The General Optimal Market Area Model

Planning delivery-by-drone micro-fulfilment centres

A queuing–location–allocation model for designing a capacitated bus garage system

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