Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles

Abstract

Set Covering problems find the optimal provision of service locations while guaranteeing an acceptable level of accessibility for every demand points in a given area. Other than reliance on static, exogenously-imposed accessibility measures, these problems either exclude substantive infrastructurevehicle interactions or only include fragmented infrastructure-vehicle interactions related to the routing considerations of households seeking refueling service as a requirement of performing routine, daily activities. Here, we address this problem by coupling a Location-Routing Problem (LRP) that uses the set covering model as a location strategy to the Household Activity Pattern Problem (HAPP) as the mixed integer scheduling and routing model that optimizes households' participation in out-of-home activities.The problem addressed includes multiple decision makers: the public/private sector as the service provider, and the collection of individual households that make their own routing decisions to perform a given set of "out-of-home activities" together with a visit to one of the service locations. A solution method that does not necessarily require the full information of the coverage matrix is developed to reduce the number of HAPPs that needs to be solved. The performance of the algorithm, as well as comparison of the results to the set covering model, is presented. Although the application is focused on identifying the optimal locations of Hydrogen Fuel Cell Vehicle (HFCV) refueling stations, this proposed formulation can be used as a facility location strategy for any service activity that is generally toured with other activities.

On the way or around the corner? Observed refueling choices of alternative-fuel drivers in Southern California

“….commuting routes are perhaps an important consideration in designing an effective distribution network for new fuels'' (p. 243). Activity-based approaches have become more common in transportation modeling (Pendyala et al, 2002), and even incorporate trip chaining explicitly in station location modeling (Kang and Recker, 2012), signaling that refueling events may be linked to differing kinds of trips. Left unanswered was what AFV drivers considered to be more convenient: station proximity to home, or availability of refueling stations along their frequently traveled paths.…”

Stochastic frontier analysis of excess access to mid-trip battery electric vehicle fast charging

“…Second, one oft-cited refueling behavior in locating AFV refueling stations is that drivers prefer to stop along their way to destination rather than make a special-purpose trip solely to refuel their vehicles (Kitamura and Sperling, 1987;Upchurch and Kuby, 2010), so driver's intended path should be taken into account in the analysis of mid-trip fast-charging behavior. Third, refueling is reasonable to be fitted into users' daily activities (Kitamura and Sperling, 1987;kang and Recker, 2012), but information about activities during trips is not provided by this trial. Above all, the samples used in this study are mid-trip fast-charging events that can be initiated at the next actually used charging location (fast charging or normal charging) or at home after the last trip of a day (normal charging), which solves the path and activity considerations simultaneously.…”

Refueling hydrogen fuel cell vehicles with 68 proposed refueling stations in California: Measuring deviations from daily travel patterns

“…For detailed up-to-date refueling station siting work, with a focus on variations of Flow Refueling Location Problems, readers are referred to MirHassani and Ebrazi [12]. Kang and Recker [13] proposed a refueling siting formulation, in the category of a Location Routing Problem. While their work can be viewed as a tour-based model, it not only includes the tours, but also the capability of making the tour construction within the model.…”

“…However, the goal of this process is to match the total production and the total attraction of each zone rather than deriving e or collecting e a set of real path demand [20]. In an effort to include individual traveler's fundamental travel decision making procedures, some studies incorporate recent advances in activity-based travel modeling within station siting work; Kang and Recker [13] used Statewide Travel Survey Data and integrated travel demand models in their proposed formulation. However, they used a small sample data set, and such a sample size may be sufficient for model development but not for representing real demand.…”