Jee Eun Kang

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

A hydrogen refueling station siting model that considers scheduling and routing decisions of individual vehicles is presented. By coupling a location strategy of the set covering problem SCP and a routing and scheduling strategy of the household activity pattern problem, this problem falls into the category of location routing problems. It introduces a tour-based approach to refueling station siting, with tour-construction capability within the model. There are multiple decision makers in this problem: the public 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 a refueling location. A solution method that does not require the full information of the coverage matrix is developed to reduce the computational burden. Compared to the point-based SCP the results indicate that the minimum infrastructure requirement may be overestimated when vehicle refueling demand-infrastructure refueling supply interactions with daily out-of-home activities are excluded.

Constructing the feasible space–time region of the Household Activity Pattern Problem

ABSTRACT In this paper, a method of constructing the feasible region in space–time dimension of the well-known Household Activity Pattern Problem (HAPP) is developed. HAPP is an optimization model that generates an optimal set of full-day activity-travel decisions. Based on the definition of an activity and its spatial and temporal constraints, feasible space–time region for the completion of one activity is derived. Then, a full-day feasible space–time region is determined as an intersection of a set of feasible regions for activities to be performed on a given travel day. This feasible space–time region is shown to be consistent with the concept of space–time prism. An algorithm is developed for the construction of an activity-specific feasible space–time region for a network-based travel environment. The algorithm generates a set of time windows for each activity and for each node that later converts to link-based time windows of accessibility.

Inventory Rebalancing through Pricing in Public Bike Sharing Systems

Abstract This paper presents a new conceptual approach to improve the operational performance of public bike sharing systems using pricing schemes. Its methodological developments are accompanied by experimental analyses with bike demand data from Capital Bikeshare program of Washington, DC (USA). An optimized price vector determines the incentive levels that can persuade system customers to take bikes from, or park them at, neighboring stations so as to strategically minimize the number of imbalanced stations. This strategy intentionally makes some imbalanced stations even more imbalanced, creating hub stations. This reduces the need for trucks and dedicated staff to carry out inventory repositioning. For smaller networks, a bi-level optimization model with a single level reformulation is introduced to minimize the number of imbalanced stations optimally. The results are compared with a heuristic approach that adjusts route prices by segregating the stations into different categories based on their current inventory profile, projected future demand, and maximum and minimum inventory values calculated to fulfill certain desired service level requirements. We use a routing model for repositioning trucks to show that the proposed optimization model and the latter heuristic approach, called the iterative price adjustment scheme (IPAS), reduce the overall operating cost while partially or fully obviating the need for a manual repositioning operation.

On Activity-based Network Design Problems

Abstract This paper examines network design where OD demand is not known a priori , but is the subject of responses in household or user itinerary choices to infrastructure improvements. Using simple examples, we show that falsely assuming that household itineraries are not elastic can result in a lack in understanding of certain phenomena; e.g., increasing traffic even without increasing economic activity due to relaxing of space-time prism constraints, or worsening of utility despite infrastructure investments in cases where household objectives may conflict. An activity-based network design problem is proposed using the location routing problem (LRP) as inspiration. The bilevel formulation includes an upper level network design and shortest path problem while the lower level includes a set of disaggregate household itinerary optimization problems, posed as household activity pattern problem (HAPP) (or in the case with location choice, as generalized HAPP) models. As a bilevel problem with an NP-hard lower level problem, there is no algorithm for solving the model exactly. Simple numerical examples show optimality gaps of as much as 5% for a decomposition heuristic algorithm derived from the LRP. A large numerical case study based on Southern California data and setting suggest that even if infrastructure investments do not result in major changes in link investment decisions compared to a conventional model, the results provide much higher resolution temporal OD information to a decision maker. Whereas a conventional model would output the best set of links to invest given an assumed OD matrix, the proposed model can output the same best set of links, the same daily OD matrix, and a detailed temporal distribution of activity participation and travel from which changes in peak period OD patterns can be observed.