Suchergebnisse

In this study, transit-served areas (TSAs) are defined as areas within a reasonable distance (e.g., 800 meters) of transit services. TSAs have two key dimensions: physical features (e.g., land-use density and mix) and performance (regarding human behaviors). Non-traditional data (NTD) (e.g., social media check-ins and cellular network data) can supplement traditional data (TD) (e.g., interviews and censuses) to enhance studies and monitoring of TSAs. A case study of Shenzhen, China, illustrates how to combine NTD and TD to evaluate the features and performance of 167 TSAs along metro lines. It finds that NTD can be used to formulate new indicators to measure and monitor the two dimensions of TSAs; the features and performance of different TSAs vary significantly; point of interest (POI) efficiency, or the average users attracted by each POI, can be a useful indicator to differentiate TSAs' performance; the POI efficiency of a single TSA can vary across days and the POI efficiency of an extremely efficient or inefficient TSA can be totally different across days; and the combination of NTD and TD can effectively help locate extreme TSAs and explain factors contributing to the extremity.

AbstractWe consider an integrated production and delivery scheduling problem with non‐stationary demand in a two‐stage supply chain, where orders arrive dynamically and the demand is time‐varying. Orders should be first processed on identical machines and then delivered to a single next‐stage destination by the transporters with fixed departure times. The objective is to minimize the order waiting time via production‐delivery scheduling. We formulate the problem into a Markov decision process model and develop an approximate dynamic programming (ADP) method. To shrink action (decision) space, we propose the shorter processing time first and first completion first delivery (SPTm/FCFD) principle to determine order processing sequences and order delivery, and then we establish two constraints to eliminate a fraction of inferior actions. Based on the SPTm/FCFD principle, we propose the SPT/FCFD rule, and show its optimality for two scenarios. In addition, we deploy five basis functions to approximate the value function. The superior performance of ADP policy is validated via numerical experiments, compared with four benchmark policies. We also empirically study the impact of demand features on the waiting time, and results show that these features significantly affect the performances of all polices. In practice, it is suggested to postpone the peak demand, when total demand exceeds the available production capacity.