English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Traditional mathematical models for locating/allocating vehicles and facilities are reviewed and extended to illustrate how to formulate and solve a problem of minimized response time, given resource constraints. Results indicate that the average response time can be significantly improved through strategically allocating vehicles throughout the service area. Furthermore, the modified model was shown to outperform the traditional model as the number of vehicles allocated to a fixed number of facilities increase. Implications are identified for applications such public transit systems, wholesale and distribution operations. LITERATURE REVIEW arly literature on location problems was based upon the traditional set covering problem. Set covering describes the minimum number of facilities needed to cover all system demand. A population is considered served, or covered, when a facility is sited within the maximal service distance. Church and Revelle (5) presented the Maximal Covering Location Problem (MCLP) to maximize the total amount of population served within a maximal service distance, given a fixed number of facilities. Under this objective, some population is left uncovered by the model. To address this problem Church and Revelle created the MCLP with mandatory closeness constraints and a bicriterion model that maximizes the amount of demand covered within the maximal distance and minimizes the distance traveled from the uncovered demand to its nearest facility, simultaneously (4). Hakimi (12) developed a model similar to the MCLP termed the P-median problem. The P-median problem weights the distance between demand nodes and facilities by the associated demand quantity and calculates the total weighted travel distance between demand nodes and facilities. The model then seeks to find the location of P facilities so as to minimize the total demand weighted travel distance between demands and facilities. Additional research has been conducted to expanded upon the p-median approach to the emergency vehicle location problem and analyze sources of modeling errors (8,9). These models offer techniques to improve the efficiency of the traditional p- median problem. A review of the EMS system in Austin, Texas that relied upon MCLP based models exposed the necessity to construct alternative models that addressed the issue of vehicle availability. Daskin's (7) Maximal Expected Coverage Location Problem (MEXCLP) examines equipment availability within the context of location models. The objective of the MEXCLP is to locate emergency vehicles so as to maximize the expected coverage area, even in the event that multiple vehicles are in use. The model is based on the assumption that the probability that a randomly chosen vehicle is busy is independent of any other vehicle being in use. Bernardo and Repede (2) later modified this model to incorporate time varying demand and multiple states of vehicle availability.

Developing A Mathematical Model For Locating Facilities And Vehicles To Minimize Response Time