Relocating emergency service vehicles with multiple coverage and critical levels partition
Betül YILDIRIM
Makale/Derleme | 2023 | Computers & Industrial Engineering , pp.1 - 17
A relocation strategy repositions idle emergency service (ES) vehicles to prevent some demand points from being uncovered. In this study, we consider an ES system, such as natural gas emergency services system, which involves satisfying multiple-coverage requirements of demand points. This system uses a compliance table strategy that denotes the base stations of available ES vehicles depending on the system state. As different from the relocation literature, we allow multiple vehicle assignments to stations. Additionally, we categorize the system states as critical and non-critical. We proposed a mathematical model, which maximizes . . . the expected safely covered population to construct the compliance table. According to our results, the developed model opens fewer stations and provides better safe coverage. In order to help the dispatcher, who is mostly under pressure while making relocation decisions, we also introduced vehicle moves required for relocation into the compliance table. So the dispatcher can see the relocation plan and desired stations in the compliance table in advance. For this purpose, we present a network flow model and a heuristic approach to find the relocation plan for all emergency site cases. We performed a detailed simulation study, which also provided information about the performances of each vehicle. Accordingly, the proposed reloA relocation strategy repositions idle emergency service (ES) vehicles to prevent some demand points from being
uncovered. In this study, we consider an ES system, such as natural gas emergency services system, which involves satisfying multiple-coverage requirements of demand points. This system uses a compliance table strategy
that denotes the base stations of available ES vehicles depending on the system state. As different from the
relocation literature, we allow multiple vehicle assignments to stations. Additionally, we categorize the system
states as critical and non-critical. We proposed a mathematical model, which maximizes the expected safely
covered population to construct the compliance table. According to our results, the developed model opens fewer
stations and provides better safe coverage. In order to help the dispatcher, who is mostly under pressure while
making relocation decisions, we also introduced vehicle moves required for relocation into the compliance table.
So the dispatcher can see the relocation plan and desired stations in the compliance table in advance. For this
purpose, we present a network flow model and a heuristic approach to find the relocation plan for all emergency
site cases. We performed a detailed simulation study, which also provided information about the performances of
each vehicle. Accordingly, the proposed relocation strategy is significantly better regarding response time related
measures. cation strategy is significantly better regarding response time related measures.
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