Abstract
This paper proposes a new network model for the building evacuation problem considering congestion levels and provides a mixed integer linear programming (MILP) model and an efficient heuristic algorithm solving the problem. Constructing an optimization model with several congestion levels, we introduce a new network called the multi-class time-expanded (MCTE) network having several exclusive arcs connecting the same tail and head nodes. The MCTE networks make both the MILP model and the heuristic algorithm reflect a realistic situation in congested networks. Considering MCTE networks makes the problem difficult to solve, which motivates us to develop an efficient heuristic algorithm. We test our heuristic algorithm using several real-world networks such as a multiplex cinema, a subway station, and a large-size complex shopping mall in addition to an artificial network for clear comparison between the proposed algorithm and the MILP approaches. The results indicate that the proposed algorithm runs fast and produces a near-optimal solution compared with those from MILP models with a commercial solver.
Highlights
Recent tragedies such as the Manchester Arena terrorist attack, the Grenfell Tower fire in London and the Bataclan concert hall attack in Paris are motivating the development of time-critical evacuation plans
We find a clue for improving the speed of the algorithm from the solutions of the mixed integer linear programming (MILP) model for the multi-class time-expanded (MCTE) network
In large-size networks, the MILP model for the MCTE network is not solvable, so we compare the solution of our heuristic algorithm to the solutions of the MILP models with single-class arcs; see Section IV-B for an explanation
Summary
Recent tragedies such as the Manchester Arena terrorist attack, the Grenfell Tower fire in London and the Bataclan concert hall attack in Paris are motivating the development of time-critical evacuation plans. The Internet of Things (IoT) technology enables us to collect and share essential building information such as the number of evacuees in each space and availability of each space in real-time and plays as an essential infrastructure for implementing real-time evacuation plans from data. Inspired by such technological advancement, we set our goal to develop an algorithm adequate for emergent evacuating situations.
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