Optimizing Crowd Evacuation In The Emergency Route Planning Problem - Khalid, Mohd Nor Akmal (2015)

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Situasi bencana, yang berlaku secara semula jadi (kebakaran, banjir, taufan) atau buatan manusia (contohnya pengeboman pengganas, tumpahan bahan kimia, dan lain-lain), telah meragut ribuan nyawa, mencetuskan keperluan untuk pemindahan kecemasan. Biasanya, mengoptimumkan pelan pemindahan kecemasan melibatkan berkesanan pemodelan orang ramai dan pemilihan laluan, dimana pelan yang optimum penting dalam masalah perancangan laluan kecemasan (ERP). Pelbagai pendekatan ERP telah dibangunkan dimana diklasifikasikan kepada pendekatan matematik, keputusan sokongan, heuristik, dan meta-heuristik. Ulasan kesusasteraan menyeluruh telah menunjukkan kepentingan untuk merapatkan jurang antara pemodelan dan pemilihan laluan, di mana di mana pendekatan bersepadu dan berdaya maju diperlukan. Dalam kajian ini, satu perancangan pemindahan rangka kerja bersepadu menggunakan model pemindahan orang ramai dan sistem imun (AIS) algoritma tiruan, yang dipanggil iEvaP, telah dicadangkan. iEvaP telah disahkan terhadap Lu et al. (2003) dan parameternya telah ditentukan untuk prestasi yang optimum. Disastrous situations, either natural (e.g. fires, floods, hurricane) or man-made (e.g. terrorist bombings, chemical spills, etc.), have claimed the lives of thousands, triggering the needs for emergency evacuation. Typically, optimizing an emergency evacuation plan involves both the effectiveness in crowd modelling and route selection, where an optimum evacuation plan is vital in the emergency route planning (ERP) problem. Various ERP approaches have been developed which are classified into mathematical, decision-support, heuristic, and meta-heuristic approaches. Exhaustive literature reviews have shown the significance of bridging the gap between modeling and routing, where an integrated and viable approach is needed. In this study, an integrated evacuation planning framework utilizing crowd evacuation model and an artificial immune system (AIS) algorithm, called iEvaP, was proposed. iEvaP was validated against Lu et al. (2003) and its parameters were calibrated for optimum performance.