Online algorithms for ambulance routing in disaster response with time-varying victim conditions

Abstract

We present a novel online optimization approach to tackle the ambulance routing problem on a road network, specifically designed to handle uncertainties in travel times, triage levels, required treatment times of victims, and potential changes in victim conditions in post-disaster scenarios. We assume that this information can be learned incrementally online while the ambulances get to the scene. We analyze this problem using the competitive ratio criterion and demonstrate that, when faced with a worst-case instance of this problem, neither deterministic nor randomized online solutions can attain a finite competitive ratio. Subsequently, we present a variety of innovative online heuristics to address this problem which can operate with very low computational running times. We assess the effectiveness of our online solutions by comparing them with each other and with offline solutions derived from complete information. Our analysis involves examining instances from existing literature as well as newly generated large-sized instances. One of our algorithms demonstrates superior performance when compared to the others, achieving experimental competitive ratios that closely approach the optimal ratio of one.

Competitive Analysis of Algorithms for an Online Distribution Problem

“…We refer to Borodin and El-Yaniv [3] and to Hentenryck and Bent [4] for a comprehensive description of online algorithms and competitive analyses, to Albers [5] for a survey on online algorithms, and to Jaillet and Wagner [6] for a survey of online vehicle routing problems. Recent contributions have been presented by Chen et al [7] for an online machine minimization problem; Ma et al [8] for online knapsack problems; Ber ǵe et al [9] for the online k-Canadian traveller problem; Li et al [10,11], Yu and Jacobson [12], Shamsaei et al [13], and Jiang et al [14] for online scheduling problems; Akbari et al [15] for a post-disaster road restoration problem; Zhang et al [16] for the management of online orders in modern crowdsourced truck logistics platforms; Shiri et al [17] for ambulance routing in disaster response with partial or no information on victim conditions; Fujii et al [18] for the Secretary problem with predictions; Arnosti et al [19] for static threshold policies in the prophet Secretary problem; Salem et al [20] for Secretary problems with biased evaluations using partial ordinal information; Shiri et al [21] for the ambulance routing problem on a road network; and Chen et al [22] for a review of online integrated production and distribution scheduling. Finally, for an extensive overview of the most recent contributions on online algorithms, we refer the work by to Höhne et al [23][24][25] and Amouzandeh et al [26].…”