Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

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

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

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization

We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based Integer Programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a General Variable Neighborhood Search algorithm. We evaluate the IP model and the metaheuristic on various small, medium, and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses

Using simulation to investigate impact of different approaches to coordination on a healthcare system’s resilience to disasters

Many disasters that have happened in the last decades have caused a shortage of healthcare resources and change in healthcare activities. Coordination of healthcare facilities is one of the emergency medical response strategies to ensure the continued provision of medical services during disasters. The importance of coordination in healthcare systems during disasters is well recognised in the literature, but to the best of our knowledge there has been no review of the published research in this area. In this thesis, a focused literature review of models for the coordination in the healthcare system is provided. Additionally, measures of coordination effectiveness that denote resilience are discussed. In the field of medical management, there are two types of coordination including integrative care and collaborative care. Both types of coordination aim to improve the emergency medical response by ensuring the continuity of medical services and improving healthcare capability during disasters. Integrative care mainly investigates the resource allocation within a common governance, whereas collaborative care is mainly focused on the sharing of healthcare resources across governances. Thus, integrative care is mainly implemented within a healthcare provider setting, while collaborative care is mainly implemented between the settings. However, resilience is usually perceived at community level rather than at an individual institution when responding to disasters. Improving resilience during disasters requires the capability of different healthcare providers, which can be achieved by collaborative care, rather than integrative care. In addition, the literature has commonly addressed collaborative care using optimisation approach, not simulation approach. In this regard, this study presents simulation models for resilience of the healthcare network during disasters. In collaboration with the health authorities and medical staff in Thailand who experienced a number of disasters we investigated real-world activities that took place in emergency medical responses. We developed novel discrete event simulation models of collaboration in an emergency medical response in a healthcare network during disasters with the aim to improve the resilience of the healthcare network. Three strategies for collaboration in the healthcare network were defined including non-collaborative care, semi-collaborative care, and a new proposed collaborative care. Non-collaborative care strategy was in place in response to Tsunami in Phuket in 2004, while semi-collaborative care strategy is the current strategy which was implemented during the boat capsizing in Phuket in 2018. We propose a new collaborative care strategy which is defined by considering the disadvantages of the current semi-collaborative care strategy. It addresses a new collaboration in the network that enables information sharing and the classification of healthcare providers. The strategies differ with respect to the first treatment provision of patients, sharing of resources, and patient transportation The simulation models were validated and verified by using the boat capsizing real-world event. The model validations were in line with the available system outputs including the number of patients in different categories, resource allocation, patient allocation and average patient waiting times at healthcare providers. A generic metric of resilience proposed in the literature was adapted to be used in healthcare context. Our analysis yielded managerial insights into the emergency planning as follows. In all defined scenarios, the new collaborative care strategy had a considerable impact on improving the resilience and enabled faster return to the pre-disaster state of healthcare network than other strategies. The semi-collaborative care strategy frequently provided the worst resilience in almost all the defined scenarios. However, it provided better resilience than the non-collaborative care strategy when the number of affected patients was relatively small. Even though simulation enabled investigation of the impact of different strategies for collaboration in the network on the resilience, the patient allocation might not be optimal. We developed a mixed integer programming model to address the allocation of patients in collaborative care in which ambulances transport multiple patients to healthcare providers in one trip. The developed model will provide further insights into the collaborative care in disasters management

Using simulation to investigate impact of different approaches to coordination on a healthcare system’s resilience to disasters

Many disasters that have happened in the last decades have caused a shortage of healthcare resources and change in healthcare activities. Coordination of healthcare facilities is one of the emergency medical response strategies to ensure the continued provision of medical services during disasters. The importance of coordination in healthcare systems during disasters is well recognised in the literature, but to the best of our knowledge there has been no review of the published research in this area. In this thesis, a focused literature review of models for the coordination in the healthcare system is provided. Additionally, measures of coordination effectiveness that denote resilience are discussed. In the field of medical management, there are two types of coordination including integrative care and collaborative care. Both types of coordination aim to improve the emergency medical response by ensuring the continuity of medical services and improving healthcare capability during disasters. Integrative care mainly investigates the resource allocation within a common governance, whereas collaborative care is mainly focused on the sharing of healthcare resources across governances. Thus, integrative care is mainly implemented within a healthcare provider setting, while collaborative care is mainly implemented between the settings. However, resilience is usually perceived at community level rather than at an individual institution when responding to disasters. Improving resilience during disasters requires the capability of different healthcare providers, which can be achieved by collaborative care, rather than integrative care. In addition, the literature has commonly addressed collaborative care using optimisation approach, not simulation approach. In this regard, this study presents simulation models for resilience of the healthcare network during disasters. In collaboration with the health authorities and medical staff in Thailand who experienced a number of disasters we investigated real-world activities that took place in emergency medical responses. We developed novel discrete event simulation models of collaboration in an emergency medical response in a healthcare network during disasters with the aim to improve the resilience of the healthcare network. Three strategies for collaboration in the healthcare network were defined including non-collaborative care, semi-collaborative care, and a new proposed collaborative care. Non-collaborative care strategy was in place in response to Tsunami in Phuket in 2004, while semi-collaborative care strategy is the current strategy which was implemented during the boat capsizing in Phuket in 2018. We propose a new collaborative care strategy which is defined by considering the disadvantages of the current semi-collaborative care strategy. It addresses a new collaboration in the network that enables information sharing and the classification of healthcare providers. The strategies differ with respect to the first treatment provision of patients, sharing of resources, and patient transportation The simulation models were validated and verified by using the boat capsizing real-world event. The model validations were in line with the available system outputs including the number of patients in different categories, resource allocation, patient allocation and average patient waiting times at healthcare providers. A generic metric of resilience proposed in the literature was adapted to be used in healthcare context. Our analysis yielded managerial insights into the emergency planning as follows. In all defined scenarios, the new collaborative care strategy had a considerable impact on improving the resilience and enabled faster return to the pre-disaster state of healthcare network than other strategies. The semi-collaborative care strategy frequently provided the worst resilience in almost all the defined scenarios. However, it provided better resilience than the non-collaborative care strategy when the number of affected patients was relatively small. Even though simulation enabled investigation of the impact of different strategies for collaboration in the network on the resilience, the patient allocation might not be optimal. We developed a mixed integer programming model to address the allocation of patients in collaborative care in which ambulances transport multiple patients to healthcare providers in one trip. The developed model will provide further insights into the collaborative care in disasters management