Evacuation routing optimizer (EROP) / Azlinah Mohamed … [et_al.]

This report presents the solution to the two of the most critical processes in planning for flash Hood evacuation: the evacuation vehicle assignment problem (EVAP) and the evacuation vehicle routing problem (EVRP). With these solutions, the evacuation routing optimizer (EROP) is constructed. The EVAP is firstly solved, followed by the EVRP. For EVAP, discrete particle position is proposed to support the implementation of discrete particle swarm optimization called myDPSOVAP-A. Particle positions are initially calculated based on the average passenger capacity of each evacuation vehicle. We experiment with different numbers of the potential flooded areas (PFA) using two types of sequences for vehicle capacity; random and sort ascending order. Both of these sequences are tested with different inertia weights, constriction coefficients (CF), and acceleration coefficients. We analyse the performance of each vehicle allocation in four experiment categories: myDPSOVAP-A using inertia weight with random vehicle capacity, myDPSOVAP-A using inertia weight with sort ascending order of vehicle capacity; myDPSOVAP-A using CF with random vehicle capacity, and myDPSOVAP-A using CF with sort ascending of vehicle capacity. Flash flood evacuation datasets from Malaysia are used in the experiment. myDPSOVAP-A using inertia weight with random capacity was found to give the best results for both random and sort ascending order of vehicle capacity. Solutions reached by analyses with CF random and inertia weight sorted in ascending order were shown to be competitive with those obtained using inertia weight with random capacity. Overall, myDPSOVAP-A outperformed both a genetic algorithm with random vehicle capacity and a genetic algorithm with sort ascending order of vehicle capacity in solving the EVAP. Consequently EVRP, myDPSOVRPl is modified and named as myDPSO_VRP_2, adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Computational experiments involve EVRP dataset from road network for flash flood evacuation in Johor State, Malaysia. The myDPSOVRPl and myDPSO_VRP_2 are respectively compared with a genetic algorithm (GA) using solution mapping for EVRP. The results indicate that the proposed myDPSO_VRP_2 are highly competitive and show good performance in both fitness value and processing time. Overall, DPSOVRP2 and myDPSOVAP-A which are the main component in the EROP gave good performance in maximizing the number of people to vehicles and minimizing the total travelling time from vehicle location to PFA. EROP was embedded with the DPSOVRP2 and retrieved the generated capacitated vehicles from the myDPSOVAP-A. EROP is also accommodated with the routing of vehicles from PFA to relief centres to support the whole processes of the evacuation route planning

Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

An Agent-Based Approach to Self-Organized Production

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively ("socially"), the blockings in the system are reduced. A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which gives conclusions about the consequencies for the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c

An Integrated Contraflow Strategy for Multimodal Evacuation

To improve the efficiency of multimodal evacuation, a network aggregation method and an integrated contraflow strategy are proposed in this paper. The network aggregation method indicates the uncertain evacuation demand on the arterial subnetwork and balances accuracy and efficiency by refining the local road subnetworks. The integrated contraflow strategy contains three arterial configurations: noncontraflow to shorten the strategy setup time, full-lane contraflow to maximize the evacuation network capacity, and bus contraflow to realize the transit cycle operation. The application of this strategy takes two steps to provide transit priority during evacuation: solve the transit-based evacuation problem with a minimum-cost flow model, firstly, and then address the auto-based evacuation problem with a bilevel network flow model. The numerical results from optimizing an evacuation network for a super typhoon justify the validness and usefulness of the network aggregation method and the integrated contraflow strategy

Visual analytics approach to user-controlled evacuation scheduling

Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster-affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information

A social vulnerability-based genetic algorithm to locate-allocate transit bus stops for disaster evacuation in New Orleans, Louisiana

In the face of severe disasters, some or all of the endangered residents must be evacuated to a safe place. A portion of people, due to various reasons (e.g., no available vehicle, too old to drive), will need to take public transit buses to be evacuated. However, to optimize the operation efficiency, the location of these transit pick-up stops and the allocation of the available buses to these stops should be considered seriously by the decision-makers. In the case of a large number of alternative bus stops, it is sometimes impractical to use the exhaustive (brute-force) search to solve this kind of optimization problem because the enumeration and comparison of the effectiveness of a huge number of alternative combinations would take too much model running time. A genetic algorithm (GA) is an efficient and robust method to solve the location/allocation problem. This thesis utilizes GA to discover accurately and efficiently the optimal combination of locations of the transit bus stop for a regional evacuation of the New Orleans metropolitan area, Louisiana. When considering people’s demand for transit buses in the face of disaster evacuation, this research assumes that residents of high social vulnerability should be evacuated with high priority and those with low social vulnerability can be put into low priority. Factor analysis, specifically principal components analysis, was used to identify the social vulnerability from multiple variables input over the study area. The social vulnerability was at the census block group level and the overall social vulnerability index was used to weight the travel time between the centroid of each census block to the nearest transit pick-up location. The simulation results revealed that the pick-up locations obtained from this study can greatly improve the efficiency over the ones currently used by the New Orleans government. The new solution led to a 26,397.6 (total weighted travel time for the entire system measured in hours) fitness value, which is much better than the fitness value 62,736.3 rendered from the currently used evacuation solution

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event