a fast heuristic for routing in post disaster humanitarian relief logistics

Abstract In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

A fast heuristic for routing in post-disaster humanitarian relief logistics

In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

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

Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining

Recently, hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques, where frequent patterns found in high-quality solutions can lead to an efficient exploration of the search space, along with a significant reduction of computational time. In this work, a GRASP-based state-of-the-art heuristic for the Minimum Latency Problem (MLP) is improved by means of data mining techniques for two MLP variants. Computational experiments showed that the approaches with data mining were able to match or improve the solution quality for a large number of instances, together with a substantial reduction of running time. In addition, 88 new cost values of solutions are introduced into the literature. To support our results, tests of statistical significance, impact of using mined patterns, equal time comparisons and time-to-target plots are provided.Comment: This document is a dissertation fil

Tactical design of last mile logistical systems

Tactical Design of Last Mile Logistical Systems Alexander M. Stroh 161 Pages Directed by Dr. Alan Erera and Dr. Alejandro Toriello This dissertation consists of three distinct logistical topics, unified by a focus on the intelligent design of last mile logistical systems at a tactical level. The three design problems all arise within package delivery supply chains, though the mathematical models and solution techniques developed in these studies can be applied to other logistics systems. We propose models that do not attempt to capture granular minute by minute operational decision making, but rather, system behavior on average so that we may approximate the impact of various design choices. In Chapter 2, we study tactical models for the design of same-day delivery (SDD) systems. While previous literature includes operational models to study SDD, they tend to be detailed, complex, and computationally difficult to solve. Thus, such models may not provide any insight into tactical SDD design variables and their impact on the average performance of the system. We propose a simplified vehicle dispatching model that captures the average behavior of an SDD system from a single depot location by utilizing continuous approximation techniques. We analyze the structure of vehicle dispatching policies given by our model for various families of problem instances and develop techniques to find optimal dispatching policies that require only simple computations. Our models can help answer various tactical design questions including how to select a fleet size, determine an order cutoff time, and combine SDD and overnight order delivery operations. In Chapter 3, we study the tactical optimization of SDD systems under the assumption that service regions are allowed to vary over the course of each day. In most existing studies of last mile logistics problems, service regions are assumed to be static. Service regions which are designed too small or cutoff SDD availability too soon may potentially lose SDD market share, while regions which are designed too large or accept orders too late may result in costly operations or failed deliveries, resulting in a loss of customer goodwill. We use a continuous approximation approach to capture average system behavior and derive optimal dynamic service region areas and tactical vehicle dispatching policies which maximize the expected number of SDD orders served per day. Furthermore, we compare such designs to fixed service region designs or capacitated service region designs. In Chapter 4, we introduce the concept of cycle time considering capacitated vehicle routing problems, which are motivated by the desire to decrease the average time packages spent within a delivery network. Traditional vehicle routing models focus on the resource usage of the system whereas our models instead consider the impact of routing policies on the units being served. We explicitly consider pre-routing waiting times at a depot, total demand-weighted accumulated routing times, vehicle capacity constraints, and designing repeatable delivery routes in our models. We present two set partitioning formulations for such problems and derive efficient solution techniques so that the impact of various design parameters can be assessed.Ph.D