No abstract

Highlights• We addressed the scheduling and routing of a repair crew after a disaster.• We present a dynamic programming model that solves small/mid-sized problems • We develop a IGRCP procedure to solve large problem instances efficiently.• Our work has societal impact as it helps to efficiently repair a network damaged by a disaster.• Considering the routing of the repair crew makes the problem more realistic AbstractEvery year, hundreds of thousands of people are affected by natural disasters. The number of casualties is usually increased by lack of clean water, food, shelter, and adequate medical care during the aftermath. One of the main problems influencing relief distribution is the state of the post-disaster road network. In this paper, we consider the problem of scheduling the emergency repair of a rural road network that has been damaged by the occurrence of a natural disaster. This problem, which we call the Network Repair Crew Scheduling and Routing Problem addresses the scheduling and routing of a repair crew optimizing accessibility to the towns and villages that demand humanitarian relief by repairing roads. We develop both an exact dynamic programming (DP) algorithm and an iterated greedy-randomized constructive procedure to solve the problem and compare the performance of both approaches on small-to medium-scale instances. Our numerical analysis of the solution structure validates the optimization model and provides managerial insights into the problem and its solutions.

Planning a trip with an electric vehicle requires consideration of both battery dynamics and the availability of charging infrastructure. Recharging costs for an electric vehicle, which increase as the battery's charge level increases, are fundamentally different than refueling costs for conventional vehicles, which do not depend on the amount of fuel already in the tank. Furthermore, the viability of any route requiring recharging is sensitive to the availability of charging stations along the way. In this paper, we study the problem of finding an optimal adaptive routing and recharging policy for an electric vehicle in a network. Each node in the network represents a charging station and has an associated probability of being available at any point in time or occupied by another vehicle. We develop efficient algorithms for finding an optimal a priori routing and recharging policy and then present solution approaches to an adaptive problem that build on a priori policy. We present two heuristic methods for finding adaptive policies-one with adaptive recharging decisions only and another with both adaptive routing and recharging decisions. We then further enhance our solution approaches to a special case of grid network. We conduct numerical experiments to demonstrate the empirical performance of our solutions.

Purpose Following a large-scale disaster, medical assistance is a critical component of the emergency response. The paper aims to discuss this issue. Design/methodology/approach Academic and practitioner literature is used to develop a framework studying the effectiveness of the humanitarian medical supply chain (HMSC). The framework is validated by using the findings of interviews conducted with experts and the case study of a serious humanitarian medical crisis (Ebola outbreak in 2014). Findings The factors affecting the effectiveness of the HMSC are identified. Research limitations/implications To get an expert opinion on the major logistical challenges of the medical assistance in emergencies only 11 interviews with practitioners were conducted. Originality/value While the existing academic literature discusses the distribution of various supplies needed by the affected population, limited research focuses specifically on studying the HMSC aspect of the response. This paper closes this gap by describing the HMSC in the case of disaster response, and identifying the factors affecting its effectiveness, especially focusing on the factors that are unique to the medical aspect of the humanitarian supply chain.

In this paper we consider the optimization of a functional F defined as the convolution of a function f with a Gaussian kernel. We propose this type of objective function for the optimization of the output of complex computational simulations, which often present some form of deterministic noise and need to be smoothed for the results to be meaningful. We introduce a derivative-free algorithm that computes trial points from the minimization of a regression model of the noisy function f over a trust region. The regression model is constructed from function values at sample points that are chosen randomly around iterates and trial points of the algorithm. The weights given to the individual sample points in the regression problem are obtained according to an adaptive multiple importance sampling strategy. This has two advantages. First, it makes it possible to re-use all noisy function values collected over the course of the optimization. Second, the resulting regression model converges to the second-order Taylor approximation of the convolution functional F. We prove that, with probability one, each limit point of the iterates is a stationary point of F. Computational experiments on a set of benchmark problems with noisy functions compare the proposed algorithm with the deterministic derivative-free trust-region method the proposed method is based on. It is demonstrated that the proposed algorithm performs similarly efficiently in the early stages of the optimization and is able to overcome convergence problems of the original method, which might get trapped in spurious local minima induced by the noise.