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

The multi-depot k-traveling repairman problem

In this paper, we study the multi-depot k-traveling repairman problem. This problem extends the traditional traveling repairman problem to the multi-depot case. Its objective, similar to the single depot variant, is the minimization of the sum of the arrival times to customers. We propose two distinct formulations to model the problem, obtained on layered graphs. In order to find feasible solutions for the largest instances, we propose a hybrid genetic algorithm where initial solutions are built using a splitting heuristic and a local search is embedded into the genetic algorithm. The efficiency of the mathematical formulations and of the solution approach are investigated through computational experiments. The proposed models are scalable enough to solve instances up to 240 customers

A multi-period location-allocation model for nursing home network planning under uncertainty

This paper proposes a multi-period location- allocation problem arising in nursing home network planning. We present a strategic model in which the improvement of service accessibility through the planning horizon is appropriately addressed. Unlike previous research, the proposed model modifies the allocation pattern to prevent unacceptable deterioration of the accessibility criterion. In addition, the problem is formulated as a covering model in which the capacity of facilities as well as the demand elasticity are considered. The uncertainty in demands within each time period is captured by adopting a distributionally robust approach. The model is then applied to a real case study for nursing home planning network in Shiraz city, Iran

The distributionally robust machine scheduling problem with job selection and sequence-dependent setup times

This paper proposes an interesting variant of the parallel machine scheduling problem with sequence-dependent setup times, where a subset of jobs has to be selected to guarantee a minimum profit level while the total completion time is minimized. The problem is addressed under uncertainty, considering both the setup and the processing times as random parameters. To deal with the uncertainty and to hedge against the worst-case performance, a risk-averse distributionally robust approach, based on the conditional value-at-risk measure, is adopted. The computational complexity of the problem is tackled by a hybrid large neighborhood search metaheuristic. The efficiency of the proposed method is tested via computational experiments, performed on a set of benchmark instances

Addressing the Challenges of Last-mile: The Drone Routing Problem with Shared Fulfillment Centers

With the easing of restrictions worldwide, drones will become a preferred transportation mode for last-mile deliveries in the coming years. Drones offer, in fact, an optimal solution for many challenges faced with last-mile delivery as congestion and emissions and can streamline the last leg of the supply chain. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision, among which the limited drone range and payload. To overcome this issue, big companies such as Amazon, are already filing up patents for the development of fulfilment centers where drones can be restocked before flying out again for another delivery, effectively extending their range. Only a few authors have addressed the joint problem of operating these facilities and providing services to retail companies. This paper addresses this problem and proposes a mathematical formulation to show the viability of the proposed approach

A hybrid reactive GRASP heuristic for the risk-averse k-traveling repairman problem with profits

This paper addresses the k-traveling repairman problem with profits and uncertain travel times, a vehicle routing problem aimed at visiting a subset of customers in order to collect a revenue, which is a decreasing function of the uncertain arrival times. The introduction of the arrival time in the objective function instead of the travel time, which is common in most vehicle routing problems, poses compelling computational challenges, emphasized by the incorporation of the stochasticity in travel times. For tackling the solution of the risk-averse k-traveling repairman problem with profits, in this paper is proposed a hybrid heuristic, where a reactive greedy randomized adaptive search procedure is used as a multi-start framework, equipped with an adaptive local search algorithm. The effectiveness of the solution approach is shown through an extensive experimental phase, performed on a set of instances, generated from three sets of benchmark instances containing up to 200 nodes

The Electric Vehicle Route Planning Problem with Energy Consumption Uncertainty

Electric freight vehicles (EVs) are a sustainable alternative to conventional internal combustion freight vehicles. The driving autonomy of EVs is a fundamental component in the planning of EV routes for goods distribution. In this respect, a complicating factor lies in the fact that EVs' energy consumption is subject to a great deal of uncertainty, which is due to a number of endogenous and exogenous factors. Ignoring such uncertainties in the planning of EV routes may lead a vehicle to run out of energy, which-given the scarcity of recharging stations-may have dire effects. Thus, to foster a widespread use of EVs, we need to adopt new routing strategies that explicitly account for energy consumption uncertainty. In this paper, we propose a new two-stage stochastic programming formulation for the single electric vehicle routing problem with stochastic energy consumption. Furthermore, we develop a decomposition algorithm for this problem. We provide an illustrative example showing the added value of incorporating uncertainty in the route planning process. We perform a variety of computational experiments and show that our decomposition algorithm is capable of efficiently solving instances with 20 customers and 30 scenarios

The selective minimum latency problem under travel time variability: An application to post-disaster assessment operations

In this paper, we consider a new selective routing problem, where a subset of customers should be serviced by a limited fleet of vehicles with the aim of minimizing the total latency. A service level constraint is added to guarantee that a minimum system performance is achieved. Assuming that the travel times are uncertain, we address the problem through a mean-risk approach. The inclusion of risk in the objective function makes the problem computationally challenging. To solve it, we propose an efficient heuristic, relying on a variable neighbourhood search mechanism, able to strike the balance between service level and latency. A detailed discussion of the model, which includes simulation tests and a sensitivity analysis, is carried out to illustrate the applicability of our approach in a post-disaster scenario, taking as a case study the Haiti earthquake in 2010. Additional computational experiments show that the proposed heuristic is effective for this difficult problem and often matches optimal solutions for small and medium-scale benchmark instances

A heuristic Approach for the k-Traveling Repairman Problem with Profits under Uncertainty

This paper addresses the k-traveling repairman problem with profits under uncertain travel times, a new vehicle routing problem aimed at visiting a subset of customers in order to collect a revenue, defined as decreasing function of the uncertain arrival times. We adopt a risk-averse approach, enabling the decision maker to manage and control risk, and develop a mean-risk model in which only the first and the second moment of the travel times distribution are required to be known. We propose an adaptive local search heuristic in which, in each iteration, a Greedy Randomized Adaptive Search Procedure is used to generate the initial solution. The effectiveness of the solution approach is shown by the computational experiments performed on a set of instances