Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand

Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose two-echelon vehicle routing frameworks for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed frameworks, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present two decision frameworks, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem. The first framework addresses the problem in two stages: providing telecommunications capabilities in the first stage and satisfying the resulting demands in the second. To that end, two types of drones are considered. Hotspot drones have the capability of providing cell phone and internet reception, and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. The second framework, on the other hand, addresses the problem as a stochastic emergency aid delivery problem, which uses a two-stage robust optimization model to handle demand uncertainty. To solve the resulting models, we propose efficient and novel solution approaches

Route activity tracking and management using available technology

Small organizations that maintain their own fleet and make their own deliveries are responsible for ensuring their drivers are utilizing the most efficient routes while delivering products to their customers. Furthermore, efficient delivery requires that drivers spend as little time as possible dropping off and picking up products, since these activities are referred to as “non-value added activities,� although they are necessary tasks in the order cycle process. To aid in reducing order cycle times, large organizations that can afford it have employed transportation management systems. Unfortunately, small organizations with limited resources are less likely to adopt transportation management systems, despite the need for such automation. One solution is to use available productivity software to track and manage driver route activity in an effort to improve and maintain driver productivity by reducing non-value time and identifying optimal routes. This paper will outline how office productivity software such as Microsoft® Access can meet the needs of small organizations with limited resources by describing the development and use of a route activity database that employs an easy-to-use multi-user interface. This paper also includes the details of the underlying infrastructure and the user interface

Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood

The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest. Yet, despite extensive methodological progress, many recent studies are hampered by the limited access to simple and efficient open-source solution methods. Given the sophistication of current algorithms, reimplementation is becoming a difficult and time-consuming exercise that requires extensive care for details to be truly successful. Against this background, we use the opportunity of this short paper to introduce a simple -- open-source -- implementation of the hybrid genetic search (HGS) specialized to the capacitated vehicle routing problem (CVRP). This state-of-the-art algorithm uses the same general methodology as Vidal et al. (2012) but also includes additional methodological improvements and lessons learned over the past decade of research. In particular, it includes an additional neighborhood called SWAP* which consists in exchanging two customers between different routes without an insertion in place. As highlighted in our study, an efficient exploration of SWAP* moves significantly contributes to the performance of local searches. Moreover, as observed in experimental comparisons with other recent approaches on the classical instances of Uchoa et al. (2017), HGS still stands as a leading metaheuristic regarding solution quality, convergence speed, and conceptual simplicity

Two agent-based approaches for solving multi-objective multi-constraint optimization problems

Master'sMASTER OF SCIENC

Optimization of Airfield Parking and Fuel Asset Dispersal to Maximize Survivability and Mission Capability Level

While the US focus for the majority of the past two decades has been on combatting insurgency and promoting stability in Southwest Asia, strategic focus is beginning to shift toward concerns of conflict with a near-peer state. Such conflict brings with it the risk of ballistic missile attack on air bases. With 26 conflicts worldwide in the past 100 years including attacks on air bases, new doctrine and modeling capacity are needed to enable the Department of Defense to continue use of vulnerable bases during conflict involving ballistic missiles. Several models have been developed to date for Air Force strategic planning use, but these models have limited use on a tactical level or for civil engineer use. This thesis presents the development of a novel model capable of identifying base layout characteristics for aprons and fuel depots to maximize dispersal and minimize impact on sortie generation times during normal operations. This model is implemented using multi-objective genetic algorithms to identify solutions that provide optimal tradeoffs between competing objectives and is assessed using an application example. These capabilities are expected to assist military engineers in the layout of parking plans and fuel depots that ensure maximum resilience while providing minimal impact to the user while enabling continued sortie generation in a contested region

An investigation of multilevel refinement in routing and location problems

Multilevel refinement is a collaborative hierarchical solution technique. The multilevel technique aims to enhance the solution process of optimisation problems by improving the asymptotic convergence in the quality of solutions produced by its underlying local search heuristics and/or improving the convergence rate of these heuristics. To these aims, the central methodologies of the multilevel technique are filtering solutions from the search space (via coarsening), reducing the amount of problem detail considered at each level of the solution process and providing a mechanism to the underlying local search heuristics for efficiently making large moves around the search space. The neighbourhoods accessible by these moves are typically inaccessible if the local search heuristics are applied to the un-coarsened problems. The methodologies combine to meet the multilevel technique's aims, because, as the multilevel technique iteratively coarsens, extends and refines a given problem, it reduces the possibility of the local search heuristic becoming trapped in local optima of poor quality. The research presented in this thesis investigates the application of multilevel refinement to classes of location and routing problems and develops numerous multilevel algorithms. Some of these algorithms are collaborative techniques for metaheuristics and others are collaborative techniques for local search heuristics. Additionally, new methods of coarsening for location and routing problems and enhancements for the multilevel technique are developed. It is demonstrated that the multilevel technique is suited to a wide array of problems. By extending the investigations of the multilevel technique across routing and location problems, the research was able to present generalisations regarding the multilevel technique's suitability, for these and similar types of problems. Finally, results on a number of well known benchmarking suites for location and routing problem are presented, comparing equivalent single-level and multilevel algorithms. These results demonstrate that the multilevel technique provides significant gains over its single-level counterparts. In all cases, the multilevel algorithm was able to improve the asymptotic convergence in the quality of solutions produced by the standard (single-level) local search heuristics or metaheuristics. The multilevel technique did not improve the convergence rate of the single-level's local search heuristics in all cases. However, for large-scale problems the multilevel variants scaled in a manner superior to the single-level techniques. The research also demonstrated that for sufficiently large problems, the multilevel technique was able to improve the asymptotic convergence in the quality of solutions at a sufficiently fast rate, such that the multilevel algorithms were able to produce superior results compared to the single-level versions, without refining the solution down to the most detailed level

The method for evaluation of efficiency of the concept of centrally managed distribution in cities

The paper describes proecological solution dedicated for organizing logistics services in urban areas. Proposed solution is based on cross-docking processes combined with consolidation centres. Authors proposed new method of estimating economic and social benefits from implementing centrally managed cooperation of logistics operators using common city consolidation hubs. Developed mathematical model bases on Vehicle Routing Problem (VRP) with vehicles of different types, limited loading capacities and multiply depots characterized by limited throughput. Proposed approach was supported by case study of integration of distribution processes in Warsaw (Poland) performed by three medium-size logistics operators. The central management of distribution was investigated in variants assuming using existing warehouses and with new configuration of logistics network developed with using SIMMAG 3D tools. As it was proved for analysed case, total costs of distribution in the city after implementation of centrally managed distribution were reduced by 8.1% for variant with current depots and by 26.5% for variant with new logistics network, while emission of carbon monoxide (CO) was reduced respectively by 7.8 and 16.7%

An auction for collaborative vehicle routing: Models and algorithms

Increasing competition and expectations from customers pressures carriers to further improve efficiency. Forming collaborations is essential for carriers to reach their targeted efficiency levels. In this study, we investigate an auction mechanism to facilitate collaboration amongst carriers while maintaining autonomy for the individual carriers. Multiple auction implementations are evaluated. As the underlying decision problem (which is a traditional vehicle routing problem) is known to be NP-hard, this auction mechanism has an important inherent complexity. Therefore, we use fast and efficient algorithms for the vehicle routing problem to ensure that the auction can be used in operational decision making. Numerical results are presented, indicating that the auction achieves a savings potential better than the thus far reported approaches in the literature. Managerial insights are discussed, particularly related to the properties of the auction and value of the information

Innovative Hybrid Approaches for Vehicle Routing Problems

This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs). The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality. The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community

Multi-Objective Optimization of Green Transportation Operations in Supply Chain Management

Supply chain is the integration of manufacturing process where raw materials are converted into final products, then delivered to customers. Supply chains consists of two basic integrated process that interact together: (1) production and inventory and (2) distribution and logistics. Maximizing competitiveness and profitability are of the main goals of a supply chain. Accounting only for economic impacts as variable and fixed costs does not serve the main goal of the supply chain. Therefore, considering customer satisfaction measures in distribution models is essential in supply chain management. Models that addressed the three objectives simultaneously handled one of the objectives as a constraint with a certain threshold in the problem, while others used weighted utility functions to address the problem objective in deterministic environment. This thesis focuses on the multi-objective Vehicle Routing Problem (VRP) in green environment. The proposed Green VRP (GVRP) deals with three different objectives simultaneously that considers economic, environmental, and social aspects. A new hybrid search algorithm to solve the capacitated VRP is presented and validated in Chapter 2. The developed algorithm combines the evolutionary genetic search with a new local search heuristic that considers both locations and demand quantities of the nodes to be visited in routing decisions, not just the distances travelled. The algorithm is then used to solve the multi-objective GVRP in Chapter 3. The objectives of the developed GVRP model are minimizing the total transportation operations cost, minimizing the fuel consumption, and maximizing customer satisfaction. Moreover, a new overlap index is developed to measure the amount of overlap between customers’ time windows that provides an indication of how tight/constrained the problem is. The model is then adapted to consider the uncertainty in travel times, service times, and unpredictable demands of customers in Chapter 4. Pareto fronts were obtained and trade-offs between the three objectives are presented in both deterministic and stochastic forms. Furthermore, analysis of the effects of changing vehicle capacity and customer time windows relaxation are presented