Development of Heuristic Approaches for Last-Mile Delivery TSP with a Truck and Multiple Drones

Unmanned Aerial Vehicles (UAVs) are gaining momentum in many civil and military sectors. An example is represented by the logistics sector, where UAVs have been proven to be able to improve the efficiency of the process itself, as their cooperation with trucks can decrease the delivery time and reduce fuel consumption. In this paper, we first state a mathematical formulation of the Travelling Salesman Problem (TSP) applied to logistic routing, where a truck cooperates synchronously with multiple UAVs for parcel delivery. Then, we propose, implement, and compare different sub-optimal routing approaches to the formulated mFSTSP (multiple Flying Sidekick Travelling Salesman Problem) since the inherent combinatorial computational complexity of the problem makes it unattractable for commercial Mixed-Integer Linear Programming (MILP) solvers. A local search algorithm, two hybrid genetic algorithms that permutate feasible and infeasible solutions, and an alternative ad-hoc greedy method are evaluated in terms of the total delivery time of the output schedule. For the sake of the evaluation, the savings in terms of delivery time over the well-documented truck-only TSP solution are investigated for each proposed routing solution, and this is repeated for two different scenarios. Monte Carlo simulations corroborate the results

An Overview of Drone Energy Consumption Factors and Models

At present, there is a growing demand for drones with diverse capabilities that can be used in both civilian and military applications, and this topic is receiving increasing attention. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to this, it appears that it is necessary to identify the factors affecting the energy consumption of the unmanned air vehicle (UAV) during the mission process, as well as examine the general factors that influence the consumption of energy. This chapter aims to provide an overview of the current state of research in the area of UAV energy consumption and provide general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models

Developing a Vans-and-Drones System for Last-Mile Delivery

The e-commerce industry is experiencing rapid growth, and growing customer expectations and demand challenges the industry to find more cost-efficient ways of performing the last-mile deliveries. Drones have in recent years been a hot topic, and with high versatility and several application areas it may be the answer to the challenge. In this project a Vans-and-Drones System for Last-Mile Delivery have been developed considering effective task allocation and route scheduling. A literature review is presented on the topic of drone technology and application areas, especially emphasizing utilization of drones in logistic operations and routing problems. A mathematical model for the Vehicle Routing Problem with Drones is derived based on the classical Capacitated Vehicle Routing Problem, and the formulation is modeled in Jupyter Notebook with Python programming language and solved with CPLEX solver. A case study is carried out to examine the effects of integrating drones into the delivery system for a vaccine distribution scenario in a sparsely populated area, Ofoten region, considering vehicle employment cost, delivery time and emission impact. Results show that the proposed vans-and-drones system outperforms a truck-only delivery system for this purpose

트럭을 이동형 드론 기지로 사용하는 한정용량 트럭-드론 경로 배정 문제

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Drones initially received attention for military purposes as a collective term for unmanned aerial vehicles (UAVs), but recently, efforts to use them in logistics have been actively underway. If drones are put into places where low-weight and high-value items are currently difficult to deliver by existing delivery means, it will have the effect of greatly reducing costs. However, the disadvantages of drones in delivery are also clear. In order to improve the delivery capacity of drones, the size of drones must increase when drones are equipped with large-capacity batteries. This thesis introduced two methods and presented algorithms for each method among VRP-D. First of all, CVP-D is a method in which carriers such as trucks and ships with large capacity and slow speed carry robots and drones with small capacity. Next, in the CVRP-D, the vehicle and the drone move different paths simultaneously, and the drone can visit multiple nodes during one sortie. The two problems are problems in which restrictions are added to the vehicle route problem (VRP), known as the NP-hard problem. The algorithm presented in this study derived drone-truck routes for two problems within a reasonable time. In addition, sensitivity analysis was conducted to observe changes in the appropriate network structure for the introduction of drone delivery and the main parameters of the drone. In addition, the validity of the proposed algorithm was verified through comparison with the data used as a benchmark in previous studies. These research results will contribute to the creation of delivery routes quickly, considering the specification of a drone.드론은 무인항공기(UAV)의 통칭으로 초기에는 군사적 목적으로 주목을 받았으나 최근 물류에서 사용하려는 노력이 적극적으로 진행되고 있다. 드론이 저중량-고가치 물품을 배송에서 현재 기존 배송수단에 의해 배송이 어려운 곳에 투입이 된다면 큰 비용절감의 효과가 있을 것이다. 하지만 배송에 있어서 드론의 단점도 명확하다. 드론의 배송능력을 향상시키기 위해서는 드론이 대용량 배터리를 탑재하면 드론 크기가 증가하여야 한다. 이러한 단점을 극복하기 위해서 드론과 트럭을 결합하여 운영하는 방식이 연구되어왔다. 이러한 방식 중 본 연구에서는 두 가지 방식을 소개하고, 각각의 방식에 대한 알고리즘을 제시하였다. 먼저, CVP-D는 용량이 크고 속도가 느린 트럭이나 배 등의 캐리어가 용량이 작은 로봇, 드론 등을 싣고 다니면서 배송을 하는 방식이다. 다음으로, CVRP-D는 차량과 드론이 동시에 각기 다른 경로를 이동하며, 드론은 1회 비행(sortie)시 다수의 노드를 방문하는 것이 가능하다. 두 문제는 차량경로문제(VRP)에 제약이 더해진 문제이다. VRP는 대표적인 NP-hard 문제로 해를 구하기 위해서 휴리스틱 알고리즘이 요구된다. 본 연구에서 제시하는 알고리즘은 합리적인 시간 내 두문제의 드론-트럭 경로를 도출하였다. 또한 민감도 분석을 실시하여 드론 배송 도입을 위한 적절한 네트워크 구조 및 드론의 주요 파라미터에 변화에 따른 변화를 관찰하였다. 이는 차후 드론의 성능에 관한 의사결정 시 고려해야 할 요소들에 대한 기준이 될 수 있을 것으로 기대된다. 또한 선행연구에서 벤치마크로 사용되는 데이터와의 비교를 통해 제안하는 알고리즘의 타당성을 검증하였다. 본 연구는 드론 도입이 배송시간을 감소시키며, 운영방법에 따라서 배송시간의 차이가 발생함을 보였다. 이러한 연구 성과는 드론 배송 시 환경과 기계적 성능을 고려한 배송 경로를 단시간내 생성하여 상업적으로 이용가능 할 것이다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Purpose 3 1.3 Contribution of Research 4 Chapter 2. Literature review 5 2.1 Vehicle Routing Problems with Drone 5 2.2 Carrier Vehicle Problem with Drone(CVP-D) 10 2.3 Capacitated VRP with Drone(CVRP-D) 12 Chapter 3. Mathematical Formulation 14 3.1 Terminology 14 3.2 CVP-D Formulation 15 3.3 CVRP-D Formulation 19 Chapter 4. Proposed Algorithms 23 4.1 Heuristic Algorithm 23 4.1.1 Knapsack Problem 23 4.1.2 Parallel Machine Scheduling (PMS) 25 4.1.3 Set Covering Location Problem (SCLP) 27 4.1.4 Guided Local Search (GLS) Algorithm 28 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 39 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 42 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53석

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

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