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An adaptive large neighborhood search metaheuristic for a passenger and parcel share-a-ride problem with drones
... Yuan et al. [15] proposed a two-stage model combining railway freight with trucking, improving the economic feasibility of long-distance transport by optimizing railway bidding strategies and refining truck-based short-distance delivery routes, thereby achieving synergistic gains in efficiency and profitability. Cheng et al. [16] introduced a dual-tier delivery approach that integrates public transit with drones, where buses transport both goods and passengers and drones launch and land on bus rooftops to enable flexible delivery, significantly enhancing transport efficiency. While most studies focus on improving delivery efficiency and reducing empty load rates, research on environmental impacts and cargo loss remains limited. ...
... Equation (15) represents the objective function, which aims to minimize the total distribution costs of a heterogeneous fleet under the segmented transshipment mode. Equations (16) and (17) set the maximum load capacity constraints for fuel-powered and electric vehicles, respectively. Equations (18) and (19) ensure that each node is serviced exactly once. ...
To address the challenges of environmental impact and distribution efficiency in fresh food logistics, a segmented transshipment model involving the coordinated operation of gasoline and electric vehicles is proposed. The model minimizes total distribution costs by considering transportation, refrigeration, product damage, carbon emissions, and penalties for time window violations. The k-means++ clustering algorithm is used to determine transshipment points, while an improved adaptive multi-objective ant colony optimization algorithm (IAMACO) is employed to optimize the delivery routes for the heterogeneous fleet. The case study results show that compared to the traditional model, the segmented transshipment mode reduces the total distribution costs, carbon emissions, and time window penalty costs by 22.13%, 28.32%, and 41.08%, respectively, providing a viable solution for fresh food logistics companies to achieve sustainable and efficient growth.
... In this study, we have developed an adaptive large neighborhood search (ALNS) metaheuristic to address the routing problem. The ALNS has proven to be highly effective in solving various vehicle routing problem variants (Ropke and Pisinger, 2006;Li et al., 2016;Cheng et al., 2023b). The ALNS algorithm was implemented in C++. ...
The expansion of e-commerce and the sharing economy has paved the way for crowdshipping as an innovative approach to addressing last-mile delivery challenges. Previous studies and implementations have predominantly concentrated on private vehicle-based crowdshipping, which may lead to increased traffic congestion and emissions due to additional trips made specifically for deliveries. To circumvent these possible adverse effects, this paper explores a public transport (PT)-based crowdshipping concept as a complementary solution to the traditional parcel delivery systems. In this model, PT users leverage their routine journeys to perform delivery tasks. We propose a methodology that includes a parcel locker location model and a vehicle routing model to analyze the effect of PT-based crowdshipping. Notably, the parcel locker location model aids in planning a PT-based crowdshipping network and identifying obstacles to its development. A case study conducted in the central district of Copenhagen utilizing real-world data assesses the effects of PT-based crowdshipping. The findings suggest that PT-based crowdshipping can decrease the total kilometers traveled by vehicles, the overall working hours of drivers, and the number of vans required for last-mile deliveries, thereby alleviating urban traffic congestion and environmental pollution. Nevertheless, the growth of PT-based crowdshipping may be limited by the availability of crowdshippers, indicating that initiatives to increase the number of crowdshippers are essential.
... Metaheuristic techniques have been applied across diverse domains for numerous purposes since they exhibited exceptional performance in general optimization tasks. Some examples include addressing complex challenges in emerging industries [53], healthcare applications [54,55,56], software testing [57,58], engineering [59,60], intrusion detection [61,62,63], finance [64,65], and transportation [66,67,68]. Furthermore, metaheuristics have also shown promising results in time series forecasting including oil prices [69,70], green energy generation and consumption [71,72], air pollution [73,74], gold prices [75,76] and cryptocurrency trends predictions [77,78]. ...
Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge.
Technological progress and evolving business strategies are propelling the advancement of connected, shared, autonomous, green and electric solutions in urban logistics. These solutions' efficiency and sustainability benefit from harnessing the vast data generated by passengers, drivers, and vehicles. This study aims to examine literature on smart urban logistics by reviewing key articles discussing intelligent models for optimizing goods flow management in urban areas. The goal is to promote eco-friendly distribution while mitigating environmental and urban challenges like traffic congestion. After defining urban logistics and last-mile delivery, we present our research methodology, followed by an exploration of smart urban logistics solutions. We then discuss our findings and conclude by highlighting emerging trends and suggesting future research directions.
The promotion of urban mobility by integrating people-and-goods transportation has attracted increasing attention in recent years. Within this framework, diversified forms such as co-modality, freight on transit, and crowdshipping have been proposed, piloted or implemented. The success of the implementation and market penetration depends on not only the novelties of the concept but also the planning and operational efficiency. Thus, a comprehensive review focusing on the operation of integrated people-and-goods transportation systems and associated critical decisions and subproblems is performed. Different practical forms in which people and goods are transported in an integrated manner are identified. The critical decisions associated with each form and subproblem are discussed, along with corresponding models and solution approaches. Notably, because integrated transportation systems are in the early exploration stage at present, new forms are expected to emerge. Therefore, this paper proposes a general framework to realise the planning and operation of new forms in the future. The decisions and subproblems identified from existing forms are fed to the proposed general framework to identify two key research opportunities: to improve or extend existing research and to conduct pioneering research to fill the gaps in the frameworks for operating potential forms of integrated people-and-goods transportation.
This paper is motivated by a non-emergency ambulance transportation service provider that picks up and drops off patients while considering both the time window for medical appointments and the maximum ride-time constraint for each patient. Varying travel times based on departure times further complicates the feasibility evaluation of a given route under both constraints. This problem aims to maximize the net profit which is calculated as the collected reward of serving the selected requests minus the total travel cost of the designed route. The problem is modeled as a time-dependent profitable dial-a-ride problem (TD-PDARP) with a single-vehicle using a mixed-integer linear programming (MILP) model. We propose a tailored feasibility evaluation procedure to handle the complicated maximum ride-time constraint under the time-dependent travel time model, which is then embedded in a hybrid algorithm to solve the proposed problem. This hybrid algorithm leverages an adaptive large neighborhood search (ALNS) for large-scale exploration together with local search (LS) techniques to exploit local regions comprehensively. We evaluate the performance of the proposed algorithm on newly generated TD-PDARP instances. The experiments show that our ALNS-LS algorithm can solve large instances that cannot be solved by commercial solvers in a reasonable time. Furthermore, for all instances that can be solved by the solver within 12 h, our proposed heuristic algorithm is able to obtain the optimal solutions and takes only 1.03% of the average run time required by the solver.
Over the last few years, a large emphasis has been devoted to Autonomous Vehicles (AVs), as vehicle automation promises a large number of benefits such as: improving mobility and minimization of energy and emissions. Additionally, AVs represent a major tool in the fight against pandemics as autonomous vehicles can be used to transport people while maintaining isolation and sterilization. Thus, manufacturers are racing to introduce AVs as fast as possible. However, laws and regulations are not yet ready for this change and the legal sector is following the development of autonomous vehicles instead of taking the lead. This paper provides a comprehensive review of the previous studies in the transportation field that involve AVs with the aim of exploring the implications of AVs on the safety, public behavior, land use, economy, society and environment, public health, and benefits of autonomous vehicles in fighting pandemics.
The Share-a-Ride Problem with Flexible Compartments (SARPFC) is an extension of the Share-a-Ride Problem (SARP) where both passenger and freight transport are serviced by a single taxi network. The aim of SARPFC is to increase profit by introducing flexible compartments into the SARP model. SARPFC allows taxis to adjust their compartment size within the lower and upper bounds while maintaining the same total capacity permitting them to service more parcels while simultaneously serving at most one passenger. The main contribution of this study is that we formulated a new mathematical model for the problem and proposed a new variant of the Simulated Annealing (SA) algorithm called Simulated Annealing with Mutation Strategy (SAMS) to solve SARPFC. The mutation strategy is an intensification approach to improve the solution based on slack time, which is activated in the later stage of the algorithm. The proposed SAMS was tested on SARP benchmark instances, and the result shows that it outperforms existing algorithms. Several computational studies have also been conducted on the SARPFC instances. The analysis of the effects of compartment size and the portion of package requests to the total profit showed that, on average, utilizing flexible compartments as in SARPFC brings in more profit than using a fixed-size compartment as in SARP.
The shared autonomous vehicle (SAV) is a new concept that meets the upcoming trends of autonomous driving and changing demands in urban transportation. SAVs can carry passengers and parcels simultaneously, making use of dedicated passenger and parcel modules on board. A fleet of SAVs could partly take over private transport, taxi, and last-mile delivery services. A reduced fleet size compared to conventional transportation modes would lead to less traffic congestion in urban centres. This paper presents a method to estimate the optimal capacity for the passenger and parcel compartments of SAVs. The problem is presented as a vehicle routing problem and is named variable capacity share-a-ride-problem (VCSARP). The model has a MILP formulation and is solved using a commercial solver. It seeks to create the optimal routing schedule between a randomly generated set of pick-up and drop-off requests of passengers and parcels. The objective function aims to minimize the total energy costs of each schedule, which is a trade-off between travelled distance and vehicle capacity. Different scenarios are composed by altering parameters, representing travel demand at different times of the day. The model results show the optimized cost of each simulation along with associated routes and vehicle capacities.
We address the problem of delivering parcels in an urban area, within a given time horizon, by conventional vehicles, i.e., trucks, equipped with drones. Both the trucks and the drones perform deliveries, and the drones are carried by the trucks. We focus on the energy consumption of the drones that we assume to be influenced by atmospheric events. Specifically, we manage the delivery process in a such a way as to avoid energy disruption against adverse weather conditions. We address the problem under the field of robust optimization, thus preventing energy disruption in the worst case. We consider several polytopes to model the uncertain energy consumption, and we propose a decomposition approach based on Benders’ combinatorial cuts. A computational study is carried out on benchmark instances. The aim is to assess the quality of the computed solutions in terms of solution reliability, and to analyze the trade-off between the risk-adverseness of the decision maker and the transportation cost.
Public transportation offers a promising opportunity for freight transportation in urban areas. Through the sharing of infrastructure, vehicles, or even space inside wagons, synergies can be realized which can lead to a more efficient and environmentally-friendly transportation of goods in cities. This paper conducts a comprehensive investigation of the state-of-the-art of freight on public transportation by means of a systematic literature review. We start by giving an overview of the various ways in which freight on public transportation can be realized. Then we analyze the identified references, classified into qualitative and quantitative approaches, regarding methodology, mode of transportation, shared aspects, underlying network, and utilized data. We find that freight on urban public transportation is a highly dynamic and diverse research field. Finally, we describe opportunities and barriers for freight on public transportation and identify the main fields for future research, among others the further inclusion of external costs and the more frequent application of stochastic and real-time data.
More and more studies have aimed to optimize the ground vehicle (GV) and unmanned aerial vehicle (UAV) system in which GVs function as mobile satellites and UAVs are dispatched from GVs for last-mile deliveries. From the two-echelon scheme perspective, GV routes originating at the depot are on one echelon, and UAV routes originating at satellites are on the other echelon. A change in a GV route may affect some UAV routes, which indicates the satellite synchronization. In the past decade, the optimization of vehicle routes in two-echelon networks has attracted increasing attention in the operations research community. We classify routing problems of two-echelon networks based on the modeling mechanism connecting the two echelons. Different formulations for describing connection mechanisms of the two-echelon scheme, especially constraints on capacitated satellites, satellite synchronization, vehicle coupling/decoupling at satellites, etc., are briefly introduced. There are several modeling challenges of optimizing delivery routes for a fleet of GV–UAV combinations that include new connection mechanisms between the two echelons. Some important variants, especially those involving mobile satellite synchronization, and GV–UAV flexibly coupling/decoupling, require new mathematical formulations and algorithms catering to more general and practical situations.
In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.
Emerging autonomous driving technologies are claimed to create new opportunities for realizing smart and sustainable urban mobility initiatives. In this perspective, some studies identified shared autonomous vehicles (SAVs) as being a crucial element of on-demand mobility services. There is, however, limited evidence from practice on what roles SAVs play in delivering smart urban mobility. In other words, SAV in the context of smart urban mobility is an understudied area of research with limited review studies. To bridge this gap in the literature, this study aims to investigate and map out the service attributes and impacts of SAVs on urban mobility, infrastructure and land use, travel behavior, and the environment. As the methodological approach, the paper adopts the systematic literature review technique. The study findings reveal that: (a) Providing dynamic ridesharing services could result in significant reduction of SAV fleet size, congestion, travel cost, parking demand, vehicle ownership, and emissions; (b) Positive environmental outcomes could be enhanced with full electrification of SAV fleet through renewable energy charging; (c) Integration of SAV, as a smart urban mobility system with dynamic ridesharing services, could promote sustainability and social and transportation equity, and their adoption. In the light of the findings, this study advocates the consolidation of urban/transport policy to achieve desired urban mobility outcomes.
In this paper, we propose a new parcel delivery system consisting of a public train and drones. The train, an already existing mobile platform in our neighbourhood, follows its normal predefined route and timetable to transport passengers. In the meanwhile, some parcels to be delivered to some customers and a delivery drone are stored on its roof. The drone can launch from the train, deliver the parcel to a customer, and return to the train. It can also travel with the train and replace its battery on the roof via an automatic battery swap system. As the parcel delivery system cannot manage the movement of the train, the route and the timetable of the train need to be accounted carefully to schedule the deliveries. We formulate an optimization problem to minimize the total delivery time of a given set of parcels, and we propose two algorithms. Though the exact algorithm gives the optimal schedule, its computational complexity is exponential to the number of parcels. To make the proposed system possible to be implemented in practice, a suboptimal algorithm is developed, which is more efficient than the exact algorithm and can achieve close performance with the exact algorithm. Additionally, we propose a simple but effective strategy to deal with the uncertainty associated with the train’s timetable. Moreover, the proposed algorithm for the single drone case is modified to deal with the multiple drone case. The effectiveness of the proposed algorithms is verified via computer simulations and comparison with existing methods. The results show that the presented approach is more cost-efficient than the Truck only scheme and the Truck plus Drone scheme. Moreover, the parcel delivery time can be reduced and the delivery area can be enlarged compared to the scheme using drones only.
Parcels delivery is the most expensive phase of the distribution logistics. Everyday, several vehicles, usually internal combustion engine vehicles, have to serve a high number of customers spatially distributed in an urban area. Their presence generates several negative externalities, such as, noise, congestion and pollutant emissions. Drones have become a valid alternative to support the delivery process and several big companies, such as, Amazon and DHL, have started to use them for parcels deliveries. On the one hand, drones drastically reduce negative externalities, allowing a more sustainable delivery process. On the other hand, several technical aspects must be carefully taken into account. In particular, they have a limited flight endurance and capacity. In addition, several restrictions related to safety and flight area must be considered. Indeed, not all countries allow the use of drones in the urban area. In this work, we provide a qualitative and quantitative analysis on benefits and drawbacks in using drones in the parcels delivery process. We analyze three different transportation systems with incremental use of drones for the delivery. In particular, we address the problem of delivering parcels without drone, known as vehicle routing problem, the problem in which the deliveries are performed by a fleet of drones starting from the central depot, and a hybrid transportation system where the classical vehicles are equipped with drones. In the latter case, the classical vehicles perform the deliveries and the drones can get in charge some deliveries. The drone takes off from the vehicle, carries out the delivery, and lands to the same vehicle at a randevouz-location. During the drone delivery, the classical vehicle continues its work. The three transportation systems are formalized via mathematical programming models. The solutions obtained by solving the models via a general-purpose solver are compared and insights on the use of drones in the urban area are provided.
The increasing demand for goods, especially in urban areas, together with the technological advances are creating both opportunities and challenges for planning urban freight systems. One of these promising opportunities is to use the underused assets in people-based systems to transport goods. In this paper, we consider an integrated system in which a set of freight requests needs to be delivered using a fleet of grounded, and autonomous, pickup and delivery (PD) robots where a public transportation service (referred to as scheduled line (SL)) can be used as part of PD robot's journey. Passengers and PD robots (carrying freight) share the available capacity on SLs where passengers are prioritised, and their transport demand is stochastic. Thus the number of available places for PD robots is only revealed upon shuttle arrival to the corresponding SL station. We first formulate this problem as a Pickup and Delivery Problem with Time Windows and Scheduled Lines (PDPTW-SL). We then introduce a sample average approximation (SAA) method along with an Adaptive Large Neighbourhood Search (ALNS) algorithm for solving the stochastic optimization problem. Finally, we present an extensive computational study, analyse its results and give some directions for future research.
Autonomous vehicles (AV) are poised to induce disruptive changes, with significant implications for the economy, the environment, and society. This article reviews prior research on AVs and society, and articulates future needs. Research to assess future societal change induced by AVs has grown dramatically in recent years. The critical challenge in assessing the societal implications of AVs is forecasting how consumers and businesses will use them. Researchers are predicting the future use of AVs by consumers through stated preference surveys, finding analogs in current behaviors, utility optimization models, and/or staging empirical “AV-equivalent” experiments. While progress is being made, it is important to recognize that potential behavioral change induced by AVs is massive in scope and that forecasts are difficult to validate. For example, AVs could result in many consumers abandoning private vehicles for ride-share services, vastly increased travel by minors, the elderly and other groups unable to drive, and/or increased recreation and commute miles driven due to increased utility of in-vehicle time. We argue that significantly increased efforts are needed from the AVs and society research community to ensure 1) the important behavioral changes are analyzed and 2) models are explicitly evaluated to characterize and reduce uncertainty.
Five years ago the project Cargo Hitching started with the goal to use the unused capacity of public transport passenger vehicles
for freight and parcel transport. Like many new city logistics initiatives it is a difficult challenge to setup a profitable private
business model. A rural pilot project was developed in the East of the Netherlands, building on Dutch government funding
(Dinalog), with several Dutch universities, the province of Gelderland, public transport service provider Connexxion and city
logistics service provider Binnenstadservice. The paper describes how viability for the cargo hitching project was organized,
providing important social and environmental benefits as well as a sustainable business model for the system.
In the realm of human urban transportation, many recent studies have shown that comparatively smaller fleets of shared autonomous vehicles (SAVs) are able to provide efficient door-to-door transportation services for city dwellers. However, because of the steady growth of e-commerce and same-day delivery services, new city logistics approaches will also be required to deal with last-mile parcel delivery challenges. We focus on modeling a variation of the people and freight integrated transportation problem (PFIT problem) in which both passenger and parcel requests are pooled in mixed-purpose compartmentalized SAVs. Such vehicles are supposed to combine freight and passenger overlapping journeys on the shared mobility infrastructure network. We formally address the problem as the share-a-ride with parcel lockers problem (SARPLP), implement a mixed-integer linear programming (MILP) formulation, and compare the performance of single-purpose and mixed-purpose fleets on 216 transportation scenarios. For 149 scenarios where the solver gaps of the experimental results are negligible (less than 1%), we have shown that mixed-purpose fleets perform in average 11% better than single-purpose fleets. Additionally, the results indicate that the busier is the logistical scenario the better is the performance of the mixed-purpose fleet setting.
Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.
Recently, several prominent logistic companies in Europe and U.S. are seriously considering the idea of using
drones launched from trucks and working in parallel to deliver packages. In the relevant literature, a novel problem formulation called Traveling Salesman Problem with Drone (TSP-D) has been introduced, and some modeling and solution approaches have been presented. Existing approaches are based on the main ssumption that the truck can dispatch and pick up a drone only at a node, i.e., the depot or a customer location. In this work, we present a novel approach aimed to maximize the drone usage in parcel delivering. We consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (en-route). In this way, the operations of a drone are not strictly related to the customers’ position, but it can serve a wider area along the route. We tested the proposed heuristic on benchmark instances and analyzed the benefits introduced with the en-route approach.
Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.
In this paper, we introduce the vehicle routing problem with drones (VRPD). A fleet of trucks equipped with drones delivers packages to customers. Drones can be dispatched from and picked up by the trucks at the depot or any of the customer locations. The objective is to minimize the maximum duration of the routes (i.e., the completion time). The VRPD is motivated by a number of highly influential companies such as Amazon, DHL, and Federal Express, actively involved in exploring the potential use of commercial drones for package delivery. After stating our simplifying assumptions, we pose a number of questions in order to study the maximum savings that can be obtained from using drones; we then derive a number of worst-case results. The worst-case results depend on the number of drones per truck and the speed of the drones relative to the speed of the truck.
Combining trucks and drones in package delivery provides a promising venue for a future logistics system that is more efficient and sustainable than the existing one. However, how to coordinate trucks and drones, particularly under uncertain traffic conditions (thus, travel time), remains a critical question in this field. To address this challenge, this study proposes and solves a truck–drone hybrid routing problem with time-dependent road travel time (TDHRP-TDRTT) to address the truck–drone cooperation issue. TDHRP-TDRTT is formulated as a cost minimization problem with constraints associated with logistics demand and supply. An iterative local search heuristic algorithm based on intra-pair and inter-pair customer exchanges and link re-optimization is developed to solve TDHRP-TDRTT. Our results on small-scale and benchmark instances show that the proposed algorithm has better computational performance than CPLEX solver, the adaptive large neighborhood search, hybrid genetic-sweep algorithm, and variable neighborhood search. A case study using traffic data from Chongqing, China shows that the truck–drone solution improves the timeliness of delivery, undertakes sensitivity analysis considering four road congestion states, significantly reduces trucking mileage, and facilitates overcoming terrain limitations. Therefore, the proposed model and algorithm are of practical significance in reducing operating cost, improving transportation efficiency, and facilitating a smart and sustainable urban logistics distribution system.
The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.
When demand for transportation is low or highly variable, traditional public bus services tend to lose their efficiency and typically frustrate (potential) passengers. In the literature, a large number of demand-responsive systems, that promise improved flexibility, have therefore been developed. At present, however, a comprehensive survey of these systems is lacking. In this paper, we fill this gap by presenting a unifying framework that classifies all demand-responsive public bus systems. The classification is mainly based on three degrees of responsiveness: dynamic online, dynamic offline, and static. For each system we discuss the specific optimization problem modeled, whether realistic data is considered, and the size of the instances used for testing. Where possible, we try to draw conclusions on the current state of the literature and try to identify potential avenues for future research. Different tables are included to structure and summarize the information of all papers.
Urban logistics, which is centered around defining and analyzing the logistical problems of the modern city, and developing solutions and models, is one of the most important contemporary fields of urbanization. The integration of the logistics activities in a city with technology is what constitutes smart urban logistics. This study aims to provide a review of smart urban logistics-related publications by collecting the main papers in the field for classification and analysis. Within the scope of the study, both the academic and industrial literature of smart urban logistics is examined and important points and gaps are emphasized. In the relevant literature, many different sub-topics are mentioned and a host of innovative technologies are evaluated, but there is currently no existing literature review that analyzes both the sub-topics and technologies in the field. This is what constitutes the original contribution of this study. The gaps revealed as a result of this research can serve as a guide for future investigation.
The cooperation of trucks and unmanned aerial vehicles (UAV) has become a new delivery method in the area of logistics and transportation. In this form of cooperation, the trucks are not only able to provide services to the customers, but also serve as a ‘launch pad’ for the drones, in which the drones can be launched to service a customer and then recovered at the rendezvous node. This study intends to explore this cooperation by developing a model for the vehicle routing problem with drones that considers the presence of customer time windows (VRPTWD). A mixed-integer programming (MIP) model is presented to minimize the total travelling time of all trucks. Then, a simple yet effective variable neighborhood search (VNS) procedure with a novel solution representation is proposed as a solver. The numerical results indicate the ability of the proposed VNS to solve the VRPTWD, as well as the improvement of delivery performance using drones.
The integration of passenger and freight transport has been the subject of debate among scholars from the beginning of the century, with a peak observed in the last five years. Considering the relatively recent interest in the topic, most authors have highlighted the heterogeneous and explorative approaches adopted so far, indicating a lack of systematic analyses. This is confirmed by the different names given to the same concept (e.g., co-modality, cargo hitching, system with mixed passengers and goods, share-a-ride, integrated passenger and freight logistics, and collaborative passenger and freight transport). This study conducts a comprehensive literature review based on the scientific contributions indexed in Scopus and selected through a semiautomatic data extraction. First, a descriptive analysis is conducted, which includes types of publication, geographical areas, sources of publication, research methods, and research design adopted. Then, a text mining analysis identifies the main content-related aspects, including a semantic investigation of the most frequently occurring terms, their clustering in homogeneous groups, the transport means considered, and the territorial scales that have been investigated. This analysis is used to define the future challenges related to the topic, which span from the provision of more robust quantitative analyses (studies providing real data and adopting ad-hoc models are still very limited) to policy-related issues. However, the definition of a normative framework that integrates both systems is essential to deal with passenger–freight transport in a combined manner.
The e-commerce boom has increased the complexity of last-mile logistics operations in urban environments. In this context, unmanned aerial vehicles (UAVs), also known as delivery drones, and ground autonomous delivery devices (GADDs) show great potentialities. The objective of this paper is to provide strategic insights to adequately match these autonomous technologies with some given characteristics of cities and help define relevant decision variables. Using continuous approximation equations, the operations costs as well as the externalities induced by a) GADDs in association with an urban consolidation center (UCC) and b) truck-launched UAVs are estimated. Then, the developed mathematical formulations are applied in two different use cases: a part of the Paris suburbs (France) and the historical center of Barcelona (Spain). In less dense and larger service regions such as the Paris suburbs, truck-launched delivery drones seem more suitable to reduce the carriers’ operations costs. In denser neighborhoods such as the Barcelona historical center, GADDs are expected to be more economically profitable. In both use cases, GADDs would generate less externalities. Finally, considering the high uncertainty of some input parameters, a sensitivity analysis of the models is done.
We consider the problem of routing a large fleet of drones to deliver packages simultaneously across broad urban areas. Besides flying directly, drones can use public transit vehicles such as buses and trams as temporary modes of transportation to conserve energy. Adding this capability to our formulation augments effective drone travel range and the space of possible deliveries but also increases problem input size due to the large transit networks. We present a comprehensive algorithmic framework that strives to minimize the maximum time to complete any delivery and addresses the multifaceted computational challenges of our problem through a two-layer approach. First, the upper layer assigns drones to package delivery sequences with an approximately optimal polynomial time allocation algorithm. Then, the lower layer executes the allocation by periodically routing the fleet over the transit network, using efficient, bounded suboptimal multi-agent pathfinding techniques tailored to our setting. We demonstrate the efficiency of our approach on simulations with up to 200 drones, 5000 packages, and transit networks with up to 8000 stops in San Francisco and the Washington DC Metropolitan Area. Our framework computes solutions for most settings within a few seconds on commodity hardware and enables drones to extend their effective range by a factor of nearly four using transit.
Considering autonomous delivery robots in urban logistics has attracted a great deal of attention in recent years. In the meantime, new technology has led to new operational challenges, such as the routing and scheduling of vehicles and delivery robots together that are currently outside the logistics service providers’ capability. In this paper, a vehicle routing problem with time windows and delivery robots (VRPTWDR) as a variant of the classical VRP is studied. The investigated problem is concerned with the routing of a set of delivery vans equipped with a number of self-driving parcel delivery robots. To tackle the VRPTWDR, an Adaptive Large Neighborhood Search heuristic algorithm is developed. Experiments show the performance and effectiveness of the algorithm for solving the VRPTWDR, and provide insights on the use of self-driving parcel delivery robots as an alternative last mile service.
We propose a model for solving a parcel delivery problem with a fleet of trucks embedded with drones. When appropriate, drones are loaded with a parcel, launched directly from the truck, and sent to a client. Afterward, the drones autonomously return to the truck to be replenished and recharged. Inspired by the case of a large European logistics provider, the proposed modeling framework confronts realistic delivery problems involving time windows, limited drone autonomy, and the eligibility of clients to be served by drones. The considered global cost function includes fixed daily vehicle fares, driver wages, and the fuel and electricity consumption to power trucks and drones. To solve the problems at hand, we propose a mixed‐integer linear programming formulation and an adaptive large neighborhood search. Moreover, we introduce an efficient modeling framework to manage the numerous synchronization constraints induced by the simultaneous use of trucks and drones. We analyze the benefits of this new transportation concept for delivery problems involving up to 100 parcels. Results show that truck‐and‐drone solutions can reduce costs up to 34% compared to traditional truck‐only delivery. From a managerial perspective, we show that a certain percentage of client locations must be reachable by drone to make truck‐and‐drone solutions competitive (i.e., if the fixed costs of the drones are compensated for by the savings on truck routes) and compare the cost structures of truck‐and‐drone versus truck‐only solutions.
We address the problem of routing a fleet of trucks equipped with unmanned aerial vehicles, commonly known as drones, to perform deliveries in last‐mile delivery process. The customers can be served by either a truck or a drone within the respective time window of each. Each capacitated truck carries drones that can be launched to perform deliveries. The drone takes off from a truck located either at a customer or at the depot and it must land on the same truck after visiting a customer to be served. The aim is to serve all customers at minimum cost, under time window, capacity, and flying endurance constraints. We formulate the problem as a mixed integer linear program (MILP) and develop a heuristic procedure where a two‐phase strategy is embedded in a multi‐start framework. The computational results are carried out on instances generated by starting from vehicle routing problem with time windows benchmarks. We analyze the behavior of the considered transportation system by mean of the solutions provided by the MILP. The proposed formulation is able to solve instances with up to 15 customers. The solutions of the MILP are used as benchmark to assess the effectiveness of the proposed heuristic procedure.
Taking a taxi at the railway station is difficult in many large Chinese cities, especially during the night. Passengers off trains always have carry-on luggage, which may affect the travel choices for the next trip. This study integrates the passenger and freight transportation at the railway station by bus-pooling service. A two-stage model with passenger incentive is built. In the first stage, passengers are matched in a fair way while in the second stage, parcels are inserted into the bus routes. An algorithm is designed to find the fair bus-pooling plan in which a large neighborhood search is tailored to generate group rides. To assess the performance of the proposed model and algorithm, cases are presented based on real-life taxi data related to Dalian North Railway Station. The results indicate that the fairness, waiting time of passengers, walking distance of recipients and time span have impacts on the matching rate. Relaxing the fairness constraint could improve the application of the fair bus-pooling in practice.
The interest in using drones in various applications has grown significantly in recent years. The reasons are related to the continuous advances in technology, especially the advent of fast microprocessors, which support intelligent autonomous control of several systems. Photography, construction, and monitoring and surveillance are only some of the areas in which the use of drones is becoming common. Among these, last-mile delivery is one of the most promising areas. In this work we focus on routing problems with drones, mostly in the context of parcel delivery. We survey and classify the existing works and we provide perspectives for future research.
Unmanned Aerial Vehicles, commonly known as drones, have attained considerable interest in recent years due to the potential of revolutionizing transport and logistics. Amazon were among the first to introduce the idea of using drones to deliver goods, followed by several other distribution companies working on similar services.
The Traveling Salesman Problem, frequently used for planning last-mile delivery operations, can easily be modified to incorporate drones, resulting in a routing problem involving both the truck and aircraft. Introduced by Murray and Chu (2015), the Flying Sidekick Traveling Salesman Problem considers a drone and truck collaborating. The drone can be launched and recovered at certain visits on the truck route, making it possible for both vehicles to deliver goods to customers in parallel. This generalization considerably decreases the operational cost of the routes, by reducing the total fuel consumption for the truck, as customers on the routes can be serviced by drones without covering additional miles for the trucks, and hence increase productivity.
In this paper a mathematical model is formulated, defining a problem similar to the Flying Sidekick Traveling Salesman Problem, but for the capacitated multiple-truck case with time limit constraints and minimizing cost as objective function. The corresponding problem is denoted the Vehicle Routing Problem with Drones. Due to the difficulty of solving large instances to optimality, an Adaptive Large Neighborhood Search metaheuristic is proposed. Finally, extensive computational experiments are carried out. The tests investigate, among other things, how beneficial the inclusion of the drone-delivery option is compared to delivering all items using exclusively trucks. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.
This paper surveys the state-of-the-art optimization approaches in the civil application of drone operations (DO) and drone-truck combined operations (DTCO) including construction/infrastructure, agriculture, transportation/logistics, security/disaster management, entertainment/media, etc. In particular, this paper reviews ongoing research on various optimization issues related to DO and DTCO including mathematical models, solution methods, synchronization between a drone and a truck, and barriers in implementing DO and DTCO. First, the paper introduces DO and DTCO and their applications, and explores some previous works including survey papers. In addition, this paper surveys the state of the art of DO and DTCO studies and discusses the research gaps in the literature. Furthermore, the detailed review of DTCO models and solution methods are reviewed. Finally, future research directions are discussed.
The rise of e-commerce has increased the demands placed on pickup and delivery operations, as well as customer expectations regarding the quality of services provided by those operations. One strategy a logistics provider can employ for meeting these increases in demands and expectations is to complement and coordinate its fleet operations with those of for-hire, third-party logistics providers. Herein, we study an optimization problem for coordinating these operations: the time-dependent profitable pickup and delivery problem with time windows. In this problem, the logistics provider has the opportunity to use its fleet of capacitated vehicles to transport shipment requests, for a profit, from pickup to delivery locations. Owing to demographic and market trends, we focus on an urban setting, wherein road congestion is a factor. As a result, the problem explicitly recognizes that travel times may be time-dependent. The logistics provider seeks to maximize its profits from serving transportation requests, which we compute as the difference between the profits associated with transported requests and transportation costs. To solve this problem, we propose an adaptive large neighborhood search algorithm. The results of our extensive computational study show that the proposed algorithm can find high-quality solutions quickly on instances with up to 75 transportation requests. Furthermore, we study its impact on profits when explicitly recognizing traffic congestion during planning operations.
We extend the Traveling Repairman Problem (TRP) by assuming a single truck which can stop at customer locations and launch drones multiple times from each stop location to serve customers. The problem is mathematically modeled, several bound analyses are conducted to determine the maximum possible improvements in customer waiting times, and an efficient hybrid Tabu Search-Simulated Annealing algorithm is developed to solve the problem. The results of evaluations on several problem instances suggest that the system can yield considerable reductions in customer waiting time for a wide range of model parameters compared to the conventional delivery models.
In recent years, drone routing and scheduling has become a highly active area of research. This research introduces a new routing model that considers a synchronized truck-drone operation by allowing multiple drones to fly from a truck, serve one or multiple customers, and return to the same truck for a battery swap and package retrieval. The model addresses two levels (echelons) of delivery: primary truck routing from the main depot to serve assigned customers and secondary drone routing from the truck, which behaves like a moveable intermediate depot to serve other sets of customers. The model takes into account both trucks' and drones’ capacities with the objective of finding optimal routes of both trucks and drones that minimizes the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by formulated mixed integer programming (MIP) for the small-size problem, and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). Numeric results from the experiment compare the performance of both heuristics against the MIP method in small/medium-size instances from the literature. A sensitivity analysis is conducted to show the delivery time improvement of the proposed model over the previous truck-drone routing models.
We present a mathematical formulation and a heuristic solution approach for the optimal planning of delivery routes in a multi-modal system combining truck and Unmanned Aerial Vehicle (UAV) operations. In this system, truck and UAV operations are synchronized, i.e., one or more UAVs travel on a truck, which serves as a mobile depot. Deliveries can be made by both UAVs and the truck. While the truck follows a multi-stop route, each UAV delivers a single shipment per dispatch. The presented optimization model minimizes the waiting time of customers in the system. The model determines the optimal allocation of customers to truck and UAVs, the optimal route sequence of the truck, and the optimal launch and reconvene locations of the UAVs along the truck route. We formulate the problem as a Mixed-Integer Linear Programming (MILP) model and conduct a bound analysis to gauge the maximum potential of the proposed system to reduce customer waiting time compared to a traditional truck-only delivery system. To be able to solve real-world problem size instances, we propose an efficient Truck and Drone Routing Algorithm (TDRA). The solution quality and computational performance of the mathematical model and the TDRA are compared together and with the truck-only model based on a variety of problem instances. Further, we apply the TDRA to a real-world case study for e-commerce delivery in São Paulo, Brazil. Our numerical results suggest significant reductions in customer waiting time to be gained from the proposed multi-modal delivery model.
An unmanned aerial vehicle (UAV), commonly known as a drone, offers the advantage of speed, flexibility, and ease in delivering goods to customers. They are particularly useful for tasks that are dull, hazardous, or dirty. Whether the use of drone delivery is beneficial to the environment and cost saving is still a topic under debate. Ideally, drones yield lower energy consumption and reduce greenhouse gas emissions, thus reducing the carbon footprint and enhancing environmental sustainability. In this research, we analytically study the impact of UAVs on CO 2 emission and cost. We propose a mixed-integer (0–1 linear) green routing model for UAV to exploit the sustainability aspects of the use of UAVs for last-mile parcel deliveries. A genetic algorithm is developed to efficiently solve the complex model, and an extensive experiment is conducted to illustrate and validate the analytical model and the solution algorithm. We find that optimally routing and delivering packages with UAVs would save energy and reduce carbon emissions. The computational results strongly support the notion that using UAVs for last-mile logistics is not only cost effective, but also environmentally friendly.
The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last mile to their customers. One technology-enabled opportunity that recently has received much attention is the use of drones to support deliveries. An innovative last-mile delivery concept in which a truck collaborates with a drone to make deliveries gives rise to a new variant of the traveling salesman problem (TSP) that we call the TSP with drone. In this paper, we model this problem as an integer program and develop several fast route-first, cluster-second heuristics based on local search and dynamic programming. We prove worst-case approximation ratios for the heuristics and test their performance by comparing the solutions to the optimal solutions for small instances. In addition, we apply our heuristics to several artificial instances with different characteristics and sizes. Our experiments show that substantial savings are possible with this concept compared to truck-only delivery.
The online appendix is available at https://doi.org/10.1287/trsc.2017.0791 .
This research introduces an extension of the share-a-ride problem (SARP), called the general share-a-ride problem (G-SARP). Similarly to SARP, a taxi in G-SARP can service passenger and package requests at the same time. However, G-SARP allows the taxi to transport more than one passenger at the same time, which is more beneficial in practical situations. In addition, G-SARP has no restrictions on the maximum riding time of a passenger, and the number of parcel requests that can be inserted between the pick-up and drop-off points of a passenger is limited only by vehicle capacity. A simulated annealing (SA) algorithm is proposed to solve G-SARP. The proposed SA algorithm is compared with basic SA and tabu search (TS) algorithms. The results show that the proposed SA algorithm outperforms basic SA and TS algorithms. Moreover, further analysis shows that G-SARP solutions are better than those of SARP in most cases.
The Pickup and Delivery Problem with Time Windows, Scheduled Lines and Stochastic Demands (PDPTW-SLSD) concerns scheduling a set of vehicles to serve a set of requests, whose expected demands are known in distribution when planning, but are only revealed with certainty upon the vehicles’ arrival. In addition, a part of the transportation plan can be carried out on limited-capacity scheduled public transportation line services. This paper proposes a scenario-based sample average approximation approach for the PDPTW-SLSD. An adaptive large neighborhood search heuristic embedded into sample average approximation method is used to generate good-quality solutions. Computational results on instances with up to 40 requests (i.e., 80 locations) reveal that the integrated transportation networks can lead to operational cost savings of up to 16% compared with classical pickup and delivery systems.
We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.
The Pickup and Delivery Problem with Time Windows and Scheduled Lines (PDPTW-SL) concerns scheduling a set of vehicles to serve freight requests such that a part of the journey can be carried out on a scheduled public transportation line. Due to the complexity of the problem, which is NP-hard, we propose an Adaptive Large Neighborhood Search (ALNS) heuristic algorithm to solve the PDPTW-SL. Complex aspects such as fixed lines' schedules, synchronization and time-windows constraints are efficiently considered in the proposed algorithm. Results of extensive computational experiments show that the ALNS is highly effective in finding good-quality solutions on the generated PDPTW-SL instances with up to 100 freight requests that reasonably represent real life situations.
The Share-a-Ride Problem (SARP) aims at maximizing the profit of serving a set of passengers and parcels using a set of homogeneous vehicles. We propose an adaptive large neighborhood search (ALNS) heuristic to address the SARP. Furthermore, we study the problem of determining the time slack in a SARP schedule. Our proposed solution approach is tested on three sets of realistic instances. The performance of our heuristic is benchmarked against a mixed integer programming (MIP) solver and the Dial-a-Ride Problem (DARP) test instances. Compared to the MIP solver, our heuristic is superior in both the solution times and the quality of the obtained solutions if the CPU time is limited. We also report new best results for two out of twenty benchmark DARP instances.
Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.
Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.