Cooperative Delivery Research Articles

Blockchain adoption to eliminate dispute in platform-supplier cooperative delivery

Inefficient delivery services undermine consumers’ online shopping experience, posing a crucial challenge for e-commerce platforms and suppliers. In a platform-supplier cooperative delivery system, the issue of unclear responsibility for quality defects often arises. By utilizing real-time traceability enabled by blockchain, the platform can promptly identify supplier delivery accidents and return defective products directly to the supplier. This blockchain-enabled return mechanism could significantly reduce disputes in cooperative delivery processes. We develop a game theoretical model to analyze delivery strategies in supply chains with and without blockchain, and further examine the impact of blockchain on members’ delivery services and their profits. Our analysis indicates that blockchain incentivizes the platform to conduct high-level delivery services. However, it may either encourage or discourage the supplier’s provision of high-level delivery services. Moreover, blockchain acts as a double-edged sword in the supply chain, enhancing consumer satisfaction while also increasing wholesale prices. The interaction between these two effects determines whether blockchain negatively impacts on the supply chain’s profit, with the disposal cost of returned products by the supplier playing a crucial role. When the supplier’s disposal cost is relatively high, blockchain may result in supply chain members being worse off, despite its enhancement of consumer satisfaction. Furthermore, we find that blockchain enhances both the delivery service level and the profit of the supply chain if neither the supplier’s disposal cost nor the platform’s cost of high-level service are high. This study provides valuable insights into the potential benefits and challenges associated with blockchain adoption in cooperative delivery systems.

Layered coded cache placement and cooperative delivery with sharing links in satellite-terrestrial integrated networks

Layered coded cache placement and cooperative delivery with sharing links in satellite-terrestrial integrated networks

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

AbstractEdge caching at access points (APs) is a promising approach to alleviate the fronthaul burden and reduce user‐perceived delay. However, the edge caching placement is still challenging considering the coupling between caching and AP‐user association, limited fronthaul capacity, and multi‐AP deployment in the cell‐free (CF) massive MIMO systems. To this end, the authors establish a framework for the joint problem of AP‐user association and caching to minimize the content delivery delay which considers both cooperation delivery delay and radio access delay. It is an integer nonlinear programming problem and NP‐hard. The optimization problem is first decomposed into an AP‐user association sub‐problem and a caching placement sub‐problem to address this problem. A two‐stage matching algorithm is further proposed to achieve AP‐user association and a modified genetic algorithm to determine caching placement. A computationally efficient iterative algorithm is developed to solve the joint optimization problem. Finally, the global convergence and computational complexity of the proposed strategy are analyzed theoretically. Simulation results reveal that the proposed strategy can achieve better delivery delay performance than benchmark schemes.

Cooperative trucks and drones for rural last-mile delivery with steep roads

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed model, an improved adaptive large neighborhood search (IALNS) algorithm is introduced, which incorporates several novel operators designed based on the characteristics of the problem. The effectiveness of the IALNS algorithm and the feasibility of the GVRPD-SR are verified through extensive computational experiments. Furthermore, a detailed sensitivity analysis is conducted on several critical parameters to derive managerial insights.

Multi-trucks-and-drones cooperative pickup and delivery problem

This study aims to propose a decision methodology on scheduling trucks and drones for truck-and-drone cooperative delivery and pickup system. A fleet contains multiple truck groups; each truck group is a truck with carrying multiple drones. The fleet serves a set of dispersed customers who have the requirements of pickup and delivery services as well as their due time for service. A mixed-integer linear programming (MILP) model is formulated in this study for routing the trucks and drones in the fleet so that each customer’s pickup or delivery requirements could be served by either a truck or a drone before their required due time. For solving the MILP model efficiently, this study designs a novel hybrid algorithm by combining the column generation and the logic-based Benders decomposition. Based on the main frame of column generation algorithm, the hybrid algorithm uses logic-based Benders decomposition to solve the pricing problem, and dynamic programming to solve subproblems of logic-based Benders decomposition for the purpose of accelerating the whole algorithm’s solving process. Numerical experiments are also conducted on the context of the Hangzhou city so as to validate the efficiency of the proposed hybrid algorithm. Some managerial implications are also derived on the basis of some sensitivity analysis. The proposed methodology, i.e., the MILP model and the novel hybrid algorithm, is potentially useful for platform operators who run the truck-and-drone based urban delivery systems.

Optimization of Takeaway Delivery Based on Large Neighborhood Search Algorithm

<div>The drone logistics distribution method, with its small size, quick delivery, and zero-touch, has progressively entered the mainstream of development due to the global epidemic and the rapidly developing global emerging logistics business. In our investigation, a drone and a delivery man worked together to complete the delivery order to a customer’s home as quickly as possible. We realize the combined delivery network between drones and delivery men and focus on the connection and scheduling between drones and delivery men using existing facilities such as ground airports, unmanned stations, delivery men, and drones. Based on the dynamic-vehicle routing problem model, the establishment of a delivery man and drone with a hybrid model, in order to solve the tarmac unmanned aerial vehicle for take-out delivery scheduling difficulties, linking to the delivery man and an adaptive large neighborhood search algorithm solves the model. The objective function is to reduce customer waiting time. The average delivery time for orders was subsequently shortened thanks to the optimization of the cooperative delivery solution for drones and delivery personnel.</div>

CO2 emissions and delivery time of last‐mile drone delivery using trucks

AbstractThe shift in consumer behaviour from in‐person to online shopping has led to an increase in parcel delivery volume and its associated negative impacts, such as CO2 emissions in cities. With the emergence of drone‐delivery technologies, the authors analyzed a joint delivery method using drones and trucks, which is an emerging alternative solution for last‐mile delivery in terms of CO2 emissions and delivery time. The analysis verified the highest possible level of reduction in CO2 emissions for the simultaneous and strategic operation of drones and trucks compared to diesel‐ or electric‐only truck operations. Moreover, this approach leads to reduced delivery times. A sensitivity analysis was performed to optimize the delivery‐drone flight performance in a drone‐and‐truck delivery strategy. It was found that a 1.5‐km drone flight performance was sufficient when considering the reasonable assumptions adopted in this study. Furthermore, based on the analysis results, drone‐and‐truck cooperative delivery strategies may not provide a significant advantage in terms of CO2 emissions compared with alternative transportation modes, such as fuel‐cell trucks with sufficiently low emissions. It has been empirically verified that changes in CO2 emissions are proportional to the number of clusters. However, there is a risk of local optima due to microscopic fluctuations among neighbouring cluster numbers, which occurs during the search for the optimal number of clusters.

Scheduling trucks and drones for cooperative deliveries

Truck and drone based cooperative delivery system is an emerging instant delivery mode for transporting packages in a more timely and efficient way than traditional delivery mode. This paper studies the scheduling of a fleet of truck groups, each of which can carry multiple drones. We formulate a mixed integer programming model for truck groups routes, and for the timing of the drones’ launching and return to their dedicated truck. The model objective is to minimize the total operational cost, including truck travel cost, drone travel cost, using truck groups fixed cost, and potential penalty for late delivery at customer locations. Column generation-based heuristic algorithm and some acceleration techniques are designed for solving the model efficiently. We carry out numerical experiments for validating the effectiveness of the mathematical model, algorithm, and acceleration techniques. In addition, sensitivity analyses are performed to derive some managerial implications.

A humanitarian vehicle routing problem synchronized with drones in time-varying weather conditions

This paper applies the cooperative delivery mode of trucks synchronized with drones to humanitarian logistics (HVRP-SD), considers the impact of time-varying weather conditions on the synchronous delivery from two dimensions (HVRP-SD-TVW): drone safety and drone delivery efficiency, and investigate a multi-objective optimization problem. This issue concerns not only the delivery timeliness of relief supplies, but also the fairness of supplies allocation. First, we establish a basic multi-objective mixed integer programming model for HVRP-SD, and then establish an extended mathematical model for HVRP-SD-TVW. To solve the multi-objective optimization problem, we develop a hybrid multi-objective evolutionary algorithm (HMOEA). According to the characteristics of HVRP-SD-TVW, HMOEA designs a variable-length two-dimensional array encoding method, fitness evaluation rules, multimodal mutation, and specialized local search operators. A set of numerical experiments prove that HMOEAS is highly competitive, and both the multimodal mutation and local search have significant effects on improving the quality of solutions. Further, taking the emergency supplies distribution in flood-stricken areas of Gongyi City, China in 2021 as a sample case, we prove that weather conditions play an important role in the truck-drone synchronized delivery decision by comparing the results of the basic model and extended model. In addition, we respectively analyze the variation in solutions under different weather scenarios, departure times, and drone risk resilience to guide decision-making.

Multivisit Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery considering No-Fly Zones

The employment of drones for the distribution of goods represents a significant avenue for addressing logistical challenges at the end of the supply chain. The truck-drone cooperative delivery model overcomes drone limitations such as limited capacity and endurance and has emerged as a crucial mode of drone participation in logistics delivery. This delivery model effectively reduces delivery costs and shortens delivery times. Herein, we examine a variant of the truck-drone routing problem, which encompasses the strategic deployment and routing of multiple fleets of trucks, each equipped with an auxiliary drone. The objective is to fulfill all the pickup and delivery demands of a designated customer base while minimizing the overall route cost. Within this problem domain, drones are authorized to serve multiple customers within their capacity and endurance limits, providing both pickup and delivery services during each trip. However, the utilization of drones for servicing all customers is impeded by the existence of no-fly zones that have been implemented in numerous cities worldwide. These prescribed no-fly zones cause significant challenges when attempting to optimize the routing of truck-drone operations. Thus, this study constructs a mixed integer linear programming (MILP) model for the path optimization problem of joint service of trucks and drones considering no-fly zones and simultaneous pickup and delivery. Given the intricacy of the MILP model, we propose a two-stage heuristic algorithm based on a simulated annealing approach, combined with strategies for rectifying infeasible solutions and expediting algorithmic processes. During the phase of computational experimentation, we explore the advantages derived from enabling drones to serve multiple customers and assess the effectiveness of the proposed model and two-stage heuristic algorithm. Finally, sensitivity analysis is conducted on two key parameters.

무인 배송 로봇 기술의 특허 동향 분석과 전망

With the advent of the untact era due to COVID-19, demand in the existing online market and the courier and logistics industry is accelerating. At the same time, the problem of decreasing available labor force due to decreasing population growth rate and aging is growing, causing difficulties due to labor shortage. In this paper, we examine the patent application trends of unmanned delivery systems by country, technology, and major applicants, and identify the degree of patent activity of major companies through index analysis. To this end, we analyzed domestic and foreign patents on unmanned delivery robot technology filed up to July 2021, and classified them according to the technology classification system to present the outlook of unmanned delivery technology. The analysis showed that robots, drones, and unmanned delivery boxes, which combine IoT technologies of the Fourth Industrial Revolution based on last-mile delivery technology, are expanding their scope of use. In addition, the development of cooperative delivery services combining robots and drones is expected, and the differentiation of delivery costs between rapid delivery by drones and regular delivery by robots is expected. This paper is expected to raise awareness of unmanned delivery technology among related companies and researchers, and to serve as a reference for future research and development for efficient last-mile delivery

The time-dependent electric vehicle routing problem with drone and synchronized mobile battery swapping

Logistics enterprises are moving towards high efficiency and green. As an emerging technology, unmanned aerial vehicle (UAV, also known as drone) is very useful to cope with flexible and fast delivery. Electric vehicles (EVs), as new energy vehicles, are the technical support for green logistics development. This paper considers the last-mile delivery system in which a fleet of homogeneous EVs, equipped with a UAV each, serve a set of customers, and meanwhile battery swapping vehicles (BSVs) offer mobile battery swapping service for EVs. A mixed integer programming (MIP) model is formulated to minimize the sum of depreciation cost, driver wages and energy consumption cost of EVs, UAVs, and BSVs, by considering the nonlinear energy consumption of each vehicle, time-dependent speeds of EVs and BSVs and monetary time value on depreciation cost. A large neighborhood search algorithm based on Q-learning (LNS-QL) is designed in which Q-learning is used to select a combination of tailored destroy and repair operators to solve three sub-problems, (1) the UAV multi-visit problem, (2) the EV and UAV synchronized scheduling problem, (3) the EV and BSV synchronized mobile battery swapping problem. Besides, this paper also designs Lagrangian relaxation algorithm for comparison with LNS-QL. Extensive experiments verify the correctness of the model by testing small-scale instances. Further analysis demonstrates the accuracy and stability of LNS-QL on medium and large-scale instances as well as computational time and verifies the effectiveness of the mobile battery swapping mode in the cooperative delivery system of EVs and UAVs. Actual cases analyze the characteristics of EV delivery from the cost and emissions perspectives by comparing with FV delivery.

A Review on the Truck and Drone Cooperative Delivery Problem

As an emerging delivery style in logistics, the cooperation between trucks and drones can significantly improve the efficiency of parcel delivery, especially in some typical scenes, such as mountainous areas, high buildings, or post-disaster material delivery. In recent years, the truck and drone cooperative delivery problem (TDCDP) has attracted more and more attention from logistic research and commercial sectors. This paper proposes a taxonomy for TDCDP and systematically summarizes the related research. First, the impacts of changes in customers and environments on truck and drone delivery modes are analyzed in detail. Second, by using the proposed taxonomy, the delivery modes in TDCDP are classified into four types: parallel delivery, mixed delivery, drone delivery with truck-assisting, and truck delivery with drone-assisting. The roles of trucks and drones are analyzed in different scenes. Then, for different delivery modes, this paper summarizes the TDCDP models and analyzes the common assumptions, constraints, and objective functions. This paper also combs the exact algorithms, heuristic algorithms, and hybrid algorithms used to solve different kinds of TDCDP. Finally, the current research status and future research trends are discussed, and the challenges of TDCDP are highlighted.

Optimal scheduling and quantitative analysis for multi-flying warehouse scheduling problem: Amazon airborne fulfillment center

The outstanding advantages of unmanned aerial vehicles (UAVs) have led to an intensive study of the UAV system over the past decade. A variety of potential UAV applications are emerging nowadays, and UAV delivery is one in the limelight. However, one of the critical issues for UAV delivery service is how UAVs can be persistently used within their fundamental limited battery capacity. One recent approach to alleviate the weaknesses is using a cooperative system of UAVs with movable stations for the delivery logistics. The movable stations move across the field of operation, providing persistent delivery service using UAVs by efficiently replenishing the consumables of UAVs. In this study, one of the compelling cooperative delivery services called the flying warehouse system is investigated. The concept of the flying warehouse delivery service has firstly filed as a patent by the e-commerce giant Amazon. The service utilizes airborne fulfillment centers (AFCs), an airship that floats at an altitude of around 45,000. The airship has stocked with inventory, and when a customer places an order, UAVs fly down and deliver the package. The AFC is a novel delivery system that allows delivery in under 10 min and expands Amazon Prime members' access to UAV shipping. On the other hand, AFC is a challenging system that must extract the simultaneous operating schedule of the different system elements. For this purpose, mixed integer linear programming (MILP) is developed. The proposed MILP enables the derivation of service schedules of multiple AFCs and their components. The validity of the MILP is tested through case studies and quantitatively investigated system managerial issues. In addition, we proposed a clustering-based solution approach to provide computational efficiency for real-world case problems.

Crowdsourced last-mile delivery with parcel lockers

Crowdshipping is increasingly known as a sustainable solution to address the challenges of last mile delivery (LMD) in urban areas. While employing the crowd to perform LMD appears to be an operationally and financially appealing model, it comes with several challenges in practice, including low willingness to participate in delivery work due to low financial incentive and additional travel effort. Inspired by the Physical Internet concept, in this paper we propose a novel crowdsourced LMD problem and solution approach, which allows a delivery task to be performed by one or multiple crowdshippers using parcel lockers as exchange points. The utilisation of parcel lockers in a crowdshipping network allows for shorter trip detour and better geographical coverage. To achieve this objective, we develop a novel model for locating parcel lockers and allocating delivery tasks. A two-phase algorithm is then developed to rank and choose parcel lockers from the potential locations, which first classifies jobs into single and joint delivery sets and then scores each prospective locker by its utilisation in cooperative delivery. A second algorithm is then designed with three selection strategies of random, roulette, and inverse roulette to assign jobs to crowdshippers for single or joint delivery. To evaluate the performance of the algorithms, experiments were conducted in small and large instances based on a real-world case study. While the exact solution was only capable to deal with small-sized problems, the proposed algorithms were able to produce (sub-)optimal results with significantly low computational expenses. Numerical analyses conducted on large instances showed that enabling joint delivery can improve the success delivery rate by up to 5%, which can be achieved by having a small number of parcel lockers hired at ‘critical’ locations.

Branch-price-and-cut for trucks and drones cooperative delivery

The truck and drone-based cooperative model of delivery can improve the efficiency of last mile delivery, and has thus increasingly attracted attention in academia and from practitioners. In this study, we examine a vehicle routing problem and apply a cooperative form of delivery involving trucks and drones. We propose a mixed-integer programming model and a branch-price-and-cut-based exact algorithm to address this problem. To reduce the computation time, we design several acceleration strategies, including a combination of dynamic programming and calculus-based approximation for the pricing problem, and various effective inequalities for the restricted master problem. Numerical experiments are conducted to validate the effectiveness and efficiency of the proposed solution.

Simultaneous cooperation of Refrigerated Ground Vehicle (RGV) and Unmanned Aerial Vehicle (UAV) for rapid delivery with perishable food

With the growth of e-commerce, various types of last-mile delivery services have emerged to meet customer needs. Among the most notable of these are “rapid delivery” and “perishable food delivery” services. Owing to their advantages, the use of unmanned aerial vehicles (UAVs), which are both fast and inexpensive, has received considerable interest for rapid delivery. However, because of the inherent limitations of UAVs, they are used in conjunction with other ground vehicles. In contrast, maintaining freshness during delivery of perishable food is crucial for customer satisfaction and hygiene. Consequently, the cooperative operation of refrigerated ground vehicles (RGVs) with UAVs is necessary for rapid and fresh delivery services. RGVs can focus on the delivery of perishable foods and large-sized general products in such an operation, whereas UAVs can focus on small-sized general goods. In this study, the cooperative operation of RGVs and UAVs was investigated for the rapid delivery of perishable food. Mixed integer linear programming (MILP) was developed to derive optimal delivery schedules for RGVs and UAVs with the objective of minimizing the maximum service completion time of delivery vehicles. Additionally, an RGV-UAV route generating algorithm (R-U RGA) was presented to efficiently derive optimal, near-optimal, or qualified vehicle schedules when the MILP fails to derive optimal schedules in a reasonable time. The validity of the proposed MILP model was verified through a case study. The usefulness of the R-U RGA was confirmed through numerical experiments conducted on instances of various sizes. Additionally, numerical tests were performed to compare the benefits of the RGV-only delivery system and the proposed cooperative delivery system. These tests conducted in terms of economic, environmental, and service factors; they quantitatively elucidate the tradeoffs between the two systems.

Milk handling practices and utilization at dairy farms and collection centers under rural and peri-urban milk value chain systems in Nakuru county, Kenya

There are increasing expectations on the compliance of food products to safety and quality standards due to consumer demand for high-quality food. The aim of this study was to determine the quality tests that are carried out on raw milk and its utilization at three milk collection centers in Olenguruone and Dundori regions of Nakuru as well as some selected dairy farms. Using a semi-structured questionnaire, data were collected from milk collection centers’ staff and farmers. Milk sampling for quality control testing was done at both the cooperative delivery points and farm level. The quality of milk handled and stored in different containers was assessed. Descriptive statistics, Chi-square and logistic regression analysis were carried out on the data. Results indicated that the average quantity of milk received at all milk collection centers was about 3687 liters per day. It was noted that most of the milk collection centers’ staff (operators) had certificates or diplomas in dairy science. Their average job experience period in the milk sector was 7 years. Majority of the farmers (90%) and transporters (94%) used plastic containers for milk handling and storage. Farmers who used plastic containers for milking were approximately three times more likely to have their milk rejected compared to those who used mazzi cans, aluminium or stainless-steel containers (p<0.05; Odds ratio =3.20). The alcohol and lactometer tests were carried out on milk received at all collection centers studied. Resazurin test was only carried out in one collection center at Olenguruone that had the required laboratory equipment. Milk quality assessment was not done at the farm level. Traditional fermented milk was the common dairy product produced from evening milk in most dairy farmers’ households. Regular education programs and seminars on milk safety and quality should be provided to both collection centers’ operators and farmers.

Two-Stage Interference Cancellation for Device-to-Device Caching Networks.

Wireless device-to-device (D2D) caching networks are studied, in which n nodes are distributed uniformly at random over the network area. Each node caches M files from the library of size and independently requires a file from the library. Each request will be served by cooperative D2D transmission from other nodes having the requested file in their cache memories. In many practical sensor or Internet of things (IoT) networks, there may exist simple sensor or IoT devices that are not able to perform real-time rate and power control based on the reported channel quality information (CQI). Hence, it is assumed that each node transmits a file with a fixed rate and power so that an outage is inevitable. To improve the outage-based throughput, a cache-enabled interference cancellation (IC) technique is proposed for cooperative D2D file delivery which first performs IC, utilizing cached files at each node as side information, and then performs successive IC of strongly interfering files. Numerical simulations demonstrate that the proposed scheme significantly improves the overall throughput and, furthermore, such gain is universally achievable for various caching placement strategies such as random caching and probabilistic caching.

Проекты северных потоков в контексте сотрудничества России и Евросоюза в газовой сфере

The article considers the key issues of cooperation between Russia and the European Union in the energy sector through the prism of the Nord Streams. Analysis of the gas industry is presented in the global format as well as part of the relationship between Russia and the European Union in particular. The Nord Streams are considered as the main gas pipelines of Russia. Along with economic are shown political factors affecting cooperation delivery between the RF and the EU in the gas sector. The authors of the article made an attempt to accumulate the results achieved in the implementation of the Nord Streams agreements, as well as give an analysis of ways to solve the problems.