Delivery Tasks Research Articles

UAV control in autonomous object-goal navigation: a systematic literature review

Research interest in autonomous control of unmanned aerial vehicles (UAVs) has increased rapidly over the past decade. They are now widely used in civilian, military, and private areas. Applications include surveillance, search and rescue, and delivery tasks. More broadly, they excel at solving problems where a significant amount of space must be covered and traveled. However, using UAVs to solve navigation problems with full autonomy necessitates the mastering of complex subtasks. A solution that includes control, planning, localization, and mapping remains an open challenge. Object-goal navigation contains the same navigation problems where the main objective is to reach a target object. The search and identification of this target are central to the vehicle’s navigation. This requires an understanding of what it is and where it can be located to move around the scene. This paper presents a systematic literature review on object-goal navigation and its subtasks, using autonomous UAVs. Survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. The review analyzed 67 articles found between 2011 and 2022. They were found in the ACM, IEEE Xplore, WebOfScience, Science Direct, and Scopus databases. This review revealed essential issues related to autonomous navigation task dependencies. Moreover, it highlighted gaps in UAV development and framework standardization. Open challenges for autonomous UAV control for object-goal navigation must address the research on finding methods for problems. For example, autonomy level and comparison metrics, considering safety, ethics, and legal implications.

Mental Distress, Label Avoidance, and Use of a Mental Health Chatbot: Results From a US Survey.

For almost two decades, researchers and clinicians have argued that certain aspects of mental health treatment can be removed from clinicians' responsibilities and allocated to technology, preserving valuable clinician time and alleviating the burden on the behavioral health care system. The service delivery tasks that could arguably be allocated to technology without negatively impacting patient outcomes include screening, triage, and referral. We pilot-tested a chatbot for mental health screening and referral to understand the relationship between potential users' demographics and chatbot use; the completion rate of mental health screening when delivered by a chatbot; and the acceptability of a prototype chatbot designed for mental health screening and referral. This chatbot not only screened participants for psychological distress but also referred them to appropriate resources that matched their level of distress and preferences. The goal of this study was to determine whether a mental health screening and referral chatbot would be feasible and acceptable to users. We conducted an internet-based survey among a sample of US-based adults. Our survey collected demographic data along with a battery of measures assessing behavioral health and symptoms, stigma (label avoidance and perceived stigma), attitudes toward treatment-seeking, readiness for change, and technology readiness and acceptance. Participants were then offered to engage with our chatbot. Those who engaged with the chatbot completed a mental health screening, received a distress score based on this screening, were referred to resources appropriate for their current level of distress, and were asked to rate the acceptability of the chatbot. We found that mental health screening using a chatbot was feasible, with 168 (75.7%) of our 222 participants completing mental health screening within the chatbot sessions. Various demographic characteristics were associated with a willingness to use the chatbot. The participants who used the chatbot found it to be acceptable. Logistic regression produced a significant model with perceived usefulness and symptoms as significant positive predictors of chatbot use for the overall sample, and label avoidance as the only significant predictor of chatbot use for those currently experiencing distress. Label avoidance, the desire to avoid mental health services to avoid the stigmatized label of mental illness, is a significant negative predictor of care seeking. Therefore, our finding regarding label avoidance and chatbot use has significant public health implications in terms of facilitating access to mental health resources. Those who are high on label avoidance are not likely to seek care in a community mental health clinic, yet they are likely willing to engage with a mental health chatbot, participate in mental health screening, and receive mental health resources within the chatbot session. Chatbot technology may prove to be a way to engage those in care who have previously avoided treatment due to stigma.

Designing Automation for Pickup and Delivery Tasks in Modern Warehouses Using Multi Agent Path Finding (MAPF) and Multi Agent Reinforcement Learning (MARL) Based Approaches

A warehouse pickup and delivery problem finds its solution using multi agent path finding (MAPF) approach. Also, the problem has been used to showcase the capabilities of the multi agent reinforcement learning (MARL). The warehouse pickup and delivery work needs the agent to pick up a requested item and successfully deliver it to the intended location within the warehouse. The problem has been solved based on two approaches that include single shot and lifelong problem solution. The single shot solution has the delivery as the final goal and thus once it reaches the delivery address, it stops whereas in case of lifelong, the agent needs to deliver the item which it had picked, deliver it to the required place and then again pick up new item until requests are satisfied. The strategy used by multi agent path finding (MAPF) approach aims at constructing collision free paths to reach the delivery location but in case of multi agent reinforcement learning (MARL), the agents’ decision making tactics (or policies) are learned which are then used to help agents decide path to be followed based on environment state and agent’s position. The results show that the lifelong conflict based search (CBS) is a better option when the agents are less in number as in that case, the re-planning will take overall less time but when the agents are large in number then this re-planning can take very long to produce conflict free paths from source to goal nodes. In this case, shared experience action critic (SEAC) which is based on multi agent reinforcement learning (MARL) approach can be more efficient choice as it takes the current environment state to give the most suitable action for that time t. For this study the agents taken for learning are homogeneous in nature that can pickup and deliver any type of requested item. We can address the same pickup and delivery problem when the agents are not all same and differ in their capabilities and the type of item they can handle.

Crowdsourced vehicles and UAVs for last-mile delivery application using blockchain-hosted matching mechanism

The crowdsourcing paradigm has been used for last-mile delivery applications to offer cost-efficient delivery through crowdsourced vehicles. The increasing demand from users for fast delivery led to the adoption of Unmanned Aerial Vehicles (UAVs) due to their speed and cost, for instance, in peak hours. However, existing frameworks do not offer a comprehensive solution that utilizes vehicles and UAVs for transparent, timely, and cost-efficient delivery. This paper proposes a novel supply chain management framework on a consortium blockchain with last-mile delivery through crowdsourced vehicles and UAVs. The blockchain-based framework aims to provide last-mile delivery for time-critical tasks in a transparent and cost-efficient manner. A blockchain-hosted Gale-Shapley Matching mechanism is designed for allocating delivery tasks to UAVs and vehicles that will perform them timely and cost-efficiently. The allocation mechanism is incorporated in the proposed management framework consisting of smart contracts that autonomously respond to members' interactions. In addition, the framework stores the supply chain process data transparently and immutably. The performed experiments explore the feasibility of the proposed solution showing the increase in task allocation percentage (at least 6%) and reward (at least 50%) while reducing the delivery time (at least 23%) compared to the considered benchmarks.

A simulation-based performance evaluation model for decision support on drone location and delivery scheduling

PurposeThis paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.Design/methodology/approachThis simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.FindingsAn extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.Originality/valueThe goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Evaluation of Some Selected Medical Equipment in Olabisi Onabanjo University Teaching Hospital for Staphylococcus aureus of Nosocomial Threat

Hospital is an establishment where patients that are diseased, infected, ill or injured receive medical care. A medical staff and equipment are required for the task of medical care delivery. The equipment that are employed in hospital could be classified as critical, semi-critical and non-critical depending on their design and tasks. A total of Eighty-five (85) samples of hospital equipment swab were obtained from Olabisi Onabanjo Teaching Hospital of which 40 samples of Staphylococcus aureus were isolated. The isolates were Gram stained followed by conventional biochemical test for the identification of Staphylococcus aureus. Antibiogram of the isolates was determined. The isolates exhibited resistance to ampiclox (87.5%), zinnacef (92.5%), amoxicillin (92.5%), rocephin (80%), and septrin (55%), while susceptible to pefloxacin (77.5%), gentamicin (80%), streptomycin (72.5%), and ciprofloxacin (52.5%). Some of the resistant isolates were exposed to plasmid DNA analysis and were found to be plasmid borne of varied molecular weight, which could be responsible for resistance to the antibiotics observed. There is therefore a need for regular disinfection, properly sterilization and preservation of medical equipment before and after use, which could curtailed or reduce the spread of equipment borne nosocomial infection.

Secure decentralized firmware update delivery service for Internet of Things

With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.

Modeling and solving the two-level generalized median tour problem

In many real-world situations, the beneficiaries of a distribution system may be grouped into clusters, requiring a transportation structure to serve each cluster of beneficiaries efficiently. For instance, in public services, such as health, education, and emergencies, delivery tasks rely on the local authority of each district or cluster. In this context, a two-level distribution system named the Two-Level Generalized Median Tour Problem (TLGMTP) is introduced. The first level addresses product distribution using a specialized vehicle, starting and ending at a depot and visiting some clusters. In this manner, products are delivered to one or more nodes belonging to the visited clusters. The second level comprises smaller vehicles that start their trips from the nodes belonging to the first level and transport the products to one or more nodes located in a non-visited cluster, ensuring that all non-visited clusters of the first level are visited in the second level. Then, the non-visited nodes in each cluster must reach a node in the same cluster to collect their products. In this study, we present, model, and solve the TLGMTP to minimize the total transportation costs. We develop three mathematical formulations and solve them using a branch-and-cut algorithm. Exhaustive computational experiments involving tests and real-world instances are presented to show the efficiency and advantages of the proposed methodology.

A Molecular Dynamics Simulation of Complexes of Fullerenes and Lysine-Based Peptide Dendrimers with and without Glycine Spacers.

The development of new nanocontainers for hydrophobic drugs is one of the most important tasks of drug delivery. Dendrimers with hydrophobic interiors and soluble terminal groups have already been used as drug carriers. However, the most convenient candidates for this purpose are peptide dendrimers since their interiors could be modified by hydrophobic amino acid residues with a greater affinity for the transported molecules. The goal of this work is to perform the first molecular dynamics study of the complex formation of fullerenes C60 and C70 with Lys-2Gly, Lys G2, and Lys G3 peptide dendrimers in water. We carried out such simulations for six different systems and demonstrated that both fullerenes penetrate all these dendrimers and form stable complexes with them. The density and hydrophobicity inside the complex are greater than in dendrimers without fullerene, especially for complexes with Lys-2Gly dendrimers. It makes the internal regions of complexes less accessible to water and counterions and increases electrostatic and zeta potential compared to single dendrimers. The results for complexes based on Lys G2 and Lys G3 dendrimers are similar but less pronounced. Thus, all considered peptide dendrimers and especially the Lys-2Gly dendrimer could be used as nanocontainers for the delivery of fullerenes.

An AI-Driven Secure and Intelligent Robotic Delivery System

Last-mile delivery has gained much popularity in recent years, it accounts for about half of the whole logistics cost. Unlike container transportation, companies must hire significant number of employees to deliver packages to the customers. Therefore, many companies are studying automated methods such as robotic delivery to complete the delivery work to reduce the cost. It is undeniable that the security issue is a huge challenge in such a system. In this article, we propose an AI-driven robotic delivery system, which consists of two modules. A multilevel cooperative user authentication module for delivering parcel using both PIN code and biometrics verification, i.e., voiceprint and face verification. Another noncooperative user identification module using face verification which detects and verifies the identification of the customer. In this way, the robot can find the correct customer and complete the delivery task automatically. Finally, we implement the proposed system on a Turtlebot3 robot and analyze the performance of the proposed schema. Experimental results show that our proposed system has a high accuracy and can complete the delivery task securely.

Experiences of community health workers on adopting mHealth in rural Malawi: A qualitative study.

The use of mobile health technology (mHealth) by community health workers (CHWs) can strengthen community-based service delivery and improve access to and quality of healthcare. This qualitative study sought to explore experiences and identify factors influencing the use of an integrated smartphone-based mHealth called YendaNafe by CHWs in rural Malawi. Using pre-tested interview guides, between August and October 2022, we conducted eight focus group discussions with CHWs (n = 69), four in-depth interviews with CHW supervisors, and eight key informant interviews in Neno District, Malawi. We audio-recorded and transcribed the interviews verbatim and organized them for analysis in Dedoose V9.0.62. We used an inductive analysis technique to analyze the data. We further applied the six domains of the socio-technical system (STS) framework to map factors influencing the use of YendaNafe. User experiences and facilitators and barriers were the two main themes that emerged. mHealth was reported to improve the task efficiency, competence, trust, and perceived professionalism of CHWs. CHWs less frequently referred to cultural factors influencing app uptake. However, for other social systems, they identified relationships and trust with stakeholders, availability of training and programmatic support, and performance monitoring and feedback as influencing the use of YendaNafe. From the STS technical domain, the availability and adequacy of hardware such as phones, mobile connectivity, and usability influenced the use of YendaNafe. Despite the initial discomfort, CHWs found mHealth helpful in supporting their service delivery tasks. Identifying and addressing social and technical factors during mHealth implementation may help improve end users' attitudes and uptake.

Scheduling‐location problem with drones

AbstractDrone operation, a new driving force for logistics innovation, is struggling to overcome practical challenges. One of the concerns for drone utilization is limited flight ranges, and different concepts of facilities are continually developed to support drone delivery. These new facilities prompt the need to integrate decision‐making across different phases. In particular, the deployment of facilities that complement the physical limitations of drones and the scheduling of drones to perform delivery tasks are closely related. Therefore, we developed a scheduling‐location problem with drones, a new methodology for integrating operational and strategic planning decisions. The integrated decision‐making determines the location of the drone facilities by not only considering the critical distance of facilities but also by taking into account whether the delivery schedule is implemented. In our model, additional drone facilities are sometimes opened considering available drones due to the feasibility of the delivery schedule. An extended formulation and a restricted master heuristic are proposed to solve problems time‐efficiently. Computational results show that the restricted master heuristic outperforms the mathematical model in finding solutions for large‐scale instances. The developed model and heuristic algorithm provide drone delivery services even in areas that are not easily reachable by drones due to being located far from the warehouse and can be effectively applied to humanitarian logistics.

SCIENTIFIC AND TECHNICAL APPROACHES TO BUILDING A SYSTEM OF DELIVERY OF MATERIAL AND TECHNICAL EQUIPMENT FOR THE NEEDS OF THE AIR FORCE UNITS OF THE ARMED FORCES OF UKRAINE WITH THE USE OF TRUCK UAV COMPLEXES

The possibility of using cargo unmanned aerial systems to solve the tasks of operational delivery of spare parts for weapons and military equipment, other material and technical means to units and separate units of the Air Force of the Armed Forces of Ukraine was considered. The analysis of modern developments of cargo unmanned aerial systems (unmanned aerial vehicles) of the leading countries of the world for military needs was carried out. The question of the formation of external accounting of unmanned aerial systems based on transport unmanned aerial vehicles has been voiced), scientific and technical approaches to the construction of a perspective system of providing material and technical means to military units and units of the Air Force of the Armed Forces of Ukraine using unmanned aerial means of cargo delivery have been proposed.
 The main advantage of using cargo unmanned aerial systems and devices as part of the supply system of material and technical means for the needs of air defense units is to increase the efficiency of solving the tasks of delivering, first of all, the necessary acutely scarce elements of spare parts that have a limited working life before failure. The timely replenishment of spare parts sets is an important task within the framework of their planned deliveries, but it becomes most relevant in the event of the need for emergency delivery of a spare element required during the current repair of the sample, but for some reason not included in the regular set.
 The specified scientific and technical approaches determine the possibilities of creating a promising system for the delivery of military cargo using specialized flying robotic systems based on transport (cargo) unmanned aerial vehicles, which allows solving the task of delivering military cargo with an economically justified method in various conditions of the operational and logistical situation, including and to individual units of the Air Force of the Armed Forces of Ukraine, which are located in hard- to-reach areas in case of possible destruction of road infrastructure facilities.

Parallel drone scheduling vehicle routing problems with collective drones

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behavior of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently.In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones and we introduce for the first time the variant with multiple trucks, called the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones. For the latter version of the problem, we propose two Constraint Programming models and a Mixed Integer Linear Programming model.An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models’ behavior on the version with multiple trucks. Some insights about future research are finally discussed.

Mathematical models for the electric vehicle routing problem with time windows considering different aspects of the charging process

This paper addresses the electric vehicle routing problem with time windows (E-VRPTW), considering the battery’s state of charge (SoC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SoC$$\\end{document}) and the recharging process's linearity and non-linearity. We compare two proposed models: the first assumes a linear charging process, and the second evaluates the impact generated by including the non-linearity of the battery recharging process. The non-linear model considers the limitation of the state of charge and restricts the deep battery discharge during movement. Additionally, the effect of overload on the supplied energy process has been evaluated to extend the batteries' useful life. The models are tested on instances commonly used in the literature. The obtained results verify that including the non-linearity recharging process reduces the total time of the routes. Indeed, by accessing the upper sections of the recharge curve (>85%SoC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 85\\% \\; SoC$$\\end{document}, the more significant degradation), autonomy is obtained to avoid unnecessary visits to stations. In addition, including the option to carry out a fast recharge could reduce the total time, even reducing the number of vehicles necessary to carry out the delivery tasks and the maximum time defined by each route.

Optimization of the drone-assisted pickup and delivery problem

The proposition of utilizing unmanned aerial vehicles (UAVs), colloquially known as drones, for the purpose of delivery was ushered into the mainstream by Amazon in 2013. Since then, UAVs have quickly gained traction as a feasible option for last-mile operations in transport and logistics. In the context of this research, we construct a mathematical framework aiming to depict the collaborative interaction between trucks and drones in coordinating pickup and delivery tasks with the objective of minimizing operational costs. The drone-assisted pickup and delivery problem (DAPDP) is a variant of the problem in which a single truck departs a depot with parcels and a UAV on board. As the truck picks up packages and makes deliveries, the UAV can also be used to make deliveries to customers near the truck’s position. As the unmanned aerial vehicle (UAV) embarks on its delivery task, the truck continues on its route, making further deliveries along the way and retrieving the UAV at another customer location different from the launch point. The model is presented as a mixed integer linear program (MILP), and a novel heuristic solution approach based on the classic Clarke–Wright savings heuristic is proposed. Our heuristic’s efficacy is evaluated in comparison to a scenario involving only trucks, with a comprehensive series of numerical experiments conducted to underscore the advantages of incorporating UAVs into the pickup and delivery problem. Finally, we perform a comprehensive sensitivity analysis of key drone parameters in order to demonstrate their impact.

Modeling package delivery acceptance in Crowdshipping systems by Public Transportation Passengers: A latent class approach

The subject of this research is to model the probability of accepting package delivery tasks in Crowd-Shipping (CS) systems focusing on shipping packages by Public Transportation (PT) passengers. Real-world implementation of such a PT-based CS initiative requires a proper grasp of the behavior of crowd shippers, package receivers, and senders, among which crowdshippers are at the center of attention. Given survey data collected from 2,208PT passengers in the Sydney metropolitan area, this study constructs a model for package delivery task acceptance among PT passengers who express a willingness to participate as crowdshippers. A Latent Class (LC) choice model is developed and estimated to explore different classes of participant characteristics and preferences for accepting CS delivery tasks, under different levels of offered incentive, package weight, and required detour distance at the destination. As a key finding, the model identifies three classes, namely “leisurely”, “avid”, and “skeptical” users with class sizes of 19%, 53%, and 28%, respectively. Leisurely users tend to be majorly employed with relatively higher ages and lower PT trip frequency per month. This class shows a potential desire to take CS tasks as long as they are not logistically burdensome. Avid users, forming the largest class of the population, are mostly younger PT passengers who are less sensitive to task difficulty and might accept even CS tasks with minimum incentives (around AU$3). Skeptical users, displaying high PT trip frequency per month and high income, are unwilling to accept the CS tasks unless get compensated well.

Prioritization of crowdsourcing models for last-mile delivery using fuzzy Sugeno–Weber framework

Modern technologies provide new opportunities for the industry but raise the expectations of the customers as well. This is valid also for the postal, courier, and logistics industries. The biggest challenge for the companies in the field is to improve the efficiency of the final phase in the transfer of shipments – last-mile delivery. A contemporary solution relates to collaboration between delivery companies and citizens who are willing to perform the delivery tasks as an additional activity to their established job or routine. Such a concept is known as crowdsourcing. There are several approaches within crowdsourcing, for example, transportation by car, utilizing public transport, etc. To make an appropriate choice, multiple criteria should be considered. This paper aims to propose an original methodology for solving the explained multi-criteria decision-making problem. The proposed framework is based on the application of the Sugeno–Weber nonlinear functions in a fuzzy environment. The application of the fuzzy Sugeno–Weber weighted assessment methodology showed that the proposed methodology has adaptability, a high degree of generalization, and stability of results. This enables the applicability of the methodology to various tasks from a broad spectrum of areas, which represents an additional value. A real-life case study is provided to solve the prioritization of crowdsourcing models in suburban municipalities of Belgrade, Serbia. The available alternatives and the most important criteria for the crowdsourced delivery model are identified. All criteria are divided into four groups, which define the dimensions of sustainability, including the technical aspect. The results of the conducted research indicate a high level of convenience in using one’s car or daily bus lines for crowdsourced delivery to optimize the entire process of shipment transfer in the observed territory.

Exploring fairness in food delivery routing and scheduling problems

Demand for delivery of take-away meals to customers has been growing worldwide, with deliveries often performed by non-specialised gig economy couriers working for online platform operators such as Deliveroo or Just Eat. This has led to the introduction of the ‘meal delivery problem’, characterised by a series of individual pickup and delivery tasks to be assigned to available couriers. While there is a vast set of algorithms proposed in the literature that aim to minimise total workload, very little attention has been given to equitably distributing work between couriers. We propose a new multi-objective problem that is aiming at distributing orders equitably between couriers as well as minimising total workload, where all information is known upfront. We propose an integer linear programming (ILP) model with a weighted objective function that is used to derive the Pareto front in small-scale problems by exploiting the ϵ−constraint approach. This formulation has been proven to solve in a reasonable time for problems with up to 60 orders, however, the optimal Pareto front can only be computed within a reasonable time for problems up to 30 orders. For problems with more orders, we propose a Variable Neighbourhood Search (VNS) algorithm, for which the fitness evaluation evolves in order to explore a wider set of the solution space. The VNS is compared against the ILP and also tested on more realistic size instances with up to 3123 orders, improving the performance over the business as usual and shows that equitable distribution of work can be achieved alongside reducing the total travelled distance.

Efficient Package Delivery Task Assignment for Truck and High Capacity Drone

Efficient Package Delivery Task Assignment for Truck and High Capacity Drone