Occasional Drivers Research Articles

Integrated People and Freight Transportation: A Literature Review

Increasing environmental and economic pressures have led to numerous innovations in the logistics sector, including integrated people and freight transport (IPFT). Despite growing attention from practitioners and researchers, IPFT lacks extensive research coverage. This study aims to bridge this gap by presenting a general framework and making several key contributions. It identifies, researches, and explains relevant terminologies, such as cargo hitching, freight on transit (FoT), urban co-modality, crowd-shipping (CS), occasional drivers (OD), crowdsourced delivery among friends, and share-a-ride, illustrating the interaction of IPFT with different systems like the sharing economy and co-modality. Furthermore, it classifies IPFT-related studies at strategic, tactical, and operational decision levels, detailing those that address uncertainty. The study also analyzes the opportunities and challenges associated with IPFT, highlighting social, economic, and environmental benefits and examining challenges from a PESTEL (political, economic, social, technological, environmental, and legal) perspective. Additionally, it discusses practical applications of IPFT and offers recommendations for future research and development, aiming to guide practitioners and researchers in addressing existing challenges and leveraging opportunities. This comprehensive framework aims to significantly advance the understanding and implementation of IPFT in the logistics sector.

Spatio-temporal task pricing for shared electric micro-mobility battery-swapping platform with reinforcement learning

Spatial crowdsourcing has emerged in shared electric micro-mobility platforms, compensating occasional drivers (ODs) per task of swapping micro-mobility batteries. As ODs autonomously select tasks only when satisfied with predetermined compensation and travel distance, a traditional uniform pricing strategy results in possible low task completion. To resolve the imbalance between ODs and tasks, this study introduces a spatio-temporal pricing strategy where task prices differ by region and time interval. Considering the daily variations in task distribution and OD availability, the goal is to minimise the platform costs equal to the sum of total OD wages and penalties for uncompleted tasks. The reinforcement learning approach with proximal policy optimisation (PPO) is implemented to generate real-time continuous task prices. A domain-specific masking technique is incorporated to improve the learning process by disregarding the data from inactive grids in loss calculations. Computational results show that the PPO agent strategically raises prices in regions with insufficient ODs according to the OD density level. Further comparison with the mixed integer programming model with perfect information on ODs' willingness-to-accept parameters demonstrates the superior capability of our algorithm in navigating the uncertainties of OD task acceptance. A sensitivity analysis provides insights into the decision of system parameters.

Bundle generation for the vehicle routing problem with occasional drivers and time windows

AbstractIn this paper, we address the vehicle routing problem (VRP) with occasional drivers (ODs) and time windows (TWs). The problem (VRP-OD-TW) is an extension of the VRP-OD, where ODs serve customers within given TWs. Differently from the basic version of VRP-OD-TW, we assume that ODs not only accept single requests, but they can also serve bundles of requests. To deal with the bundle-to-driver assignment problem, an auction-based system has been designed; a company offers a set of bundles to the ODs, who bid for all the bundles they consider attractive. There is no limit on the number of bids a driver can place, but at most one bid per OD can be assigned to avoid infeasible workloads. This system could yield a large cost reduction for the company, but its success is strongly related to the bundles offered. Hence, determining bundles which are attractive for ODs and profitable for the company, becomes a crucial issue. We propose two different bundling strategies, which make use of a spatial-temporal representation of customers in a three-dimensional (3D) space. The former is based on the generation of 3D corridors, while the latter relies on 3D clustering techniques. Through extensive computational results, we show that the former technique outperforms the latter in terms of both solution quality and computational times and that both the approaches strongly outperform bundle generation techniques that neglect the temporal dimension and rely only on spatial information.

Bi-objective Sustainable Crowdshipping with Multi Types of Occasional Drivers

The current work presents a deterministic bi-objective sustainable crowdshipping problem optimizing both the cost and CO2 emissions, referred to as the Bi-Objective Vehicle Routing Problem with Multi types of Occasional Drivers and Time Windows (BO-VRPMODTW). The goals of this work are to verify the trade-of between transportation cost and environmental emissions generation, considering different types of ODs, and to verify the impact in different location spreading for customers. Preliminary experimental results show the gain both in terms of cost and CO2 emissions of having ODs of different types. The use of ODs in the non-dominated solutions is also analyzed.

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

The two-echelon vehicle routing problem with time windows, intermediate facilities, and occasional drivers

The pickup and delivery problem with multiple depots and dynamic occasional drivers in crowdshipping delivery

In recent years, crowdshipping has gained much popularity because it not only has economic and environmental advantages, but also is regarded as an effective way to increase the resilience of supply chains and logistics. In this work, we focus on a last-mile delivery system that supports its O2O e-commerce partner for processing and fulfilling online orders in an urban area. Besides the full-time regular drivers, the system also relies on occasional drivers from society who dynamically announce their availability over the time horizon. We model the studied problem as a multi-depot pickup and delivery problem with dynamic occasional drivers. An event-based rolling horizon solution approach is proposed to solve the problem through iteratively calling for a re-optimization procedure to update the delivery plan at each decision epoch. Besides, we investigate the scenarios of disruptions to the delivery system. Extensive numerical experiments have proven the effectiveness of our proposed approach and have also demonstrated the studied crowdshipping system can help achieve the supply chain and logistics resilience. Moreover, sensitivity analysis on several important parameters have been carried out and managerial insights are drawn.

Towards a framework for development of crowd logistics: Paving the way for sustainable logistics

Crowd logistics (CL) can be understood as a concept in which logistics activities are outsourced to the occasional drivers selected from a pool of private or public travelers, and the operations are mediated by an online platform to attain benefits for the involved stakeholders. CL currently stands at a young state of maturity. Prior literature has identified the promising potential to help alleviate conventional business logistics' sustainability concerns, and researchers have advocated the need for extensive research for the future development of CL. However, the domain still lacks a developmental framework that explicitly studies factors affecting CL development and provides a roadmap for further growth. This study systematically reviews a portfolio of seventy-nine articles using the quantitative content analysis technique to identify the temporal trends in CL literature and collates lists of 11 barriers, 11 drivers, and 10 enablers to CL development. Following the systematic literature review, this study appraises the barriers, drivers, and enablers using content analysis and proposes a preliminary framework for CL development. At the theoretical level, we propose a developmental framework and supplement it with relevant recommendations for operationalization and testing, followed by several research questions to open avenues for future research. At a managerial level, our study helps practitioners with suggestions to adapt the proposed framework for CL businesses to foster sustainable CL systems. This study offers valuable insights about the factors that are currently driving [hindering] (or are likely to drive [hinder]) CL development and how the facilitating aspects can be explored to ensure further development of CL.

Deep reinforcement learning for stochastic last-mile delivery with crowdshipping

We study a setting in which a company not only has a fleet of capacitated vehicles and drivers available to make deliveries but may also use the services of occasional drivers (ODs) willing to make deliveries using their own vehicles in return for a small fee. Under such a business model, a.k.a crowdshipping, the company seeks to make all the deliveries at the minimum total cost, i.e., the cost associated with their vehicles plus the compensation paid to the ODs.We consider a stochastic and dynamic last-mile delivery environment in which customer delivery orders, as well as ODs available for deliveries, arrive randomly throughout the day, within fixed time windows.We present a novel deep reinforcement learning (DRL) approach to the problem that can deal with large problem instances. We formulate the action selection problem as a mixed-integer optimization program.The DRL approach is compared against other optimization under uncertainty approaches, namely, sample-average approximation (SAA) and distributionally robust optimization (DRO). The results show the effectiveness of the DRL approach by examining out-of-sample performance.

The Two-Echelon Vehicle Routing Problem with Transshipment Nodes and Occasional Drivers: Formulation and Adaptive Large Neighborhood Search Heuristic

This research introduces a new variant of the two-echelon vehicle routing problem (2EVRP) called the two-echelon vehicle routing problem with transshipment nodes and occasional drivers (2EVRP-TN-OD). In addition to city freighters in the second-echelon network, a set of occasional drivers (ODs) is available to serve customers. ODs are the basis of a crowd-shipping system in which crowds with planned trips are willing to take detours to deliver packages in exchange for some compensation. To serve customers, ODs collect the assigned packages at either satellite served by first-echelon trucks or transshipment nodes served by city freighters. We formulate this problem as a mixed-integer nonlinear programming model and develop an adaptive large neighborhood search (ALNS) to solve it. New problem-specific destroy and repair operators and a tailored local search procedure are embedded into ALNS to deal with the problem’s unique characteristics. The experiments show that the proposed ALNS effectively solves 2EVRP-TN-OD by outperforming Gurobi in terms of both solution quality and computational time. Moreover, the experiments confirm that employing occasional drivers leads to lower operational costs. Sensitivity analyses on the characteristics of occasional drivers and the impact of transshipment nodes are presented as interesting managerial insights from 2EVRP-TN-OD.

Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy

Nowadays, last-mile logistics represents the least efficient stage of supply chains, covering up to 28% of the total delivery cost and causing significant environmental emissions. In the last few years, a wide range of collaborative economy business models has emerged across the globe, rapidly changing the way services were traditionally provided and consumed. Crowd logistics (CL) is a new strategy for supporting fast shipping services, entrusting the management of the last-mile delivery to the crowd, i.e., normal people, who agree to deliver goods to customers located along the route they have to travel, using their own transport means, in exchange for a small reward. Most existing studies have focused on evaluating the opportunities and challenges provided by CL through theoretical analysis and literature reviews, while others have proposed models for designing such emerging distribution networks. However, papers analyzing real successful applications of CL worldwide are lacking, despite being in high demand. This study attempted to fill this gap by providing, at first, an overview of real CL applications around the globe to set the stage for future successful implementations. Then, the implementation potential of CL in northern Italy was assessed through a structured questionnaire delivered to a panel of 214 people from the Alma Mater Studiorum University of Bologna (Italy) to map the feasibility of a crowd-based system in this area. The results revealed that about 91% of the interviewees were interested in using this emerging delivery system, while the remaining respondents showed some concern about the protection of their privacy and the safeguarding of the goods during transport. A relevant percentage of the interviewees were available to join the system as occasional drivers (ODs), with a compensation policy preference for a fixed fee per delivery rather than a variable reward based on the extra distance traveled to deliver the goods.

The vehicle routing problem with simultaneous pickup and delivery and occasional drivers

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total compensation paid to employed occasional drivers. We cast the problem into a mixed integer linear programming model and propose a simulated annealing (SA) heuristic with a mathematical programming-based construction heuristic to solve newly generated VRPSPDOD benchmark instances. The proposed SA incorporates a set of neighborhood operators specifically designed to address the existence of regular vehicles and occasional drivers. Extensive computational experiments show that the proposed SA obtains comparable results with the state-of-the-art algorithms for solving VRPSPD benchmark instances – i.e., the special case of VRPSPDOD – and outperforms the off-the-shelf exact solver – i.e., CPLEX – in terms of solution quality and computational time for solving VRPSPDOD benchmark instances. Lastly, sensitivity analyses are presented to understand the impact of various OD parameters on the objective value of VRPSPDOD and to derive insightful managerial insights.

Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints

In this work, we study last-mile delivery with the option of crowd shipping. A company uses occasional drivers to complement its fleet in the activity of delivering products to its customers. We model it as a variant of the stochastic capacitated vehicle routing problem. Our approach is data-driven, where not only customer orders but also the availability of occasional drivers are uncertain. It is assumed that marginal distributions of the uncertainty vector are known, but the joint distribution is difficult to estimate. We optimize considering a worst-case joint distribution and model with a strategic planning perspective, where we calculate an optimal a priori solution before the uncertainty is revealed. A limit on the infeasibility of the routes due to the capacity is imposed using probabilistic constraints.We propose an extended formulation for the problem using column-dependent rows and implement a branch-price-and-cut algorithm to solve it. We also develop a heuristic approximation to cope with larger instances of the problem. Through computational experiments, we analyze the solution and performance of the implemented algorithms.

An Auction-Based Multiagent Simulation for the Matching Problem in Dynamic Vehicle Routing Problem with Occasional Drivers

This research incorporated an auction mechanism into the vehicle routing problem with occasional drivers and produced simulations in an agent-based environment. Auctions were used to match online orders with potential occasional drivers. While a centralized system optimizes system performance under global objectives, the novel decentralized approach presented here illustrates emergent phenomena resulting from the interaction of individual entities in highly dynamic cases. In the simulations, the auctions were executed after a fixed time interval called a rolling time horizon. Our results suggest that the appropriate rolling time horizon produces a lower average unit compensation cost because better matches can be found when the accumulation of online orders and occasional drivers is maintained at a certain level. The simulation results also indicate that the use of an auction mechanism instead of simple nonauction rules can improve the average unit compensation cost by up to 25.1%.

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

In this work, we study a flexible compensation scheme for last-mile delivery where a company outsources part of the activity of delivering products to its customers to occasional drivers (ODs), under a scheme named crowdshipping. All deliveries are completed at the minimum total cost incurred with their vehicles and drivers plus the compensation paid to the ODs. The company decides on the best compensation scheme to offer to the ODs at the planning stage. We model our problem based on a stochastic and dynamic environment where delivery orders and ODs volunteering to make deliveries present themselves randomly within fixed time windows. The uncertainty is endogenous in the sense that the compensation paid to ODs influences their availability. We develop a deep reinforcement learning (DRL) algorithm that can deal with large instances while focusing on the quality of the solution: we combine the combinatorial structure of the action space with the neural network of the approximated value function, involving techniques from machine learning and integer optimization. The results show the effectiveness of the DRL approach by examining out-of-sample performance and that it is suitable to process large samples of uncertain data, which induces better solutions.

Prevalence of medical factors related to aging among older car drivers: a multicenter, cross-sectional, descriptive study

AimAn increasing number of older adults in Japan are at an increased risk of road traffic crashes. This study aimed to investigate the prevalence of potential underlying medical factors that increase the risk of road traffic crashes among older people.MethodsThis cross-sectional observational study was conducted in 11 medical institutions in Japan using self-administered questionnaires and physical examination from January to May 2021. The background and social data, data on the use of nursing care insurance, and clinical data suggestive of polypharmacy, sarcopenia, cognitive impairment, and frailty/oral frailty were obtained. The prevalence of these factors was compared between everyday and occasional drivers.ResultsData of 127 patients were collected; their median (interquartile range) age was 73 (70–78) years. Of the total participants, 82 were men (64.6%) and 45 were women (35.4%). There were 77 everyday drivers and 50 occasional drivers. Of these, 121 (95.3%) had not applied for nursing care insurance, but the numbers of those who required help 1 and 2 were 1 (0.8%) and 3 (2.4%), respectively. Prevalence of medical factors was as follows: polypharmacy, 27.6%; sarcopenia, 8.7%; dementia, 16.4%; frailty, 15.0%; and oral frailty, 54.3%; it was not significantly different between every day and occasional drivers. Intention to return the car license was significantly higher among the occasional drivers (2.6% vs. 14.0%; odds ratio: 6.7, 95% confidence interval: 1.2–70.6, p = 0.024).ConclusionWe uncovered the prevalence of medical factors that can be associated with road traffic crashes among Japanese older people aged ≥ 65 years in our community.

The crowd-shipping with penalty cost function and uncertain travel times

The vehicle routing problem with time windows and occasional drivers (VRPODTW) is an extension of the vehicle routing problem with time windows, where ordinary people may perform deliveries supporting company drivers to serve a set of customers. We consider a VRPODTW variant with uncertain travel times and a penalty for each missed delivery, i.e., when either company drivers or occasional drivers arrive after the ending of the time window and the delivery does not take place. We formulate the problem with a chance-constrained stochastic model imposing a probability on the maximum number of missing deliveries. Then, we propose an equivalent adjustable robust formulation via uncertain polytope whose feasibility guarantees the probability constraint. We define two optimal solution approaches based on Benders’ decomposition and column-and-row generation. For the former, we consider logic and optimality cuts. The column-and-row generation relies on a relaxation of the uncertain polytope where meaningful realizations of the uncertain travel times are included on the fly. Numerical results are collected on benchmarks for the VRPODTW, properly modified to take into account the uncertainty. We analyze the behavior of the proposed optimal strategies, and discuss the benefit of addressing the uncertain problem showing better resource allocation with the robust solutions compared with the nominal ones, via a sampling analysis.

Multi-Objective Two-Echelon City Dispatching Problem With Mobile Satellites and Crowd-Shipping

Recently, a two-echelon city dispatching model with mobile satellites (2ECD-MS) has been proposed to reduce costs effectively. However, in addition to costs, speeds of delivery to customers are increasingly demanding in urban dispatching. This work extends 2ECD-MS to 2ECD-MS-CS by adopting the crowd-shipping model in the second-echelon dispatching, which uses occasional drivers of private vehicles to deliver parcels to improve the delivery speed. Furthermore, existing works generally consider the optimization from a single aspect, e.g., the delivery company. However, the sustainable development of a logistics company must also focus on other subjects in logistics activities, such as customers and delivery employees. So, we define a multi-objective model considering company cost, customer satisfaction, and income satisfaction of crowd-shippers simultaneously. The multi-objective optimization problem of 2ECD-MS-CS is solved by a multi-directional evolutionary algorithm (MDEA). In MDEA, multiple neighborhood operators are designed and combined with the multi-directional search strategy to fully explore the Pareto Front. Finally, we generate 40 new 2ECD-MS-CS instances based on existing common vehicle routing datasets. Experimental results show that 2ECD-MS-CS reduces the average cost by 3.4% and improves the delivery speed by 42% against 2ECD-MS in 40 instances with different customer scales, numbers of mobile satellites, and geographic scopes. The proposed MDEA outperforms several popular multi-objective optimization algorithms in both convergence and diversity. These illustrate the advantages of 2ECD-MS-CS especially in terms of delivery speed and the effectiveness of the proposed MDEA.

Use of mobile technologies for driving, road accidents, health and psychological variables in Colombian drivers

The aim of this investigation is to show the relationships between the use of Mobile Driving Applications and Devices (mdad) with self-reported road accidents, anger, physical health, and subjective wellbeing, in three types of Colombian drivers. The sample is non-randomized (n = 1149) and 93.2% are male drivers. Results showed that (a) for professional drivers, there is an inverse correlation between the use of mdad with lower driving frequency during weekdays and a higher educational level, (b) for occasional drivers, gps use was directly associated with fatal road accidents and low driving frequency during weekdays, (c) for private drivers, correlations were found between mdad broader usage, higher levels of both aggression and perceived driving difficulty as well as physical health problems mainly hypertension and diabetes. It is necessary to conduct in-depth research on mdad uses, health, and driving styles, by controlling social desirability.

Crowdshipping: An open VRP variant with stochastic destinations

E-commerce continues to grow throughout the world due to people’s preference to stay at home rather than going to a brick-and-mortar retail store. COVID-19 has exacerbated this trend. Concurrently, crowd-shipping has been gaining in popularity due to both the increase in e-commerce and the current pressures due to COVID-19. We consider a setting where a crowd-shipping platform can fulfill heterogeneous delivery requests from a central depot with a fleet of professionally driven vehicles and a pool of capacitated occasional drivers. We divide delivery requests into sectors to represent different neighborhoods in a city. Occasional drivers have unknown destinations that can be anywhere inside the sectors. Route duration constraints are modeled to motivate participation and increase the probability of route-acceptance by keeping routes short. We assume that occasional drivers will choose routes that are better compensated and that the probability of route-acceptance is dependent on other routes being offered. We propose a two-stage stochastic model to formulate the problem. We use a branch-and-price algorithm capable of solving 50-customer instances, and develop a heuristic that can solve larger 100-customer instances quickly. An upper bound for the total number of occasional drivers is used to reduce the number of constraints in the master problem and reduce the complexity of the pricing problems. We show that occasional drivers with destinations far from the depot reduce the cost by over 30%, while occasional drivers with destinations that are near the depot reduce the cost by 20%. We show that route duration constraints and capacity constraints can restrict the occasional driver routes and both need to simultaneously increase in order to have cost reductions. This setting of crowd-shipping is a viable option for last-mile deliveries.

Estimation of the arrival time of deliveries by occasional drivers in a crowd-shipping setting

The success of e-commerce business offering same-day delivery depends on customer satisfaction. To speed up deliveries and lower costs, some companies have been using private individuals as non-dedicated drivers to perform pickup and delivery tasks for online customers. Such delivery systems are known as crowd-shipping. Customers have come to expect an accurate estimate for the delivery times of their online orders. The coordination of online deliveries with private individuals is done by a crowd-shipping platform. In this paper, we focus on the estimation of pickup and delivery times. This is a challenging job because not only are the requests unknown and submitted dynamically, but so is the pool of drivers, i.e. delivery capacity. We model the problem as a Markov decision process and integrate it into a simulation study. To improve the estimates that can be done by a naive policy, we propose two policies that use lookahead: one with a fixed lookahead horizon and one with a dynamic. Our numerical experiments demonstrate that a lookahead policy with dynamically adjusted horizon outperforms the other two policies in terms of estimation accuracy, which is up to 19% higher in some instances.