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Drones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the singlecapacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system. [ABSTRACT FROM AUTHOR]

This study introduces a novel parcel locker location problem in the last mile delivery where each parcel locker is equipped with a set of selectable volume sizes and provides services for two types of customers: commuters and home-based customers. We establish a pickup cost model to quantify the effort exerted by customers during the retrieval process. Furthermore, we design a satisfaction function to measure customers' willingness to retrieve items from a specific parcel locker. Finally, we implement a financial incentive mechanism to enhance customer satisfaction by reducing the pickup cost. To solve this problem, we formulate a problem-specific scatter search algorithm. Extensive experiments demonstrate the computational effectiveness of the proposed algorithm. Furthermore, sensitivity analyses show that the selectable volume size model can reduce the total cost by up to 16.8%, while "pickup on the way" can achieve a reduction of up to 63.97% in our instances. [ABSTRACT FROM AUTHOR]

The increasing demand for last-mile delivery has created a challenge for urban consolidation centers (UCCs). Due to UCC's limited delivery capacity, it has to purchase spare capacity from carriers. In the procurement, the UCC cannot observe the carriers' efforts, resulting in low-quality logistics services. Thus, we propose a reverse auction mechanism considering logistics service quality (LSQ) for the UCC's procurement of last-mile delivery capacity. First, we construct the benchmark of the mechanism by introducing the LSQ in last-mile delivery and presenting a reverse auction. Then, we develop an auction model with ideas of ensuring the UCC's incentive and awarding business to the carriers who deliver high-quality services. The case study shows that the proposed mechanism is an efficient procurement tool for the UCC with a guarantee of LSQ, and the UCC will benefit from the proposed mechanism by significant total cost saving as well as effective use of resources. [ABSTRACT FROM AUTHOR]

Last mile delivery is a significant component in the growth of e-commerce, especially in urban areas. The deliveries in dense cities cause several serious issues such as air/noise pollution and congestion. In this study, we compare the financial costs and CO2 emissions from different last mile delivery strategies of grocery bins in a scenario in Paris with four types of delivery vehicles (diesel/electric vans, cargo bikes, and autonomous delivery vehicles). We use costs associated with each vehicle type, location data with constraints for establishments in Paris, and delivery scenarios obtained by solving the vehicle routing problem for an overall cost comparison. We consider an economic analysis of the daily delivery to optimise the costs and estimate the CO2 emissions. The results highlight the competitiveness among last mile delivery and reflect the transition knowing the transport expenses since 2018: electric van costs become competitive with diesel ones, it concluded by some perspectives with cargo bikes and autonomous vehicles with their current limits. The strategies show a great impact on overall cost and emissions, which are vital in the development of sustainable urban last-mile delivery. [ABSTRACT FROM AUTHOR]

With the increasing challenges in last mile logistics, a variety of promising approaches of autonomous delivery concepts have been emerging in recent years. Due to the fast pace of advancements, several approaches have been developed, the characterization of them often by purpose rather than systematically. This study builds upon a multi-stage research process that includes a systematic literature review and structured taxonomy building process, as well as a hierarchical cluster analysis of 45 existing autonomous delivery concepts. Based on that, two main results were achieved. First, a holistic taxonomy with 13 categories - including technological, process-related and customer acceptance characteristics - was developed to achieve characterizing of autonomous delivery concepts in a standardized way. The second result centered on the taxonomy, where four clusters could be identified through the hierarchical cluster analysis. With the developed clusters it is possible to better categorize and compare individual autonomous delivery concepts. [ABSTRACT FROM AUTHOR]

The growth of e-commerce has led to an increase in delivery options, and various innovations, such as autonomous delivery robots (ADRs) are being developed to meet the important challenges of the last mile. Currently, in the testing state, consumer acceptance remains relatively unknown. This study aims to identify the factors that affect the level of consumer acceptance of autonomous robotic delivery in urban areas using consumers' current knowledge of this technology. To answer this question, the factors that negatively or positively influence user acceptance are first determined, and then the validity of the relationships between the factors and user acceptance are tested empirically. The results show that participants of this study are neutral to ADRs, which is reasonable for newly developed technology. Utilitarian factors, such as performance expectancy, are the most important for potential users, followed by social influence, trust in technology, and innovativeness. Price sensibility, perceived risk, and hedonic motivation do not seem to affect the acceptance of ADRs at the current stage of development, whereas effort expectancy shows an atypical negative effect on the behavioural intention to use this technology. However, this study has several limitations that need to be considered, notably the small sample size, which does not allow the generalisation of the results to the French population. [ABSTRACT FROM AUTHOR]

AbstractLast mile delivery is an important and growing part of the supply chain that has a sizeable negative environmental impact. This paper considers more sustainable approaches to home-delivery that are pragmatic for many non-rural environments. We address the two-echelon, multi-trip, capacitated vehicle routing problem with home-delivery and optional self-pickup services using different combinations of drones, trucks, and electric-assisted bikes (i.e. e-bikes). In the proposed approach, parcels are transported from a depot to parcel lockers by either drones or trucks and are then delivered to customer locations by either e-bikes or trucks. The four approaches range from the most sustainable (drones then e-bikes) to partly green (drone then truck or truck then e-bike) to finally the traditional (truck then truck). We formulate a mathematical model that determines the vehicle routes to minimize the total cost, which consists of vehicle operational cost and operator wages. The four types of delivery networks are assessed and compared for both costs and emissions. Experimental results suggest that with a modest increase in total cost (as little as 13%), emission reductions of up to 92%, on average, can be achieved when using the greenest delivery strategy of drones and e-bikes. The other delivery options have varying tradeoffs between costs and environmental impact. The percentage of self-pickup customers is an influencing factor to consider when choosing the delivery strategy that best meets the organization’s budget and environmental goals, especially when using e-bikes in the second echelon. [ABSTRACT FROM AUTHOR]

Last-mile delivery systems are one of the numerous industrial applications of uncrewed aircraft systems (UAS). This study analyzes UAS-based package delivery systems in the context of the integrated operations of crewed and uncrewed aircraft. It introduces a multi-trip, time-dependent vehicle routing problem aimed at optimizing UAS delivery routes within integrated operations. By conducting numerical simulations of helicopters (crewed aircraft) and UAS missions sharing the same airspace, the optimal UAS routes for resolving potential conflicts in the shared airspace can be effectively determined without any adverse impact on helicopter operations. Knowing the helicopter mission times in advance enables the efficient planning of UAS missions. The results of this study provide valuable insights regarding the implementation of integrated crewed and uncrewed aircraft operations. [ABSTRACT FROM AUTHOR]

Agri‐environmental policies in England stand on the threshold of significant change, with a new suite of Environmental Land Management schemes set to embody more of the 'public money for public goods' principle. In addition, two tranches of these schemes appear heading towards a more collaborative approch towards delivering these public goods—suggesting that landholder collaborations would be a vital key to achieving this goal on such a scale. Running in parallel with this policy change is a countryside that has been undergoing a transition over the past several decades. This has seen a growing diversification in landholder types ‐ prompting a re‐examination not only with regards to the range of landholders who should be recruited into public goods delivery but the incentivisation strategies needed to recruit them as well. In this article, we examine the limitations of the behavioural approach utilised by past agri‐environmental schemes to incentivise farmer uptake. We then propose the use of a Theories of Practice and Theory of Capital framework that shifts the approach towards a more targeted pattern of incentivisation, one which enables the recruitment of a much broader set of public goods providers into landholder collaboration. To demonstrate how this framework can be applied, we present a case study around a range of collaboration models. Our findings suggest that in order for collaborations to be sustained in the long term, policymakers will need to think more directly with regard to the different aspects of collaboration that different landholders place value in. This would ensure opportunities for various forms of capital to be generated or for the recrafting of practices through intervention points. We conclude that the recrafting of the collaborative conservation practice not only can be accomplished through its constituent elements but by changing its practitioners as well—as exemplified by the different configurations of landholders that make up each of our five models of collaboration. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the intention of carriers to switch to green vehicles in last-mile delivery (GVLDs) in an emerging market. Design/methodology/approach: A research model was developed based on the association between consumption value theory (CVT), perceived GVLD knowledge and environmental concern. An online survey was administered to carriers who use conventional vehicles for last-mile delivery. This study examined the model using structural equation modeling. Findings: Results indicate that environmental concern and attitudes toward adopting GVLDs are significantly cultivated through consumption value. Specifically, epistemic value was the most significant in promoting environmental awareness, while social value was paramount in fostering attitudes toward GVLDs. Furthermore, perceived GVLD knowledge motivated consumption value. Similarly, environmental concern positively impacted attitudes toward adopting GVLDs. Finally, environmental concerns and attitudes induced carriers' intention to switch to GVLDs. Practical implications: The findings can help policymakers, manufacturers and logistics providers understand carriers' decisions of shifting to GVLDs from a perspective of perceived consumption value, green product knowledge and environmental preservation while discussing practical recommendations for promoting the switch to GVLDs. Originality/value: This study is an early endeavor that elucidates the switching behavior toward adopting GVLDs. It emphasizes incorporating perceived GVLD knowledge and environmental concern into CVT in the context of a developing country. This study enriches the understanding of how to promote environmental awareness and attitudes toward adopting GVLDs. [ABSTRACT FROM AUTHOR]

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators' parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application. [ABSTRACT FROM AUTHOR]

The development and potential adoption of drones or unmanned aerial vehicles as delivery vehicles creates incredible opportunities and unique challenges for last mile delivery. This research first presents a last mile delivery fleet model with drones that can be further modified and expanded over time. The model shows the optimal number of drones needed based on deterministic or stochastic demand using both traditional charging and battery swapping. The research then compares the carbon emissions of four delivery modes: traditional internal combustion delivery vehicles, all-electric vehicles, plug-in hybrids, and drones within the context of last mile delivery. Findings reveal that the breakdown of carbon emissions by delivery modality depends on parameter assumptions, ambient temperature, delivery radius, electric grid pollution rate, and number of customers. [ABSTRACT FROM AUTHOR]

In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform's control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers' decisions largely affect the platform's revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers' decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics. Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant "Investissements d'Avenir ANR-16-IDEX-0008"]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129. [ABSTRACT FROM AUTHOR]

This paper introduces a two-echelon optimization model for the integrated routing of an electric vehicle (EV) and a traditional internal combustion engine vehicle (ICEV) in an urban environment. The scientific context of this study is sustainable urban logistics. The case study focuses on the distribution of pharmaceuticals in the metropolitan area of Rome. Distributing pharmaceuticals in large cities presents significant challenges, including heavy traffic congestion, the need for strict temperature control, and the maintenance of the integrity and timely delivery of sensitive medications. Furthermore, the complexity of urban logistics and adherence to regulatory requirements introduce additional layers of difficulty. Therefore, the implementation of fast and sustainable distribution mechanisms is crucial in this context. Specifically, the model seeks to minimize both total CO2 emissions and transportation costs while optimizing the use of an EV and an ICEV, all while ensuring that service level requirements are met. Computational results demonstrate the effectiveness of the proposed method in improving the sustainability of pharmaceutical distribution. [ABSTRACT FROM AUTHOR]

As an emergent technology, drones have grown increasingly integral to the logistics and supply chain management (SCM) sectors. This review paper seeks to address this gap by analyzing a carefully curated dataset of 96 journal articles, sourced from Scopus and Web of Science, concerning drone applications in logistics and SCM. Our study reveals a rising trend in drone-related logistics and SCM research and highlights the growing significance of this technology. Using latent Dirichlet allocation (LDA), we distilled the corpus into ten distinct topics and provided a comprehensive overview of the field's thematic landscape. The most prominently studied themes were drones for humanitarian logistics and SCM and optimization of last-mile delivery using drones and trucks in urban logistics. These areas underscore the promise of drones in addressing humanitarian challenges and urban logistics optimization, respectively. Conversely, the least explored topics were drones safety and utility and drone delivery and consumer behavior in food logistics. To our knowledge, this is the first paper applying LDA to examine drone applications in logistics and SCM, offering novel insights into the current state of the literature. Moreover, based on our findings, we propose a future research agenda to encourage further exploration into under-studied areas and to sustain the momentum of this exciting field. [ABSTRACT FROM AUTHOR]

In view of success stories of unicorn startups from the sharing and gig economy such as Airbnb, DiDi, or Uber, it is not surprising that postal service providers try to transfer the sharing idea toward their last-mile delivery services: owners of under-used assets (here private crowdshippers traveling anyway) are connected with users willing to pay for the use of these assets (here postal service providers having to deliver parcels). In this paper, we consider a special form of crowdshipping where public transport users, steered by a smartphone app, pick up parcels from parcel lockers, take these shipments with them on their subway rides, and deposit these parcels into other lockers. Finally, the actual recipients can pick up their shipments from their most convenient parcel lockers, e.g., on their own way back home from work. We formulate the optimization problem that matches crowdshipping demand and supply and determines the routes along lockers and crowdshippers each parcel takes. Specifically, we allow that each parcel is moved by multiple cooperating crowdshippers and solve this problem with different objective functions capturing the individual aims of the main stakeholders: shippers, crowdshippers, recipients, and the platform provider. We evaluate the relationship of these objectives and quantify the efficiency loss of a more restricted matching policy, where only a single crowdshipper can be assigned to each parcel's complete path between origin and destination. Finally, we also explore the impact of delays and investigate whether specific objectives protect against unforeseen events. [ABSTRACT FROM AUTHOR]

Investments in the digital welfare state are often driven by the promise of removing intermediaries between the state and citizens, yet they continue to play a key role in the last mile delivery of state services. By intermediaries, I mean people who interface between bureaucrats and citizens. Their work, often as proxies for citizens, is not only to simplify bureaucratic procedures for them, but also help insulate them from bureaucratic apathy. Based on 18-months of ethnographic fieldwork, I describe the work of intermediaries around government offices, who (in)visibly support citizens in navigating the bureaucratic procedures of enrolling into Aadhaar, India's biometrics-based national identity number. Building on Julia Elyachar's conception of "phatic labor," I position such intermediaries themselves as infrastructure and illustrate how their affective networks can be leveraged to orchestrate a form of distributive justice to ensure that being marginal does not preclude a citizen's access to welfare services. [ABSTRACT FROM AUTHOR]

In the logistics industry, unmanned aerial vehicles (UAVs) are mostly used for last-mile delivery, in combination with other types of vehicles. There is currently no operator in Taiwan that is completely reliant upon the usage of UAVs for cargo delivery services. Therefore, this study proposes a routing and scheduling model for UAVs by utilizing the network flow technique and mathematical programming methods. All advance requests must be satisfied, and the related operating constraints ensured in the model. The model aims to minimize the total operating cost. To effectively solve large problems that may occur in practice, this study develops a relax-and-fix heuristic. Numerical tests are conducted to preliminarily examine whether the model, coupled with the heuristic algorithm, could be applied in practice. The test results indicate that the proposed model and solution algorithm are effective and thus could be useful for UAV operators to perform delivery routing and scheduling. [ABSTRACT FROM AUTHOR]

Logistics service quality (LSQ) plays a vital role in providing excellent customer experience, particularly in e-commerce. Using mobile devices for food and fresh product orders is very common, but delivering these products is very challenging. In this study, we aim to evaluate the factors influencing logistics service quality (LSQ) in the context of fresh product e-commerce. The relevant literature was reviewed and a preliminary survey among field experts was conducted to establish the proposed LSQ scale. A qualitative study was carried out on fresh product e-commerce customers. A survey involving 222 participants was analyzed, and an LSQ evaluation scale was formed and evaluated scientifically and empirically. Research results showed that reliability, convenience, freshness, and personnel contact quality are the four key dimensions of the LSQ scale in the e-commerce platform for fresh and perishable items. The results of the study can help the managers of e-commerce companies to understand the LSQ criteria that determine customer satisfaction and consequently make the appropriate LSQ improvements. [ABSTRACT FROM AUTHOR]

Purpose: Strategic integration of enablers and the realization of drone delivery benefits emerge as essential strategies for business organizations to enhance operational efficiency and stay competitive in last-mile logistics. This paper aims to explore the benefits of drone-based last-mile delivery in the Indian logistic sector by providing a framework for ranking drone delivery benefits (DDBs) due to the adoption of its enablers. Design/methodology/approach: This study proposes a novel hybrid framework applied in the Indian logistic sector by integrating a sentence boundary extraction algorithm for extracting benefits from literature, a spherical fuzzy analytical hierarchy process (SF-AHP) for evaluating primary enablers, unsupervised fuzzy C-means clustering (FCM) for clustering benefits and a spherical combined compromised solution (SF-CoCoSo) for ranking benefits with respect to primary enablers. Findings: The results reveal that technological and infrastructure enablers (TIE), government and legislation enablers (GLE) and operational and service quality enablers (OSE) are the most significant enablers for drone implementation in logistics. Top-ranked benefits increase the efficiency of last-mile delivery (DDB10), foster supply chain management and logistic sustainability (DDB16) and increase delivery access to rural area and vulnerable people (DDB17). Practical implications: This research assists scholars, entrepreneurs and policymakers in the sustainable deployment of drone delivery in the logistics sector. This study facilitates the use of drones in delivery services and provides a foundation for all stakeholders in logistics. Originality/value: The assessments involve considering judgment from a highly knowledgeable and experienced group in India, characterized by a large volume of inputs and a high level of expertise. [ABSTRACT FROM AUTHOR]

Autonomous delivery robots offer a promising solution to the challenges of last mile logistics, a crucial topic in times of increasing logistics volume, environmental concern, and ongoing urbanization. This study investigates the acceptance of such robots in various application scenarios for last mile delivery in Germany. A first, quantitative, study investigating the overall acceptance of autonomous delivery robots for the primary predominant usage scenarios of meal and package delivery tested an adapted technology acceptance model via a structural equation model. A second, qualitative, study was conducted to better understand possible future use cases for these robots included semistandardized interviews with 14 individuals and employed a qualitative content structuring analysis method for data analysis. The results of Study 1 confirm that performance expectancy and effort expectancy influence the acceptance of delivery robots, with performance expectancy and behavioral intention being significantly higher for the package than for meal deliveries. However, in both cases, the average intention to use these robots only slightly exceeded the scale mean. Study 2 reveals that autonomous delivery robots are perceived as more convenient than existing alternatives in grocery and pharmaceutical delivery and return processing. The further application of emergency situations, such as illness or the risk of infection, also emerged from the data; even respondents who generally reject the idea of such robots would still use them in such exceptional situations. These results should be considered in the further development of autonomous delivery robot systems. [ABSTRACT FROM AUTHOR]

Drone 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. [ABSTRACT FROM AUTHOR]

The economic and environmental challenges associated with Last Mile Delivery (LMD) give rise to the self-collection concept, yet the practical adoption is often below expectations. In this consumer-centric segment, adequate understandings of consumers are vital but are often lacking. To address this gap, this study examines consumers' LMD preference with the Integrated Choice and Latent Variable framework. A stated choice experiment was commissioned in Singapore during the transition of COVID pandemic toward endemic. Our results showed that consumers accept unattended delivery methods and are no longer concerned with the delivery time window, a traditionally critical delivery attribute. Regarding latent perceptions, this study confirmed the significant effects of value perception on self-collection preferences. Nonetheless, individuals' environmental consciousness was found to be insignificant. The relative effects of specific value statements were quantified using the marginal rate of substitution and the mediation roles of value perception on the sociodemographic-preferences relationships were validated. [ABSTRACT FROM AUTHOR]

Acknowledging the significant impact of temperature deviations on vaccine efficacy, this study examines the prevalence of exposure to non-recommended temperatures, particularly focusing on freeze-sensitive vaccines. This study aims to identify critical points of temperature breaches during transportation from storage to last-mile delivery. In this cross-sectional study, the temperature integrity of vaccines has been examined during transportation across three states in India (Karnataka, Maharashtra, and Rajasthan), utilizing data loggers to track and record temperatures in real time. By analyzing instances of exposure to temperatures beyond the recommended 2–8 °C range, the study aims to identify the critical junctures within the vaccine supply chain susceptible to temperature excursions. The methodology entailed a systematic data logger analysis at each stage of transportation up to the last mile. In this study, descriptive statistics with the analysis of variance test has been applied to investigate the significance of variations in the duration of temperature breaches. The novelty of this study lies in its comprehensive examination of vaccine temperature integrity across critical transportation phases in India, utilizing real-time data-logging technology. Based on the results, the study provides recommendations using the ABCD strategic framework, encompassing awareness, best practices, continuous monitoring, and data management and documentation. This framework will be instrumental in addressing the identified challenges and significantly improving the temperature monitoring flexibility. The findings will inform targeted interventions, optimizing the cold chain to enhance the preservation of vaccine potency and identifying flexible solutions for maintaining the required temperatures from storage to transportation. [ABSTRACT FROM AUTHOR]

The aim of this paper was to propose a model for the evaluation of the flow of goods delivered to urban centers based on a systematic approach, generating possible scenarios of delivery activities and selecting those criteria for evaluation that contributed to the development of delivery activities and their evaluation by individual stakeholders. The number of users of delivery services is increasing, which leads to an increase in the volume of goods in the city center, creating additional traffic congestion—so-called bottlenecks—which results in an increased noise level and the emission of harmful exhaust gases, increasing dissatisfaction with the quality of life of the city population. It is, therefore, necessary for decision-makers to make a decision that achieves the best consensus among stakeholders. A test was carried out in the city of Rijeka. Based on a review of the literature and the spatial planning of the city of Rijeka, this paper proposed the following possible scenarios for the delivery of goods within the city center: status quo, delivery from one consolidation center, delivery from two consolidation centers, delivery by environmentally friendly vehicles from one consolidation center and livability. The criteria were evaluated by relevant stakeholders to determine the weight of each criterion for the development of goods deliveries in the city center. This paper used a multi-actor multi-criteria analysis (MAMCA) based on the optimization and ranking of scenarios in accordance with the given objective, the research problem, the defined criteria and the interests of the stakeholders. The solution of the MAMCA analysis evaluated the scenario with two consolidation centers best in accordance with the specified optimization objective. [ABSTRACT FROM AUTHOR]

Background: Various innovations have been proposed to improve the efficiency and sustainability of last-mile delivery in urban and rural environments. Notable examples of innovative delivery solutions are parcel lockers, cargo bicycles, crowdsourced delivery, and so on. Methods: This study contributes to the ongoing research by developing a large-scale digital model of England and Wales to evaluate a new generation of solutions for last-mile delivery challenges being faced in both rural areas and cities. The two innovations chosen for comparison in this study are (i) carrier consolidation and (ii) alternative delivery locations (i.e., delivery to the workplace instead of the home). As well as the effect on any individual locations, the digital model evaluates both the benefits for England and Wales as a whole. Furthermore, the influence of the market share on the results, as well as the effect of changing the number of depots, is assessed. Results: By delivering to the customer's workplace instead of the home, the vehicle kilometres travelled (VKT) reduce slightly (less than 10%). Carrier consolidation shows significant potential in reducing the overall VKT (up to 53%). When looking at individual areas in isolation, the consolidation option reduces the VKT or changes it up and down all within tolerance. Naturally, the first option causes a significant shift in delivery activity across England and Wales. Areas of central London would see in-excess of a 10-fold increase in the number of parcels delivered, whereas the demand for parcels in rural areas is further, and significantly reduced. Conclusions: This study highlights the importance of large-scale and detailed digital models that not only calculate the overall benefits of an innovation but also their effect on each individual area. [ABSTRACT FROM AUTHOR]

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry's digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry. [ABSTRACT FROM AUTHOR]

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV. [ABSTRACT FROM AUTHOR]

Consumers often receive and evaluate temporal information, such as the number of days it will take a package to arrive or the number of days a vacation will last. Across eight preregistered studies (N  =   4,758), we examine how using days-of-the-week information in descriptions of temporal intervals (e.g. "ordered on Monday, February 1, delivered on Saturday, February 6" vs. "ordered on February 1, delivered on February 6") affects consumers' duration perceptions. We propose that the days-of-the-week framing prompts people to rely on narrow-span implicit scales characterized by lower thresholds for magnitude categories. These implicit scales lead people to judge objectively equivalent temporal intervals to be longer, which in turn has downstream consequences for consumers' time-versus-money tradeoffs, work planning, and provider-switching decisions. This research adds to our understanding of how decision context can activate different implicit scales and how these scales shape consumer judgments. [ABSTRACT FROM AUTHOR]

This article reviews and compares the experience of foreign and Russian companies using unmanned aerial vehicles (hereinafter referred to as UAV, drone) in the logistics industry. SWOT-analysis (strengths, weaknesses, opportunities, threats) is conducted which identifies strengths and weaknesses, opportunities and threats of using drones. Moreover, due to the current exi stence of a large number of prohibitions and restrictions on the application of this technology, the challenges of using drones in logistics are indicated. With the help of drones, companies are able to reduce costs and the likelihood of human error in work and thus increase efficiency by automating processes. These devices can optimise the process and improve logistics operations in a company. UAVs are the future not only of logistics but also of the technology industry as a whole. With the increasing scale of activity, each company tries to find and create new technologies that attract consumers and make its work more unique. The introduction of UAVs may become the means by which organisations can not only improve their activities by optimising and modernising processes, but also increase the visibility and scale of this performance.

Truck-and-drone routing problems have become an important research topic in the last decade because of their applications for last-mile deliveries. Despite the many publications in this area, the most efficient exact algorithms designed thus far struggle to solve the benchmark instances with 39 or more customers. This fact holds even for one of the simplest variants involving one truck and one drone whose routes must synchronize at customers' locations: the basic traveling salesman problem with a drone (TSP-D). In this article, we devise a new algorithm for the TSP-D that solves every benchmark instance with up to 59 customers, and it scales up to 99 customers when the drone is much faster than the truck. The core of our method is a dynamic programming algorithm that is applied for column generation and variable fixing within tailored decremental state-space relaxation strategies. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Fondo para la Investigación Científica y Tecnológica [Grant PICT-2018-2961] (Ministry of Science, Argentina). Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0390. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and implementation of a versatile delivery robot which integrates a secure locking system, GPS navigation, customer authentication, and a camera system for real-time monitoring. The advanced locking system ensures the protection of delivered goods during transit, addressing security concerns. GPS navigation optimizes routes for efficient last-mile deliveries, reducing overall delivery times. Customer authentication adds an extra layer of security, allowing only the designated customer, equipped with a unique authentication method, to unlock and retrieve their parcel. The integrated camera system provides continuous monitoring throughout the delivery process. The combination of these features showcases technological innovation and also addresses critical aspects of security and customer trust in autonomous deliveries. The successful integration of these features positions the delivery robot as a reliable and customer-friendly solution for the evolving landscape of last-mile logistics. [ABSTRACT FROM AUTHOR]

Driven by Chinese double carbon policy, logistics companies should ensure both environmental preservation and fluency in the last-mile delivery. With the objective of minimizing the total distribution distance, a model for location of mobile recharge stations and the route planning is formulated, which considers some constraints such as load, power and service capacity of recharge stations. Subsequently, a two-stage algorithm is proposed. The genetic algorithm is used to generate the initial routing plans in the first stage, and the RSI algorithm is designed to locate recharge stations and adjust routing plans in the second stage. Tested by the CVRP benchmark cases, the results show that the average increasing rate of travel distances caused by visiting recharge station is less than 5%. Compared with PSO and SA, the results between the proposed algorithm and the other two algorithms are respectively -4.04% and -3.65%. Also, the adaptability of the model is verified through the sensitivity analysis with the main model parameters such as power consumption rate. Therefore, the mode is feasible if logistics companies can afford to use exclusive mobile recharge stations and accept the increase of travel distances which is less than 8%. [ABSTRACT FROM AUTHOR]

The primary goal of this research is to identify which factors most significantly influence customer satisfaction in the last-mile delivery (LMD) process. The sample comprised 907 participants (63.4% female) with a mean age of 34.90. All participants completed three questionnaires regarding LMD, customer satisfaction, and trust in courier service. Furthermore, participants answered questions related to significant aspects of the delivery process: speed, price, and courier call before delivery. To determine which factors most significantly influence customer satisfaction in LMD, structural equation modeling (SEM) was applied. The tested SEM model showed a good fit. The results indicated that within the LMD dimension, visual appeal was a significant predictor in a negative direction, and all other LMD dimensions (except parcel tracking) were positive and significant predictors of customer satisfaction. Trust in courier service, delivery price, speed, and courier call before delivery were statistically significant predictors of customer satisfaction in last-mile delivery, all in a positive direction. [ABSTRACT FROM AUTHOR]

With the improvement of productivity and the development of internet and information technology, many e-commerce systems and supply chain logistics systems have emerged, allowing for online purchases of food such as vegetables and fruits, as well as clothing and shoes. Nowadays, online shopping platforms such as Duoduo Maicai, Taobao, JD, and Meituan Youxuan have been integrated into people's lives, and various supermarkets and shopping malls are also everywhere. Therefore, the safety, efficiency, and real-time location of goods delivery need to be taken into account. Traditional supply chain logistics has problems such as requiring a large amount of manpower, inaccurate system positioning, inaccurate designed delivery routes, low efficiency, and inability to understand the status of drivers and vehicles. GIS (Geographic Information System) can assist systems in designing delivery routes, obtaining real-time locations of drivers or goods, and navigating. Therefore, in order to save manpower and ensure the safety and efficiency of goods delivery, this article used genetic algorithms to optimize and design a supply chain logistics intelligent management information system for GIS. This system had three modules, namely the client, driver, and terminal. The client mainly had functions such as publishing orders, checking the estimated arrival time of goods, and learning the real-time location of goods. The driver end mainly had functions such as real-time positioning, navigation, communication, viewing delivered orders, and monitoring cameras. The terminal mainly had functions such as viewing driver monitoring cameras and managing background information. Experiments showed that optimizing GIS could more accurately locate drivers and goods, assist drivers in navigation, assist systems in arranging delivery routes, and calculate the estimated arrival time of goods, resulting in an efficiency improvement of nearly 12%. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a last-mile delivery concept that is well suited for urban areas. By jointly optimizing the tour of a truck and a cargo bike, we ensure that each vehicle is used optimally. Here, we assume that the bike is restocked by meeting up with the truck so that no dedicated mini-hubs have to be constructed. We model different objective functions and analyze the different variants in comparison to the traveling salesperson problem as well as the capacitated vehicle routing problem. In an experimental evaluation, we compare MIP formulations for different problem variants and assess several heuristic approaches to solve large-scale instances. These results show that we can outperform the truck-only delivery in terms of completion time while reducing the distance driven by the truck. [ABSTRACT FROM AUTHOR]

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products. [ABSTRACT FROM AUTHOR]

Despite the rapid growth of online shopping during COVID-19, a significant number of consumers still prefer in-store shopping. This study leverages two years (i.e. pre-pandemic and pandemic) of smartphone location data to develop machine learning (ML) models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for predicting community (e.g. block group (BG)) level in-store shopping trips for department stores, shopping malls, supermarkets, and wholesale stores. This study identifies that temperature, accessibility to stores, and the number of online shopping last-mile delivery are the three most important factors influencing shopping trips; specifically, the extent of online shopping is a critical determinant for supermarkets and wholesale store trip-makings before and during the pandemic. The models developed and important determinants of shopping trips will provide useful insight for shopping trip demand forecasting as well as impact assessments of relevant policies on in-store shopping demand during emergencies. [ABSTRACT FROM AUTHOR]

Automated parcel locker (APL) systems have been proposed as a means of overcoming last-mile delivery challenges, including long delivery times and high operating costs. Recently, APL systems have attracted attention because of the increasing demand for parcel delivery and the preference for contactless delivery during the COVID-19 era. The aim of the present study aims to gain a more comprehensive understanding of the psychological and latent factors underlying the decision to adopt APL systems. Structural equation modeling (SEM) is applied to present an extended technology acceptance model (TAM) using data from 459 consumers in Seoul, Korea. The findings provide valuable insights into the reasons for adoption of APL systems. Specifically, the perceived risk of COVID-19, distrust, and their personal innovativeness significantly influenced their adoption of the systems by affecting their perceived usefulness and perceived ease of use. In addition, use experience has a significant moderating effect on the adoption. The findings revealed in this study could help policy makers, urban planners, and system operators to accelerate the adoption of APL systems. • Factors influencing the behavioral adoption of automated parcel locker systems are explored. • Hypothesized model is developed based on extended technology acceptance model. • Structural Equation Modeling is applied to test the hypothesized model. • Perceived risk of COVID-19, distrust, and innovativeness have indirect effects on behavioral intention. • Multi-group analysis confirmed the moderation effects of use experience. [ABSTRACT FROM AUTHOR]

Based on the insights derived from practical trials conducted with carriers operating in London, this article proposes a last-mile delivery model that involves both driving and walking. A particularly innovative aspect of the model is the shared use of a third-party portering service between the carriers as an enabler to collaborative provision, and shared micro-consolidation points for temporary storage of goods. The model gives rise to a complex routing problem, for which a tabu search algorithm is developed. The algorithm is able to suggest vehicle routes that consolidate parcels in shared locations and porter paths that deliver items from various companies, reducing the overall delivery cost. We applied this algorithm to a manifest dataset obtained from two carriers operating in London. The results from the model suggested that sharing a third party portering service for the last-mile delivery element between the rival carriers in the case study reduced overall transport costs by up to 14%. [ABSTRACT FROM AUTHOR]

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Purpose: Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis. Design/methodology/approach: The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs). Findings: Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model's utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations. Originality/value: Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning. [ABSTRACT FROM AUTHOR]