1,013 research outputs found

    Innovative business-to-business last-mile solutions:models and algorithms

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    Blockchain Based Secure Package Delivery via Ridesharing

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    © 2019 IEEE. Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy. Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack. In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering. However, such tamper-resistance property comes at the cost of a long confirmation delay caused by the consensus process. A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds. The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack. Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput

    Exploring Logistics-as-a-Service to integrate the consumer into urban freight

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    E-commerce established the consumer as a freight actor. This new reality in the e-commerce supply chain holds economic, social, and environmental opportunities. First, logistics service providers can capitalize on the willingness to pay of consumers with tailored logistics services. Second, consumers can be confronted with the correct costs of delivery options, raising awareness and influencing their choices\u27 sustainability. Third, policymakers can steer the consumer directly, nudging their behaviour to reach urban freight policy objectives. Until now, the lack of interaction between the logistics service provider and the consumer prevented exploiting these opportunities. In this paper, we look at passenger transport, specifically the concept of Mobility-as-a-Service (MaaS), for inspiration on how to integrate the consumer into the logistics market. We propose conceptualizations for a Logistics-as-a-Service (LaaS) platform with different levels of integration and discuss the role of various stakeholders. We conclude with a suite of research questions that deserve attention to develop further the LaaS idea and its proof of concept for consumer logistics

    Sharing Economy Last Mile Delivery: Three Essays Addressing Operational Challenges, Customer Expectations, and Supply Uncertainty

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    Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice

    Sharing Economy Last Mile Delivery: Three Essays Addressing Operational Challenges, Customer Expectations, and Supply Uncertainty

    Get PDF
    Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice

    Towards Mobility Data Science (Vision Paper)

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    Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

    Optimization under Uncertainty for E-retail Distribution: From Suppliers to the Last Mile

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    This thesis examines problems faced in the distribution management of e-retailers, in different stages of the supply chain, while accounting for sources of uncertainty. The first problem studies distribution planning, under stochastic customer demand, in a transshipment network. To decide on a transportation schedule that minimizes transportation, inventory and outsourcing costs, the problem is formulated as a two-stage stochastic programming model with recourse. Computational experiments demonstrate the cost-effectiveness of distribution plans generated while considering uncertainty, and provide insights on conditions under which the proposed model achieves significant cost savings. We then focus our attention on a later phase in the supply chain: last-mile same-day delivery. We specifically study crowdsourced delivery, a new delivery system where freelance drivers deliver packages to customers with their own cars. We provide a comprehensive review of this system in terms of academic literature and industry practice. We present a classification of industry platforms based on their matching mechanisms, target markets, and compensation schemes. We also identify new challenges that this delivery system brings about, and highlight open research questions. We then investigate two important research questions faced by crowdsourced delivery platforms. The second problem in this thesis examines the question of balancing driver capacity and demand in crowdsourced delivery systems when there is randomness in supply and demand. We propose models and test the use of heatmaps as a balancing tool for directing drivers to regions with shortage, with an increased likelihood, but not a guarantee, of a revenue-producing order match. We develop an MDP model to sequentially select matching and heatmap decisions that maximize demand fulfillment. The model is solved using a stochastic look-ahead policy, based on approximate dynamic programming. Computational experiments on a real-world dataset demonstrate the value of heatmaps, and factors that impact the effectiveness of heatmaps in improving demand fulfillment. The third problem studies the integration of driver welfare considerations within a platform's dynamic matching decisions. This addresses the common criticism of the lack of protection for workers in the sharing economy, by proposing compensation guarantees to drivers, while maintaining the work hour flexibility of the sharing economy. We propose and model three types of compensation guarantees, either utilization-based or wage-based. We formulate an MDP model, then utilize value function approximation to efficiently solve the problem. Computational experiments are presented to assess the proposed solution approach and evaluate the impact of the different types of guarantees on both the platform and the drivers

    Competition policy review

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    This is the first comprehensive review of Australia’s competition laws and policy in over 20 years. The National Competition Policy Review (The Hilmer Review) of 1993 underpinned the development of the National Competition Policy – a co-operative initiative of the Commonwealth and State and Territory governments that the Productivity Commission found contributed to a surge in productivity, directly reduced some prices and stimulated business innovation. The subsequent Review of the Competition Provisions of the Trade Practices Act (The Dawson Review) of 2003 examined the operation of the competition laws and resulted in some strengthening of the provisions. There has been considerable change in the Australian economy since the Hilmer Report of the early 1990s and the boost in productivity that underpinned the growth in living standards over the past two decades is waning. The Competition Policy Review will examine the broader competition framework to ensure that it continues to play a role as a significant driver of productivity improvements and to ensure that the current laws are operating as intended and are effective for all businesses, big and small.   MESSAGE FROM THE PANEL This is our Final Report reviewing Australia’s competition policy, laws and institutions. The Panel undertook a stocktake of the competition policy framework across the Australian economy. Although reforms introduced following the Hilmer Review led to significant improvements in economic growth and wellbeing, the Panel believes that renewed policy effort is required to support growth and wellbeing now and into the future. To this end, we have reviewed Australia’s competition policy, laws and institutions to assess their fitness for purpose. Taken together, our recommendations comprise an agenda of reinvigorated microeconomic reform that will require sustained effort from all jurisdictions. We believe this commitment is necessary if Australia is to boost productivity, secure fiscal sustainability and position our economy to meet the challenges and opportunities of a rapidly changing world. Given the forces for change already bearing on the Australian economy, delaying policy action will make reform more difficult and more sharply felt. An early response will make the reform effort more manageable over time, allowing Australians to enjoy higher living standards sooner rather than later. The recommendations and views expressed in this Final Report draw upon the expertise and experience of each member of the Panel. Importantly, we have also had the benefit of hearing from a wide cross-section of the Australian community and from participants in all sectors of the economy
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