Crowd-shipping with time windows and transshipment nodes

Crowd-shipping is a delivery policy in which, in addition to standard vehicle routing practices, ordinary people accept to deviate from their route to deliver items to other people, for a small compensation. In this paper we consider a variant of the problem by taking into account the presence of intermediate depots in the service network. The occasional drivers can decide to serve some customers by picking up the parcels either from the central depot or from an intermediate one. The objective is to minimize the total cost, that is, the conventional vehicle cost, plus the occasional drivers’ compensation. We formulate the problem and present a variable neighborhood search heuristic. To analyze the benefit of the crowd-shipping transportation system with intermediate depots and to assess the performance of our heuristic, we consider small- and large-size instances generated from the Solomon benchmarks. A computational analysis is carried out with the aim of gaining insights into the behavior of both conventional vehicles and occasional drivers, and of analyzing the performance of our methodology in terms of effectiveness and efficiency. Our computational results show that the proposed heuristic is highly effective and can solve large-size instances within short computational times.</p

Crowd-shipping with time windows and transshipment nodes

Crowd-shipping is a delivery policy in which, in addition to standard vehicle routing practices, ordinary people accept to deviate from their route to deliver items to other people, for a small compensation. In this paper we consider a variant of the problem by taking into account the presence of intermediate depots in the service network. The occasional drivers can decide to serve some customers by picking up the parcels either from the central depot or from an intermediate one. The objective is to minimize the total cost, that is, the conventional vehicle cost, plus the occasional drivers’ compensation. We formulate the problem and present a variable neighborhood search heuristic. To analyze the benefit of the crowd-shipping transportation system with intermediate depots and to assess the performance of our heuristic, we consider small- and large-size instances generated from the Solomon benchmarks. A computational analysis is carried out with the aim of gaining insights into the behavior of both conventional vehicles and occasional drivers, and of analyzing the performance of our methodology in terms of effectiveness and efficiency. Our computational results show that the proposed heuristic is highly effective and can solve large-size instances within short computational times.</p

Crowd-shipping: a new efficient and eco-friendly delivery strategy

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Waste Collection Vehicle Routing Problem Model with Multiple Trips, Time Windows, Split Delivery, Heterogeneous Fleet and Intermediate Facility

Waste Collection Vehicle Routing Problem (WCVRP) is one of the developments of a Vehicle Routing Problem, which can solve the route determination of transporting waste. This study aims to develop a model from WCVRP by adding characteristics such as split delivery, multiple trips, time windows, heterogeneous fleet, and intermediate facilities alongside an objective function to minimize costs and travel distance. Our model determines the route for transporting waste especially in Cakung District, East Jakarta. The additional characteristics are obtained by analyzing the characteristics of waste transportation in the area. The models are tested using dummy data to analyze the required computational time and route suitability. The models contribute to determining the route of transporting waste afterward. The WCVRP model has been successfully developed, conducted the numerical testing, and implemented with the actual characteristics such as split delivery, multiple trips, time windows, heterogeneous fleets, and intermediate facilities. The output has reached the global optimal for both dummy and real data

A simulation-optimization approach for the management of the on-demand parcel delivery in sharing economy

the use of multiple delivery options and crowd drivers, reflecting the synchromodality in the urban context. We propose a multi-stage stochastic model, and we solve the problem by using a simulation-optimization strategy. It relies on a Monte Carlo simulation and a large neighborhood search (LNS) heuristic for optimization. We conduct a case study in the medium-sized city of Turin (Italy) to measure the potential impact of integrating cargo bikes and crowd drivers in parcel delivery. Experimental results show that combining crowd drivers and green carriers with the traditional van to manage the parcel delivery is beneficial in terms of economic and environmental cost-saving, while the operational efficiency decreases. Besides, the green carriers and crowd drivers are promising delivery options to deal with online customer requests in the context of stochastic and dynamic parcel delivery. The resulting set of policies are part of the outcomes of the Logistics and Mobility Plan 2019-2021 in the Piedmont region

Using drones for parcels delivery process

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

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