Dynamic Collection Scheduling Using Remote Asset Monitoring: Case Study in the UK Charity Sector

Remote sensing technology is now coming onto the market in the waste collection sector. This technology allows waste and recycling receptacles to report their fill levels at regular intervals. This reporting enables collection schedules to be optimized dynamically to meet true servicing needs in a better way and so reduce transport costs and ensure that visits to clients are made in a timely fashion. This paper describes a real-life logistics problem faced by a leading UK charity that services its textile and book donation banks and its high street stores by using a common fleet of vehicles with various carrying capacities. Use of a common fleet gives rise to a vehicle routing problem in which visits to stores are on fixed days of the week with time window constraints and visits to banks (fitted with remote fill-monitoring technology) are made in a timely fashion so that the banks do not become full before collection. A tabu search algorithm was developed to provide vehicle routes for the next day of operation on the basis of the maximization of profit. A longer look-ahead period was not considered because donation rates to banks are highly variable. The algorithm included parameters that specified the minimum fill level (e.g., 50%) required to allow a visit to a bank and a penalty function used to encourage visits to banks that are becoming full. The results showed that the algorithm significantly reduced visits to banks and increased profit by up to 2.4%, with the best performance obtained when the donation rates were more variable

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

Last-mile logistics optimization in the on-demand economy

L'abstract è presente nell'allegato / the abstract is in the attachmen

Modeling and analysis of alternative distribution and Physical Internet schemes in urban area

Urban logistics is becoming more complicated and costlier due to new challenges in recent years. Since the main problem lies on congestion, the clean vehicle is not necessarily the most effective solution. There is thus a need to redesign the logistics networks in the city. This paper proposes a methodology to evaluate different distribution schemes in the city among which we find the most efficient and sustainable one. External impacts are added to the analysis of schemes, including accident, air pollution, climate change, noise, and congestion. An optimization model based on an analytical model is developed to optimize transportation means and distribution schemes. Results based on Bordeaux city show that PI scheme improves the performances of distribution

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

Enhancing Courier Scheduling in Crowdsourced Last-Mile Delivery through Dynamic Shift Extensions: A Deep Reinforcement Learning Approach

Crowdsourced delivery platforms face complex scheduling challenges to match couriers and customer orders. We consider two types of crowdsourced couriers, namely, committed and occasional couriers, each with different compensation schemes. Crowdsourced delivery platforms usually schedule committed courier shifts based on predicted demand. Therefore, platforms may devise an offline schedule for committed couriers before the planning period. However, due to the unpredictability of demand, there are instances where it becomes necessary to make online adjustments to the offline schedule. In this study, we focus on the problem of dynamically adjusting the offline schedule through shift extensions for committed couriers. This problem is modeled as a sequential decision process. The objective is to maximize platform profit by determining the shift extensions of couriers and the assignments of requests to couriers. To solve the model, a Deep Q-Network (DQN) learning approach is developed. Comparing this model with the baseline policy where no extensions are allowed demonstrates the benefits that platforms can gain from allowing shift extensions in terms of reward, reduced lost order costs, and lost requests. Additionally, sensitivity analysis showed that the total extension compensation increases in a nonlinear manner with the arrival rate of requests, and in a linear manner with the arrival rate of occasional couriers. On the compensation sensitivity, the results showed that the normal scenario exhibited the highest average number of shift extensions and, consequently, the fewest average number of lost requests. These findings serve as evidence of the successful learning of such dynamics by the DQN algorithm