Crowd-Sourced Delivery Services (CDS) use in-store customers, as crowd-shippers, to deliver online orders directly to other customers. As independent contractors, the crowd-shippers are free to decide whether to accept or reject the online orders assigned by the retailer. High order rejection rates can significantly influence the retailer’s delivery costs due to frequent reassignments and shifting the orders to more expensive professional fleet. To incentivize crowd-shippers to accept the matched orders, in this work, we propose a two-stage optimization framework that integrates bipartite matching with an individual compensation scheme. The first stage of the optimization framework computes the optimal matching between crowd-shippers and online orders to minimize the delivery detours and unassigned orders. Given the matching solutions as inputs, the second stage computes personal compensation for each crowd-shipper based on the characteristic of the matched order and his or her acceptance behavior uncertainty, with the goal of minimizing the expected total delivery cost of the retailer. Numerical experiments are conducted using the survey data to illustrate the performance of the proposed framework and compare it with existing matching and pricing strategies in the literature. Our results show that the proposed framework reduces the delivery cost by up to more than 15% and reduces the crowd-shippers’ rejection rate by an average of 55%.@en

Vehicle proactive guidance strategies are used by ride-hailing platforms to mitigate supply–demand imbalance across regions by directing idle vehicles to high-demand regions before the demands are realized. This article presents a data-driven stochastic optimization framework for computing idle vehicle guidance strategies. The objective is to minimize drivers’ idle travel distance, riders’ wait time, and the oversupply costs (OSCs) and undersupply costs (USCs) of the platform. Specifically, we design a novel neural network that integrates gated recurrent units (GRUs) with mixture density networks (MDNs) to capture the spatial-temporal features of the rider demand distribution.@en

Providing high-quality matching between drivers and riders is imperative for sustaining the growth of ride-sharing platforms. A user-focused matching mechanism design plays a key role in terms of ensuring user satisfaction. In this paper, we consider the matching problem in the community ride-sharing setting, where drivers and riders have strong personal preferences over the matched counterparties. Obtaining high-quality solutions that accommodate drivers’ and riders’ preferences in such a setting is particularly challenging as drivers and riders maybe reluctant to share with the platform their personal preferences over their ride-sharing counterparties due to privacy and ethical concerns. To this end, we propose a VOting-based MAtching (VOMA) mechanism to compute near-optimal matching solutions for drivers and riders, while preserving their privacy. The mechanism is a distributed implementation of the simulated annealing meta-heuristic, which computes matching solutions by guiding drivers and riders in the distributed search process using an iterative voting protocol. We evaluate the performance of VOMA using test cases generated based on New York taxi data sets. The experiment results show that the proposed matching mechanism achieves on average 90.9% efficiency compared with optimal solutions. We also show that VOMA improves the vehicle miles traveled (VMT) savings by up to 35% compared to an alternative voting-based greedy matching mechanism. System scalability and other practical issues regarding the implementation of such a matching mechanism in community ride-sharing platforms are also discussed.@en

This paper proposes an integrated dispatching framework for matching drivers with riders in ride-hailing systems. The goal is to compute matching solutions that maximize social welfare and benefit both sides of the market, such that the sustainable growth of the ride-hailing system is ensured. The proposed framework integrates data-driven proactive guidance strategies with batched matching optimization to increase social welfare, improve matching rate and reduce rider wait time. Proactive guidance strategies are computed by leveraging short-term demand forecasts based on historical data. Taken the resulting guidance strategies as inputs, the batched matching algorithm computes optimal bipartite matching between drivers and riders in a batch. Using New York City taxi data from 2016 March 1st to March 31st as input, we conduct a numerical study to evaluate the performance of the proposed framework and compare it with existing approaches in the literature. Our results show that the proposed framework improves social welfare for up to 50%. It also increases the matching rate by an average of 20% and reduces the average rider wait time by over 15%. This implies a strong potential for the proposed dispatching framework to improve service quality in ride-hailing systems.@en

A new sialon Eu_3.60LiSi_13.78Al_6.03O_6.82N_22.59 has been discovered via the single-particle diagnosis approach. Its crystal structure (space group P3m1) was solved and refined from single-crystal X-ray diffraction data. It has the interesting feature of two types of disorder at the Eu2 site: positional disorder (Eu2a/Eu2b) and substitutional disorder with (Si/Al)₂(O/N). The structure is generalized to the formula A_4-mB_nC_19+2mX_29+m (A = Sr, La, Eu, Ce; B = Li; C = Si, Al; X = O, N; 0 ≤ m ≤ 1; 0 ≤ n ≤ 1), of which Sr_3.61LiSi_14.27Al_5.61O_6.19N_23.25 (Sr-sialon, m = 0.41, n = 1) and La_2.85Sr_0.76LiSi_14.86Al_4.93O_2.89N_26.51 (LaSr-sialon, m = 0.40, n = 1) are two examples that have been obtained as a single-phase powder. Sr-sialon:Eu and LaSr-sialon:Eu both show blue to yellow emission, depending on the Eu concentration, whereas Sr-sialon:1% Ce shows a deep-blue emission band centered at 422 nm with a full width at half-maximum of 80 nm and an internal quantum efficiency of 80% (λ_ex = 355 nm). The latter phosphor has very good thermal stability of both emission intensity and color. A white light-emitting diode (LED) containing the newly discovered Sr-sialon:5% Ce as the blue phosphor component shows excellent color-rendering indices (R_a = 96 and R₁₂ = 97) with a correlated color temperature of 4255 K. This indicates that Sr-sialon:Ce is a highly promising deep-blue phosphor for illumination grade white LEDs.

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La₂O₃, LaN, Si₃N₄, AlN and CeO₂ have been used as starting materials to synthesize Ce doped LaAl(Si_6−zAl_z)(N_10−zO_z) (z ≈ 1, termed the JEM phase) phosphors, via a solid-state reaction method in a gas pressure furnace at a high temperature. Nearly single phase JEM:Ce phosphors have been obtained by carefully controlling the synthesis conditions. The 5% Ce doped JEM phosphor displays a broad excitation band extending from UV to 425 nm, with a maximum at 355 nm. Excitation with 355 nm light results in a Ce³⁺ 5d–4f emission band (FWHM = 81 nm) centered at 430 nm, with a high internal quantum efficiency of 75%. The emission of the JEM:0.01Ce phosphor has only quenched 3% at room temperature as compared to the intensity at 4 K and still 57% of the luminescence is left at 573 K, which is superior to JEM:Eu phosphors. These performances make JEM:Ce phosphors very promising blue-emitting phosphors for white LED applications.@en