Various issues confront instant delivery,such as elevated labor expenses,low efficiency,and courier accidents.Consequently,e-commerce and logistics providers have turned their attention towards autonomous delivery veh...Various issues confront instant delivery,such as elevated labor expenses,low efficiency,and courier accidents.Consequently,e-commerce and logistics providers have turned their attention towards autonomous delivery vehicles(ADVs).There are no studies on customers who use instant delivery that focus on their preferences for ADVs in relation to cargo dam-age in conjunction with other delivery attributes such as instant delivery service use fre-quency and price of orders.The objective of this study is to examine customers’preferences for ADVs in comparison to traditional courier delivery.To account for the heterogeneity of customers,this study employs the random parameter logit(RPL)model with interactions to quantify the relevance of attributes and their interaction effects.This study is the first to consider cargo damage as an alternative-specific attribute(ASA)in the context of preference studies for instant delivery modes.We also examine the inter-action effects between delivery price and personal and delivery attributes,considering the significance and notable preference variations regarding delivery price across different populations.For data collection,a survey employing stated-preference(SP)approach was conducted in China,resulting in 309 effective surveys.The findings indicate that cus-tomer preference heterogeneity regarding delivery price and cargo damage both follow normal distributions.And gender,privacy,instant delivery service use frequency,and price of orders all show significant effects on customers’preferences for ADVs.Analysis of the survey answers also revealed statistically significant positive interaction effects on delivery price associated with income and instant delivery service use frequency.This study con-tributes to understanding customer preferences for ADVs,thereby assisting logistics provi-ders in identifying target customers for ADVs.展开更多
In this study,we introduce an integrated schedule of order picking and delivery for instant delivery.Order picking,including order batching and picking sequencing,is scheduled online under real-time order arrival,whic...In this study,we introduce an integrated schedule of order picking and delivery for instant delivery.Order picking,including order batching and picking sequencing,is scheduled online under real-time order arrival,which integrates order delivery by depicting order location dispersion in an online order picking strategy.Order delivery,including delivery person assignment and route planning,is modeled to minimize the total duration of order fulfillment by considering the influence of the order picking completion time.A rule-based online order picking strategy is established,and a customized ant colony optimization(ACO)algorithm is proposed to optimize order delivery.Experiments on 16 simulated instances of different scales demonstrate that our online order picking schedule considering order delivery outperforms existing approaches and that the customized ACO algorithm for order delivery is effective.展开更多
The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and u...The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and uncertainties.This study introduces two order dispatching strategies,namely task buffering and dynamic batching,as potential solutions to address these challenges.The task buffering strategy aims to optimize the assignment timing of orders to couriers,thereby mitigating demand uncertainties.On the other hand,the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery dis tances.To model the instant delivery problem and evaluate the performances of order dis patching strategies,Adaptive Agent-Based Order Dispatching(ABOD)approach is developed,which combines agent-based modelling,deep reinforcement learning,and the Kuhn-Munkres algorithm.The ABOD effectively captures the system’s uncertainties and heterogeneity,facilitating stakeholders learning in novel scenarios and enabling adap tive task buffering and dynamic batching decision-makings.The efficacy of the ABOD approach is verified through both synthetic and real-world case studies.Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction,up to 275.42%,while simultaneously reducing the deliv ery distance by 11.38%compared to baseline policies.Additionally,the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios.As a result,this approach offers valuable sup port to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.展开更多
A quick tap on your phone onyour way to work has yourusual coffee arriving at theoffice before you do.Preparing for an evening event,a new foundation shade arrives in under 30 minutes,no store visit required.At a week...A quick tap on your phone onyour way to work has yourusual coffee arriving at theoffice before you do.Preparing for an evening event,a new foundation shade arrives in under 30 minutes,no store visit required.At a week-end picnic,pet treats show up from across town just as easily as lunch.Wake up at 2 a.m.with a sick child?Medicine is at your door within min-utes.This is lifte with China's rapidly developing sales model known as instant retail.展开更多
基金supported in part by the National Natural Science Foundation of China(No.72101188)the Shanghai Municipality Science and Technology Commission Soft Science Research Project(No.24692106100).
文摘Various issues confront instant delivery,such as elevated labor expenses,low efficiency,and courier accidents.Consequently,e-commerce and logistics providers have turned their attention towards autonomous delivery vehicles(ADVs).There are no studies on customers who use instant delivery that focus on their preferences for ADVs in relation to cargo dam-age in conjunction with other delivery attributes such as instant delivery service use fre-quency and price of orders.The objective of this study is to examine customers’preferences for ADVs in comparison to traditional courier delivery.To account for the heterogeneity of customers,this study employs the random parameter logit(RPL)model with interactions to quantify the relevance of attributes and their interaction effects.This study is the first to consider cargo damage as an alternative-specific attribute(ASA)in the context of preference studies for instant delivery modes.We also examine the inter-action effects between delivery price and personal and delivery attributes,considering the significance and notable preference variations regarding delivery price across different populations.For data collection,a survey employing stated-preference(SP)approach was conducted in China,resulting in 309 effective surveys.The findings indicate that cus-tomer preference heterogeneity regarding delivery price and cargo damage both follow normal distributions.And gender,privacy,instant delivery service use frequency,and price of orders all show significant effects on customers’preferences for ADVs.Analysis of the survey answers also revealed statistically significant positive interaction effects on delivery price associated with income and instant delivery service use frequency.This study con-tributes to understanding customer preferences for ADVs,thereby assisting logistics provi-ders in identifying target customers for ADVs.
基金supported by the National Natural Science Foundation of China(72032001,71972071)the Ministry of Education,Humanities and Social Sciences Research Planning Foundation(21YJA630057).
文摘In this study,we introduce an integrated schedule of order picking and delivery for instant delivery.Order picking,including order batching and picking sequencing,is scheduled online under real-time order arrival,which integrates order delivery by depicting order location dispersion in an online order picking strategy.Order delivery,including delivery person assignment and route planning,is modeled to minimize the total duration of order fulfillment by considering the influence of the order picking completion time.A rule-based online order picking strategy is established,and a customized ant colony optimization(ACO)algorithm is proposed to optimize order delivery.Experiments on 16 simulated instances of different scales demonstrate that our online order picking schedule considering order delivery outperforms existing approaches and that the customized ACO algorithm for order delivery is effective.
基金This work was supported in part by the National Natural Science Foundation of China[72101188]the Shanghai Municipal Science and Technology Major Project[2021SHZDZX0100]the Fundamental Research Funds for the Central Universities.
文摘The volume of instant delivery has witnessed a significant growth in recent years.Given the involvement of numerous heterogeneous stakeholders,instant delivery operations are inherently characterized by dynamics and uncertainties.This study introduces two order dispatching strategies,namely task buffering and dynamic batching,as potential solutions to address these challenges.The task buffering strategy aims to optimize the assignment timing of orders to couriers,thereby mitigating demand uncertainties.On the other hand,the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery dis tances.To model the instant delivery problem and evaluate the performances of order dis patching strategies,Adaptive Agent-Based Order Dispatching(ABOD)approach is developed,which combines agent-based modelling,deep reinforcement learning,and the Kuhn-Munkres algorithm.The ABOD effectively captures the system’s uncertainties and heterogeneity,facilitating stakeholders learning in novel scenarios and enabling adap tive task buffering and dynamic batching decision-makings.The efficacy of the ABOD approach is verified through both synthetic and real-world case studies.Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction,up to 275.42%,while simultaneously reducing the deliv ery distance by 11.38%compared to baseline policies.Additionally,the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios.As a result,this approach offers valuable sup port to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.
文摘A quick tap on your phone onyour way to work has yourusual coffee arriving at theoffice before you do.Preparing for an evening event,a new foundation shade arrives in under 30 minutes,no store visit required.At a week-end picnic,pet treats show up from across town just as easily as lunch.Wake up at 2 a.m.with a sick child?Medicine is at your door within min-utes.This is lifte with China's rapidly developing sales model known as instant retail.
