Optimization of train formation plan based on technical station under railcar demand fluctuation

Train formation planning (TFP) is essential for rail freight logistics services. The fluctuation of railcar flows dramatically compared with before the outbreak of COVID-19. This paper studies train formation planning, considering three types of train services provided for railcar flow between pairs of technical stations (TS), including direct trains, district trains, and pickup trains. This paper introduces an optimization model with average railcars flow data (OMAD) and an optimization model with dynamic railcars flow data (OMDD) for the train formation planning based on TS under railcar demand fluctuation while minimizing railcar-hour consumption. The OMAD is a deterministic model, and the OMDD is a probability constraint model. To solve the OMDD, an approach for transforming probability constraints into deterministic constraints is presented. Various groups of scenarios are given to verify the effectiveness of the proposed models. [ FROM AUTHOR] Copyright of Journal of Industrial & Production Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Support vector regression model for flight demand forecasting

Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

Research on Location Selection of Dairy Cold Chain Logistics Distribution Center

With the development of economy and the gradual improvement of material living standards, people's demand for nutritious and fresh products such as fresh, dairy products, fruits and vegetables is also increasing. Under the influence of the COVID-19, people put forward higher requirements for the distribution timeliness and radiation radius of cold chain logistics enterprises. Based on the existing research, this paper conducts an optimization study on the location selection of the distribution center of cold chain logistics for large-scale enterprises with self-operated operation mode. Moreover, a location model based on cluster analysis is proposed. The clustering results are corrected by gravity center method. Secondly, the optimization model of cycle picking is used to consider transportation cost, cooling cost, time constraint and so on. A better distribution center node is selected from the alternative points. Finally, the validity of the model is verified by case analysis. © 2023 SPIE.

Fostering Customer Adoption of Curbside Pick-up Service: An Abstract

Brick-and-mortar retailers are facing increased pressure to innovate to remain competitive against online retailers. In recent years, brick-and-mortar retailers have implemented the addition of curbside service, which allows customers to order ahead and pick up their items without leaving their cars ("click and collect”). While curbside pick-up service has surged 208% during the COVID-19 pandemic (Thomas 2020), it is unclear whether the service will persist in a post-pandemic world. Little empirical research has examined curbside pick-up antecedents or customer behaviors, despite investigations into curbside pick-up being an important issue for marketers (Grewal et al. 2021). This research explores the interplay between customer perceptions of curbside pick-up, time saved by customers, and downstream financial consequences for firms that provide curbside pick-up. Drawing upon the planning fallacy (Kahneman & Tversky 1977), which explains how people underestimate the length of time a task will take to complete, two studies test outcomes at both the firm and customer level. Study 1, an experimental design, examines the impact of itemized versus grouping time saving perceptions for consumers. This study shows that when firms "unpack” the amount of time saved by curbside service, then the perceived value is differentially higher than when time savings are grouped together. Study 2 uses data from a large retailer in the Midwest area of the United States. In Study 2, the authors show that the introduction of curbside service increases the repurchase time for consumers initially, but over time these effects diminish. Overall, the results of these studies show that offering curbside pick-up increases repurchase time for customers, that customers cannot accurately judge how valuable curbside pick-up will be to them, and that firms can convey the value of curbside pick-up via "unpacking" (i.e., itemizing) potential time savings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Strategic responses to the pandemic: a case study of the US department store industry

PurposeThe study focuses on (1) the success of three strategies employed during the pandemic – two "persevering” strategies, curbside pickup and return window extension and one innovative strategy, virtual try-on technology and (2) whether the strategies are likely to be successful in the post-pandemic world.Design/methodology/approachThe authors utilize a panel dataset containing 17 department store chains in the US The panel includes weekly sales by the retailers at the city level from 2018 to 2021, encompassing both a pre-COVID-19 period and a period during the pandemic. A two-way fixed effects model, including retailer-city fixed effects and year-week fixed effects, is used to estimate department store sales.FindingsThe authors find that the two persevering strategies offset the negative impact of government-imposed containment and health measures on sales performance. On the other hand, the innovative strategy is more effective with a low level of containment and health measures, leading to our observation that virtual try-on may be more sustainable than the other two strategies in a post-pandemic environment.Originality/valueThis paper makes the following contributions: First, the authors contribute to the literature on strategies that may be used to respond to crises. Second, the authors contribute to the retail management literature, assessing the impact of the three retail strategies on department store sales. Finally, the authors compare the impact on sales of the two persevering strategies to the innovative strategy and conclude that a mix of these types of strategies may be most effective at generating short-term sales during a crisis and longer-term sales post crisis.

Application of machine learning to cluster hotel booking curves for hotel demand forecasting

Accurate demand forecasting is integral for data-driven revenue management decisions of hotels, but an unprecedented demand environment caused by COVID-19 pandemic has made the forecasting process more difficult. This study aims to propose a new approach for daily hotel demand forecasting by using clusters of stay dates generated from historical booking data. This new approach is fundamentally different from traditional forecasting approaches for hotels that assume the booking curves and patterns tend to be similar during the trailing period approach. In this study, historical booking curves are clustered by a machine learning algorithm using an auto-regressive manner and the additive pickup model is used to forecast daily occupancy up to 8 weeks. The efficacy of a new forecasting approach is tested using real hotel booking data of three hotels and results show that forecasts of hotel demand are more accurate when they are generated at cluster-level for all forecasting horizons. © 2023 Elsevier Ltd

Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels

Demand forecast accuracy is critical for hotels to operate their properties efficiently and profitably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages;and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied. © 2023, The Author(s).

Artificial intelligence-based internet hospital pharmacy services in China: Perspective based on a case study.

Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk. Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic. Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the "medicine pick-up code," which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational. Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The "offline self-pick-up" mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable. Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists' working efficiency.

Is There any Effect of COVID-19 on Follicular Environment in Women Underwent ART?

There is insufficient data on the impact of severe acute respiratory coronavirus-2 (SARS-CoV-2) on the reproductive tissues, its possible risk of cross-contamination, transmission and adverse effect on in vitro fertilization (IVF) outcome. Until today, there is no report associated with viral RNA in both follicular fluid and embryo culture medium from SARS-COV-2 positive women. In this case report, a 24-year-old woman with SARS-CoV-2 was presented. We investigated the SARS-COV-2 positivity in the follicular fluid and embryo culture medium of mildly symptomatic woman on oocyte pick up (OPU) day. We could not detect viral RNA in neither the follicular fluid nor the embryo culture medium. In addition, although the response of ovarian stimulation was normal, the number and maturity of the retrieved oocytes were low.

Eat-in or eat-out? A joint model to analyze the new landscape of dinner meal preferences

In this paper, we examine the non-home-cooked meal (NHCM) preferences of individuals for their dinner meal by studying the monthly count of NHCM meals by channel type: eat-out, eat-in takeout, and eat-in delivery. Data from a 2022 online survey collected in Texas is employed to estimate a multivariate joint model. Model estimation results indicate that the most frequent customers of the eat-out channel are white individuals, individuals from 3+ motorized vehicle-owning households, those in non-joint families, those in households with no children, full-time employees who never work from home or do so only for a small fraction of their workdays, and those residing in areas with a high density of restaurants. The distinct consumer segments for the eat-in takeout channel include young individuals, those with high household incomes, those working from home all their workdays or a substantial fraction of their workdays, and urban residents;the most enthusiastic consumers of the eat-in delivery channel are white individuals, those with less than three vehicles in the household, individuals with children, urban residents, and those worried about pandemic-related personal health risks. Older individuals, non-white individuals, individuals with a graduate degree, individuals in fewer motorized vehicle-owning households and in joint families, those with children in the household, and rural residents constitute the most committed population segments of the home-cooked meal (HCM) consumption channel. The results suggest the important impact of workplace location on dining channel choice. The results also show clear evidence of complementary and substitution effects at play;the delivery channel complements eating out but substitutes takeout. Similarly, eat-out has a substitution effect on eat-in takeout. These effects have important implications for activity-travel behavior due to emerging technology-based ordering options for dining choices, especially in the aftermath of the COVID pandemic. © 2023 Elsevier Ltd

Strategic responses to the pandemic: a case study of the US department store industry

PurposeThe study focuses on (1) the success of three strategies employed during the pandemic - two "persevering" strategies, curbside pickup and return window extension and one innovative strategy, virtual try-on technology and (2) whether the strategies are likely to be successful in the post-pandemic world.Design/methodology/approachThe authors utilize a panel dataset containing 17 department store chains in the US The panel includes weekly sales by the retailers at the city level from 2018 to 2021, encompassing both a pre-COVID-19 period and a period during the pandemic. A two-way fixed effects model, including retailer-city fixed effects and year-week fixed effects, is used to estimate department store sales.FindingsThe authors find that the two persevering strategies offset the negative impact of government-imposed containment and health measures on sales performance. On the other hand, the innovative strategy is more effective with a low level of containment and health measures, leading to our observation that virtual try-on may be more sustainable than the other two strategies in a post-pandemic environment.Originality/valueThis paper makes the following contributions: First, the authors contribute to the literature on strategies that may be used to respond to crises. Second, the authors contribute to the retail management literature, assessing the impact of the three retail strategies on department store sales. Finally, the authors compare the impact on sales of the two persevering strategies to the innovative strategy and conclude that a mix of these types of strategies may be most effective at generating short-term sales during a crisis and longer-term sales post crisis.

Eat-in or eat-out? A joint model to analyze the new landscape of dinner meal preferences

In this paper, we examine the non-home-cooked meal (NHCM) preferences of individuals for their dinner meal by studying the monthly count of NHCM meals by channel type: eat-out, eat-in takeout, and eat-in delivery. Data from a 2022 online survey collected in Texas is employed to estimate a multivariate joint model. Model estimation results indicate that the most frequent customers of the eat-out channel are white individuals, individuals from 3+ motorized vehicle-owning households, those in non-joint families, those in households with no children, full-time employees who never work from home or do so only for a small fraction of their workdays, and those residing in areas with a high density of restaurants. The distinct consumer segments for the eat-in takeout channel include young individuals, those with high household incomes, those working from home all their workdays or a substantial fraction of their workdays, and urban residents;the most enthusiastic consumers of the eat-in delivery channel are white individuals, those with less than three vehicles in the household, individuals with children, urban residents, and those worried about pandemic-related personal health risks. Older individuals, non-white individuals, individuals with a graduate degree, individuals in fewer motorized vehicle-owning households and in joint families, those with children in the household, and rural residents constitute the most committed population segments of the home-cooked meal (HCM) consumption channel. The results suggest the important impact of workplace location on dining channel choice. The results also show clear evidence of complementary and substitution effects at play;the delivery channel complements eating out but substitutes takeout. Similarly, eat-out has a substitution effect on eat-in takeout. These effects have important implications for activity-travel behavior due to emerging technology-based ordering options for dining choices, especially in the aftermath of the COVID pandemic.

Fulfilment of last-mile urban logistics for sustainable and inclusive smart cities: a case study conducted in Portugal

While industry tends towards mass personalisation and instant deliveries, the last mile of urban logistics is being challenged to decrease the number of vehicles in circulation and the distances they travel in city centres. The COVID-19 pandemic helped expose the inefficiency of cities in fulfilling citizens' real-time needs. Moreover, the first aim of this paper is to understand the barriers which policymakers face in providing a personalised response to citizens' needs and the second to ascertain how they can proactively serve their communities. In line with these concerns, empirical evidence was collected through a questionnaire to Portuguese policymakers, and the results were discussed in a focus group with experts. The results suggest a lack of tools to enable real-time visualisation and study of scenarios for implementing and organising means of delivery and storage. Moreover, although policymakers feel confident in their capacity to manage the last mile, they would struggle to operate autonomously. Therefore, this paper conceptualises an initial algorithm based on the dynamic collaboration of stakeholders and sharing of resources to guarantee fulfilment of citizens' necessities. Furthermore, future discussions shall emerge about the relationships and technical standards between stakeholders to provide the necessary logistical means for city management and operations.

Mobile Application for After School Pickup Solution: Malaysia Perspectives

School pickup is a daily routine for most parents during the school term. The use of private vehicles as daily commute to transport students to and from schools has caused school-related traffic congestion and air pollution problems. The after school waiting bay of most schools in urban areas is normally very crowded with students waiting for transport to travel home. Teachers on duty and school student council members are assigned to monitor and control the crowded situation. Some schools implement drive-thru pickup procedure in which the teacher on duty need to be very attentive and observe the cars approaching the bay for pickup in order to queue up the students at the pickup bay. With the outbreak of pandemic COVID-19, the teachers not only need to observe the vehicles heading to the pickup bay but to ensure the students obey social distancing norms. To alleviate after school pickup related problems, a mobile application for after school pickup is proposed. With this application, the school teachers would be able to obtain a list of pickup requests from the parents via the mobile application and queue up the students accordingly with safe social distancing at the pickup bay. This can ease the crowded situation, minimize parents’ waiting time at the pickup lane, and reduce air pollution problem caused by vehicle engines that are running while waiting at the pickup lane. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The BISTIM study: first RCT comparing dual ovarian stimulation on the same cycle (duostim) vs 2 conventional ovarianstimulations in poor ovarian responders undergoing IVF

Study question: Is the number of cumulated oocytes with dual ovarian stimulation on the same cycle (duostim) higher compared to 2 consecutive antagonist cycles in poor responders? Summary answer: Considering the number of total and mature oocytes collected in poor ovarian responders, there is no benefit of duostim vs two consecutive antagonist cycles. What is known already: Several waves of follicular development exist on the same cycle. Recent studies have shown the ability to obtain oocytes with equivalent quality in the luteal phase, even after a previous ovarian stimulation in the follicular phase. During stimulation, smaller follicles are recruited and sensitized, which may increase the selection of follicles available on the second stimulation. In poor ovarian responders (POR) this potentialization may have a great interest, as 2 stimulations on the same cycle could give a higher number of oocytes compared to two conventional stimulations. However, these preliminary data need to be confirmed with a randomized controlled trial. Study design, size, duration: This is a multicenter, open-labeled randomized control trial (2018, september-2021, march). The primary objective was to demonstrate that two ovarian stimulations within the same cycle (first in the follicular phase, followed by a second in the luteal phase) lead to the retrevial of 1.5 more oocytes than the cumulative number of oocytes from two consecutive conventional stimulation, in POR women. According to this hypothesis, 44 patients were needed in each group. Participants/materials, setting, methods: 88 POR women, defined with Bologna criteria (AFC≤5 and/or AMH≤1.2ng/ml and ≤3 oocytes if previous IVF) were randomized, 44 in duostim group (D) and 44 in conventional group (C). Fertistart Kit®300IU/day with antagonist protocol was used except in luteal phase stimulation of group D. In group D, oocytes were pooled and inseminated after the second retrieval, with freeze all embryos. Fresh transfer was performed in group C. The analysis is presented in intention to treat. Main results and the role of chance: There was no difference between the groups regarding demographics, ovarian reserve markers (AFC, AMH) and stimulation parameters. The mean number of cumulated oocytes retrieved with 2 ovarian stimulation was not statistically different in group D and C, respectively 5.0+/-3.4 and 4.6+/-3.4 (p=0.56). The mean number of cumulated mature oocytes was not statistically different, 3.7+/-3.3 in group D vs 3.1+/-3.0 in group C (p=0.38). The mean number of embryos was significantly lower in the group D, 0.8+/-1.3 vs group C 1.6+/-1.3 (p<0.01). There was no statistical difference of the mean number of oocytes retrieved per cycle in cycle 1 vs cycle 2 in both group D and C. The delay, between the first and the second day 1 of stimulation was statistically different in group D 14.4 days (10-19) vs group C 90.6 (28-232). The ongoing pregnancy rate in group D 17.9% (7/39) was not statistically different with group C 29.3% (12/41), (p=0.23). And the mean time to ongoing pregnancy tends to be longer in group D (144 days) vs group C (115 days) but was not statistically different (p=0.21). Limitations, reasons for caution: The RCT was impacted by Covid pandemia and stop of IVF activities for 10 weeks. Delays were recalculated to exclude this period, however one women in group D cannot have the luteal stimulation. We also faced unexpected good ovarian responses and pregnancies after the first oocyte pick-up in group C. Wider implications of the findings: In routine practice, the benefit of duostim in patients with POR is not confirmed. Firstly, because there is no potentialization on the number of oocyte retrieved in luteal phase after follicular phase stimulation. Secondly, because the freeze all strategy avoids a pregnancy with fresh embryo transfer after the first cycle.

Optimising workforce efficiency in healthcare during the COVID-19: a computational study of vehicle routeing method for homebound vaccination

This study presents an optimisation model for scheduling homebound vaccination in a more efficient way to address the existing workforce management challenge. We consider a home healthcare routeing challenge for people to be vaccinated at home based on limited resources. There are different types of patients that are categorised based on the services they require and should be served by appropriate workforce teams or a single medical staff, where teams are transported by rental vehicles. In this context, our goal is to minimise the total cost of transportation while considering patient requirements and workforce qualifications, as well as resource constraints and the time limit within which the vaccine must be administered. To pursue this goal, a mathematical formulation, based on the vehicle routeing dynamics is proposed, along with an algorithm to address the challenge. A case study with a Physician who administers vaccinations at home in southeastern Italy is analysed. Driving and working times are subject to uncertainty and are defined by empirical data. Our approach allows the physician to identify the most promising solutions and thus the best one in terms of reducing work time and risk. The resulting schedule maximises the vaccine delivery rate.

Impact of SARS-CoV-2 Pandemic on Antiretroviral Access at a Large Treatment Centre in Lagos, Nigeria.

INTRODUCTION: The SARS-CoV-2 pandemic has ravaged the world with significant implications on the socio-economic and health status of individuals. Several measures were put in place to curtail the pandemic especially movement restrictions. The effect of this pandemic and the restriction measures could further compound the health needs of PLWHA. This study thus described the trend in access to HIV/AIDS care services before and during the COVID-19 induced lockdown and the possible consequences on their treatment outcome. METHODS: A secondary analysis of client data was carried out at the HIV clinic of the Nigerian Institute of Medical Research. The sociodemographic and clinical data [type of ART, duration on ART, clinical services 3-months before and during the COVID-19 induced Lockdown] were extracted from the electronic medical records. Data obtained was analyzed using the SPSS version 22.0. RESULTS: The median age of PLWHA was 45 years (IQR: 39-51), with the predominant age group being 25-49 years (65.4%). The majority were females (68.7%), married (59.5%), had at least secondary education (82.8%), and employed (81.5%). The median duration on ART was 102 months (IQR: 67-138) with the majority on non-Protease Inhibitor based regimen (77.7%). In the 3 months before the lockdown, there was an increase in drug pick-up of approximately 25% over the booked appointment compared to a decline of 40% when the lockdown was enacted. CONCLUSION: The significant decline in drug pick-up during the SARS-CoV-2 pandemic raises the need for measures to ensure continued access to drugs and care among PLWHA.

INTRODUCTION: La pandémie de SRAS-CoV-2 a ravagé le monde avec des implications significatives sur l'état socio- économique et sanitaire. Plusieurs mesures ont été mises en place pour freiner la pandémie, en particulier la restriction des déplacements. L'effet de cette pandémie et les mesures de restriction pourraient encore aggraver les besoins de santé des PVVIH. Cette étude décrit ainsi la tendance des services de soins du VIH / SIDA avant et pendant le verrouillage induit par le COVID-19 et les conséquences possibles sur l'issue de leur traitement. MÉTHODES: Une étude transversale parmi les PVVIH fréquentant la clinique VIH de l'Institut nigérian de recherche médicale. Les données sociodémographiques et cliniques [type de TAR, durée de la TAR, services cliniques 3 mois avant et pendant le verrouillage induit par COVID-19] ont été extraites des dossiers médicaux électroniques. Les données obtenues ont été analysées à l'aide de la version 22.0 de SPSS. RÉSULTATS: L'âge médian des PVVIH était de 45 ans (IQR (39-51), le groupe d'âge prédominant étant 25-49 ans (65,4%). La majorité étaient des femmes (68,7%), mariées (59,5%), avaient au moins secondaire (82,8%) et employé (81,5%). La durée médiane des TAR était de 102 mois (IQR: 67-138), la majorité sous traitement sans inhibiteur de protéase (77,7%). Au cours des 3 mois précédant le verrouillage , il y a eu une augmentation de la cueillette de médicaments d'environ 25% par rapport au rendez-vous réservé, comparativement à une baisse de 40% lorsque le verrouillage a été décrété. CONCLUSION: La baisse significative de la collecte de médicaments pendant la pandémie de SRAS-CoV-2 fait ressortir la nécessité de prendre des mesures pour assurer un accès continu aux médicaments et aux soins parmi les PVVIH. Mots Clés: SRAS-CoV-2, antirétroviral, COVID-19, ramassage de médicaments, VIH, verrouillage.

A multi-objective humanitarian pickup and delivery vehicle routing problem with drones

This paper applies the truck and drone cooperative delivery model to humanitarian logistics and proposes a multi-objective humanitarian pickup and delivery vehicle routing problem with drones (m-HPDVRPD) which contains two subproblems: cooperative routing subproblem and relief supplies allocation subproblem. The m-HPDVRPD is formulated as a multi-objective mixed integer linear programming (MILP) model with two objectives, which simultaneously minimizes the maximum cooperative routing time and maximizes the minimum fulfillment rate of demand nodes. We develop a hybrid multi-objective evolutionary algorithm with specialized local search operators (HMOEAS) and a hybrid multi-objective ant colony algorithm (HACO) for the problem. A set of numerical experiments are performed to compare the performance of the two algorithms and their three variants. And the experimental results prove that HMOEAS is more effective than other methods. Taking the Corona Virus Disease 2019 (COVID-19) epidemic in Wuhan as a case, we compare the truck-drone cooperative delivery model with the truck-only delivery and the drone-only delivery models, and find that our model has advantages in the delivery efficiency of anti-epidemic materials. Moreover, based on the real-world case, a sensitivity analysis is conducted to investigate the impact of different drone parameters on the efficiency of truck-drone cooperative delivery. [ FROM AUTHOR] Copyright of Annals of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A bi-objective optimization model for the medical supplies' simultaneous pickup and delivery with drones.

In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.

Emergence of Curbside Pickup: Consumers’ Usage Intention Amid COVID-19 Pandemic

This study examines how consumers’ intention to use a curbside pickup responds to the COVID-19 vaccination rates. With our first survey conducted in March 2021, we find that a low (high) vaccination rate is associated with consumers’ high (low) intention to maintain contact avoidance and their high (low) anticipation for shipping delays. Heightened contact avoidance and anticipation for shipping delays may encourage consumers to use a curbside pickup. Our results also show that when a product is needed immediately, and a consumer expects shipping delays, s/he is more likely to use a curbside pickup. However, with our second survey conducted in November 2021, we find heterogeneous consumer responses to the vaccination rates. Specifically, consumers’ political affiliation moderates the relationship between the vaccination rates and their intention to maintain contact avoidance. The association between the vaccination rates and the anticipation for shipping delays is also weakened compared to March 2021. Our empirical results illustrate how consumers’ intention to use a curbside pickup emerges and changes amid the COVID-19 pandemic. © 2022, Asia Pacific Journal of Information Systems. All Rights Reserved.