A state aggregation approach for stochastic multiperiod last-mile ride-sharing problems

National Research Foundation (NRF) Singapore under SMART Centr

Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers

National Research Foundation (NRF) Singapore under Corp Lab @ University scheme; Fujitsu Lt

Should bike sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China

Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike sharing users will be exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike sharing. Based on the trip data of bike sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspective of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike sharing during the pandemic. The results show that pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults are more dependent on bike sharing systems. Besides, the trip decrease reduces user contact and increases idle bikes. And a new concept of user distancing is proposed to avoid transmission risk and activate idle bikes. This study evaluates the role of shared micro-mobility during the COVID-19 pandemic, and also inspires the blocking of viral transmission within the city.Comment: 30 pages, 7 figures, 6 table

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

DATA-DRIVEN MODELS OF CUSTOMER BEHAVIOR TO IMPROVE OPERATIONAL EFFICIENCY IN SERVICE SYSTEMS

In 2015, on a global level, the service industry represented 66% of GDP and employed over 51% of the total working population making it one of the largest industries. Some of the sectors in this industry include finance, transportation, call centers, retail, and health care. Customers and service providers are key players in this industry. A successful transaction between these two results in a valued service for the customer and revenue to the provider. The primary objective of the providers, therefore, is to understand the customer’s needs, meet their requirements, provide quality service and achieve customer satisfaction. In this thesis, I utilize large data sets on customer-provider transactions to study two important issues. First, I build micro-demand models to predict true demand incorporating customer behavior, time and spatial dynamics. I utilize the predicted demand to optimally allocate the resources for improved operational performance. The study contexts I focus on are Bike Sharing systems and Street-Hail Taxi services. Second, I build micro models to understand the factors driving customer provided satisfaction measure on logistics service and their impact on purchase probability in E-commerce platforms. In the first chapter, I analyze the optimal allocation of bikes in a network of stations to improve ridership under non-stationarity demand and station substitution. Using large datasets on the censored trip and minute-level inventory, walking distance between stations and a stochastic model, I predict true demand at each station. Then I determine an optimal allocation of bikes across stations at the start of the day utilizing a dynamic program to maximize ridership in the network. I find the optimal policy could improve ridership and service level by 7.60% and 1.69% respectively. In the second chapter, I examine the impact of logistics performance metrics such as delivery delays, customer's promised speed of delivery, order split, etc. on logistics service ratings of sellers on an e-commerce platform. Using a large dataset of customer orders from an e-commerce platform, I find logistics ratings are negatively impacted by delivery delays, but positively impacted by faster-promised speed of delivery and total order amount paid. I also find that logistics ratings impact customer purchasing behavior positively. Lastly, I show that a reduction in delivery delay by one day can improve the average weekly sales by as much as 2.5%. In the third chapter, I study passenger demand estimation problem in Street-Hail Taxi services. I utilize large-scale datasets on GPS information of pick-ups and drop-off from New York Yellow Taxi services for this study. I first develop a stochastic model (double-ended queue) to predict passenger demand in location and time. The model allows for non-stationarity, randomness in arrivals and reneges of both drivers and passengers. Using sample path information along with Maximum Likelihood Estimation, I develop a framework to estimate true passenger demand, drivers and passengers renege rate. The predicted demand can be used to analyze the optimal timing of drivers change their shift to maximize revenue under the current status quo of delay in shift changeover. Overall, the thesis focuses on the analysis of large and granular transactional data to build micro demand models incorporating customer behavior and incorporate the models into planning to improve operational efficiency.Doctor of Philosoph