Recommending personalized schedules in urban environments

• Main Author: CHEN, Cen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: planning and scheduling; mobile crowdsourcing; workforce scheduling; orienteering problem; multi-agent task assignment; agent coordination; Computer Engineering; Digital Communications and Networking; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/etd_coll_all/22; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1032&context=etd_coll_all
• Institution: Singapore Management University

In this thesis, we are broadly interested in solving real world problems that involve decision support for coordinating agent movements in dynamic urban environments, where people are agents exhibiting different human behavior patterns and preferences. The rapid development of mobile technologies makes it easier to capture agent behavioral and preference information. Such rich agent specific information, coupled with the explosive growth of computational power, opens many opportunities that we could potentially leverage, to better guide/influence the agents in urban environments. The purpose of this thesis is to investigate how we can effectively and efficiently guide and coordinate the agents with a personal touch, which entails optimized resource allocation and scheduling at the operational level. More specifically, we look into the agent coordination from three specific aspects with different application domains: (a) crowd control in leisure environments by providing personalized guidance to individual agents to smooth the congestions due to the crowd; (b) mobile crowdsourcing by distributing location-based tasks to part-time crowd workers on-the-go to promote the platform efficiency; (c) workforce scheduling by better utilizing full-time workforce to provide location-based services at customers' homes. For each, we propose models and efficient algorithms, considering agent-level preferences and problem-specific requirements. The proposed solution approaches are shown to be effective through various experiments on real-world and synthetic datasets.