AnEfficient Worker Selection and Task Allocation Scheme for Mobile Crowdsourcing System

Abstract

The rapid proliferation of mobile computing devices has facilitated mobile crowd sourcing systems (MCS) to emerge as a powerful model for allocating tasks to a distributed workforce. The efficiency of such systems is significantly dependent on two critical factors: the selection of appropriate workers and the distribution of tasks to workers. Addressing these challenges is crucial for optimizing task accom plishment, minimizing task completion time, and improving satisfaction for both workers and task requesters. Online Food delivery (OFD), a specialized applica tion of mobile crowdsourcing, represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. Assigning food delivery orders to workers in a way that optimizes service quality by maximizing workers’ profit while minimizing order completion time to simultaneously enhance customer satisfaction is a challenging problem. The growing complexity of order fulfillment features and rising expecta tions for service quality have made the task of efficiently assigning riders for long distance, cross regional deliveries a major engineering challenge. Existing studies in the literature are limited as they tend to focus solely on either reducing order completion time or minimizing order delivery costs, failing to meet both objectives thoroughly. Prior research frequently depended on conventional order allocation i Abstract ii methods that either failed to notice varying capacities, or utilized non-intelligent systems that inadequately addressed fluctuating order demands and service delays. In this dissertation, we aim to develop a framework for optimal assignment of food delivery orders for both regional and cross regional online food delivery sys tem. At first, we propose a framework for the optimal assignment of food delivery orders to workers, formulated as a multi-objective linear programming (MOLP) problem, which balances the trade-off between maximizing worker profit and en hancing customer satisfaction, providing a comprehensive solution that addresses both objectives simultaneously. A Water Wave Optimization based metaheuristic assignment algorithm is developed for the online Food Delivery system that bal ances worker’s profit and customer satisfaction by selecting appropriate workers to complete the orders. The experiment results show the assignment significantly improves the performance of the OFD system in terms of average worker profit, customer satisfaction, average service time. The second contribution of this thesis is the development of the system compo nents and functional architecture of a cross regional online food delivery (XROFD) system, designed to facilitate real-time deliveries across regions efficiently. A Mixed Integer Linear Programming (MILP) optimization framework has been designed to minimize the total service time and delivery cost for cross regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. In the XROFD system, food orders are primarily transported by transfer vehicles, such as pickup vans, from restaurants to meeting point locations. To enhance the predictive accuracy of the XROFD sys tem, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands Abstract iii accurately, reflecting the dynamic nature of the marketplace. Additionally, Ex treme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and re source allocation within the MILP framework. These machine-learning techniques significantly bolster the MILP framework by providing detailed, accurate predic tions that improve decision-making processes and adaptability to real-time condi tions. Proven that the above MILP is an NP-hard problem, we further enhance our approach by integrating a metaheuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehi cles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders