We investigate the problem of large-scale mobile crowdtasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker’s current location; each worker then independ...

We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is how to cope with the diversity of tasks and the task-dependent reliability of workers; for example, a worker may be good at recognizing the names of sports teams but not be familiar with cosmetics brands. We refer to th...

This paper develops an integer mathematical programming model to design the cellular manufacturing systems under data envelopment analysis. Since workers have an important role in doing jobs on machines, assignment of workers to cells becomes a crucial factor for fully utilization of cellular manufacturing systems (CMS). The aim of the proposed is to minimize backorder costs and intercellul...

This paper focuses on task assignments to workers in mobile crowdsoucing systems. The current method does not work so well since it considers only workers who are ready to work at the time of optimization. Our method handles workers’ day-long schedules, creates a ‘timeextended’ worker-task graph that expresses the relationships between workers and tasks over a time period and finds the best set...

This paper presents a theoretical model that combines employers learning about worker productivity, human capital acquisition, job-assignment and resolution of worker uncertainty regarding disutility of work from a job, to show how widely documented findings on both wage and promotion dynamics and turnover can be captured in a single set-up. Specifically we show how our model can capture result...

In this paper a new problem called Assembly Line Worker Assignment and Balancing Problem (ALWABP) is introduced. This problem arises in those assembly lines where we have certain limited resources available (normally workers) in which the operation time for every task is different depending on who executes the task, and where there are also some task-worker incompatibilities defined. The proble...

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels – allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The ...