Abnormality Management in Spatial Crowdsourcing for Multi-skilled Workers Assignment

Crowdsourcing is dependent on a number of skilled workers who are needed to accomplish spatial tasks. This has been an active area of research and is gaining wide popularity now. Most of these tasks can be completed online due to convenience, but this method fails when there is a need of completing a task at actual physical locations. This has led to a new area called Spatial crowd sourcing that consists of location-specific tasks that require people who can accomplish them to physically arrive at specific locations. The tasks which require specific skillsets, completion times or other constraints are matched with workers who can meet these constraints and complete them. In this report we consider a situation where the jobs are at different locations with sequential sub-tasks, each with time and skill constraints, and are to be completed within the given interval by workers who have those required skills and are dispersed. The aim is to finish a majority of tasks in the environment before a final cap time given the constraints of this environment. First workers are assigned to tasks appropriately so that each worker has the skill needed to complete each of the tasks allocated. After the assignment is complete, a variant of the vehicle routing problem called vehicle routing problem with time windows (VRPTW) is used to assign these workers the paths and visiting times that they need to follow to reach specific task locations and finish them within the required time intervals. The vehicle routing problem with time windows (VRPTW) is a generalization of the vehicle routing problem where the service of a customer can only begin at time within the time window defined by the earliest and the latest times. We also consider the case when a worker cannot reach a particular task location in an abnormal situation and perform a re-assignment that does not need to re-assign tasks to all workers and is faster. By following these approaches, we aim to create a technique that can be applied to many real-world problems in the spatial crowd-sourcing environment with such practical events

Abnormality Management in Spatial Crowdsourcing for Multi-skilled Workers Assignment

Crowdsourcing is dependent on a number of skilled workers who are needed to accomplish spatial tasks. This has been an active area of research and is gaining wide popularity now. Most of these tasks can be completed online due to convenience, but this method fails when there is a need of completing a task at actual physical locations. This has led to a new area called Spatial crowd sourcing that consists of location-specific tasks that require people who can accomplish them to physically arrive at specific locations. The tasks which require specific skillsets, completion times or other constraints are matched with workers who can meet these constraints and complete them. In this report we consider a situation where the jobs are at different locations with sequential sub-tasks, each with time and skill constraints, and are to be completed within the given interval by workers who have those required skills and are dispersed. The aim is to finish a majority of tasks in the environment before a final cap time given the constraints of this environment. First workers are assigned to tasks appropriately so that each worker has the skill needed to complete each of the tasks allocated. After the assignment is complete, a variant of the vehicle routing problem called vehicle routing problem with time windows (VRPTW) is used to assign these workers the paths and visiting times that they need to follow to reach specific task locations and finish them within the required time intervals. The vehicle routing problem with time windows (VRPTW) is a generalization of the vehicle routing problem where the service of a customer can only begin at time within the time window defined by the earliest and the latest times. We also consider the case when a worker cannot reach a particular task location in an abnormal situation and perform a re-assignment that does not need to re-assign tasks to all workers and is faster. By following these approaches, we aim to create a technique that can be applied to many real-world problems in the spatial crowd-sourcing environment with such practical events.</p