Fair Task Allocation in Crowdsourced Delivery

Abstract

Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries, including online shopping, on-demand local delivery, and on-demand transportation. The power of crowdsourced delivery stems from the large number of workers potentially available to provide services and reduce costs. It has been shown in social psychology literature that fairness is key to ensuring high worker participation. However, existing assignment solutions fall short on modeling the dynamic fairness metric. In this work, we introduce a new assignment strategy for crowdsourced delivery tasks. This strategy takes fairness towards workers into consideration, while maximizing the task allocation ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase allocation model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that minimizes unfairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task allocation ratio (TAR), as well as unfairness and assignment ratio. Experiments show that F-Aware runs around 10 7 × faster than the TAR-optimal solution and allocates 96.9% of the tasks that can be allocated by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm.

Fairness of Task Allocation in Crowdsourcing Workflows

“…For i � 1 to |S| Do (8) Get the list of nominees for t i (9) Group nominees by similarity (10) Set t i • v for each group (11) Get candidates by multicasting k nominees by group (12) Calculate the overallFairness ( 13 Mathematical Problems in Engineering complete a workflow with 1000 tasks, whereas the F-Aware costs only 347 cents. One hundred tasks can be completed for 29 cents because the average cost of a task is 0.3 cents.…”

“…e summary of the principal contributions of this work is provided as follows: e fairness-aware task allocation problem in crowdsourcing workflows is formulated as a constraint optimization problem based on the customized fairness. A generic two-phase task allocation model inspired by [8] is designed to cover various crowdsourcing workflow scenarios based on the new fair criterion. Four heuristic strategies are proposed to solve the current problem.…”

Model-Based Rolling Matching Strategy for Crowdsourced Drivers and Delivery Tasks Considering Uncertain Transportation Duration

“…It can be seen in the existing studies ( 7, 24, 33, 34 ) that the results of one-to-one matching are the basis of one-to-many matching between drivers and delivery tasks. Usually, the model for one-to-many matching can be obtained according to the transformation of the model for one-to-one matching.…”

“…usually complex, since multiple factors need to be considered, including each crowdsourced driver's origin, destination, earliest departure time and latest arrival time, and each delivery task's origin, destination, earliest pickup time and latest delivery time, and so forth (2,24). Therefore, the determination of rational matching between crowdsourced drivers and delivery tasks is an important research problem with a realistic background in the operations of crowdsourced delivery platforms.…”

“…According to the information provided by crowdsourced drivers and senders, the crowdsourced delivery platform needs to determine rational matching between crowdsourced drivers and delivery tasks to implement the delivery tasks and make a profit. It is important to highlight that the determination of rational matching between crowdsourced drivers and delivery tasks is usually complex, since multiple factors need to be considered, including each crowdsourced driver’s origin, destination, earliest departure time and latest arrival time, and each delivery task’s origin, destination, earliest pickup time and latest delivery time, and so forth ( 2, 24 ). Therefore, the determination of rational matching between crowdsourced drivers and delivery tasks is an important research problem with a realistic background in the operations of crowdsourced delivery platforms.…”