Optimized Long-distance Online Take-out Food Delivery Using Mobile Crowdsourcing

With the rapid development of the Internet and the popularization of mobile intelligent devices,intelligent devices with rich sensing and computing capabilities are becoming more and more popular.In this context,mobile crowdsourcing as a new perception mode has gradually developed.Different from the general way of distribution,mobile crowdsourcing mainly depends on the widely distributed people carrying mobile devices in the city.These users can collect relevant data through their mobile devices,and efficiently complete some large-scale sensing tasks.Among them,logistics distribution is one of the important application directions of mobile crowdsourcing.Due to delivery method based on crowdsourcing needs to arrange the driving route,taxi drivers in the city need to cooperate with each other.At present,most of the previous studies simply consider the problems of single task assignment,which only need to choosing the right participants for a perceptual task without considering the participants selection in multi-task assignment problem.Meanwhile,the existing allocation problem does not consider the impact of task pricing on task completion.Therefore,this work focuses on the take-out food delivery in crowdsensing platform and studies the related issues under this background.The main work of this paper is as follows:1)Periodical multitask allocation for mobile crowdsourcing tasks: because the goal of the platform is often to maximize the benefits of the system,it is necessary to consider the current risks and future returns in the distribution in different time periods.The objective of this problem is to find the distribution scheme with the minimum global cost and the maximum future revenue.In this paper,an initial solution generation algorithm based on genetic algorithm is proposed,and then a local scheduling algorithm is introduced to solve the scheme.The modern portfolio theory is used to consider the route risk and optimize the overall scheme.2)Instant multitask allocation for mobile crowdsourcing tasks: in order to solve the urgent situation of the task in the distribution,we consider to quickly allocate appropriate distribution vehicles for take out orders,and improve the efficiency of distribution.The optimization goal of the problem is to maximize the number of tasks to be completed,improve the task completion rate,and minimize the total distance to complete the task to reduce the time to complete the task.In this paper,the construction algorithm is proposed to generate the initial solution,and the worst deletion method and the Shaw deletion method are proposed to solve the problem.The experimental results show that this method can distribute the urgent distribution tasks reasonably and effectively.3)Dynamic pricing strategy for mobile crowdsourcing tasks: in order to encourage participants to participate in crowdsourcing distribution,it is necessary to set reasonable distribution price for different stages of the platform,and encourage participants to complete the distribution task with high quality.The goal of this problem is to maximize the revenue of participants and minimize the time of task moving.Firstly,this paper proposes a static pricing strategy based on optimal reservation price.Then,according to the dynamic change of supply-demand relationship,a local dynamic pricing strategy with confidence upper bound is proposed to recommend the appropriate distribution unit price for different regions.