Deep reinforcement learning approach for solving takeout delivery problem
This paper took the minimization of the rider's cost-benefit ratio as the optimization objective and used the mini-mum ratio traveling salesman problem to model the takeout delivery problem.Aiming at the issues of low accuracy and poor stability of current algorithms for solving this problem,this paper proposed a DRL-MFA algorithm based on deep reinforcement learning.Firstly,the algorithm defined the takeout delivery problem as a Markov decision model to simulate the process between agent and environment.Secondly,the algorithm used a multi-feature aggregation embedding sublayer in the encoder to achieve the advantageous complementarity among the features and improve the modelling ability of nonlinear problems.Finally,the algorithm calculated the probability distribution of the solution by the attention mechanism and pointer network in the decoder and used the strategy gradient to train the network.Through the experimental analysis of classic examples and simu-lation cases in Changchun,the results show that the proposed algorithm can effectively solve the takeout delivery problem,and has higher stability and accuracy than other heuristic algorithms.In addition,this paper conducted the sensitivity experiment to explore the impact of different pricing strategies on takeout delivery,which makes the research more realistic and practical.
takeout deliveryminimum ratio traveling salesman problemdeep reinforcement learningmulti-feature embed-dingattention mechanism
万方数据
维普
