Research on Coordination and Optimization of Order Allocation and Delivery Route Planning in Take-Out System

Sun, Guofeng; Tian, Zhongyuan; Liu, Renhua; Jing, Yun; Ma, Yawen

doi:10.1155/2020/7248492

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Based on the DP model with dominant conditions, the advantage condition of OLMP has been proposed. Sun et al [14] established an optimization model for the problem using cumulative time with the goal of minimizing the total delivery cost and added a time dimension to the order allocation and path optimization dimensions. The main algorithm is a dynamic allocation algorithm designed from the perspective of scheduling efficiency, and the sub-algorithm is an improved genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm

Shi,

Li,

Sun

et al. 2024

Applied Sciences

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With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25.

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Section: Related Workmentioning

confidence: 99%

Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm

Shi,

Li,

Sun

et al. 2024

Applied Sciences

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“…Reliability redundancy allocation can help improving the system reliability and minimize the cost, weight or volume [56,43]. Order allocation becomes more and more important to commercial companies like passenger transportation service companies [29,7], food delivery companies [50] and also other logistic companies [24]. Optimal allocation directly influences the efficiency and the profit of such companies that are relying on limited resources.…”

Section: Related Workmentioning

confidence: 99%

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

Tian¹,

Han²,

Kulkarni³

et al. 2022

Preprint

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Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussiansoftmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussiansoftmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.

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“…Li et al studied the order fulfillment problems of online retailers and proved through experiments that product overlap between different warehouses will affect the operating costs of e-commerce companies [18]. Sun et al proposed order allocation and delivery path optimization as the common consideration when studying the order allocation and unilateral soft time window constrained delivery route delivery problem [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Order Fulfillment Decision under Multiwarehouse Collaborative Delivery

Shuanjun

Longguang

Yuanyi

et al. 2021

Mathematical Problems in Engineering

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Aiming at the high cost of multicategory orders fulfillment under multiwarehouse collaborative distribution, comprehensively considering the fulfillment costs of different orders fulfillment strategies, an order fulfillment strategy selection model is proposed. The first step of the model uses the linear programming algorithm to solve the cost of suborder merge transportation after the order is split. The second step calculates the cost of the current “greedy algorithm” of the e-commerce platform for order split fulfillment. Then, the cost of each strategy is compared and the lowest cost one is chosen. The calculation example analysis shows that the order fulfillment strategy is closely related to the delivery location of the order and the SKU category. When the delivery location is far away and SKU categories are many in the order, the merged transportation strategy of suborders after the order is split will be significantly better than the cost of separate transportation. The multiwarehouse collaborative distribution fulfillment strategy proposed in this paper can provide a decision basis for the e-commerce platform to choose which fulfillment method.

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Research on Coordination and Optimization of Order Allocation and Delivery Route Planning in Take-Out System

Cited by 10 publications

References 18 publications

Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm

Online and Offline Model for Housekeeping Order Assignment Based on Dynamic Programming Algorithm

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

Order Fulfillment Decision under Multiwarehouse Collaborative Delivery

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