Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning

Abstract

This paper is concerned with the multi-task multiagent allocation problem via many-objective optimization for multi-agent systems (MASs). First, a novel layered MAS model is constructed to address the multi-task multi-agent allocation problem that includes both the original task simplification and the many-objective allocation. In the first layer of the model, the deep Q-learning method is introduced to simplify the prioritization of the original task set. In the second layer of the model, the modified shift-based density estimation (MSDE) method is put forward to improve the conventional Strength Pareto Evolutionary Algorithm 2 (SPEA2) in order to achieve many-objective optimization on task assignments. Then, an MSDE-SPEA2-based method is proposed to tackle the many-objective optimization problem with objectives including task allocation, makespan, agent satisfaction, resource utilization, task completion, and task waiting time. As compared with existing allocation methods, the developed method in this paper exhibits an outstanding feature that the task assignment and the task scheduling are carried out simultaneously. Finally, extensive experiments are conducted to 1) verify the validity of the proposed model and the effectiveness of two main algorithms; and 2) illustrate the optimal solution for task allocation and efficient strategy for task scheduling under different scenarios.

Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

“…Cao and others proposed comparing the space required to store a certain type of goods with the average outbound volume of the goods as an indicator of picking bit allocation [8]. Li and others analyzed that when manual picking and automatic picking systems are used for partition picking and parallel picking, the manual picking area or unallocated area will affect the picking path of operators and then affect the picking efficiency [9]. Martin and others studied the influence of location allocation on picking efficiency in this type of automatic three-dimensional warehouse with multiple lanes, selected several influencing parameters, i.e., cargo correlation and shipment volume, established the picking time model, and selected heuristic algorithm genetic algorithm to search the optimal solution [10].…”

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

“…One of the main applications for this technique is unmanned ground vehicles or unmanned surface vessels [219]. There are very few applications for containment and surrounding control in the literature and this area needs further investigation to find suitable applications in future connected smart cities [220].…”

“…Examples of such applications are presented in energy [222,223], transportation [148,224], health care [225], and supply chains [226,227]. Task allocations in cloud computing platforms are considered as one of the main applications of distributed multi-agent optimization in smart cities [220]. Distributed estimation can be also reformulated as a distributed optimization problem in which the main goal is to reduce computation time and estimation error by splitting the task between multiple agents [228,229].…”

Tracking and Data Association Based on Reinforcement Learning

“…After decades of development, RL technology has many achievements, such as Q-learning, dynamic programming, Policy Gradients, Deep-Q-Network, etc. [13][14][15][16][17][18][19][20][21][22][23][24]. In essence, RL is a process in which the agent learns itself in an unknown environment under defined rules.…”