Open Access

ARTICLE

Coordinated Scheduling of Two-Agent Production and Transportation Based on Non-Cooperative Game

by Ke Xu^1,2, Peng Liu^1,*, Hua Gong^1,2

1 School of Management, Shenyang University of Technology, Shenyang, 110870, China
2 School of Science, Shenyang Ligong University, Shenyang, 110159, China

* Corresponding Author: Peng Liu. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3279-3294. https://doi.org/10.32604/iasc.2023.036007

Received 13 September 2022; Accepted 04 November 2022; Issue published 15 March 2023

Abstract

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers. The jobs of two agents compete for the processing position on a machine, and after the processed, they compete for the transport position on a transport vehicle to be transported to two agents. The two agents have different objective functions. The objective function of the first agent is the sum of the makespan and the total transportation time, whereas the objective function of the second agent is the sum of the total completion time and the total transportation time. Given the competition between two agents for machine resources and transportation resources, a non-cooperative game model with agents as game players is established. The job processing position and transportation position corresponding to the two agents are mapped as strategies, and the corresponding objective function is the utility function. To solve the game model, an approximate Nash equilibrium solution algorithm based on an improved genetic algorithm (NE-IGA) is proposed. The genetic operation based on processing sequence and transportation sequence, as well as the fitness function based on Nash equilibrium definition, are designed based on the features of the two-agent production and transportation coordination scheduling problem. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. When compared to heuristic rules such as the Longest Processing Time first (LPT) and the Shortest Processing Time first (SPT), the objective function values of the two agents are reduced by 4.3% and 2.6% on average.

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Keywords

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