Proportionate Flow Shop Scheduling with Multi-agents to Maximize Total Gains of JIT Jobs

Different variants of the multi-agent scheduling have been studied in the literature due to its wide applications in artificial intelligence, decision theory, operations research, etc. Most of previous research focused on the single-machine environment and two-agent scheduling. In this paper, we add...

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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) Vol. 43; no. 2; pp. 969 - 978
Main Authors: Li, Shi-Sheng, Chen, Ren-Xia, Li, Wen-Jie
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018
Springer Nature B.V
Subjects:
Agents (artificial intelligence)
Cost function
Decision theory
Dynamic programming
Engineering
Humanities and Social Sciences
Job shop scheduling
Job shops
Lower bounds
Multiagent systems
multidisciplinary
Operations research
Polynomials
Production scheduling
Research Article - Computer Engineering and Computer Science
Science
JIT jobs
Multi-agent scheduling
FPTAS
Proportionate flow shop
ISSN:2193-567X, 1319-8025, 2191-4281
Online Access:Get full text
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Summary:Different variants of the multi-agent scheduling have been studied in the literature due to its wide applications in artificial intelligence, decision theory, operations research, etc. Most of previous research focused on the single-machine environment and two-agent scheduling. In this paper, we address a multi-agent scheduling problem on a set of m machines in a proportionate flow shop system, where the job processing times are machine independent. Each agent desires to maximize its total gains of JIT jobs which are completed exactly at the due dates. The goal is to find a feasible schedule in which each agent’s cost function value does not less than a given lower bound. When the number of agents is part of the input, we use the reduction method to show that the general problem is strongly NP -complete even if all jobs have unit processing times. When the number of agents is fixed, we first develop a dynamic programming algorithm that runs in pseudo-polynomial time, then we design a fully polynomial time approximation scheme by exploiting the technique of trimming-the-state-space. The results presented in this paper imply that by relaxing each agent’s desired cost function value a small fraction, we can obtain an efficient approximate schedule for the problem with fixed number of agents in polynomial time, while when the number of agents is part of the input, the problem become much intractable, and it needs more sophisticated methods to solve in future research.
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-017-2900-9