A new immune multi-agent system for the flexible job shop scheduling problem

Abstract

Scheduling for the flexible job shop is very important and challenging in manufacturing field. Multiagent-based approaches have been used to solve the flexible job shop scheduling problem (FJSP), in order to reduce complexity and cost, increase flexibility, and enhance robustness. However, the quality of solution obtained by the multi-agent approach is always worse than the centralized meta-heuristic algorithms. The immune system is a distributed and complicated information processing system, which can protect body from foreign antigens by immune responses. In this paper, we analyze the similarities between the FJSP and humoral immunity, which is one of the immune responses. Based on the similarities, we develop a new immune multi-agent scheduling system (NIMASS) to solve the FJSP with the objective of minimizing the maximal completion time (makespan). In order to acquire the higher-quality solution of the FJSP, we simulate humoral immunity to establish the architecture of NIMASS and the negotiation strategies of NIMASS, which are proposed for negotiation among agents. NIMASS was tested on different benchmark instances of the FJSP. In comparison with the multi-agent approaches and the centralized heuristic algorithms, the computational results indicate that NIMASS can effectively improve the quality of solution in very short time. And the computational time of NIMASS is superior to that of the centralized meta-heuristic algorithms, especially for the complex FJSPs. These results indicate that NIMASS can be very useful in applications that deal with real-time FJSPs.

CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation

“…This helps to create robust system with respect to time-varying communication. Without being exhaustive, some intelligent control systems based on MAS could be cited: Leitão [8], Isern et al [9], Xiong and Fu [10] etc. In all these citations, it is addressed that the applicability of MAS is characterized by their large scale in terms of number of agents, dynamic nature, and complex functionality.…”

“…Meanwhile, argumentation-based negotiation approach has been gained increasing popularity for its potential ability to overcome the limitations of other conventional approaches. As a continuation of developing negotiation protocols for improving manufacturing system's performance, Xiong and Fu [10] have presented the role of cooperative multi-agents. They designed an immune multi-agent scheduling system to solve a flexible job shop scheduling problem.…”

Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm

“…Yin et al [20] established a mathematical model of the FJSP to optimize three objectives, productivity, noise reduction and energy efficiency, in which processing times are controllable. Inspired by the similarities between the FJSP and humoral immunity, Xiong and Fu [21] introduced a novel immune multi-agent scheduling system for the FJSP. Piroozfard et al [22] proposed a modified multi-objective genetic algorithm for FJSP to optimize two criteria, i.e., the total carbon emission footprint and total late work.…”

Automatic Task Matching and Negotiated Vehicle Scheduling for Intelligent Logistics System

“…Thanks to its intelligence, adaptability and autonomy, the MAS outperforms other algorithms in the research of intelligent logistics systems [21][22][23][24]. For instance, the MAS has been preliminarily adopted for the optimal allocation of job-shop resources [25,26], the planning of distributed process flow [27,28] and the dynamic scheduling of the production system [29,30]. However, there is still no report on its application in logistics system for automatic task matching and negotiated scheduling of distribution vehicles.…”