An Agent-Based Approach for Scheduling Multiple Machines

National Conference on Artificial Intelligence, 1998

Scheduling of multiple parallel machines in the face of sequence dependent setups and downstream considerations is a hard problem. No single efficient algorithm is guaranteed to produce optimal results. We describe a solution for an instance of this problem, in the domain of paper manufacturing. The problem has additional job machine restrictions and fixed costs of assigning jobs to machines.

In this paper the scheduling of n independent jobs on m non-identical machines is considered for a large concrete schedule space for 30 jobs and 6 machines. The schedule space is about 1023 which is large enough to render exhaustive systematic search for the optimal schedule limited. The schedules are generated by agents that represent the jobs as they randomly select the machines on which the jobs should be processed.

Journal of Intelligent Manufacturing, 2004

ATeams—teams of autonomous agents co-operating by sharing solutions through a common memory—have been proposed as a means of solving combinatorial optimization problems. In this paper, the ATeam architecture is tested on the job-shop scheduling problem. The results show that the method can work, but that it depends on the portfolio of agents and on the way in which the memory is managed.

Theoretical Computer Science, 2006

We consider the feasibility model of multi-agent scheduling on a single machine, where each agent's objective function is to minimize the total weighted number of tardy jobs. We show that the problem is strongly NP-complete in general. When the number of agents is fixed, we first show that the problem can be solved in pseudo-polynomial time for integral weights, and can be solved in polynomial time for unit weights; then we present a fully polynomial-time approximation scheme for the problem.

J Intell Manuf, 2004

ATeamsÐteams of autonomous agents co-operating by sharing solutions through a common memoryÐhave been proposed as a means of solving combinatorial optimization problems. In this paper, the ATeam architecture is tested on the job-shop scheduling problem. The results show that the method can work, but that it depends on the portfolio of agents and on the way in which the memory is managed.

Holonic and Multi-Agent Systems for Manufacturing, 2007

This paper addresses and introduces an overview on various multi-agent architectures applied to teams of metaheuristic agents for job shop scheduling applications, whose developed and examined on distributed problem solving environments. We reported a couple of topologies; ATEAM is a centrally coordinating method, which provides very good results when well-studied, on the other hand, architectures based on peer-to-peer technology provide wider flexibility in implementing various fashions. The experimentation for each targeted topology has revealed more details and attracts more attentions.

Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160), 1998

Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled, is a ubiquitous and complex activity in everyday life. This paper presents an approach to multi-machine scheduling that follows the multiagent learning paradigm known from the field of Distributed Artificial Intelligence. According to this approach the machines collectively and as a whole learn and iteratively refine appropriate schedules. The major characteristic of this approach is that learning is distributed over several machines, and that the individual machines carry out their learning activities in a parallel and asynchronous way.

Proceedings of the Winter Simulation Conference 2014, 2014

Complex scheduling problems require a large amount computation power and innovative solution methods. The objective of this paper is the conception and implementation of a multi-agent system that is applicable in various problem domains. Independent specialized agents handle small tasks, to reach a superordinate target. Effective coordination is therefore required to achieve productive cooperation. Role models and distributed artificial intelligence are employed to tackle the resulting challenges. We simulate a NP-hard scheduling problem to demonstrate the validity of our approach. In addition to the general agent based framework we propose new simulation-based optimization heuristics to given scheduling problems. Two of the described optimization algorithms are implemented using agents. This paper highlights the advantages of the agent-based approach, like the reduction in layout complexity, improved control of complicated systems, and extendability.

Lecture Notes in Computer Science, 2009

The present work details the experience on designing a multiagent system devoted to a dynamic Job Shop setting using the PASSI methodology. The agent system is in charge of the planning and scheduling of jobs and their operations on a set of available machines, while considering the materials assigned to each operation. Dynamicity concerns job orders scheduling on-the-fly and the reschedule caused by changes to the original plan due to clients, machines and material stocks. The system has been modeled with the PASSI Toolkit (PTK) and implemented over the Jade agent platform.

2011

Job shop scheduling is one of the strongly NP-complete combinatorial optimization problems. Developing effective search methods is always an important and valuable work. Metaheuristic methods such as genetic algorithms are widely applied to find optimal or nearoptimal solutions for the job shop scheduling problem. Parallelizing genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel genetic algorithm for the job shop scheduling problem. In our approach, initial population is created in an agent-based parallel way then an agent-based method is used to parallelize the genetic algorithm. Experimental results showed that the proposed approach enhances the performance.