Autonomous Agents Applied to Manufacturing Scheduling Problems: A Negotiation-Based Heuristic Approach

In this paper, a heuristic approach to job scheduling, which is based on a multi-agent system model, is proposed. The class of scheduling problems considered is characterized by the presence of independent jobs and identical parallel machines, and by a non-regular objective, including both job earliness and tardiness penalties. The multi-agent scheduling system is composed by different classes of agents, and a schedule is progressively defined by a negotiation protocol among the agents associated with the jobs and the machines

Stochastic Local Search for Multiprocessor Scheduling for Minimum Total Tardiness

The multi-processor total tardiness problem (MPTTP) is an NP-hard scheduling problem, in which the goal is to minimise the tardiness of a set of jobs that are processed on a number of processors. Exact algorithms like branch and bound have proven to be impractical for the MPTTP, leaving stochastic local search (SLS) algorithms as the main alternative to find high-quality schedules. Among the available..

Multi-machine earliness and tardiness scheduling problem: an interconnected neural network approach

This paper addresses the problem of scheduling a set of independent jobs with sequence-dependent setups and distinct due dates on non-uniform multi-machines to minimize the total weighted earliness and tardiness, and explores the use of artificial neural networks as a valid alternative to the traditional scheduling approaches. The objective is to propose a dynamical gradient neural network, which employs a penalty function approach with time varying coefficients for the solution of the problem which is known to be NP-hard. After the appropriate energy function was constructed, the dynamics are defined by steepest gradient descent on the energy function. The proposed neural network system is composed of two maximum neural networks, three piecewise linear and one log-sigmoid network all of which interact with each other. The motivation for using maximum networks is to reduce the network complexity and to obtain a simplified energy function. To overcome the tradeoff problem encountered in using the penalty function approach, a time varying penalty coefficient methodology is proposed to be used during simulation experiments. Simulation results of the proposed approach on a scheduling problem indicate that the proposed coupled network yields an optimal solution which makes it attractive for applications of larger sized problems