Process Planning and Scheduling with Multiagent Systems

Abstract

This paper deals with the approach of application of intelligent software agents to improve information logistics in the area of process planning and production control. Thus, enterprises will be able to fulfil the requirement of flexible, reliable and fault-tolerant manufacturing. Fulfilment of these requirements is a prerequisite for successful participation in modem business alliances like supply chains, temporal logistic networks and virtual enterprises. Thus, agent-based improvements of information logistics enable enterprises to face the challenges of competition successfully. Current research activitiesfocus on the development of agent-based systems for integrated process planning and production control. They led to the "lntaPS" approach, which is presented in this paper.

A Machining Feature Information Model for Dynamic Manufacturing Planning

“…An evolutionary artificial neural networks based method is used to meet both objectives in process plan and production schedule. Saygin and Kilic [8] proposed hierarchical integer linear programs with different objective functions to select alternative machines (shortest path) and alternative process plans (dissimilarity maximization), and to finish time scheduling of the obtained linear process plan. Denkena et.al [9] presented a multi-agent architecture to determine operation routes and schedules.…”

“…Another aspect to overcome this problem is the integration of process planning and scheduling [7]. Ueda et.al [8] proposed an emergent synthesis approach to simultaneous process planning and scheduling. An evolutionary artificial neural networks based method is used to meet both objectives in process plan and production schedule.…”

Optimization Algorithm for Dynamic Multi-Agent Job Routing

“…As a consequence, logistics gets a new focus on optimization of the production process in a very dynamic environment. Current research in multi-agent heterarchical control for holonic systems is usually focused in real-time scheduling algorithms, where agents explore the routing or process sequencing flexibility in real-time (Denkena et al, 2002, Heragu et al, 2002, Sheremetov et al, 2003, Usher, 2001. Though there are a lot of results on scheduling heuristics and dispatching rules, few researchers have studied the influence of these approaches on the overall optimization of the production system performance.…”