The paper deals with the questions of how to develop the automated planning systems that are fast enough to be used in real-time management of supply networks, considering the manual plan corrections by the users. Several practical situations and planning system use cases are considered. The paper proposes several methods that allow the increase of the data processing speed in practical cases. The methods include parallel data processing, dynamic control of the solutions space depth search, self-regulation of the system behavior based of the specifics of the data processed.
The paper focuses on the development of methods of self-adaptation of agent societies in multi-agent scheduling systems allowing them to achieve higher scheduling quality in changing environments. The paper introduces a self-regulation method based on the agent expectation of achievable profit and on the level of truthfulness in agent interactions. It is described how the profit expectations and the declaration of these expectations affect the scheduling quality in different cases. The paper shows the importance of coordinated behaviour of agents. The approach to coordinated self-regulation and to proactive schedule improvement is proposed. Finally, the results of real data scheduling using the proposed approach are given.
This paper focuses on analysis of effective interaction techniques of agents in multi-agent systems used for real-time scheduling. The paper describes two approaches to the organization of the interaction of asynchronously working software agents. The supply network scheduling case is considered to show the difference in how the interaction goes on. The comparison shows how well each approach allows parallel processing, and subsequently, how fast the scheduling can be done on multi-core hardware. The pros and cons of the approaches are described, as well as ways to achieve better quality. Finally, the results of processing of real data using the approaches are given. The results show a higher effectiveness of one of the approaches in real-time supply scheduling.
To respond to customer demand businesses invest in capacities and supply. Any mismatch results in obsolescent stock, wasted resources and lost sales. In this paper the considerations for design & deployment of an Enterprise Grade Real-time Multi Agent System for supply chain synchronization is presented, so that each and every business involved in the supply chain can adjust their activities to minimize the wasted resources.
The paper describes main features of a strategy for managing complexity of the global market and real-time scheduling multi-agent system designed for the LEGO Company. The design is based on Multi-Agent Technology Group (MATech) own strategy blueprint and multi-agent platform, which provide real-time adaptive event-driven scheduling to replenish products to LEGO Branded Retail stores. The prototype system has been used to schedule 20 US-based LEGO retail outlets for a yearlong trial period and has achieved the following results: • Reduction of lost sale from 40% to 16%; • Increase in service level from 66% to 86%; • Increase in profitability 56% to 81%. The results show a considerable potential value for full scale LEGO supply chain multi-agent solution which would be able to dynamically and adaptively re-schedule deliveries in real time.
The paper describes main features of a real-time forecasting and scheduling multi-agent solution designed for the LEGO Company. The design is based on Knowledge Genesis Group own multi-agent platform and technology, which provide real-time adaptive event-driven scheduling to replenish products to LEGO Branded Retail stores. The prototype system has been used to schedule 20 US-based LEGO retail outlets for a yearlong trial period and has achieved the following results:
• Reduction of lost sale from 40% to 16%;
• Increase in service level from 66% to 86%;
• Increase in profitability 56% to 81%.
The results show a considerable potential value for full scale LEGO supply chain multi-agent solution which would be able to dynamically and adaptively re-schedule deliveries in real time.