Decision support tool for managing the PQ Problem Extended

New and innovative aspects

In contrast to traditional approaches that employ deterministic or heuristic methods for optimizing manufacturing and production systems, the innovative approach adopted by the Extended PQ Problem is to address the intrinsic complexity of the PQ Problem and extend it with uncertainty in both volume and mix through modeling and simulation techniques. These methods enable the capture of both internal interactions within the production system and external uncertainties, potentially leading to more realistic and effective solutions. By considering stochastic situations and applying simulation-based approaches using both discrete simulation and intelligent agent techniques, the limitation of oversimplification in traditional approaches is overcome, resulting in more informed and efficient decision-making in complex production systems.

Main advantages of its use

-Allows for the identification of current issues and potential improvements in the sequencing of manufacturing and production systems.

-Takes into account sources of uncertainty, both in production volume and mix, thereby enhancing the practical utility of the proposed solutions.

-Facilitates experimentation before implementing decision-making in real manufacturing and production systems.

Specifications

In general terms, the PQ Problem Extended simulator is a tool that allows experimentation with the well-known Goldratt PQ Problem production system. To provide greater realism and similarity to a real production system, the PQ Problem Extended has been extended with uncertainty in both production volume and mix. In this way, the PQ Problem Extended offers an appropriate framework for analyzing the behavior of managers and the impact of their decisions on manufacturing system flexibility.
To implement this PQ Problem Extended, hybrid modeling and simulation techniques have been used. Modeling has been done through Petri Nets, a common choice for capturing the dynamics of agent-based systems, representing the operation of the production system over time. Regarding simulation, discrete simulation has been used to implement a future event list, which is necessary for advancing the simulation clock and managing the sequence of discrete events that trigger actions. Additionally, intelligent agent-based simulation has been utilized, as this decentralized approach is considered highly suitable for analyzing the complex behavior of flexible manufacturing systems that rely heavily on interdependencies in variable environments. Systems are created from the interaction of their basic units, agents, so the global system is modeled as a multi-agent system, where individual agents interact in a changing environment and cooperate to achieve collective objectives.
Please do not hesitate to contact the University of Burgos for further information regarding the technical features of this software.

Applications

This simulation tool enables the analysis of a manufacturing system's behavior while considering various levels of uncertainty in production volume and product mix. Through experimentation, it becomes possible to anticipate outcomes before making changes to real production systems. The tool assists in decision-making by identifying how these sources of uncertainty can impact the economic performance of the system. Furthermore, it offers managers insights into the system's behavior over time.

Intellectual property status

Protected by software

Current development status

Device already developed and validated for industrialization.

Desired business relationship

Commercial Agreement, License Agreement, Technical Cooperation: further development; Technical Cooperation: testing new applications; Technical Cooperation: adaptation to specific needs.