Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop

We present a stochastic optimisation model that can be used to design a resilient supply chain operating under random disruptions. The model aims to determine sourcing and network design decisions ...

A multi-cut L-shaped method for resilient and responsive supply chain network design

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the s...

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

Energy-Aware Decision Support Models in Production Environments: A Systematic Literature Review

Enhanced agents in shared factory: Enabling high-efficiency self-organization and sustainability of the shared manufacturing resources

With the increased awareness of the market competition and protection of the environment, many studies have examined sustainable manufacturing, which combines lean production and sustainable perfor...

Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment

An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.

Gang Guo

Papers

Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment

Research on Intelligent Decision Method of Optimal Production Planning and AGV In-time Delivery in Mixed-Model Assembly Line