Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

Abstract

Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.

Moving Towards a Sustainable Model: Societal, Economic and Environmental

“…Mazumder et al (2021) presented research books related to power electronic innovations in cyber-physical systems (CPS), more precisely new energy distribution architectures, protection techniques taking into account a wide integration of renewable energies in smart grids, simultaneous transmission of energy and information, etc. Nouiri et al (2019b) proposed a multi-agent architecture named EasySched, based on the collaboration between goods producers (energy consumers) and renewable energy producers. Two types of ICPS are considered in this example: cyber-physical production systems (CPPS) and cyber-physical energy supply systems (CPES).…”

CODAS HFLTS Method to Appraise Organizational Culture of Innovation and Complex Technological Changes Environments

“…The productive processes in the context of Industry 4.0 are modified throughout the industrial phase of value creation through the digital interconnection of people, machines and objects, which offers numerous possibilities to increase production efficiency [3,4,22], as well as the costs reduction, optimization of energy consumption and product life cycle management improvement [14,85]. These processes are complex given the constant evolution of technology.…”

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

“…Typical processes range from additive processes, such as casting or forging [16], to subtractive processes such as machining (e.g., milling) [17], laser cutting [18] or electrochemical machining (ECM) [19]. Some processes require the use of thermal energy (e.g., heating to a high temperature) or chemical energy, but the main problem is electricity consumption [20].…”

“…Energy efficiency can be improved by applying less energy-intensive modern technologies and advanced machine tools or operating methods at the system level, such as energy efficient scheduling (EES) [16]. Currently, there is an increasing number of publications about EES [16][17][18][19][20][21][22]. Different models of machine energy consumptions processes [14] and all shop floor types have been investigated in the literature, such as single machine scheduling [23][24][25][26][27], flow shop [28][29][30][31][32][33][34][35][36][37][38][39][40][41], job shop [42][43][44][45][46][47], and hybrid systems for specific issues [48][49][50].…”