Dynamic agent-based bi-objective robustness for tardiness and energy in a dynamic flexible job shop

Nowadays, manufacturing systems are shifting rapidly with the significant change in technology, business, and industry to become more complex and involved in more difficult issues, customised products, variant services and products, unavailable machines, and rush jobs. In the current practices, there are limited models or approaches that are dealing with these complexities. Most of the scheduling models in literature are proposed as centralised approaches. Researchers recently started to pay attention to reduce energy consumption in manufacturing due to the rising cost and the environmental impact. The energy consumption factor has been lately introduced into scheduling research among other traditional objectives such as time, cost and quality. Although reducing energy in manufacturing systems is very important, few researchers have considered energy consumption factor into scheduling in dynamic flexible manufacturing systems. This paper proposes an agent-based dynamic bio-objective robustness for energy and time in a job shop. Two types of agent are introduced which are machine agent and product agent. A new decision making and negotiation model for multi-agent systems is developed. Two types of dynamic unexpected events in the shop floor are introduced: dynamic job arrival and machines breakdown. A case study is provided in order to verify the result

A Linear Programming Model for Renewable Energy Aware Discrete Production Planning and Control

Industrial production in the EU, like other sectors of the economy, is obliged to stop producing greenhouse gas emissions by 2050. With its Green Deal, the European Union has already set the corresponding framework in 2019. To achieve Net Zero in the remaining time, while not endangering one's own competitiveness on a globalized market, a transformation of industrial value creation has to be started already today. In terms of energy supply, this means a comprehensive electrification of processes and a switch to fully renewable power generation. However, due to a growing share of renewable energy sources, increasing volatility can be observed in the European electricity market already. For companies, there are mainly two ways to deal with the accompanying increase in average electricity prices. The first is to reduce consumption by increasing efficiency, which naturally has its physical limits. Secondly, an increasing volatile electricity price makes it possible to take advantage of periods of relatively low prices. To do this, companies must identify their energy-intensive processes and design them in such a way as to enable these activities to be shifted in time. This article explains the necessary differentiation between labor-intensive and energy intensive processes. A general mathematical model for the holistic optimization of discrete industrial production is presented. With the help of this MILP model, it is simulated that a flexibilization of energy intensive processes with volatile energy prices can help to reduce costs and thus secure competitiveness while getting it in line with European climate goals. On the basis of real electricity market data, different production scenarios are compared, and it is investigated under which conditions the flexibilization of specific processes is worthwhile

A Dual Scheduling Model for Optimizing Robustness and Energy Consumption in Manufacturing Systems

[EN] Manufacturing systems involve a huge number of combinatorial problems that must be optimized in an efficient way. One of these problems is related to task scheduling problems. These problems are NP-hard, so most of the complete techniques are not able to obtain an optimal solution in an efficient way. Furthermore, most of real manufacturing problems are dynamic, so the main objective is not only to obtain an optimized solution in terms of makespan, tardiness, and so on but also to obtain a solution able to absorb minor incidences/disruptions presented in any daily process. Most of these industries are also focused on improving the energy efficiency of their industrial processes. In this article, we propose a knowledge-based model to analyse previous incidences occurred in the machines with the aim of modelling the problem to obtain robust and energy-aware solutions. The resultant model (called dual model) will protect the more dynamic and disrupted tasks by assigning buffer times. These buffers will be used to absorb incidences during execution and to reduce the machine rate to minimize energy consumption. This model is solved by a memetic algorithm which combines a genetic algorithm with a local search to obtain robust and energy-aware solutions able to absorb further disruptions. The proposed dual model has been proven to be efficient in terms of energy consumption, robustness and stability in different and well-known benchmarks.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been supported by the Spanish Government under research project TIN2013-46511-C2-1 for the Spanish government and the TETRACOM EU project FP7-ICT-2013-10-No 609491.Escamilla Fuster, J.; Salido Gregorio, MA. (2016). A Dual Scheduling Model for Optimizing Robustness and Energy Consumption in Manufacturing Systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 1(1):1-12. https://doi.org/10.1177/0954405415625915S1121

Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing

Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA

Real-Time Analysis and Control of Serial Production Lines for Energy Efficient Manufacturing

Productivity analysis, operation control and energy consumption reduction have been the central topics in manufacturing research and practice. They are closely related to each other. Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems, while system performance analysis serves as the base of production control. On the other hand, effective operation control can result to energy efficiency in manufacturing. Steady state analysis has been investigated extensively; however, transient analysis remained largely unexplored. Our research focuses on system modeling, performance analysis, and real-time operation control of serial production lines with unreliable machines and finite buffers, especially in transient period, with Bernoulli or geometric reliability. Analytical results, practical case studies and applications for energy efficient manufacturing are provided. A simulation using ARENA software to reproduce and analyze brewery production line is performed

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd

Towards an Energy Information System Reference Architecture for Energy-Aware Industrial Manufacturers on the Equipment-Level

The research goal of this thesis is to support the development of energy information systems for energy-aware industrial manufacturers in the form of reusable artifacts

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Minimisation of Energy Consumption Variance in Manufacturing through Production Schedule Manipulation

In the manufacturing sector, despite the vital role it plays, the consumption of energy is rarely considered as a manufacturing process variable during the scheduling of production jobs. Due to both physical and contractual limits, the local power infrastructure can only deliver a finite amount of electrical energy at any one time. As a consequence of not considering the energy usage during the scheduling process, this limited capacity can be inefficiently utilised or exceeded, potentially resulting in damage to the infrastructure. To address this, this thesis presents a novel schedule optimisation system. Here, a Genetic Algorithm is used to optimise the start times of manufacturing jobs such that the variance in production line energy consumption is minimised, while ensuring that typical hard and soft schedule constraints are maintained. Prediction accuracy is assured through the use of a novel library-based system which is able to provide historical energy data at a high temporal granularity, while accounting for the influence of machine conditions on the energy consumption. In cases where there is insufficient historical data for a particular manufacturing job, the library-based system is able to analyse the available energy data and utilise machine learning to generate temporary synthetic profiles compensated for probable machine conditions. The performance of the entire proposed system is optimised through significant experimentation and analysis, which allows for an optimised schedule to be produced within an acceptable amount of time. Testing in a lab-based production line demonstrates that the optimised schedule is able to significantly reduce the energy consumption variance produced by a production schedule, while providing a highly accurate prediction as to the energy consumption during the schedules execution. The proposed system is also demonstrated to be easily expandable, allowing it to consider local renewable energy generation and energy storage, along with objectives such as the minimisation of peak energy consumption, and energy drawn from the National Grid