Högskolan i Skövde

Efficient energy management is particularly significant for the automotive industry due to its high consumption in foundry operations. To satisfy ambitious environmental goals, it is imperative to develop tools and methods for optimizing energy consumption in such contexts. The foundry is currently equipped with sensors and data collection equipment, which presents equipment-specific graphs of energy consumption over time. Although these graphs can reveal patterns and trends in energy consumption by themselves, more systematic methods are needed to utilize this data to investigate and optimize improvements that reduce energy waste. This article investigates the use of Discrete Event Simulation as a tool for leveraging collected historical energy consumption data. The aim is to explore how such data can be collected and translated into simulation variables to generate insights that support improvement initiatives. A case study was conducted to explore this approach, demonstrating that tracking energy consumption data provides a valuable input for Discrete Event Simulation modeling. The findings suggest some methods for data collection of different equipment, its modelling, and that further investigation in this direction could reveal opportunities for optimized energy management in energy-intensive industries.

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

In today’s global and aggressive market system, for manufacturing companies to remain competitive, they must manufacture high-quality products that can be produced at a low cost; they also must respond efficiently to customers’ predictable and unpredictable needs and demand variations. Increasingly shortened product lifecycles, as well as product customization degrees, lead to swift changes in the market that need to be supported by capable and flexible resources able to produce faster and deliver to the market in shorter periods while maintaining a high degree of cost-efficiency. To cope with all these challenges, the setup of production systems needs to shift toward Reconfigurable Manufacturing Systems (RMSs), making production capable of rapidly and economically changing its functionality and capacity to face uncertainties, such as unforeseen market variations and product changes. Despite the advantages of RMSs, designing and managing these systems to achieve a high-efficiency level is a complex and challenging task that requires optimization techniques. Simulation-based optimization (SBO) methods have been proven to improve complex manufacturing systems that are affected by predictable and unpredictable events. However, the use of SBO methods to tackle challenging RMS design and management processes is underdeveloped and rarely involves Multi-Objective Optimization (MOO). Only a few attempts have applied Simulation-Based Multi-Objective Optimization (SMO) to simultaneously deal with multiple conflictive objectives. Furthermore, due to the intrinsic complexity of RMSs, manufacturing organizations that embrace this type of system struggle with areas such as system configuration, number of resources, and task assignment. Therefore, this dissertation contributes to such areas by employing SMO to investigate the design and management of RMSs. The benefits for decision-makers have been demonstrated when SMO is employed toward RMS-related challenges. These benefits have been enhanced by combining SMO with knowledge discovery and Knowledge-Driven Optimization (KDO). This combination has contributed to current research practices proving to be an effective and supportive decision support tool for manufacturing organizations when dealing with RMS challenges.

I dagens globala och högst föränderliga marknad för att vara konkurrenskraftig måste tillverkandebolag producera högkvalitativa produkter som produceras till låga kostnader och möter kunders behov samt är anpassningsbara till marknadens variationer i efterfrågan. De allt kortare produktlivscyklerna och graden av produktanpassning leder till snabba förändringar på marknaden som behöver stödjas av mer kapabla och flexibla produktionsresurser som ökar produktionstakten och leverera till marknaden på kortare tid med bibehållen hög kostnadseffektivitet. För att hantera en sådan utmaning måste produktionssystemens uppbyggnad skifta mot omkonfigurerbara tillverkningssystem (RMS), vilket möjliggör för produktionen att på ett snabbt och kostnadseffektivt sätt ändra sin funktion och kapacitet för att möta oförutsedda marknadsvariationer och produktförändringar. Trots de fördelar som RMS för med sig så är design och nyttjande av dessa system för med en hög effektivitetsgrad en komplex och utmanande uppgift som kräver användning av optimeringstekniker. Metoder för simuleringsbaserad optimering (SBO) har visat sig förbättra komplexa tillverkningssystem som utsätts för planerade och oplanerade händelser. Användningen av SBO-metoder för att ta itu med utmaningen rörande design och effektiv nyttjande av RMS är dock underutvecklad och där nyttjande av flermålsoptimering (MOO) är begränsad. Det har endast skett ett fåtal försök att tillämpa simulering baserad flermålsoptimering (SMO) för att hantera flera konflikterande mål. På grund av den komplexet i RMS kämpar tillverkningsorganisationer som om-famnar den här typen av system med områden som systemkonfiguration, antal resurser och uppgiftstilldelning. Följaktligen bidrar denna avhandling till de nämnda områdena genom att använda SMO för att undersöka designen och hanteringen av RMS. Fördelarna för beslutsfattare har visat sig när SMO används mot RMS-utmaningarna. Dessa fördelar har förbättrats genom att kombinera SMO med kunskapsupptäckt och kunskapsdriven optimering (KDO). Denna kombination har bidragit till nuvarande forskningspraktiker och visat sig vara ett effektivt och stödjande beslutsstödsverktyg för tillverkningsorganisationer när de hanterar RMS-utmaningar. På grund av RMS inneboende komplexitet, de tillverkande organisationer som arbetar med denna typ av system möter oftast utmaningar rörande systemkonfiguration, antal resurser och uppgiftsfördelning. Följaktligen bidrar denna avhandling till de nämnda områdena genom att använda SMO för att undersöka design och effektive nyttande av RMS system. Fördelarna med att nyttja SMO för RMS utmaning har demonstrerats för beslutsfattare. Fördelarna har en mer utvecklats genom att kombinera SMO med kunskaps extrahering och KDO. Kombinationen av dessa tekniker har bidragit till den forskning som presenteras här som visat sig vara IV ett effektivt och stödjande beslutsstödsverktyg för tillverkningsorganisationer när de hanterar RMS-utmaningar.

Nowadays, manufacturing companies face an increasing number of challenges that can cause unpredictable market changes. These challenges are derived from a fiercely competitive market. These challenges create unforeseen variations and uncertainties, including new regional requirements or regulations, new technologies and materials, new market segments, increasing demand for new product features, etc. To cope with the challenges above, companies must reinvent themselves and design manufacturing systems that seek to produce quality products while responding to the changes faced. These capabilities are encompassed in Reconfigurable Manufacturing Systems (RMS), capable of dealing with uncertainties quickly and economically. The availability of RMS is a crucial factor in establishing the production capacity of a system that considers all events that could interrupt the planned production. The impact of the availability in RMS is influenced by the configuration of the systems, including the number of resources used. This paper presents a case study in which a simulation-based multi-objective optimization (SMO) method is used to find machines’ optimal task allocation and assignment to workstations under different scenarios of availability. It has been shown that considering the availability of the machines affects the optimal configuration, including the number of resources needed, such as machines and buffers. This study demonstrates the importance of the availability consideration during the design of RMS.

Due to the nature of today's manufacturing industry, where enterprises are subjected to frequent changes and volatile markets, reconfigurable manufacturing systems (RMS) are crucial when addressing ramp-up and ramp-down scenarios derived from, among other challenges, increasingly shortened product lifecycles. Applying simulation-based optimization techniques to their designs under different production volume scenarios has become valuable when RMS becomes more complex. Apart from proposing the optimal solutions subject to various production volume changes, decision-makers can extract propositional knowledge to better understand the RMS design and support their decision-making through a knowledge discovery method by combining simulation-based optimization and data mining techniques. In particular, this study applies a novel flexible pattern mining algorithm to conduct post-optimality analysis on multi-dimensional, multi-objective optimization datasets from an industrial-inspired application to discover the rules regarding how the tasks are assigned to the workstations constitute reasonable solutions for scalable RMS. 

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.

Globalization and mass customization are commonly translated into increased levels of complexity in manufacturing systems. One of the main reasons is the increased number of variables, parameters, and interrelations on the shop floor. This intrinsic complexity can grow exponentially when considering the manufacture of large-size products with high levels of variability and variants: the mass production of large recreational motorboats with high levels of customization and low production volumes, mass customization. With the increasing role of sustainability and concepts of Industry 5.0, focusing not just on improving production systems but also human wellbeing, quick decision making becomes essential. Data and digitalization are becoming the cornerstone for system improvement, and digital data availability and analysis can facilitate the utilization of computerized tools to support decision making and maximize the performance of complex systems.

For that purpose, simulation can be a powerful analytical tool to design, maintain, and improve complex manufacturing systems. Simulation techniques usually allow handling the size and complexity commonly associated with manufacturing systems. However, in systems with highly customized and large-size products, manual processes, and limited floor space, the implementation of simulation techniques is not straightforward, especially considering the aspects of variability, data collection, model validation, and system reconfiguration. With a particular focus on large-size products and limitations of a constrained existing facility layout, this paper presents the implementation of a simulation-based reconfiguration assessment considering manual production, assembly, and internal logistics requirements.

Going through an industrial case study of large recreational motorboats manufacturing, the paper analyses the system analysis, data collection, implementation, and validation of the methodology step by step. Considering different what-if scenarios, the focus is on the capacity reconfiguration using Discrete-Event Simulation. The results can serve as a guideline for decision-makers and stakeholders working with complex mass customization manufacturing systems and space-constrained facility layouts.

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.

In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.