Högskolan i Skövde

Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. Hence, due to the multiple performance measures, supply chain decision making is much more complex than treating it as a single objective optimization problem. Thus, the aim of the doctoral thesis is to address the supply chain optimization problem within a truly Pareto-based multi-objective context and utilize knowledge extraction techniques to extract valuable and useful information from the Pareto optimal solutions. By knowledge extraction, it means to detect hidden interrelationships between the Pareto solutions, identify common properties and characteristics of the Pareto solutions as well as discover concealed structures in the Pareto optimal data set in order to support managers in their decision making. This aim is addressed through the SBO-framework where the simulation methodology is based on system dynamics (SD) and the optimization utilizes multi-objective optimization (MOO). In order to connect the SD and MOO software, this doctoral thesis introduced a novel SD and MOO interface application which allow the modeling and optimization applications to interact. Additionally, this thesis work also presents a novel SD-MOO methodology that addresses the issue of curse off dimensionality in MOO for higher dimensional problems and with the aim to execute supply chain SD-MOO in a computationally cost efficient way, in terms of convergence, solution intensification and accuracy of obtaining the Pareto-optimal front for complex supply chain problems. In order to detect evident and hidden structures, characteristics and properties of the Pareto-optimal solutions, this work utilizes Parallel Coordinates, Clustering and Innovization, which are three different types of tools for post-optimal analysis and facilitators of discovering and retrieving knowledge from the Pareto-optimal set. The developed SD-MOO interface and methodology are then verified and validated through two academic case studies and a real-world industrial application case study. While not all the insights generated in these application studies can be generalized for other supply-chain systems, the analysis results provide strong indications that the methodology and techniques introduced in this thesis are capable to generate knowledge to support academic SCM research and real-world SCM decision making, which to our knowledge cannot be performed by other methods.

Digitalization is forcing the industry to rethink current practices in all business domains, pushing for a digital transformation of business and operations at a high rate and, thus, paving the way for new business models and making others redundant. For small and medium-sized companies (SME), in particular, it is an enormous challenge to keep up with the pace of technological development. Several initiatives have argued the industry’s need for continuous digitalization, innovation, transformation ability, and future skills and competencies development. However, the advancement of the Swedish industry in this area has been uneven, where larger organizations have begun their digital transformation journey to some extent, but SMEs risk falling behind. In addition to the technological transformation, the challenges regarding the industries’ skills supply need to be solved, where a workforce with the right competencies, knowledge, and skill sets are equally, if not more, important for remaining competitive. One of the key elements to face these challenges in the companies will be to recruit knowledgeable employees or re-skill the existing ones. Efficient access to relevant knowledge and skills is still a major concern for companies that will surely affect their competitiveness for a long time to come. This paper elaborates on the opportunities and challenges that Swedish universities face in the context of lifelong learning and education for industry professionals. The paper presents results and experiences gained from a lifelong learning project for industry professionals at the University of Skövde in collaboration with ten industry partners. The results from the project show that in addition to pedagogical methods, current structures and policies within academia need to be further developed to effectively serve industry professionals. The paper also presents a concept of education for industry professionals in the lifelong learning context based on the results and experience gained from the project.

Research regarding supply chain optimisation has been performed for a long time. However, it is only in the last decade that the research community has started to investigate multi-objective optimisation for supply chains. Supply chains are in general complex networks composed of autonomous entities whereby multiple performance measures in different levels, which in most cases are in conflict with each other, have to be taken into account. In this chapter, we present a comprehensive literature review of existing multi-objective optimisation applications, both analytical-based and simulation-based, in supply chain management publications. Later on in the chapter, we identify the needs of an integration of multi-objective optimisation and system dynamics models, and present a case study on how such kind of integration can be applied for the investigation of bullwhip effects in a supply chain.

Agent-based simulation (ABS) represents a paradigm in the modelling and simulation of complex and dynamic systems distributed in time and space. Since manufacturing and logistics operations are characterised by distributed activities as well as decision making - in both time and in space - and can be regarded as complex, the ABS approach is highly appropriate for these types of systems. The aim of this chapter is to present a new framework of applying ABS and simulation-based optimisation techniques to supply chain management, which considers the entities (supplier, manufacturer, distributor and retailer) in the supply chain as intelligent agents in a simulation. This chapter also gives an outline on how these agents pursue their local objectives/goals as well as how they react and interact with each other to achieve a more holistic objective(s)/goal(s).

Purpose 

The purpose of this study is to introduce an effective methodology for obtaining Pareto-optimal solutions, when combining System Dynamics (SD) and Multi-Objective Optimization (MOO) for supply chain problems.

Design/methodology/approach 

This paper proposes a new approach that combines SD and MOO within a simulation-based optimization framework to generate the efficient frontier that supports decision- making in SupplyChain Management (SCM). It also addresses the issue of the curse of dimensionality, commonly found in practical optimization problems, through design space reduction.

Findings 

The integrated MOO and SD approach has been shown to be very useful in revealing how the decision variables in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect. The results of the in-depth Beer Game study clearly show that these three optimization objectives are in conflict with each other, in the sense that a supply chain manager cannot minimize the bullwhip effect without increasing the total inventory and total backlog levels.

Practical implications

Having a methodology that enables the effective generation of optimal trade-off solutions, in terms of computational cost, time, as well as solution diversity and intensification, not only assists decision makers to make decisions on time, but also presents a diverse and intense solution set to choose from.

Originality/value 

This paper presents a novel supply chain MOO methodology that helps to find Pareto-optimal solutions in a more effective manner. In order to do so, the methodology tackles the so-called curse of dimensionality, by reducing the design space and focusing the search of the optimization to regions of interest. Together with design space reduction, it is believed that the integrated SD and MOOapproach can provide an innovative and efficient method for the design and analysis of manufacturing supply chain systems in general.

Research  regarding  supply  chain   optimization   has been  performed  for  a  long  time.  However,  it’s  only  in  the  last decade  that  the  research  community  has  started  to  investigate multi-objective optimization for supply chains. Supply chains are in  general  complex  networks  composed  of  autonomous  entities whereby   multiple   performance   measures   in   different   levels, which  in  most  cases  are  in  conflict  with  each  other,  have  to  be taken into account. In this paper, we present a literature review of    existing    multi-objective    optimization    applications,    both analytical-based    and    simulation-based,    in    supply    chain management  publications.  Based  on  the  literature  review,  the need    for    research    in    a    multi-objective    and    multi-level optimization   framework   for   supply   chain   management   is proposed. Such a framework considers not only the optimization of  the  overall  supply  chain,  but  also  for  each  entity  within  the supply chain, in a multi-objective optimization context.

The aim of this paper is to address the dilemma of supply chain management (SCM) within a truly Pareto-based multi-objective context. This is done by introducing an integration of system dynamics and multi-objective optimisation. An extended version of the well-known pedagogical SCMproblem, the Beer Game, originally developed at MIT since the 1960s, has been used as the illustrative example. As will be discussed in the paper, the integrated multi-objective optimisation and system dynamics model has been shown to be very useful for revealing how the parameters in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimisation of inventory cost, backlog cost, and the bullwhip effect.

The aim of this paper is to address the dilemma of Supply Chain Management (SCM) within a truly Pareto-based multi-objective context. This is done by introducing an integration of System Dynamics and Multi-Objective Optimization. Specifically, the paper contrasts local optimization with global optimization for SCM in which optimal trade-off solutions in the entity level, i.e. optimizing the supply chain from the perspectives of individual (local) entities. e.g., supplier, factory, distributor and retailer, are collected and compared to those obtained from an overall supply chain level (global) optimization. An extended version of the well-known pedagogical SCM problem, the Beer Game, originally developed at MIT since the 1960s, has been used as the illustrative example. As will be discussed in the paper, the integrated multi-objective optimization and system dynamics model has been shown to be very useful for revealing that how the parameters in the Beer Game affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect.

System dynamics, which is an approach built on information feedbacks and delays in the model in order to understand the dynamical behavior of a system, has successfully been implemented for supply chain management problems for many years. However, research within in multi-objective optimization of supply chain problems modelled through system dynamics has been scares. Supply chain decision making is much more complex than treating it as a single objective optimization problem due to the fact that supply chains are subjected to the multiple performance measures when optimizing its process. This paper presents an industrial application study utilizing the simulation based optimization framework by combining system dynamics simulation and multi-objective optimization. The industrial study depicts a conceptual system dynamics model for internal logistics system with the aim to evaluate the effects of different material flow control strategies by minimizing total system work-on-process as wells as total delivery delay.

Virtual production development is adopted by many companies in the production industry and digital models and virtual tools are utilized for strategic, tactical and operational decisions in almost every stage of the value chain. This paper suggest a testbed concept that aims the production industry to adopt a virtual production development process with integrated tool chains that enables holistic optimizations, all the way from the overall supply chain performance down to individual equipment/devices. The testbed, which is fully virtual, provides a mean for development and testing of integrated digital models and virtual tools, including both technical and methodological aspects.

This paper generalizes the automated innovization framework using genetic programming in the context of higher-level innovization. Automated innovization is an unsupervised machine learning technique that can automatically extract significant mathematical relationships from Pareto-optimal solution sets. These resulting relationships describe the conditions for Pareto-optimality for the multi-objective problem under consideration and can be used by scientists and practitioners as thumb rules to understand the problem better and to innovate new problem solving techniques; hence the name innovization (innovation through optimization). Higher-level innovization involves performing automated innovization on multiple Pareto-optimal solution sets obtained by varying one or more problem parameters. The automated innovization framework was recently updated using genetic programming. We extend this generalization to perform higher-level automated innovization and demonstrate the methodology on a standard two-bar bi-objective truss design problem. The procedure is then applied to a classic case of inventory management with multi-objective optimization performed at both system and process levels. The applicability of automated innovization to this area should motivate its use in other avenues of operational research.

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.

The purpose of this study is to analyze the use of Simulation-Based Multi-Objective Optimization (SMO) for Reconfigurable Manufacturing System Configuration Analysis (RMS-CA). In doing so, this study addresses the need for efficiently performing RMS-CA with respect to the limited time for decision-making in the industry, and investigates one of the salient problems of RMS-CA: determining the minimum number of machines necessary to satisfy the demand. The study adopts an NSGA II optimization algorithm and presents two contributions to existing literature. Firstly, the study proposes a series of steps for the use of SMO for RMS-CA and shows how to simultaneously maximize production throughput, minimize lead time, and buffer size. Secondly, the study presents a qualitative comparison with the prior work in RMS-CA and the proposed use of SMO; it discusses the advantages and challenges of using SMO and provides critical insight for production engineers and managers responsible for production system configuration.

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 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.

European higher education institutions (HEIs) are facing increasing demands for more flexible learning and flexibility in learning paths.

This report from a 2023 European University Association Learning & Teaching Thematic Peer Group on “Flexible learning and teaching” explores the complexity of implementing flexible learning at HEIs, starting by defining what it means and entails for the institution, and its members and entities (staff, students, leadership, faculties). With the view that the development of flexible learning is an essential condition for the future of learning at universities, the group identified challenges and examples of practice, and offered recommendations for institutions to reflect on their strategy and build capacity for flexible learning.

Warehouses are obliged to optimize their operations with regard to  multiple  objectives,  such  as  maximizing  effective  use  space, equipment,  labor,  maximize  accessibility  of  products,  maximize amount of processed orders and all this should be achieved whilst minimizing  order  processing  times,  distance  traveled,  broken promises, errors and not to forget the operational cost. A product placement problem for a warehouse is in focus of this study and the main goal is to decrease the picking time for each pick run in order to gain higher efficiency.  To achieve this, a simulation model is built as a representation of the warehouse. As the complexity and the size of the number  of input   variable   grow   it   is   essential   to   use   simulation-based optimization in order to receive a satisfying result. A set of initial solutions  for  the  simulation-based  optimization  is  needed;  since the  number  of  products  to  place  in  the  warehouse  is  huge  this solution ought to be intelligent. This paper describes a technique for  generating  such  a  set  of  solutions  through  searching  for frequent itemsets in the transaction  database. It is  believed that frequent products usually picked simultaneously should be stored closed together.

Supply Chains (SCs) are becoming increasingly complex, and intensified competition in the end markets has started to create a situation where cooperation requirements between companies are increasing, and old mechanistic operations management solutions are becoming obsolete. In this paper we analyse a real-life situation in Alpha’s manufacturing plant in Sweden, which serves northern European countries in consumer markets. Case study findings reveal that the product-mix flexibility requirements are high and lead-time requirements in manufacturing as well as purchasing take weeks or months, not days. Based on the empirical observations, we propose an agent system for this company and discuss different levels of decision making, operative responsibilities and decision time horizons.

In this paper, knowledge transfer is defined as a process of disseminating both technological and theoretical understanding as well as enhancing both industrial and academic knowledge through conducted research to project partners collaborating within a research project. To achieve this, a new methodology called 'user groups' is introduced. It facilitates knowledge transfer between project participants in collaborative research programs engaging both experienced and unexperienced partners regardless of level of input. The introduced methodology 'user groups' provides tools for collaborating with several research partners even though their levels of engagement in the project and prior research experience may vary without dividing them into separate groups. It enables all project partners to gain new knowledge and by so doing extending the knowledge society. The case study shows that the eight engaged companies are able to cooperate, achieve their own objectives and, both jointly and individually, contribute to the overall project goals.

In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.

Designing or improving a manufacturing system involves a series of complex decisions over time to satisfy the strategic objectives of the company. To select the optimal parameters of the system entities so as to achieve the desired overall performance of the system is a very complex task that has been proven to be difficult, even for a seasoned decision maker. One of the major barriers for more efficient decision making in manufacturing is that whilst there is in principle abundant data from various levels of the factory, these data need to be organized and transferred into knowledge suitable for decision-making support. The integration of decision-making support and knowledge management has been identified to be more and more important in both scientific research and from industrial companies. The concept of deciphering knowledge from multi-objective optimization was first proposed by Deb with the term innovization (innovation via optimization). By integrating the concept of innovization with simulation, a new set of powerful tools for manufacturing systems analysis, in order to support optimal decision making in design and improvement activities, is emerged. This method is so-called Simulation-based Innovization (SBI), which has been proven to produce promising results in our previous application studies. Nevertheless, to promote the wider use of such a new method requires the development of an integrated software toolset. The goal of this paper is therefore to outline a Cloud-computing based system architecture for implementing such a SBI-based Interactive Decision Support System.

Removing bottlenecks that restrain the overall performance of a factory can give companies a competitive edge. Although in principle, it is possible to connect multiple detailed discrete-event simulation models to form a complete factory model, it could be too computationally expensive, especially if the connected models are used for simulation-based optimizations. Observing that computational speed of running a simulation model can be significantly reduced by aggregating multiple line-level models into an aggregated factory level, this paper investigates, with some loss of detail, if the identified bottleneck information from an aggregated factory model, in terms of which parameters to improve, would be useful and accurate enough when compared to the bottleneck information obtained with some detailed connected line-level models. The results from a real-world, multi-level industrial application study have demonstrated the feasibility of this approach, showing that the aggregation method can represent the underlying detailed line-level model for bottleneck analysis.

Analyzing networks of supply-chains, where each chain is comprised of several actors with different purposes and performance measures, is a difficult task. There exists a large potential in optimizing supply-chains for many companies and therefore the supply-chain optimization problem is of great interest to study. To be able to optimize the supply-chain on a global scale, fast models are needed to reduce computational time. Previous research has been made into the aggregation of factories, but the technique has not been tested against supply-chain problems. When evaluating the configuration of factories and their inter-transportation on a global scale, new insights can be gained about which parameters are important and how the aggregation fits to a supply-chain problem. The paper presents an interactive proof-of-concept model enabling testing of supply chain concepts by users and decision makers.

Reacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition. 

Simulation and optimization enables companies to take decision based on data, and allows prescriptive analysis of current and future production scenarios, creating a competitive edge. However, it can be difficult to visualize and extract knowledge from the large amounts of data generated by a many-objective optimization genetic algorithm, especially with conflicting objectives. Existing tools offer capabilities for extracting knowledge in the form of clusters, rules, and connections. Although powerful, most existing software is proprietary and is therefore difficult to obtain, modify, and deploy, as well as for facilitating a reproducible workflow. We propose an open-source web-based application using commonly available packages in the R programming language to extract knowledge from data generated from simulation-based optimization. This application is then verified by replicating the experimental methodology of a peer-reviewed paper on knowledge extraction. Finally, further work is also discussed, focusing on method improvements and reproducible results.

In an increasingly competitive market due to customer demands of customization and an increasing rate of new product variant introductions, companies need to explore new tools to support them to better predict and optimally re-configure their production networks. In terms of the factory flow level, discrete-event simulation and simulation-based optimization represent such kinds  of tools available at the disposal of production engineers or managers. For a complex factory consisting of multiple production lines, creating detailed simulation models of these lines and connecting them together can be used for optimization, but the computational complexity can be prohibitively large for acquiring results in time. Model simplification methods can be utilized to reduce the computational complexity of a model. In this study, a systematic literature review is conducted with the aim of identifying simplification methods for coded models, characteristics of the detailed model, type of industry, motivation, and validation measures. Based on the results of the literature review an experimental study wherein the limits of a specific simplification method are analyzed. We compare the output of a dynamically created model with the output of a simplified representation. A correlation can be observed between outputs for medium to large lines, but for smaller lines, there is a larger discrepancy. The simplification method allows for the reduction in simulation runtime, enabling simulation-based optimization of large lines or interconnected simplified models forming a production network, i.e., a factory, to be optimized and analyzed more efficiently, leading to competitive advantages for companies.

Modeling projects, in order to build richer understanding of the dynamics of real-world phenomena in manufacturing systems, benefit from utilizing System dynamics group model building. This paper describes a project utilizing such method in order to identify the interrelated dynamics of aging machinery equipment, competence development, and level of automation for accurate manufacturing systems development. These central aspects were identified by the project group during modeling and were considered vital in order to approach the proper Machine Strategy for the system of interest. Aspects of attention in the study also considered participants’ learning of the system of interest, participants’ perception upon model results, and the comparison between utilizing group model building and the traditional modeler-client approach. It is shown that System dynamics group model building has potential use in manufacturing, and indeed that more efforts are needed for successful use in projects. For that reason the need of a framework for supporting system dynamics projects in manufacturing is identified.

Maintenance can be performed in multiple procedures, and it is hard to justify investments in preventive work. It is a complex equation between the inherent complexity of maintenance and its tight dependencies with production, but also the aspect of direct cost and consequential costs from activities. A model is presented that quantify dynamics of maintenance performance in order to enable a systems analysis on the total of consequences from different strategies. Simulation offers experimenting and learning on how performance is generated. The model is based on parts of previous research on maintenance modelling, system dynamics, maintenance theory, and mapping of practical information flows in maintenance. Two experiments are presented that both take off from a reactive strategy of maintenance performance, and implement two different strategies for preventive maintenance. Using the model enriches the analysis on how the aspects of maintenance performance work together with different maintenance strategies.

Managing maintenance and its impact on business results is increasingly complex, calling for more advanced operational research methodologies to address the challenge of sustainable decision-making. This problem-based research has identified a framework of methods to supplement the operations research/management science literature by contributing a hybrid simulation-based optimization framework (HSBOF), extending previously reported research.

Overall, it is the application of multi-objective optimization (MOO) with system dynamics (SD) and discrete-event simulation (DES) respectively which allows maintenance activities to be pinpointed in the production system based on analyzes generating less reactive work load on the maintenance organization. Therefore, the application of the HSBOF informs practice by a multiphase process, where each phase builds knowledge, starting with exploring feedback behaviors to why certain near-optimal maintenance behaviors arise, forming the basis of potential performance improvements, subsequently optimized using DES+MOO in a standard software, prioritizing the sequence of improvements in the production system for maintenance to implement.

Studying literature on related hybridizations using optimization the proposed work can be considered novel, being based on SD+MOO industrial cases and their application to a DES+MOO software.

Industrial maintenance includes rich internaldynamic complexity on how to deliver value. While the technical development hasprovided with applicable solutions in terms of reliability and condition basedmonitoring, managing maintenance is still an act of balancing, trying to pleasethe short-termism from the economic requirements and simultaneously address thenecessity of strategic and long-term thinking. By presenting an analysis tojustify maintenance studying system behavior, this paper exemplifies thecontribution of the combined approach of a system dynamics maintenanceperformance model and multi-objective optimization. The paper reveals howinsights from the investigation, of the near optimal Pareto-front solutions inthe objective space, can be drawn using visualization of performance ofselected parameters. According to our analysis, there is no return back to thesingle use of system dynamics; the contribution to the analysis of exploringsystem behavior, from applying multi-objective optimization, is extensive.However, for the practical application, the combined approach is not areplacement – but a complement. Where the interpretation of the visualizedPareto-fronts strongly benefits from the understanding of the model dynamics, inwhich important nonlinearities and delays can be revealed, and thus facilitateon the selected strategical path for implementation.

Identifying sustainable strategies to develop maintenance performance within the short-termism framework is indeed challenging. It requires reinforcing long-term capabilities while managing short-term requirements. This study explores differently applied time horizons when optimizing the tradeoff between conflicting objectives, in maintenance performance, which are: maximize availability, minimize maintenance costs, and minimize maintenance consequence costs. The study has applied multi-objective optimization on a maintenance performance system dynamics model that contains feedback structures that explains reactive and proactive maintenance behavior on a general level. The quantified results provide insights on how different time frames are conditional to enable more or less proactive maintenance behavior in servicing production.

Managing maintenance within an economical short-termism framework, without considering the consequential long-term cost effect, is very common in industry. This research presents a novel conceptual system dynamics model for the study of the dynamic behaviors of maintenance performance and costs, which aims to illuminate insights for the support of the long-term, strategic development of manufacturing maintenance. By novel, we claim the model promotes a system's view of maintenance costs that include its dynamic consequential costs as the combined result of several interacting maintenance levels throughout the constituent feedback structures. These range from the applied combination of maintenance methodologies to the resulting proactiveness in production, which is based on the rate of continuous improvements arising from the root cause analyses of breakdowns. The purpose of using system dynamics is to support the investigations of the causal relationships between strategic initiatives and performance results, and to enable analyses that take into consideration the time delays between different actions, in order to support the sound formulation of policies to develop maintenance and production performances. The model construction and validation process has been supported by two large maintenance organizations operating in the Swedish automotive industry. Experimental results show that intended changes can have both short and long-term consequences, and that obvious and hidden dynamic behavioral effects, which have not been reported in the literature previously, may be in the system. We believe the model can help to illuminate the holistic value of maintenance on the one hand and support its strategic development as well as the organizational transformation into proactiveness on the other.

Discrete event simulation (DES) models imitates the behavior of a production system. Models can be developed to reflect different levels of the production system, e.g supply chain level or manufacturing line level. Product Lifecycle Management (PLM) systems have been developed in order to manage product and manufacturing related data. DES models is one kind of product lifecycle’s data which can be managed by a PLM system. This paper presents a method and its implementation for management of interacting multi-level models utilizing a PLM system.

Many industrial challenges can be related to the setup of manufacturing plants and supply chains. While there are techniques available for discrete event simulation of production lines, the opportunities of applying such techniques on higher manufacturing network levels are not explored to the same extent. With established methods for optimisation of manufacturing lines showing proven potential in conceptual analysis and development of production lines, the application of such optimisation methods on higher level manufacturing networks is a subject for further exploration. In this paper, an extended aggregation technique for discrete event simulation of higher level manufacturing systems is discussed, proposed, tested, and verified with real-world problem statements as a proof of concept. The contribution of the new technique is to enable the application of DES models, with reasonable computational requirements, at higher level manufacturing networks. The proposed technique can be used to generate valuable decision information supporting conceptual systems development.

In conceptual analysis of higher level manufacturing systems, for instance, when the constraint on system level is sought, it may not be very practical to use detailed simulation models. Developing detailed models on supply chain level or plant wide level may be very time consuming and might also be computationally costly to execute, especially if optimization techniques are to be applied. Aggregation techniques, simplifying a detailed system into fewer objects, can be an effective method to reduce the required computational resources and to shorten the development time. An aggregated model can be used to identify the main system constraints, dimensioning inter-line buffers, and focus development activities on the critical issues from a system performance perspective. In this paper a novel line aggregation technique suitable for manufacturing systems optimization is proposed, analyzed and tested in order to establish a proof of concept while demonstrating the potential of the technique.

In many simulation studies for factory analysis, for example, to locate the constraint of an entire factory that consists of multiple production lines, it may not be effective to put every process detail into a single model. Firstly, to develop such a factory-wide model would be very time-consuming. Secondly, it can be very computational costly to run the model, especially if simulation-based optimisation is applied to find the optimal setting from such a complex model that possesses all the details of the processes. In this regard, aggregation, with which multiple process steps are aggregated into some simpler simulation objects, is an effective method to reduce both the development and computational times. On one hand, based on the initial analysis, the simulation expert can pinpoint the sub-system that restrains the performance of the entire factory and decide if a more detailed model is needed. On the other hand, interline buffers/storages can be readily optimised by using such an aggregated model. Through an application study with data from a real-world factory, this paper introduces a novel aggregation method and illustrates the potential of the abovesaid concepts.

To increase changeability and reconfigurability of manufacturing systems, while maintaining cost-efficiency and environmental sustainability they need to be designed in accordance to the need for change. Since companies often need to convert existing manufacturing systems to handle variation, implementation of reconfigurable manufacturing systems calls for an analysis of the current system to understand to what extent they fulfil reconfigurability characteristics. This requires an assessment of the needs for reconfigurability as well as assessment of the existing ability to reconfigure the manufacturing system. Although a lot of reconfigurable manufacturing system assessment models are proposed in theory there is an evident knowledge gap pertaining to what extent the existing systems in the industry are in achieving reconfigurability. The purpose with this paper is to propose an assessment criterion for existing manufacturing systems to measure reconfigurability and their readiness to change with respect to products and volume variations. Based on a literature review of existing reconfigurability assessment models and a case study within the automotive industry, a criterion is developed and tested to analyze how reconfigurable a system is and to decide which parameters that need more attention to achieve higher degree of reconfigurability. © 2019 The Authors.

Quality inspections are essential in almost all manufacturing processes and can be undertaken using an automatic vision system. The main challenge when using AI-based vision systems is gathering enough training data for the AI to perform well. This study investigates the use of simulationgenerated data to address this challenge. A real-world industrial case study evaluates the feasibility of simulationgenerated images for training an AI to be used in an industrial vision system for quality inspection. Results from the study show that the approach has potential, but that simulationgenerated images cannot be used solely. However, real-world images must be mixed into the training data set to achieve satisfactory results.