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.

Process and casting data from different sources have been collected and merged for the purpose of predicting, and determining what factors affect, the quality of cast products in a foundry. One problem is that the measurements cannot be directly aligned, since they are collected at different points in time, and instead they have to be approximated for specific time points, hence introducing uncertainty. An approach for addressing this problem is investigated, where uncertain numeric features values are represented by intervals and random forests are extended to handle such intervals. A preliminary experiment shows that the suggested way of forming the intervals, together with the extension of random forests, results in higher predictive performance compared to using single (expected) values for the uncertain features together with standard random forests.

A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly siutable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.

In this work, we investigate ways of extracting information from simulations, in particular from simulation-based multi-objective optimisation, in order to acquire information that can support human decision makers that aim for optimising manufacturing processes. Applying data mining for analyzing data generated using simulation is a fairly unexplored area. With the observation that the obtained solutions from a simulation-based multi-objective optimisation are all optimal (or close to the optimal Pareto front) so that they are bound to follow and exhibit certain relationships among variables vis-à-vis objectives, it is argued that using data mining to discover these relationships could be a promising procedure. The aim of this paper is to provide the empirical results from two simulation case studies to support such a hypothesis.

Nowadays many production companies collect and store production and process data in large databases. Unfortunately the data is rarely used in the most value generating way, i.e.,  finding  patterns  of  inconsistencies  and  relationships  between  process  settings  and quality  outcome.  This  paper  addresses  the  benefits  of  using  data  mining  techniques  in manufacturing  applications.  Two  different  applications  are  being  laid  out  but  the  used technique  and  software  is  the  same  in  both  cases.  The  first  case  deals  with  how  data mining  can  be  used  to  discover  the  affect  of  process  timing  and  settings  on  the  quality outcome in the casting industry. The result of a multi objective optimization of a camshaft process  is  being  used  as  the  second  case.  This  study  focuses  on  finding  the  most appropriate dispatching rule settings in the buffers on the line.  The  use  of  data  mining  techniques  in  these  two  cases  generated  previously  unknown knowledge. For example, in order to maximize throughput in the camshaft production, let the dispatching rule for the most severe bottleneck be of type Shortest Processing Time (SPT) and for the second bottleneck use any but Most Work Remaining (MWKR).

Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.

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.

This paper introduces a new methodology for the design, analysis, and optimization of production systems. The methodology is based on the Innovization procedure originally introduced for unveiling new and innovative design principles in engineering design problems. Although the Innovization method is based on multi-objective optimization and post-optimality analyses of optimized solutions, it extends the scope beyond an optimization task and attempts to discover new design/operational rules/principles related to decision variables and objectives, in order to enable a deeper understanding of the problem. By integrating the concept of Innovization with discrete-event simulation, a new set of powerful tools can be developed for general systems analysis, which is particularly suitable for production systems. After describing the Simulation-based Innovization procedure and its difference from conventional simulation analysis methods, the results of an industrial case study, carried out for the improvement of an assembly line at an automotive manufacturer in Sweden, are presented.

This paper introduces a novel methodology for the optimization, analysis and decision support in production systems engineering. The methodology is based on the innovization procedure, originally introduced to unveil new and innovative design principles in engineering design problems. The innovization procedure stretches beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. By integrating the concept of innovization with simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis. The uniqueness of the approach introduced in this paper lies in that decision rules extracted from the multi-objective optimization using data mining are used to modify the original optimization. Hence, faster convergence to the desired solution of the decision-maker can be achieved. In other words, faster convergence and deeper knowledge of the relationships between the key decision variables and objectives can be obtained by interleaving the multi-objective optimization and data mining process. In this paper, such an interleaved approach is illustrated through a set of experiments carried out on a simulation model developed for a real-world production system analysis problem.

This chapter introduces a novel methodology for the analysis and optimization of production systems. The methodology is based on the innovization procedure, originally introduced for unveiling new and innovative design principles in engineering design problems. Although the innovization method is based on multi-objective optimization and post-optimality analyses of optimised solutions, it stretches the scope beyond an optimization task and attempts to discover new design/operational rules/principles relating to decision variables and objectives, so that a deeper understanding of the problem can be obtained. By integrating the concept of innovization with discrete-event simulation and data mining techniques, a new set of powerful tools can be developed for general systems analysis, particularly suitable for production systems. The uniqueness of the integrated approach proposed in this chapter lies on applying data mining to the data sets generated from simulation-based multi-objective optimization, in order to automatically or semi-automatically discover and interpret the hidden relationships and patterns for optimal production systems design/reconfiguration.

Despite the fact that Discrete Event Simulation (DES) is claimed to be one of the most potent tools for analysis and optimization of production systems, industries worldwide have not been able to fully utilize its potential. One reason is argued to be that DES projects are not time efficient enough due to extensive time consumption during the input data phases. In some companies, input data is totally missing, but even in projects where data is available it usually takes a considerable amount of time to analyze and prepare it for use in a simulation model. This paper presents one approach to the problem by implementing a software that automates several steps in the input data process such as extracting data from a database, sorting out the information needed and fitting the data to statistical distributions. The approach and the software have been developed based on a case study at Volvo Trucks in Gothenburg, Sweden. The work presented in this paper is part of a more comprehensive project called FACTS. The project scope is to develop methods and IT-tools for conceptual plant development.