Manufacturing System Design Problems Research Articles

Design Methodology and Tools in Factory of the Future

Abstract This paper presents a design method and tool developed to support the skill forming activities in the DigiFoF network (https://www.digifof.eu/). The focus is on training of manufacturing system design skills both as HEI education and vocational training, but preliminary design of new manufacturing systems is also supported (e.g in the development of small business process scenarios). We proposed a model-based methodology for solving of the manufacturing system design problems The methodology and the supporting tool are centred around a less abstract Domain-Specific Modelling Language (DSML). The language is easy to learn due to its few components. A modelling and simulation environment named Digital Production Planner Tool (DPPT) was generated from the metamodel of the DSML. The degree of abstraction used by this tool corresponds well to the intended use in training and preliminary design. Our method incorporates by design the possibility to impose constraints at the modelling language level to limit the modelling space to feasible/possible solutions. The resulting tool enforces these constraints in the use and supports the development of feasible designs even by inexperienced designers. The access to the conceptual model allows the translation of the model to other modelling language like Petri net. This extends the support for the design methodology. The whitepaper presents a use case for the developed method and tool: the design of a chocolate manufacturing line.

Approaches for handling wicked manufacturing system design problems

Due to complex interdependencies, incomplete information and uncertainty in the manufacturing requirements, manufacturing system design can be described as a wicked problem. A wicked problem is an ill-defined problem, which makes it difficult to determine if a solution has been found and cannot be solved with traditional optimization approaches. This research will present arguments to justify why such manufacturing system design problems can be considered to fall under the definition of wicked problems especially when customer requirements, hence products, evolve over time. Paradigms such as changeable manufacturing, cyber physical and evolvable production systems have emerged to deal with these issues. A critical analysis of these approaches is then presented, which exposes a gap which exists in the design stages of manufacturing systems to adequately support manufacturing system designers in handling the wicked aspect of manufacturing system design problems. This research therefore argues for the importance of developing integrated approaches which consider both business and product evolution aspects when designing manufacturing systems. Finally an integrated approach is contributed which leverages artificial intelligence to support manufacturing system design decision making whilst considering business and product evolutionary aspects.

6.5.2 MBSE Supports Manufacturing System Design

AbstractTo date, Model Based Systems Engineering (MBSE) has been deployed primarily in the product design domain. However, manufacturing system design problems are equally challenging, but there are many fewer design support tools in the hands of system designers. This paper describes how MBSE principles and methods can be used to make a powerful analysis tool readily accessible to manufacturing systems engineers. The MagicDraw™ SysML tool was used to create a domain specific language for electronics assembly, and the tool itself was customized to present an interface that was relatively easy for the manufacturing domain experts to use to create descriptions of products, resources, and processes. The domain specific language was mapped to a metamodel of the Arena™ discrete event simulation language, and the ATL© model transformation tool was used to automate the generation of the an instance of a simulation model from an instance of the electronics assembly system. The use of this kind of analysis automation can reduce by up to a factor of ten the time required to develop such simulation models.

A simulated annealing algorithm for dynamic system reconfiguration and production planning in cellular manufacturing

Most manufacturing system design problems have been studied under static conditions in which the facilities are configured on fixed shop floors for relatively long planning period by assuming constant product mix and demand. In today's dynamic business environment, shorter time periods should be considered where the product mix and demand may vary from period to period. As a result, the best facility layout for one period may not be efficient for subsequent periods. To address this issue, several authors proposed dynamic system reconfiguration models and solution procedures for manufacturing system design. In this paper, we consider an integrated problem of production planning and dynamic system reconfiguration in cellular manufacturing systems where production quantities are also decision variables. Based on this consideration, we propose a mathematical programming model for solving this problem. The solution of the model provides the planned production quantity, inventory level and system configuration for each period. Since the problem is NP-hard, we developed a heuristic algorithm based on multiple Markov chain simulated annealing to allow multiple search directions to be traced simultaneously. Numerical examples are presented to demonstrate the features of the proposed model and the computational efficiency of the developed algorithm.

A self-organized neural network metamodelling and clonal selection optimization-based approach for the design of a manufacturing system

Simulation metamodelling has captured the attention of various researchers due to its ability to estimate the simulation responses corresponding to a given combination of input variables, and to provide broad insights than simulation models alone. In this paper, the metamodelling approach has been extended by proposing a new methodology that incorporates the aid of two soft computing tools: neurofuzzy network and polynomial neural network. To perform post metamodelling analysis, an artificial immune system (AIS) based optimization algorithm has been used. Unlike in most neural-networks, the number of layers and number of nodes in each layer of the proposed hybrid network are not predetermined but can be generated dynamically through a growth process. AIS based optimization algorithm has been utilized to exploit the solution space much more extensively to generate an optimal configuration. The special features of the proposed methodology are the rate of convergence, computational speed, and quality of solution. To validate the procedures developed here, the authors have considered a well-known case study of a PCB manufacturing plant. The obtained results show that the proposed methodology can be applied to solve complex manufacturing system design problems in a more effective and efficient manner.

The use of simulation modeling and factorial analysis as a method for process flow improvement

Over the past quarter century, much effort has been devoted to the design and development of simulation modeling languages, and to methods for the development of simulation models themselves. Less effort, however, has been expended on the design of the experimental models upon which simulation studies are based. This paper describes a methodology for the determination of near optimal solutions considering experimental design and simulation modeling. Using this methodology, simulation scenarios are created and analyzed using an analysis of variance (ANOVA)-based experimental design. This paper also presents an application of this method in analyzing a manufacturing system design problem.

A MULTI CRITERIA APPROACH TO DESIGNING THE CELLULAR MANUFACTURING SYSTEM

Cellular manufacturing system design problems such as design framework, manufacturing cells layout and layout evaluation. The research objective is developing the framework to designing manufacturing cells with considering the organization and management aspects in shopfloor. In this research have compared the existing layout with proposed layout which applied the multi criteria approach. The proposed method is combining Analytical Hierarchy Process (AHP), Clustering and heuristic approach. The result has show that grouping with Single Linkage Clustering (SLC) to be selected as manufacturing cells. The comparison of clustering weight is 0,567, 0,245 and 0,188 for SLC, Complete Linkage Clustering (CLC) and Average Linkage Clustering (ALC), respectively. This result shows that generating layout by using grouping result from SLC. The evaluation result shows that types of manufacturing cells better than process layout which used the existing system.

Managing variances in manufacturing system design

The goal of this paper is to investigate how uncertainties in demand and production should be incorporated into manufacturing system design problems. We examine two problems in manufacturing system design: the resource allocation problem and the product grouping problem. In the resource allocation problem, we consider the issue of how to cope with uncertainties when we utilize two types of resources: actual processing capacity and stored capacity (inventory). A closed form solution of the optimal allocation scheme for each type of capacity is developed, and its performance is compared to that of the conventional scheme where capacity allocation and inventory control decisions are made sequentially. In the product grouping problem, we consider the issue of how we design production lines when each line is dedicated to a certain set of products. We formulate a mathematical program in which we simultaneously determine the number of production lines and the composition of each line. Two heuristics are developed for the problem.