In the rapidly changing technical-economical environment time has an increasingly important role in the product development besides creativity, cost, quality criteria. In this paper we aim to reveal methods that can decrease the cost and time spent on the product development increasing the efficiency of the process. This publication presents an insight of optimising with the use of design structure matrix, genetic algorithms, and neural network. Provides the opportunity of using the methods in different areas, so it may result a profitable industrial innovation, and is suitable for creating new ideas on the field of automobile engineering projects

Nagy, Klaudia

Szalai, Judit

English

Periodica Polytechnica (Budapest University of Technology and Economics)

Ŕ periodica polytechnicaMechanical Engineering55/2 (2011) 111–115doi: 10.3311/pp.me.2011-2.09web: http://www.pp.bme.hu/mec© Periodica Polytechnica 2011RESEARCH ARTICLEModeling and optimising alternativessuitable to advance the productdevelopment processKlaudia Nagy / Judit SzalaiReceived 2011-11-31AbstractIn the rapidly changing technical-economical environmenttime has an increasingly important role in the product develop-ment besides creativity, cost, quality criteria. In this paper weaim to reveal methods that can decrease the cost and time spenton the product development increasing the efficiency of the pro-cess. This publication presents an insight of optimising with theuse of design structure matrix, genetic algorithms, and neuralnetwork. Provides the opportunity of using the methods in dif-ferent areas, so it may result a profitable industrial innovation,and is suitable for creating new ideas on the field of automobileengineering projects.Keywordsproduct development · design structure matrix · genetic algo-rithms · neuralAcknowledgementThe work reported in the paper has been developed in theframework of the project „Talent care and cultivation in the sci-entific workshops of BME” project. This project is supported bythe grant TÁMOP-4.2.2.B-10/1–2010-0009.Klaudia NagyDepartment of Machine and Product Design,BME, H-1111 Budapest, Műe-gyetem rkp. 3., HungaryJudit SzalaiDepartment of Machine and Product Design, BME, H-1111 Budapest, Műe-gyetem rkp. 3., Hungary1 IntroductionThe product development applications have been many andvaried and have led to a deeper understanding of how to gatherand use information about the customer in the design, testing,launch, and management of new products [1].For the time and resource perspective planning of the con-struction design process a Design Structure Matrix (DSM) basedmodelling optimization procedure was worked out applying ge-netic algorithms. The application of such methods gives oppor-tunity to determinate the minimal lead time required for designactivity sequence.The intense international competition, the accelerating tech-nical development and the increasing customer requirements re-sulted new and more market compatible product nnovation to beessential for companies. At these products not only the search oftechnical solutions but the product development and design pro-cess, the resources, capacity, costs, information flow and man-agement are also emphasized along with a conscious quality as-surance and risks that inherent in innovation management [2].2 Product development principlesProduct development is the process between defining a mar-ket opportunity based on the actual and predicted customer’sneeds and the beginning of the production [3]. According toBaldwin and Clark [4, page 149–165] product development is asearch for something unknown, and the result is a description ofa thing to be made, including instructions about how to make it.The management of product development demands system-atic methods that account for the special characteristics of theprojects. Most important of the challenging factors are extremecomplexity - today’s products include tens of thousands com-ponents. High uncertainty - the precise functions, attributesand components of the product are unknown at the outset ofa product development project. Dynamic project context - thecustomer’s needs, the competitors’ products and the availabletechnologies change during the lifetime of product developmentproject. Iterative processes - engineering work is creative andinnovative. Product development processes are iterative, notrepetitive like manufacturing or business processes. As a con-Modeling and optimising alternatives suitable to advance the product development process 1112011 55 2sequence the changes are natural part of product developmentprojects that have to be considered during the planning and con-trol of such projects. Fig. 1 shows the core competence of theintegrated product development process.Fig. 1. Management of Product DevelopmentFig. 2. Effects of innovation according to individual and organizational cre-ativity [9]The main goal is the increasing success to accomplish “prof-itable” innovation. With the use of optimisation techniques sus-tainable improvement in time to market, total cost, product ma-turity, process quality and consumer satisfaction can be reached.The integration of conceptual expertise and practicability leadsto strategic alignment in product development. Efficient and ma-ture product development processes and methods combined witha marketoriented product portfolio eventuates in a superior prod-uct development strategy.3 Enhancing creativity and motivation at the field oforganizational innovationIn terms of the innovation process the idea generating has ahigh level importance, which cannot be reached without appro-priate motivation [5]. However, if you manage to achieve therequired level of motivation, it might turn into the company’smost valuable strategic advantage [6].To enhance creativity in business, three components wereneeded, expertise (technical, procedural and intellectual knowl-edge), creative thinking skills (how flexibly and imaginativelypeople approach problems), and motivation (especially intrinsicmotivation, comes from inside an individual, satisfaction, enjoy-ment of work etc) [7].Social scientists consider creativity as an integrative and cu-mulative process which recombines the existing knowledge ina new way. Creativity can be observed from the aspect of anorganisation or an individual. Main drives of the organisationalcreativity are creative and democratic leadership, opened organi-sational communication, diversity of the company and feedback.The influence of personal creativity is more divergent: personalcompetencies also take part besides the qualification of the indi-vidual [8]. Fig. 2 describes the effects of innovation accordingto individual and organizational creativity.4 Modeling and optimising of development processThe challenge in this scheme is the management of a largenumber and variety of components, as it is the case in typicalmechatronic systems. Recording the components one by one,identifying the interactions between the components, and distin-guishing the relational groupings is most of the time beyond thecapability of a single engineer and even of a group [10].4.1 Design structure matrix representationsThe majority of the traditional project management tools(Gantt-diagram, CPNM-PERTIDEF-methods) alone are notsuitable to solve the problems derived from the complexity ofthe project planning. However, the Design Structure Matrix(DSM) method gives the possibility to the project and planningprogramme managers of modeling the sequential and overlap-ping activities as well as describing relationships of interdepent-dent tasks; integrates the network planning, – and where appli-cable – the elements of cost planning [11].For the time and resource oriented planning of the construc-tion design process, a DSM (Design Structure Matrix) basedmodelling and genetic algorithm using optimization procedurewas developed. By procedures using heuristic methods, it ispossible – with given resources – to define the sequence of de-sign activities requiring the minimal turnaround time.The discrete methods can give accurate results to theresource-allocation problems with small tasks, but as the tasksgrow, they become unmanageable with discrete methods, so itis advisable to use heuristic methods. According to the compar-ison, the decision heuristics combined with genetic algorithmsachieved the best results [12].DSM is first introduced by Steward (1981) to manage the pa-rameter dependencies in the design of a complex system. It iswidely used for managing the complexity of components and in-terfacing in design. A DSM is a relational matrix that constitutesa framework for documenting and evaluation of the interface ar-chitecture. The DSM is usually created following the functionaldecomposition of the system [13].DSM can in fact be used in any domain where entities arerelated with each other on a varying relational basis. For ex-ample, it can be used for analyzing the dependencies betweenPer. Pol. Mech. Eng.112 Klaudia Nagy / Judit Szalaimarketing, operations management, and engineering decisionsfor product development [14].Fig. 3. A sample DSM (a) and its diagonalized form (b)Fig. 3a shows a sample documenting the relation between sixentitis. Entity one (E1) provides inputs to E2 and E4, and getsinput from E4. E4 gets inputs from E1 and E6. The diagonalof the matrix is redundant. The weight of the entries signifiesthe degree of the relation between two entities. Usually a largervalue signifies higher degree of relation. Accordingly, the inputrelation from E6 to E3 is higher valued compared to the inputrelation from E6 to E4.For modularizaztion it is usually the case that the relationalmatrix is “diagonalized” in the sense of bringing the largerweighted relations close to the diagonal. Diagonalization cor-responds to finding the optimal permutation of the componentsthat minimizes a cost function. This cost function decreaseswhen the larger weighted relations are placed close to the di-agonal. For example the DSM in fig. 3a can be diagonalizedas in fig. 3b. Finding the optimum permutation is an NP-hardproblem. A complete enumeration of the possible solutions getscomputationally too costly for large number of entities.Therefore, some heuristic search methods (genetic algo-rithms, simulated annealing) are used to find near-optimal so-lutions.Diagonalization is obtained by minimizing a cost functionwhich delineates the distance of the entries from the diagonal.Once the diagonalization is performed the engineer can visuallydetermine the modules identifying the groupings in the DSMalong the diagonal. In Fig. 3b the elements {E4, E1, E2}, {E3,E6}, and {E5} constitute three groups that can be named asmodules. However, when the size of the matrix is large andthe grouping is not clear, visual determination of the modules isdifficult and tedious [15].4.2 Genetic algorithms and strategy developmentThe planning of design steps’ sequence and optimization ofthe constructional design processes, genetic algorithm was usedwith integer coding.Genetic Algorithms (GAs) are currently an accepted methodfor searching large multimodal domains that have inexpensivefitness function calls. The requirement regarding the executiontime for the fitness function is needed as GAs often require sev-eral hundred thousand function calls to converge onto a solu-tion. The method used in GAs, to model the solution space, is tomaintain a population of candidate solutions. The biologicallyinspired operations of reproduction, crossover and mutation areperformed on these solutions to improve their utility over suc-cessive iterations.Input-variable dependencies, for a solution space, would pro-vide information on how inputs might be separated into difierentsub-problems. This dependency information is modeled indi-rectly by GAs. The chromosome representation for GAs andthe most common implementations of the crossover operator as-sume that coupled variables will appear near each other on thecandidate string. This bias for the crossover operation, will beshown through the examples of this section.The application of crossover within GA is an attempt to sep-arate out a sub-solution to a component sub-problem from oneparent and combine it with a sub-solution from a second par-ent. The child, so created, would therefore have the incorpo-rated solutions developed from each parent. GAs do not com-pute the explicit relationships between input variables to deter-mine where the best crossover points should be. They randomlyselect crossovers that are often not beneficial towards separatingthe problem into sub-problems without the aid of many repeatedattemptsIn practice, expert knowledge is sometimes pulled uponto place related inputs next to each other on the candidate chro-mosome string; increasing the chances that a single crossoveroperation will carry forward discovered sub-solutions. Whilethis approach is reasonable, it assumes access to expert knowl-edge for the domain at hand and presupposes that the relation-ships for this domain will fall within previously observed behav-iors [16].4.3 Neural networks for problem decompostionInteresting recent work by Khare in 2006. [16] has sought toautomatically decompose several problems by evolving neuralnetworks for each sub-problem component.As shown in Fig. 4, the method used to select among thesemodules is to start with a general population of modules witheach module accepting a random number of the input variables.Composite system models are created from these modules andeach system model trains a combining network based off oftraining data, Fig. 4.The success of each system model, in predicting a validationset, provides a fitness measure for the model. The presence ofmodules in high performing system models provide a fitnessModeling and optimising alternatives suitable to advance the product development process 1132011 55 2Fig. 4. Two Populations Maintained to Generate Co-evolutionary Module [17].measure for the module population. The presence of modulesin high performing system models provide a fitness measure forthe module population.Neutral Networks take a networked form inspired by thestructure of neurons within the brain. They have great expressiveability for modeling non-linear functions and have been usedwidely within control systems and as a non-linear replacementfor RSEs. There are a large number of excellent introductions totopic of neural networks [18].Regarding the expressive power of neural networks, there areproofs that show that there exists a network of two hidden layersthat can approximate any arbitrary function. A network with asingle hidden layer and a suficient (possibly very large) numberof nodes can represent any continuous function and all booleanfunctions [19].One would use a neural network when there are a large num-ber of varied examples for the behavior one wants to learn. Theset of data may contain errors as the training is robust to noisewithin the training set. After a network is trained, the runtimeevaluation of the regression is very rapid when compared tonearest neighbor type methods [20].The large training set and high number of weights cause longtraining times. The neural networks is also not able to explainits response to human examination. The network acts as a blackbox. Cases of medical diagnosis, for example, require an ex-plained set of humanreadable reasoning that is not availablethrough a neural networks . There is also an extremely highprobability that the training set will be overfit by the networkduring training. When a network overfits a set of data, both theinformation and noise from a set of data is fit by the model.If a validation data set is used to trim links from a network, anadditional validation set should be used to serve as an unbiasedjudge of the networks future performance. These additional datasets increase the already large sample data requirements for thenetwork [21].5 ConclusionThis working paper describes several promising ideas to max-imize desired benefits while minimizing negative aspects ofproduct development could be utilized to advance profitable in-novation.Approach to identifying a set of design alternatives and se-lecting the best candidate from within that set, engineering opti-mization was developed as a means of helping engineers to de-sign systems that are both more efficient and less expensive andto develop new ways of improving the performance of existingsystems.Optimization techniques can now be used to find creative so-lutions to larger, more complex problems than ever before. Asa consequence, optimization is now viewed as an indispensabletool of the trade for engineers working in many different indus-tries, especially the aerospace, automotive, chemical, electrical,and manufacturing industries.This study provides an application-oriented presentation ofthe full array of classical and newly developed optimizationtechniques now being used by engineers in a wide range of in-dustries.References1 Dahan E, Hauser RJ, Product Development - Managing a Dispersed Pro-cess by and Handbook of Marketing Barton Weitz and Robin Wensley, SAGEPublications, 2002.2 Bercsey T, Rick T, A konstrukciós tervezési folyamat modellezése és op-timálása heurisztikus módszerrel (A GAMF Közleményei, ed.), Vol. XXIII.évfolyam, Kecskemét, 2009, pp. 61–70.3 Tyson R, On customer value and improvement in product development pro-cesses, Browning Systems Engineering 6 (2003), no. 1, 49–61.4 Garud R, Kumaraswamy A, Langlois RN, Baldwin CY, Clark KB, Man-aging in the modular age: architectures, networks, and organizations, Black-well Publishers LTd.5 Manso G, Motivating Innovation, Journal of Finance 66 (2011), 1–59, DOI10.1111/j.1540-6261.2011.01688.x.6 Dahan E, Hauser JH, Product Development - Managing a Dispersed Pro-cess, Sage, 2001.7 Sullivan C, Harper G, The Creative Environment:Authors at Work Cam-bridge, English Association/Boydell and Brewer, 2009.8 Simonton KD, Creativity in Science: Chance, Logic, Genius, and Zeitgeist,Cambridge University Press, 2004.9 Zhou J, Shalley CE, Handbook of Organizational Creativity, PsychologyPress, 2007.10 Holmqvist TKP, Persson ML, Analysis and improvement of product modu-larization methods: Their ability to deal with complex products, System En-gineering 6 (2003), no. 3, 195–209, DOI 10.1002/sys.10046.11 Chen CH, Ling SF, Chen W, Project Scheduling for collaborative productPer. Pol. Mech. Eng.114 Klaudia Nagy / Judit Szalaidevelopment using DSM, Internetional Journal of Project Management 21(2003), no. 4, 291–299, DOI 10.1016/S0263-7863(02)00023-6.12 Kolisch R, Hartmann S, Experimental investigation of heuritics for re-sourceconstrained project scheduling: an update, European Journal of Oper-ational Research 174 (2006), no. 1, 23–37, DOI 10.1016/j.ejor.2005.01.065.13 Thebeau RE, Knowledge Management of System Interfaces and Interactionsfor Product Development Process, Master Thesis, Massachusetts Institute ofTechnology, 2001.14 Krishnan V, Ulrich KT, Product development decisions: A reviewof the literature, Management Science 47 (2001), no. 1, 1–21, DOI10.1287/nsc.47.1.1.10668.15 Thom JB, Mustafa SE, Tetsuo T, Modular Design of Mecha-tronic Systems with Function Modeling, Mechatronics 20, 850–863, DOI10.1016/j.mechatronics.2010.02.002.16 Otero RE, Braun RD, The State of Problem Decomposition in EngineeringDesign, American Institute of Aeronautics and Astronautic (2009).17 Khare VR, Automatic Problem Decomposition using Co-Evolution and Mod-ular Neural Networks, University of Brimingham, 2006. Ph.D. thesis.18 Mitchell TM, Machine Learning, The McGraw-Hill Companies Inc. and MITPress, 1997.19 Cybenko G, Approximation by Superpositions of a Sigmoidal Function,Mathematics of Control, Signals, and Systems 2 (December 1989), no. 4,303–314, DOI 10.1007/BF02134016.20 Bishop CM, Pattern Recognition and Machine Learning, Springer Scienceand Business Media, LLC, 2006.21 Mitchell TM, Machine Learning, 1997.Modeling and optimising alternatives suitable to advance the product development process 1152011 55 2

Modeling and optimising alternatives suitable to advance the product development process

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Modeling and optimising alternatives suitable to advance the product development process

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