International Conference on Engineering, Technology and Management 15 – 16, July’ 2016, Singapore
A Soft Computing Approaches to the Complexity Management in Product Design: A Case Study of Automotive Wiring Harness Design Choosak Pornsing, Pattrawet Tharawetcharak and Noppakun Tiwapat
Tongtang Tonglim Faculty of Science and Technology Muban Chombueng Rajabhat University Ratchaburi 70150, Thailand
Dept. of Industrial Engineering and Management Faculty of Engineering and Industrial Technology Silpakorn University, Nakhon Pathom 73000, Thailand
[email protected]
Abstract— this study illustrates the efficacy of a soft computing approach that be deployed as a decision support tool in product design process. A sample company is a car maker who handles a number of car models. A subassembly, automotive wiring harness, can be designed as common parts or peculiar parts. Each of which has its own costs and benefits. Usually, the company makes use of experienced design engineers to make the decision in design. However, in this study, we propose the stateof-the-art technique, named harmony search algorithm, to support the decision management. The case simulation is conducted on the data which is drawn from the sample company. The results show that the harmony search is a useful tool. It yields the minimum total cost which better than the current practice. Furthermore, we show the application of full-factorial experimental design to find the appropriate harmony search parameters in this study.
complexity. Frequently, the process complexity is a sequel of product complexity. Furthermore, they are a flaw of product design.
Keywords- Complexity management, soft computing, metaheuristics, optimization, harmony search algorithm.
In this study, we propose a decision support tool for design engineers and supervisor of a sample company who manufactures cars and owns design center in Thailand. The design department’s responsibility is to design automotive wiring harness sets for all car models that sold in this region. This tool is being claimed that enables to encourage the design team to make a decision about the complexity of automotive wiring harness. This paper is organized as follows. In Section 2, we describe the complexity of automotive wiring harness. Section 3, the harmony search (HS) algorithm is concisely described. The case simulation results are shown in Section 4. The discussions and conclusions are drawn in Section 5.
In automotive industry, car makers challenge each other by offering a model to small market segmentation. For example, BMW offers a number of possible automobile variations in the BMW 7 Series alone could reach 1017 [3]. As a result, they have to carry on their product variety. A number of models the company handles cost it in every perspective. It is not only affecting to the finished products of the company, but also affects to the complexity of assemblies and sub-assemblies that need to be handled by the company. Accordingly, product complexity also brings the company process complexity, supply chain management complexity, and organization complexity, etc. [4].
I. INTRODUCTION The complex in business is a result of globalization, highspeed telecommunications links, regulatory requirements, and technology [1]. Large business is inherently much more complex than before. Managers and staffs of complex corporations must learn more advance technologies and processes, adapt at the speed of exponential function. Stephen and Perumal [2] divide the business complexity into three categories: product complexity, process complexity, and organizational complexity. Product complexity relates to the variety of and within the products or services the company offers to customers. Many companies extend their product complexity for enhancing its competition. However, too much variety will set the company with substantial cost, piling up inventory, and impairing its ability to deliver. Process complexity is the number of processes, process steps that involved in executing and delivering its products. The process that consists of duplication, rework, transportation, convoluted steps, and non-value-added steps are called bad process
II.
AUTOMOTIVE WIRING HARNESS COMPLEXITY AND DESIGN As described above, the large number of products variations brings in complexity to the systems and produces a higher management cost [5]. Fig. 1 shows a sample of automotive wiring harness set. It consists of connectors, outer packages, wires, cable ties, and protective sleeves. The main functions of the wiring harness are: (i) to transmit electric signals from control units to actuators, (ii) to supply of the
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International Conference on Engineering, Technology and Management 15 – 16, July’ 2016, Singapore current of the devices, (iii) to avoid an electromagnetic interference from other electronic circuits, and (iv) to properly and dependably connect under all working conditions [5]. Fig. 2 shows a sample of automotive wiring harness complexity that covers all functions of 3 car models. The GLSpec-1 is the top model among three models. The L-Spec-1 is the sub-model that has no function of FuelLid, PDrLcks-RHD, PDrLcks-Ps, and PWindows. Likewise, the LX-Spec-1 has no function of FuelLid, PDrLcks-Ps, and PWindows. If the design engineer makes decision to design peculiar wiring harness, he would design them for three types of wiring harness set. If the design engineer makes decision to design common wiring harness, he would design a wiring harness set that cover all function of three car models. Figure 2. The Variant of Automotive Wiring Harness (source: www.embedded.com)
The costs and benefits of two extreme decisions must be evaluated. In case of one common set, the cost of part acquiring is decreased because the company can save by ordering more quantity. For example, suppose each car model planned to be manufactured 10,000 cars next year, the total number of wiring harness sets that need to be ordered to the supplier is 30,000 sets. The company could save by ordering a large number of wiring harness sets and received discounted price. Furthermore, the company could save the cost of logistics and inventory in its supply chain. This is the benefit of risk pooling effect [6]. However, one common set brings the company of assembly process complexity problem. For example, if the common wiring harness sets are being used to manufacture car model of Lx-Spec-1, the wiring of FuelLid, PDrLcks-Ps, and PWindows are needed to precede “giveaway” process. It is the processes of repack, cut, re-tie, and re-attach of protective sleeves of the wiring harness sets. These processes rise up the manufacturing cost. Furthermore, it may causes of quality cost with significant probability. On the other hand, in case of three peculiar sets, the company can save some money of giveaway process and cost of quality. However, the costs of part acquiring, inventory management, and supply chain management are raised, as well.
Accordingly, the problem at hand of the design team is how many common sets of automotive wiring harness should be designed? And what configurations should it be? Suppose the design team has a complexity problem at hand of 87 functions of automotive wiring harness which divided into 37 function groups for 28 car models. The combinations may explode to 90,132 peculiar wiring harness sets. As a result, carefully determination of human (even expert supervisors) is almost impossible. III. HARMONY SEARCH FOR COMPLEXITY MANAGEMENT Harmony Search (HS) is a relatively new population based meta-heuristic algorithm which yields excellent results in the field of combinatorial optimization [7]. It was introduced by Geem [8], [9]. It imitates the musician seeking to find pleasing harmony determined by an aesthetic standard; comparable to an optimization method that finds the global optimal solution determined by its objective function [10]. In case of the musician, they iteratively improve the pitch of each music instruments to obtain the better harmony. The approaching method for unconstrained optimization problems is described as follows: Subject to
where is the objective function, is the decision variables, and are the lower and upper bounds of the feasible domain, is the number of decision variables. The procedure of HS is as follows [11]: Step 1: Initializing the optimization problem and algorithm parameters. The algorithm parameters include the harmony memory size (HMS), the harmony memory consideration rate (HMCR), the pitch adjusting rate (PAR), and the maximum iteration (T). The harmony memory is randomly initialized within the upper and lower bounds of each variable.
Figure 1. Automotive Wiring Harness (source: www.sws.co.jp)
Step 2: Initializing the harmony memory (HM).
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International Conference on Engineering, Technology and Management 15 – 16, July’ 2016, Singapore In this step, HM is filled with randomly generated solution vectors and their corresponding objective function values are calculated by Eq. (1) and stored in HM.
HMCR is too low, only few good performance harmonies are selected and the algorithm may converge slowly. Likewise, if the PAR is too high, the algorithm may work as a conventional random search; but then, too low PAR may cause limitation in search space, slow down convergence rate, and tend to converge prematurely on local optima. Lee et al. [12] recommended the parameter ranges for general HS algorithm: 0.70-0.95 for HMCR, 0.20-0.50 for PAR, and 1050 for HMS. However, for a specific optimization problem, a practitioner needs to discover them systematically.
Step 3: Improvising the harmony memory. A new harmony denoted as is improvised by deploying three rules: the memory consideration rule, the pitch adjustment rule, and randomization. Likewise an improvising of musicians, a musically pleasing harmony can be found based on three rules: (i) by playing a not from harmony memory; (ii) by playing a note which is closer to another note stored in HM; (iii) by playing an arbitrary note from entire note range. The algorithm executes as follows: (i) generating a new solution from HM (aka memory consideration); (ii) replacing a decision variable with a new one which is close to the current one (aka pitch adjustment); (iii) generating a solution vector from the possible random range (aka random selection) [11].
Step 4: Updating the harmony memory (HM). As other population-based techniques, the new harmony, , and the worst harmony in HM is compared in terms of fitness values. The better harmony is included in HM whilst the worst one is discarded. Step 5: Checking the termination criterion. The iteration is counted since step 3 until step 5. The termination criteria are examined in step 5. Usually, the number of maximum iteration is used. However, other stopping criteria may be deployed, e.g. no improvement in
Start
The memory consideration and the random selection rules are performed first. By using the predefined HMCR, Initialization
HMCR
PAR
where is a uniform random number between 0 and 1. The value of the rest of the decision variables are selected in the same manner.
Evaluation of new harmony
Then, the pitch adjustment is performed. The new decision variables are needed to determine if the pitch adjustment is necessary or not. By using PAR value the pitch adjustment rule are executed as follows:
Add new harmony to HM?
Yes
Update the HM
No
No
where is a bandwidth which is used for pitch adjustment. The value of the rest of the decision variables are selected in the same manner.
Termination criteria satisfied?
Yes
Stop
Figure 3. Flowchart of the Harmony Search Algorithm [7]
fitness value for predefined number of iterations. Fig. 3 shows the flowchart of HS algorithm.
They are worth noting that if the HMCR is too high, the most time of harmony in HM are used; as a result, the search may not explore the global optimum. On the other hand, if the
As mentioned before, the problem at hand—automotive wiring harness complexity in design phase—is an unconstrained optimization problem. The objective function is
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International Conference on Engineering, Technology and Management 15 – 16, July’ 2016, Singapore trading-off between the cost of part acquiring and in-house assembly process cost. Due to the privacy and confidential of the sample company, we could not show the structure of relevant costs in this report. The main goal of this paper is illustrating how the artificial intelligence method—HS algorithm—benefits to the sample company. IV.
it was found that increasing the number of max iterations has significant effect to computational time. TABLE I.
FACTORS AND LEVELS OF THE FULL-FACTORIAL EXPERIMENT
Factors HMS HMCR PAR Max Iteration (T)
CASE SIMULATION RESULTS AND ANALYSIS
In the year of the study, the company had to make the decision on common automotive wiring harness design of totally 87 functions which could be divided into 37 groups for 28 car models. The combinations may be exploded to 90,132 peculiar wiring harness sets. Accordingly, the company needed a decision support tool in order to maintain high competitive and retain plausible price of its products. Since the HS algorithm is a stochastic optimization method, we need to investigate its appropriate parameter settings. A two-step sequential experiment is adopted in this study. Firstly, it was aimed to initially investigate the appropriate setting of HS parameters by solving a case study of automotive wiring harness design problem. Its findings will be then applied in the next experiment that is aimed to design the complexity of automotive wiring harness design problem of the sample company. The development of simulation program is written by using VBA package on MS. Excel®. All experiments are simulated on a personal computer with CPU Intel Core i5-4210U at 1.70 GHz and 8.00 GB of RAM.
Number of levels 5 3 4 3
TABLE II.
ANOVA TABLE OF THE CASE EXPERIMENT Degree of Freedom 4 2 3 2 8 12 8 6 4 6 24 16 12 24 48 360 539
Source of Variance HMS HMCR PAR T HMS * HMCR HMS * PAR HMS * T HMCR * PAR HMCR * T PAR * T HMS * HMCR * PAR HMS * HMCR * T HMCR * PAR * T HMS * PAR * T HMS * HMCR * PAR * T Error Total
A. The Results of HS Parameters Analysis The experiment was design as a full factorial experiment because it was more efficient and necessary to avoid misleading conclusion when interactions might be present. This technique allowed the effects of a factor to be estimated at several levels of the others, yielding conclusion was valid over the wide range [13], [14]. The experiment with four factors and five replications is carried out. A dependent variable in this experiment is the fitness value (total cost). The number of levels (treatments) and the settings of each factor are shown in Table 1. There were in total of 900 runs.
Setting 10, 20, 30, 40, 50 0.75, 0.85, 0.95 0.20, 0.30, 0.40, 0.50 1000, 2000, 3000
Sum of Squares
Mean Square
9.925E+13 9.786E+14 4.712E+11 3.297E+14 1.017E+13 1.585E+12 1.032E+13 2.649E+12 9.311E+12 3.348E+12 4.051E+12 5.138E+12 7.384E+12 7.329E+12 1.564E+13 9.484E+13 1.579E+15
2.481E+13 4.893E+14 1.570E+11 1.648E+14 1272E+12 1.321E+11 1.290E+12 4.415E+11 2.327E+12 5.580E+11 1.687E+11 3.212E+11 6.153E+11 3.054E+11 3.259E+11 2.634E+11
F0
p-value
94.18 1857.24 0.60 625.84 4.83 0.50 4.90 1.68 8.84 2.12 0.64 1.22 2.34 1.16 1.24
0.000 0.000 0.618 0.000 0.000 0.914 0.000 0.126 0.000 0.051 0.905 0.250 0.007 0.277 0.145
M a in Ef f e c ts P lot f or T o ta lC o s t Da ta M e a n s HMS
HM C R
46000000 45000000
Mean
44000000 43000000 10
20
30 PA R
40
50
0. 75
0.85 T
0.95
1 00 0
2000
3000
46000000 45000000 44000000 43000000 0.2
Analysis of Variance (ANOVA) was used to investigate the effects of the main factors and their interactions. Table 2 shows the results of this analysis. For a given confidence level , all factors or interactions with the value of are statistically significant, whilst other factors may be disregarded.
0.3
0.4
0.5
Figure 4. The Main Effect Plots of HS Parameters Inte ra ctio n P lot fo r To ta lC o st Da ta M e a ns 0.75
0 .8 5
0 .9 5
1 000
200 0
300 0 48 0000 00 46 0000 00
HMS
In the case simulation, HMS, HMCR, and T yield and therefore statistically significant, within the ranges considered. However, PAR is not. It can be shown by Fig. 4. The PAR does not induce the response value (total cost) whilst HMS and HMCR do. The results show the total cost for HMS of 10 is better than, lower cost, that with 50. Similarly but conversely, HMCR of 0.95 yields lower total cost than that with 0.75. Likewise, T of 3000 is better than that with 1000. The average computation time is 408.74 seconds. Nevertheless,
44 0000 00 48 0000 00
H MS 10 20 30 40 50 H MCR 0 .7 5 0 .8 5 0 .9 5
46 0000 00 H M CR 44 0000 00 48 0000 00 46 0000 00 PAR 44 0000 00 48 0000 00 46 0000 00 T 44 0000 00
10
20
30
40
50
0.2
0.3
0 .4
0.5
Figure 5. The Interaction Plots of HS Parameters
4
PA R 0 .2 0 .3 0 .4 0 .5
T 10 00 20 00 30 00
International Conference on Engineering, Technology and Management 15 – 16, July’ 2016, Singapore B. The Results of the Case Simulation By decoding the parameters study, the HS parameters were set as follows: HMS = 10, HMCR = 0.95, HMS = 0.5, and T = 3000, the execution time of 614.72 seconds was satisfying. The total cost of HS tool is shown in Fig. 6 which compared to the current practice of the sample company. The bar graph label A means the total cost of current situation which 28 types of wiring harness are used, label B means the total cost of the improved situation which designed by the design engineers which 18 types of wiring harness are used, and label C means the total cost of the proposed designed which suggested by the harmony search algorithm which 11 types of wiring harness are used.
settings. The results of the case simulation showed that the HS algorithm is a powerful tool in design process. It can propose the alternative choice of common part design by consume a little execute time. The algorithm was coded by VBA language on MS. Excel® which has no cost to the company.
Mathematically, the option B can reduce total cost by 20.14% whilst the option C, HS algorithm, can reduce total cost by 26.06%. It is worth noting that the cost is calculated for next 2 quarter of the company’s business plan. It may not convince the design engineers to use the decision support tool. However, by comparing the time consuming of the improvement project, design engineers consumed time of 330 minutes in design process. However, the decision support tool, HS algorithm, executed the complex design only 10.24 minutes. This shows that the design team can cultivate the benefit by deploying harmony search algorithm in complexity management in their design process.
REFERENCES
Though, this design project did not consider some engineering constrained which may occur. For example, some giveaway functions may not allowed in technique because it might cause on other electronic devices interfere by generating some noise. As a result, we plan to conduct further research to apply the HS on constrained optimization problem for this matter.
[1] [2] [3]
[4] [5]
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[8] [9]
[10] Figure 6. Results Comparison [11]
V. CONCLUSIONS AND RECOMMENDATIONS The complexity in product variety can be mitigated by design process. Especially the complexity of assemblies and sub-assemblies, it can be reduced by carefully design of common parts. The sample company handles a number of car models which use some common functions of automotive wiring harness sets. We proposed the Harmony Search Algorithm to solve the problem effectively. The full-factorial experiment design is exploited to design the HS parameter
[12]
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[14]
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