doi: 10.3934/jimo.2021175
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A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

1. 

School of Management, Hangzhou Dianzi University, Hangzhou 310018, China

2. 

Department of Mathematics, China Jiliang University, Hangzhou 310018, China

* Corresponding author: Jufeng Wang

Received  February 2021 Revised  August 2021 Early access October 2021

Due to the fierce market competition, enterprises try to satisfy customers' requirements for personalized products in order to maximize profit or market share of their products. This not only needs to determine the product variants through product line design, but also needs to pay attention to resource allocation in the manufacturing process. This paper proposes a cellular manufacturing optimization model that considers the market and production. If the company excessively pursues the satisfaction of customers' personalized needs, the manufacturing time and cost may increase accordingly. Of course, with the restriction of production capacity in manufacturing cells and the expectation of reducing cost, managers cannot design attributes' levels of a product line casually, which may result in its unstable marketing share and profit. Therefore, the product demand influenced by customers' preferences could be a key factor to link market and production. The objective of propose model is to maximize product profit which consists of revenue and miscellaneous costs (material, processing, transportation, final assembly and fixed costs). A revised imperialist competitive algorithm (RICA) is developed to optimize the discrete problem. Extensive numerical experiments and t-test are carried out to verify the effect of this method. The results demonstrate the proficiency of RICA over another imperialist competitive algorithm based method and genetic algorithm in terms of solution quality.

Citation: Chunfeng Liu, Yuanyuan Liu, Jufeng Wang. A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021175
References:
[1]

M. AbdollahiA. Isazadeh and D. Abdollahi, Imperialist competitive algorithm for solving systems of nonlinear equations, Comput. Math. Appl., 65 (2013), 1894-1908.  doi: 10.1016/j.camwa.2013.04.018.  Google Scholar

[2]

M. A. AchabouS. Dekhili and A. P. Codini, Consumer preferences towards animal-friendly fashion products: An application to the italian market, Journal of Consumer Marketing, 37 (2020), 661-673.  doi: 10.1108/JCM-10-2018-2908.  Google Scholar

[3]

S. Agnew and P. Dargusch, Consumer preferences for household-level battery energy storage, Renewable and Sustainable Energy Reviews, 75 (2017), 609-617.  doi: 10.1016/j.rser.2016.11.030.  Google Scholar

[4]

M. A. ArdehM. B. MenhajE. Esmailian and H. ZandHessami, Explica: An explorative imperialist competitive algorithm based on the notion of Explorers with an expansive retention policy, Applied Soft Computing, 54 (2017), 74-92.  doi: 10.1016/j.asoc.2017.01.025.  Google Scholar

[5]

A. A. AzamiP. Payvandy and M. M. Jalili, Parameter estimation of viscoelastic model to simulate the compression behavior of artificial grass under dynamic loading using imperialist competitive algorithm, Journal of Textiles and Polymers, 9 (2021), 3-11.   Google Scholar

[6]

M. Bagheri and M. Bashiri, A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system, Proceedings of the Institution of Mechanical Engineers, 228 (2014), 458-470.  doi: 10.1177/0954405413500662.  Google Scholar

[7]

A. Ballakur, An Investigation of Part Family/Machine Group Formation in Designing A Cellular Manufacturing System, Ph. D. Thesis, University of Wisconsin, Madison, WI, 1985. Google Scholar

[8]

B. BootakiI. Mahdavi and M. M. Paydar, A hybrid GA-AUGMECON method to solve a cubic cell formation problem considering different worker skills, Computers & Industrial Engineering, 75 (2014), 31-40.   Google Scholar

[9]

D. Cao, K. Ramani and Z. Li, Guiding concept generation based on ontology for customer preference modeling, The Eighth International Symposium on Tools and Methods of Competitive Engineering, Italy, (2010), 1–14. Google Scholar

[10]

H. Garg, Handbook of research on artificial intelligence techniques and algorithms, Chapter A Hybrid GA-GSA Algorithm for Optimizing the Performance of An Industrial System by Utilizing Uncertain Data, (2015), 620–654. Google Scholar

[11]

H. Garg, A hybrid PSO-GA algorithm for constrained optimization problems, Appl. Math. Comput., 274 (2016), 292-305.  doi: 10.1016/j.amc.2015.11.001.  Google Scholar

[12]

H. Garg, A hybrid GSA-GA algorithm for constrained optimization problems, Information Sciences, 478 (2019), 499-523.   Google Scholar

[13]

S. Grasso and D. Asioli, Consumer preferences for upcycled ingredients: A case study with biscuits, Food Quality and Preference, 84 (2020), 1-9.  doi: 10.1016/j.foodqual.2020.103951.  Google Scholar

[14]

Y. GuptaM. GuptaA. Kumar and C. Sundaram, A genetic algorithm-based approach to cell composition and layout design problems, International J. Production Research, 34 (1996), 447-482.  doi: 10.1080/00207549608904913.  Google Scholar

[15]

J. A. Howard and J. N. Sheth, The Theory of Buyer Behavior, John Wiley & Sons, Inc., New York, 1969. Google Scholar

[16]

J. JouzdaniF. BarzinpourM. A. Shafia and M. Fathian, Applying simulated annealing to a generalized cell formation problem considering alternative routings and machine reliability, Asia-Pacific Journal of Operational Research, 31 (2014), 1-26.  doi: 10.1142/S0217595914500213.  Google Scholar

[17]

R. Kamalakannan and R. S. Pandian, A tabu search strategy to solve cell formation problem with ratio level data, International J. Enterprise Network Management, 13 (2018), 209-220.  doi: 10.1504/IJBIDM.2018.088431.  Google Scholar

[18]

M. Kargar and P. Payvandy, Optimization of fabric layout by using imperialist competitive algorithm, J. Textile and Polymer, 3 (2015), 55-63.   Google Scholar

[19]

A. H. KashanB. Karimi and A. Noktehdan, A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem, International J. Advanced Manufacturing Technology, 73 (2014), 1543-1556.   Google Scholar

[20]

R. KiaA. BaboliN. JavadianR. Tavakkoli-MoghaddamM. Kazemi and J. Khorrami, Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing, Comput. Oper. Res., 39 (2012), 2642-2658.  doi: 10.1016/j.cor.2012.01.012.  Google Scholar

[21]

J. R. King and V. Nakornchai, Machine-component group formation in group technology: Review and extension, International J. Production Research, 20 (1982), 117-133.  doi: 10.1080/00207548208947754.  Google Scholar

[22]

M. Kuzmanovic and M. Martic, An approach to competitive product line design using conjoint data, Expert Systems with Applications, 39 (2012), 7262-7269.  doi: 10.1016/j.eswa.2012.01.097.  Google Scholar

[23]

M. KuzmanovicM. Martic and M. Vujosevic, Designing a profit-maximizing product line for heterogeneous market, Technical Gazette, 26 (2019), 1562-1569.   Google Scholar

[24]

Y. LiX. Li and J. N. D. Gupta, Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search, Expert Systems with Applications, 42 (2015), 1409-1417.  doi: 10.1016/j.eswa.2014.09.007.  Google Scholar

[25]

C. LiuJ. WangJ. Y.-T. Leung and K. Li, Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm, International J. Production Research, 54 (2016), 923-944.  doi: 10.1080/00207543.2015.1113328.  Google Scholar

[26]

C. LiuJ. Wang and M. Zhou, Reconfiguration of virtual cellular manufacturing systems via improved imperialist competitive approach, IEEE Transactions on Automation Science and Engineering, 16 (2019), 1301-1314.  doi: 10.1109/TASE.2018.2878653.  Google Scholar

[27]

C. Liu and J. Wang, Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing, International J. Computational Intelligence Systems, 9 (2016), 765-777.  doi: 10.1080/18756891.2016.1204123.  Google Scholar

[28]

C. Liu, J. Wang and J. Y.-T. Leung, Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm, Computers & Industrial Engineering, 96 (2016), 162–179. doi: 10.1016/j.cie.2016.03.020.  Google Scholar

[29]

C. LiuJ. Wang and J. Y.-T. Leung, Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning, Applied Soft Computing, 62 (2018), 602-618.  doi: 10.1016/j.asoc.2017.10.034.  Google Scholar

[30]

E. Mehdizadeh, S. V. D. Niaki and V. Rahimi, A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training, Computers & Industrial Engineering, 101 (2016), 35–52. doi: 10.1016/j.cie.2016.08.012.  Google Scholar

[31]

J. J. MichalekO. CeryanP. Y. Papalambros and Y. Koren, Balancing marketing and manufacturing objectives in product line design, J. Mech. Des., 128 (2006), 1196-1204.  doi: 10.1115/1.2336252.  Google Scholar

[32]

J. J. MichalekP. EbbesF. AdigzelF. M. Feinberg and P. Y. Papalambros, Enhancing marketing with engineering: Optimal product line design for heterogeneous markets, International J. Research in Marketing, 28 (2011), 1-12.  doi: 10.1016/j.ijresmar.2010.08.001.  Google Scholar

[33]

S. M. MousaviR. Tavakkoli-MoghaddamB. VahdaniH. Hashemi and M. J. Sanjari, A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects, Robotics and Computer-Integrated Manufacturing, 29 (2013), 157-168.  doi: 10.1016/j.rcim.2012.04.006.  Google Scholar

[34]

K. NematiS. M. Shamsuddin and M. S. Kamarposhti, Using imperial competitive algorithm for solving traveling salesman problem and comparing the efficiency of the proposed algorithm with methods in use, Australian J. Basic and Applied Science, 5 (2011), 540-543.   Google Scholar

[35]

C. Y. Ng and K. M. Y. Law, Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning, Computers & Industrial Engineerin, 139 (2020), 1-11.  doi: 10.1016/j.cie.2019.106180.  Google Scholar

[36]

F. NiakanA. BaboliT. Moyaux and V. Botta-Genoulaz, A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria, Appl. Math. Model., 40 (2016), 2674-2691.  doi: 10.1016/j.apm.2015.09.047.  Google Scholar

[37]

S. W. Norton, The coast theorem and suboptimization in marketing channels, Marketing Science, 6 (1987), 268-285.   Google Scholar

[38]

R. S. PatwalN. Narang and H. Garg, A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units, Energy, 142 (2018), 822-837.   Google Scholar

[39]

S. J. M. RadA. F. Tab and K. Mollazade, Application of imperialist competitive algorithm for feature selection: A case study on bulk rice classification, Inter. J. Computer Appl., 40 (2012), 41-48.   Google Scholar

[40]

P. Tarasewich and R. R. McMullen, A pruning heuristic for use with multisource product design, European J. Operational Research, 128 (2001), 58-73.  doi: 10.1016/S0377-2217(99)00350-1.  Google Scholar

[41]

P. B. Tookanlou and H. Wong, Determining the optimal customization levels, lead times and inventory positioning in vertical product differentiation, Inter. J. Production Economics, 221 (2020), 1-20.  doi: 10.1016/j.ijpe.2019.08.014.  Google Scholar

[42]

P. B. Tookanlou and H. W. Wong, Product line design with vertical and horizontal consumer heterogeneity: The effect of distribution channel structure on the optimal quality and customization levels, European J. Marketing, 55 (2020), 95-131.   Google Scholar

[43]

S. TsafarakisC. SaridakisG. Baltas and N. Matsatsinis, Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables, Industrial Marketing Management, 42 (2013), 496-506.  doi: 10.1016/j.indmarman.2013.03.002.  Google Scholar

[44]

S. TsafarakisK. ZervoudakisA. Andronikidis and E. Altsitsiadis, Fuzzy self-tuning differential evolution for optimal product line design, European J. Oper. Res., 287 (2020), 1161-1169.  doi: 10.1016/j.ejor.2020.05.018.  Google Scholar

[45]

J. WangC. Liu and K. Li, A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement, Automatika, 60 (2019), 172-180.  doi: 10.1080/00051144.2019.1603264.  Google Scholar

[46]

J. WangC. Liu and M. Zhou, Improved bacterial foraging algorithm for cell formation and product scheduling considering learning and forgetting factors in cellular manufacturing systems, IEEE Systems Journal, 14 (2020), 3047-3056.  doi: 10.1109/JSYST.2019.2963222.  Google Scholar

[47]

M. ZandiehA. R. Khatami and S. H. A. Rahmati, Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm, Applied Soft Computing, 58 (2017), 449-464.   Google Scholar

[48]

S. ZhangJ. ZhangJ. Shen and W. Tang, A joint dynamic pricing and production model with asymmetric reference price effect, J. Ind. Manag. Optim., 15 (2019), 667-688.  doi: 10.3934/jimo.2018064.  Google Scholar

[49]

Z. Zhao, Study on Multi-Strategy Dynamic Scheduling Optimization Algorithm in Rotating Seru System, Master's Thesis, Dongbei University of Finance and Economics, Dalian, China, 2017. Google Scholar

[50]

A. M. ZohrevandH. Rafiei and A. H. Zohrevand, Multi-objective dynamic cell formation problem: A stochastic programming approach, Computers & Industrial Engineering, 98 (2016), 323-332.   Google Scholar

show all references

References:
[1]

M. AbdollahiA. Isazadeh and D. Abdollahi, Imperialist competitive algorithm for solving systems of nonlinear equations, Comput. Math. Appl., 65 (2013), 1894-1908.  doi: 10.1016/j.camwa.2013.04.018.  Google Scholar

[2]

M. A. AchabouS. Dekhili and A. P. Codini, Consumer preferences towards animal-friendly fashion products: An application to the italian market, Journal of Consumer Marketing, 37 (2020), 661-673.  doi: 10.1108/JCM-10-2018-2908.  Google Scholar

[3]

S. Agnew and P. Dargusch, Consumer preferences for household-level battery energy storage, Renewable and Sustainable Energy Reviews, 75 (2017), 609-617.  doi: 10.1016/j.rser.2016.11.030.  Google Scholar

[4]

M. A. ArdehM. B. MenhajE. Esmailian and H. ZandHessami, Explica: An explorative imperialist competitive algorithm based on the notion of Explorers with an expansive retention policy, Applied Soft Computing, 54 (2017), 74-92.  doi: 10.1016/j.asoc.2017.01.025.  Google Scholar

[5]

A. A. AzamiP. Payvandy and M. M. Jalili, Parameter estimation of viscoelastic model to simulate the compression behavior of artificial grass under dynamic loading using imperialist competitive algorithm, Journal of Textiles and Polymers, 9 (2021), 3-11.   Google Scholar

[6]

M. Bagheri and M. Bashiri, A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system, Proceedings of the Institution of Mechanical Engineers, 228 (2014), 458-470.  doi: 10.1177/0954405413500662.  Google Scholar

[7]

A. Ballakur, An Investigation of Part Family/Machine Group Formation in Designing A Cellular Manufacturing System, Ph. D. Thesis, University of Wisconsin, Madison, WI, 1985. Google Scholar

[8]

B. BootakiI. Mahdavi and M. M. Paydar, A hybrid GA-AUGMECON method to solve a cubic cell formation problem considering different worker skills, Computers & Industrial Engineering, 75 (2014), 31-40.   Google Scholar

[9]

D. Cao, K. Ramani and Z. Li, Guiding concept generation based on ontology for customer preference modeling, The Eighth International Symposium on Tools and Methods of Competitive Engineering, Italy, (2010), 1–14. Google Scholar

[10]

H. Garg, Handbook of research on artificial intelligence techniques and algorithms, Chapter A Hybrid GA-GSA Algorithm for Optimizing the Performance of An Industrial System by Utilizing Uncertain Data, (2015), 620–654. Google Scholar

[11]

H. Garg, A hybrid PSO-GA algorithm for constrained optimization problems, Appl. Math. Comput., 274 (2016), 292-305.  doi: 10.1016/j.amc.2015.11.001.  Google Scholar

[12]

H. Garg, A hybrid GSA-GA algorithm for constrained optimization problems, Information Sciences, 478 (2019), 499-523.   Google Scholar

[13]

S. Grasso and D. Asioli, Consumer preferences for upcycled ingredients: A case study with biscuits, Food Quality and Preference, 84 (2020), 1-9.  doi: 10.1016/j.foodqual.2020.103951.  Google Scholar

[14]

Y. GuptaM. GuptaA. Kumar and C. Sundaram, A genetic algorithm-based approach to cell composition and layout design problems, International J. Production Research, 34 (1996), 447-482.  doi: 10.1080/00207549608904913.  Google Scholar

[15]

J. A. Howard and J. N. Sheth, The Theory of Buyer Behavior, John Wiley & Sons, Inc., New York, 1969. Google Scholar

[16]

J. JouzdaniF. BarzinpourM. A. Shafia and M. Fathian, Applying simulated annealing to a generalized cell formation problem considering alternative routings and machine reliability, Asia-Pacific Journal of Operational Research, 31 (2014), 1-26.  doi: 10.1142/S0217595914500213.  Google Scholar

[17]

R. Kamalakannan and R. S. Pandian, A tabu search strategy to solve cell formation problem with ratio level data, International J. Enterprise Network Management, 13 (2018), 209-220.  doi: 10.1504/IJBIDM.2018.088431.  Google Scholar

[18]

M. Kargar and P. Payvandy, Optimization of fabric layout by using imperialist competitive algorithm, J. Textile and Polymer, 3 (2015), 55-63.   Google Scholar

[19]

A. H. KashanB. Karimi and A. Noktehdan, A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem, International J. Advanced Manufacturing Technology, 73 (2014), 1543-1556.   Google Scholar

[20]

R. KiaA. BaboliN. JavadianR. Tavakkoli-MoghaddamM. Kazemi and J. Khorrami, Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing, Comput. Oper. Res., 39 (2012), 2642-2658.  doi: 10.1016/j.cor.2012.01.012.  Google Scholar

[21]

J. R. King and V. Nakornchai, Machine-component group formation in group technology: Review and extension, International J. Production Research, 20 (1982), 117-133.  doi: 10.1080/00207548208947754.  Google Scholar

[22]

M. Kuzmanovic and M. Martic, An approach to competitive product line design using conjoint data, Expert Systems with Applications, 39 (2012), 7262-7269.  doi: 10.1016/j.eswa.2012.01.097.  Google Scholar

[23]

M. KuzmanovicM. Martic and M. Vujosevic, Designing a profit-maximizing product line for heterogeneous market, Technical Gazette, 26 (2019), 1562-1569.   Google Scholar

[24]

Y. LiX. Li and J. N. D. Gupta, Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search, Expert Systems with Applications, 42 (2015), 1409-1417.  doi: 10.1016/j.eswa.2014.09.007.  Google Scholar

[25]

C. LiuJ. WangJ. Y.-T. Leung and K. Li, Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm, International J. Production Research, 54 (2016), 923-944.  doi: 10.1080/00207543.2015.1113328.  Google Scholar

[26]

C. LiuJ. Wang and M. Zhou, Reconfiguration of virtual cellular manufacturing systems via improved imperialist competitive approach, IEEE Transactions on Automation Science and Engineering, 16 (2019), 1301-1314.  doi: 10.1109/TASE.2018.2878653.  Google Scholar

[27]

C. Liu and J. Wang, Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing, International J. Computational Intelligence Systems, 9 (2016), 765-777.  doi: 10.1080/18756891.2016.1204123.  Google Scholar

[28]

C. Liu, J. Wang and J. Y.-T. Leung, Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm, Computers & Industrial Engineering, 96 (2016), 162–179. doi: 10.1016/j.cie.2016.03.020.  Google Scholar

[29]

C. LiuJ. Wang and J. Y.-T. Leung, Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning, Applied Soft Computing, 62 (2018), 602-618.  doi: 10.1016/j.asoc.2017.10.034.  Google Scholar

[30]

E. Mehdizadeh, S. V. D. Niaki and V. Rahimi, A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training, Computers & Industrial Engineering, 101 (2016), 35–52. doi: 10.1016/j.cie.2016.08.012.  Google Scholar

[31]

J. J. MichalekO. CeryanP. Y. Papalambros and Y. Koren, Balancing marketing and manufacturing objectives in product line design, J. Mech. Des., 128 (2006), 1196-1204.  doi: 10.1115/1.2336252.  Google Scholar

[32]

J. J. MichalekP. EbbesF. AdigzelF. M. Feinberg and P. Y. Papalambros, Enhancing marketing with engineering: Optimal product line design for heterogeneous markets, International J. Research in Marketing, 28 (2011), 1-12.  doi: 10.1016/j.ijresmar.2010.08.001.  Google Scholar

[33]

S. M. MousaviR. Tavakkoli-MoghaddamB. VahdaniH. Hashemi and M. J. Sanjari, A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects, Robotics and Computer-Integrated Manufacturing, 29 (2013), 157-168.  doi: 10.1016/j.rcim.2012.04.006.  Google Scholar

[34]

K. NematiS. M. Shamsuddin and M. S. Kamarposhti, Using imperial competitive algorithm for solving traveling salesman problem and comparing the efficiency of the proposed algorithm with methods in use, Australian J. Basic and Applied Science, 5 (2011), 540-543.   Google Scholar

[35]

C. Y. Ng and K. M. Y. Law, Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning, Computers & Industrial Engineerin, 139 (2020), 1-11.  doi: 10.1016/j.cie.2019.106180.  Google Scholar

[36]

F. NiakanA. BaboliT. Moyaux and V. Botta-Genoulaz, A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria, Appl. Math. Model., 40 (2016), 2674-2691.  doi: 10.1016/j.apm.2015.09.047.  Google Scholar

[37]

S. W. Norton, The coast theorem and suboptimization in marketing channels, Marketing Science, 6 (1987), 268-285.   Google Scholar

[38]

R. S. PatwalN. Narang and H. Garg, A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units, Energy, 142 (2018), 822-837.   Google Scholar

[39]

S. J. M. RadA. F. Tab and K. Mollazade, Application of imperialist competitive algorithm for feature selection: A case study on bulk rice classification, Inter. J. Computer Appl., 40 (2012), 41-48.   Google Scholar

[40]

P. Tarasewich and R. R. McMullen, A pruning heuristic for use with multisource product design, European J. Operational Research, 128 (2001), 58-73.  doi: 10.1016/S0377-2217(99)00350-1.  Google Scholar

[41]

P. B. Tookanlou and H. Wong, Determining the optimal customization levels, lead times and inventory positioning in vertical product differentiation, Inter. J. Production Economics, 221 (2020), 1-20.  doi: 10.1016/j.ijpe.2019.08.014.  Google Scholar

[42]

P. B. Tookanlou and H. W. Wong, Product line design with vertical and horizontal consumer heterogeneity: The effect of distribution channel structure on the optimal quality and customization levels, European J. Marketing, 55 (2020), 95-131.   Google Scholar

[43]

S. TsafarakisC. SaridakisG. Baltas and N. Matsatsinis, Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables, Industrial Marketing Management, 42 (2013), 496-506.  doi: 10.1016/j.indmarman.2013.03.002.  Google Scholar

[44]

S. TsafarakisK. ZervoudakisA. Andronikidis and E. Altsitsiadis, Fuzzy self-tuning differential evolution for optimal product line design, European J. Oper. Res., 287 (2020), 1161-1169.  doi: 10.1016/j.ejor.2020.05.018.  Google Scholar

[45]

J. WangC. Liu and K. Li, A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement, Automatika, 60 (2019), 172-180.  doi: 10.1080/00051144.2019.1603264.  Google Scholar

[46]

J. WangC. Liu and M. Zhou, Improved bacterial foraging algorithm for cell formation and product scheduling considering learning and forgetting factors in cellular manufacturing systems, IEEE Systems Journal, 14 (2020), 3047-3056.  doi: 10.1109/JSYST.2019.2963222.  Google Scholar

[47]

M. ZandiehA. R. Khatami and S. H. A. Rahmati, Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm, Applied Soft Computing, 58 (2017), 449-464.   Google Scholar

[48]

S. ZhangJ. ZhangJ. Shen and W. Tang, A joint dynamic pricing and production model with asymmetric reference price effect, J. Ind. Manag. Optim., 15 (2019), 667-688.  doi: 10.3934/jimo.2018064.  Google Scholar

[49]

Z. Zhao, Study on Multi-Strategy Dynamic Scheduling Optimization Algorithm in Rotating Seru System, Master's Thesis, Dongbei University of Finance and Economics, Dalian, China, 2017. Google Scholar

[50]

A. M. ZohrevandH. Rafiei and A. H. Zohrevand, Multi-objective dynamic cell formation problem: A stochastic programming approach, Computers & Industrial Engineering, 98 (2016), 323-332.   Google Scholar

Figure 1.  The encode scheme for an example solution
Figure 2.  Flowchart of the proposed RICA
Figure 3.  Crossover of country
Figure 4.  Mutation of country
Figure 5.  The diagram of the Taguchi experiment
Figure 6.  Convergence diagram of a typical example
Table 1.  Literature review of research scenario and solution method
Authors Cell formation Consumer preference Product line design Solution method
Gupta et al. [14] $\surd$ - - GAs
Kashan et al. [19] $\surd$ - - GBPSO
Kamalakannan and Pandian [17] $ \surd $ - - TS, MGE
Bagheri and Bashiri [6] $ \surd $ - - GICA
Zohrevand et al. [50] $ \surd $ - - TS-GA
Jouzdani et al. [16] $ \surd $ - - SA
Li et al. [24] $ \surd $ - - HHS
Liu et al. [25] $ \surd $ - - DBFA
Zhao [49] $ \surd $ - - Memetic Algorithm
Mehdizadeh et al. [30] $ \surd $ - - MOVDO
Bootaki et al. [8] $ \surd $ - - GA-AUGMECON
Niakan et al. [36] $ \surd $ - - NSGA-II
Liu et al. [26] $ \surd $ - - DICAP
Howard and Sheth [15] - $ \surd $ - Buyer Behavior Theory
Norton [37] - $ \surd $ - Coase Theorem
Cao et al. [9] - $ \surd $ - Ontology-based
Ng and Law [35] - $ \surd $ $ \surd $ Fuzzy-ER
Achabou et al. [2] - $ \surd $ - Conjoint Analysis, Cluster Analysis
Agnew and Dargusch [3] - $ \surd $ $ \surd $ BWS, DCE
Grasso and Asioli[13] - $ \surd $ $ \surd $ DCMs
Michalek et al. [32] - $ \surd $ $ \surd $ Conjoint Analysis
Tookanlou and Wong [42] - $ \surd $ $ \surd $ Empirical Studies
Tsafarakis et al. [43,44] - $ \surd $ $ \surd $ Hybrid PSO, FSTDE
Kuzmanovic et al. [23] - $ \surd $ $ \surd $ Conjoint Analysis
This paper $ \surd $ $ \surd $ $ \surd $ RICA
Authors Cell formation Consumer preference Product line design Solution method
Gupta et al. [14] $\surd$ - - GAs
Kashan et al. [19] $\surd$ - - GBPSO
Kamalakannan and Pandian [17] $ \surd $ - - TS, MGE
Bagheri and Bashiri [6] $ \surd $ - - GICA
Zohrevand et al. [50] $ \surd $ - - TS-GA
Jouzdani et al. [16] $ \surd $ - - SA
Li et al. [24] $ \surd $ - - HHS
Liu et al. [25] $ \surd $ - - DBFA
Zhao [49] $ \surd $ - - Memetic Algorithm
Mehdizadeh et al. [30] $ \surd $ - - MOVDO
Bootaki et al. [8] $ \surd $ - - GA-AUGMECON
Niakan et al. [36] $ \surd $ - - NSGA-II
Liu et al. [26] $ \surd $ - - DICAP
Howard and Sheth [15] - $ \surd $ - Buyer Behavior Theory
Norton [37] - $ \surd $ - Coase Theorem
Cao et al. [9] - $ \surd $ - Ontology-based
Ng and Law [35] - $ \surd $ $ \surd $ Fuzzy-ER
Achabou et al. [2] - $ \surd $ - Conjoint Analysis, Cluster Analysis
Agnew and Dargusch [3] - $ \surd $ $ \surd $ BWS, DCE
Grasso and Asioli[13] - $ \surd $ $ \surd $ DCMs
Michalek et al. [32] - $ \surd $ $ \surd $ Conjoint Analysis
Tookanlou and Wong [42] - $ \surd $ $ \surd $ Empirical Studies
Tsafarakis et al. [43,44] - $ \surd $ $ \surd $ Hybrid PSO, FSTDE
Kuzmanovic et al. [23] - $ \surd $ $ \surd $ Conjoint Analysis
This paper $ \surd $ $ \surd $ $ \surd $ RICA
Table 2.  Attributes and levels of vacuum glass
Component Attribute Level
Component 1 Thickness Thin, thick, superthick
The shape of the support Cylindrical, spherical, oval
Glass shape Square, circular, rhombic
Component 2 Color Blue, gray, green
Light transmission Transparent, translucent, opaque
Thermal insulation General, great, excellent
Edge banding material Metallic, plastic, rubber
Component 3 Decoration Retro, fashion, chinoiserie
Welding of metallic layer Metal brazing, gastight welding, laser welding
Component Attribute Level
Component 1 Thickness Thin, thick, superthick
The shape of the support Cylindrical, spherical, oval
Glass shape Square, circular, rhombic
Component 2 Color Blue, gray, green
Light transmission Transparent, translucent, opaque
Thermal insulation General, great, excellent
Edge banding material Metallic, plastic, rubber
Component 3 Decoration Retro, fashion, chinoiserie
Welding of metallic layer Metal brazing, gastight welding, laser welding
Table 3.  Preference scores of each attribute
Attribute1 (A1) Attribute 2 (A2) Attribute 3(A3)
Level11
(L11)
Level12
(L12)
Level13
(L13)
Level21
(L21)
Level22
(L22)
Level23
(L23)
Level31
(L31)
Level32
(L32)
Level33
(L33)
Individual
1
2
(0.20)
3
(0.30)
5
(0.50)
1
(0.13)
4
(0.50)
3
(0.38)
2
(0.18)
5
(0.45)
4
(0.36)
Individual
2
2
(0.33)
3
(0.50)
1
(0.17)
3
(0.33)
4
(0.44)
2
(0.22)
3
(0.30)
4
(0.40)
3
(0.30)
Individual
3
4
(0.36)
2
(0.18)
5
(0.45)
4
(0.44)
3
(0.33)
2
(0.22)
5
(0.56)
1
(0.11)
3
(0.33)
Attribute1 (A1) Attribute 2 (A2) Attribute 3(A3)
Level11
(L11)
Level12
(L12)
Level13
(L13)
Level21
(L21)
Level22
(L22)
Level23
(L23)
Level31
(L31)
Level32
(L32)
Level33
(L33)
Individual
1
2
(0.20)
3
(0.30)
5
(0.50)
1
(0.13)
4
(0.50)
3
(0.38)
2
(0.18)
5
(0.45)
4
(0.36)
Individual
2
2
(0.33)
3
(0.50)
1
(0.17)
3
(0.33)
4
(0.44)
2
(0.22)
3
(0.30)
4
(0.40)
3
(0.30)
Individual
3
4
(0.36)
2
(0.18)
5
(0.45)
4
(0.44)
3
(0.33)
2
(0.22)
5
(0.56)
1
(0.11)
3
(0.33)
Table 4.  Utility of different products
A1 A2 A3 Individual 1 Individual 2 Individual 3
Product 1 L13 L23 L31 1.06 0.69 1.23
Competitive product 1 L12 L22 L33 1.16 1.24 0.84
Competitive product 2 L11 L23 L32 1.03 0.95 0.69
Competitive product 3 L13 L22 L31 1.18 0.91 1.34
Probability PROBi1 0.24 0.19 0.30
Demand D1
(S=300)
73
A1 A2 A3 Individual 1 Individual 2 Individual 3
Product 1 L13 L23 L31 1.06 0.69 1.23
Competitive product 1 L12 L22 L33 1.16 1.24 0.84
Competitive product 2 L11 L23 L32 1.03 0.95 0.69
Competitive product 3 L13 L22 L31 1.18 0.91 1.34
Probability PROBi1 0.24 0.19 0.30
Demand D1
(S=300)
73
Table 5.  Control parameters of RICA
Control parameters Levels
$ N_{pop} $ (Npop): Number of countries 40, 50, 60
$ N_{imp} $ (Nimp): Number of imperialists 8, 10, 12
$ \xi $ (Xi): Weight coefficient for colony profit 0.1, 0.2, 0.3
$ \rho $ (CORA): Cooling rate 0.6, 0.7, 0.8
$ U $ (UN): The number of times for which the optimal profit remains unchanged 10, 12, 15
$ \varepsilon $ (epsilon): A pre-given threshold 0.0001, 0.0005, 0,001
Control parameters Levels
$ N_{pop} $ (Npop): Number of countries 40, 50, 60
$ N_{imp} $ (Nimp): Number of imperialists 8, 10, 12
$ \xi $ (Xi): Weight coefficient for colony profit 0.1, 0.2, 0.3
$ \rho $ (CORA): Cooling rate 0.6, 0.7, 0.8
$ U $ (UN): The number of times for which the optimal profit remains unchanged 10, 12, 15
$ \varepsilon $ (epsilon): A pre-given threshold 0.0001, 0.0005, 0,001
Table 6.  Parameters of the proposed problem
Parameter Value Min Max
$ M $ : Number of components of each product type (or number of machines in each cell) 3
$ C $ : Number of cells 3
$ A $ : Number of attributes of each product type 9
$ L $ : Number of levels of each attribute 3
$ J $ : Number of company's product types 8
$ J $$ ^{'} $ : Number of competitive product types for each company's product type 5
$ I $ : Number of sample consumers 100
$ S $ : The size of represented population in the market 1000
$ \phi $ : Fixed cost per unit time 3
$ p $$ _{jsa} $ : Price of an attribute's level 10 20
$ h $$ _{jsa} $ : Material cost of an attribute's level 1 3
$ k $$ _{jsa} $ : Processing cost of an attribute's level 1 2
$ {\theta _j} $ : Material handling cost each time 25 40
$ {\Omega _j} $ : Unit assembly cost of each product type 2 5
$ {\tau _{jsa}} $ : Processing time of an attribute's level 3 8
$ \varphi $ : Preference score for an attribute's level 1 5
Parameter Value Min Max
$ M $ : Number of components of each product type (or number of machines in each cell) 3
$ C $ : Number of cells 3
$ A $ : Number of attributes of each product type 9
$ L $ : Number of levels of each attribute 3
$ J $ : Number of company's product types 8
$ J $$ ^{'} $ : Number of competitive product types for each company's product type 5
$ I $ : Number of sample consumers 100
$ S $ : The size of represented population in the market 1000
$ \phi $ : Fixed cost per unit time 3
$ p $$ _{jsa} $ : Price of an attribute's level 10 20
$ h $$ _{jsa} $ : Material cost of an attribute's level 1 3
$ k $$ _{jsa} $ : Processing cost of an attribute's level 1 2
$ {\theta _j} $ : Material handling cost each time 25 40
$ {\Omega _j} $ : Unit assembly cost of each product type 2 5
$ {\tau _{jsa}} $ : Processing time of an attribute's level 3 8
$ \varphi $ : Preference score for an attribute's level 1 5
Table 7.  Performance comparison between RICA, ICASA and GA for impact parameters
$C=6, A=5, L=5$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$M$ 6 398720 335469 322124 19 24 130
12 607478 517620 489546 17 24 203
18 797091 673714 657481 18 21 353
24 1010046 860332 819408 17 23 519
$M=14, C=5, L=5$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$A$ 3 1371713 1201124 1156277 14 19 308
6 1000975 863354 829463 16 21 344
9 963418 837029 796480 15 21 433
12 978119 831539 791116 18 24 593
$M=12, C=5, A=10$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$L$ 3 459945 402027 388192 14 18 176
10 916115 788887 741580 16 24 283
17 1343335 1154694 1104538 16 22 418
24 1601637 1344834 1285988 19 25 819
$M=17, A=7, L=3$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$C$ 3 1304929 1156539 1111787 13 17 294
18 1427445 1198392 1155033 19 24 541
13 1403110 1178416 1137835 19 23 813
18 1421519 1222470 1173408 16 21 801
$C=6, A=5, L=5$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$M$ 6 398720 335469 322124 19 24 130
12 607478 517620 489546 17 24 203
18 797091 673714 657481 18 21 353
24 1010046 860332 819408 17 23 519
$M=14, C=5, L=5$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$A$ 3 1371713 1201124 1156277 14 19 308
6 1000975 863354 829463 16 21 344
9 963418 837029 796480 15 21 433
12 978119 831539 791116 18 24 593
$M=12, C=5, A=10$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$L$ 3 459945 402027 388192 14 18 176
10 916115 788887 741580 16 24 283
17 1343335 1154694 1104538 16 22 418
24 1601637 1344834 1285988 19 25 819
$M=17, A=7, L=3$ $\mathop {\bar V}\nolimits_{RICA} $ $\mathop {\bar V}\nolimits_{ICASA} $ $\mathop {\bar V}\nolimits_{GA} $ $\mathop {\Delta \bar V}\nolimits_{ICASA}^{RICA} (\%)$ $\mathop {\Delta \bar V}\nolimits_{GA}^{RICA} (\%)$ $CPU$ (s)
$C$ 3 1304929 1156539 1111787 13 17 294
18 1427445 1198392 1155033 19 24 541
13 1403110 1178416 1137835 19 23 813
18 1421519 1222470 1173408 16 21 801
Table 8.  Statistical t-test results from SPSS for samples of entries 1-16
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