In the era of mass customization, designing optimal products is one of the most critical decision-making for a company to stay competitive. More and more customers like customized products, which will bring challenges to the product line design and the production. If a company adopts customers’ favorite levels, this may lead to lower product reliability, or incompatibility among the components that make up the product. Moreover, it is worth outsourcing certain attribute levels to reduce production cost, but customers may dislike these levels because of their delivery delay. If managers consider the compatibility issue, the quality issue, outsource determination, and the delivery due date in the product design and production stages, they will avoid unreasonable product configuration and many unnecessary expenses, thereby bringing benefits to the company. To solve this complicated problem, we establish a nonlinear program that maximizes Per-capita-contribution Margin considering Reliability Penalty. Since the integrated product line design and production problem is NP-hard, we propose an improved Discrete Imperialist Competitive Algorithm (DICA). The proposed DICA is compared with genetic algorithm (GA) and simulated annealing (SA) through extensive numerical experiment, and the results show that DICA displays 6%~17% and 5%~14% improvement over GA and SA in terms of solution quality, respectively.
Keywords: Mass customization, product line optimization, imperialist competitive algorithm, compatibility, product reliability
@article{RO_2021__55_6_3773_0,
author = {Liu, Chunfeng and Yang, Xiao and Wang, Jufeng},
title = {Optimization of product line considering compatibility and reliability \protect\emph{via} discrete imperialist competitive algorithm},
journal = {RAIRO. Operations Research},
pages = {3773--3795},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {6},
doi = {10.1051/ro/2021173},
mrnumber = {4353559},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021173/}
}
TY - JOUR AU - Liu, Chunfeng AU - Yang, Xiao AU - Wang, Jufeng TI - Optimization of product line considering compatibility and reliability via discrete imperialist competitive algorithm JO - RAIRO. Operations Research PY - 2021 SP - 3773 EP - 3795 VL - 55 IS - 6 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021173/ DO - 10.1051/ro/2021173 LA - en ID - RO_2021__55_6_3773_0 ER -
%0 Journal Article %A Liu, Chunfeng %A Yang, Xiao %A Wang, Jufeng %T Optimization of product line considering compatibility and reliability via discrete imperialist competitive algorithm %J RAIRO. Operations Research %D 2021 %P 3773-3795 %V 55 %N 6 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021173/ %R 10.1051/ro/2021173 %G en %F RO_2021__55_6_3773_0
Liu, Chunfeng; Yang, Xiao; Wang, Jufeng. Optimization of product line considering compatibility and reliability via discrete imperialist competitive algorithm. RAIRO. Operations Research, Tome 55 (2021) no. 6, pp. 3773-3795. doi: 10.1051/ro/2021173
[1] , Compartmental diffusion modeling: describing customer heterogeneity & communication network to support decisions for new product introductions. Phys. A Stat. Mech. App. 536 (2019) 1–18.
[2] and , Optimal product design using a colony of virtual ants. Eur. J. Oper. Res. 176 (2007) 498–520. | Zbl | DOI
[3] and , Sustainable product line design considering a multi-lifecycle approach. Resour. Conserv. Recycl. 149 (2019) 727–737. | DOI
[4] , and , Design optimization of real world steel space frames using artificial bee colony algorithm with levy flight distribution. Adv. Eng. Softw. 92 (2016) 1–14. | DOI
[5] , , and , Optimizing product line designs: Efficient methods and comparisons. Manage. Sci. 54 (2008) 1544–1552. | Zbl | DOI
[6] and , Rank analysis of incomplete block designs: the method of paired comparisons. Biometrika 39 (1952) 324–345. | MR | Zbl
[7] , and , Imperial competitive algorithm with policy learning for the traveling salesman problem. Soft Comput. 21 (2015) 1–13.
[8] , , and , Design for mass customization: product variety vs. process variety. CIRP Ann. – Manuf. Technol. 60 (2011) 169–174. | DOI
[9] , and , Customer-oriented product collaborative customization based on design iteration for tablet personal computer configuration. Comput. Ind. Eng. 99 (2016) 474–486. | DOI
[10] and , Product-line length as a competitive tool. J. Econ. Manage. Strategy 14 (2005) 1–28. | DOI
[11] , , , , , and , An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inf. Fusion 48 (2019) 67–83. | DOI
[12] , and , The mass customization decade: an updated review of the literature. Int. J. Prod. Econ. 138 (2012) 14–25. | DOI
[13] , , and , Scheduling dual-objective stochastic hybrid flow shop with deteriorating jobs via bi-population evolutionary algorithm. IEEE Trans. Syst. Man Cybern. Syst. 50 (2020) 5037–5048. | DOI
[14] , , , , and , A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems. IEEE-CAA J. Autom. Sinica 6 (2019) 904–916. | DOI
[15] , and , AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE-CAA J. Autom. Sinica 7 (2020) 1026–1037. | DOI
[16] , , and , A hybrid imperialist competitive algorithm for the flexible job shop problem. In: Artificial Life and Computational Intelligence, edited by , and . Springer International Publishing, Cham (2016) 221–233.
[17] , and , An integrated framework for product line design for modular products: product attribute and functionality-driven perspective. Int. J. Prod. Res. 55 (2017) 3862–3885. | DOI
[18] and , Integrated component and supplier selection for a product family. Prod. Oper. Manage. 8 (2010) 163–182. | DOI
[19] , , , , and , State-transition simulated annealing algorithm for constrained and unconstrained multi-objective optimization problems. Appl. Intell. 10 (2020) 1–13.
[20] and , Optimal design of sales and maintenance under the renewable warranty. RAIRO-Oper. Res. 52 (2018) 529–542. | MR | Zbl | Numdam | DOI
[21] , , and , Scheduling flexible job-shops with transportation times: mathematical models and a discrete imperialist competitive algorithm. Appl. Math. Model. 41 (2017) 667–682. | MR | DOI
[22] , , , , and , 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. | MR | Zbl | DOI
[23] and , Optimal product design using conjoint analysis: computational complexity and algorithms. Eur. J. Oper. Res. 40 (1989) 186–195. | MR | Zbl | DOI
[24] and , Heuristics for product line design using conjoint analysis. Manage. Sci. 36 (1990) 1464–1478. | DOI
[25] and , A profit maximising product line optimization model under monopolistic competition. Int. J. Prod. Res. 53 (2015) 1584–1595. | DOI
[26] , and , Product design under multinomial logit choices: optimization of quality and prices in an evolving product line. Manuf. Serv. Oper. Manage. 22 (2020) 1–15.
[27] , , , and , An improved simulated annealing algorithm based on residual network for permutation flow shop scheduling. Complex Intell. Syst. 7 (2021) 1173–1183. | DOI
[28] and , Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing. Int. J. Comput. Intel. Syst. 9 (2016) 765–777. | DOI
[29] , and , Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm. Comput. Ind. Eng. 96 (2016) 162–179. | DOI
[30] , , and , Solving cell formation and task scheduling in cellular manufacturing system by discrete bacteria foraging algorithm. Int. J. Prod. Res. 54 (2016) 923–944. | DOI
[31] , and , Integrated bacteria foraging algorithm for cellular manufacturing in supply chain considering facility transfer and production planning. Appl. Soft Comput. 62 (2018) 602–618. | DOI
[32] , and , Reconfiguration of virtual cellular manufacturing systems via improved imperialist competitive approach. IEEE Trans. Autom. Sci. Eng. 16 (2019) 1301–1314. | DOI
[33] , and , A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design. J. Ind. Manage. Optim. (2021). DOI:10.3934/jimo.2021175. | MR
[34] , and , Optimisation of line configuration and balancing for reconfigurable transfer lines considering demand uncertainty. Int. J. Prod. Res. 59 (2021) 444–466. | DOI
[35] , Individual choice behavior: a theoretical analysis. J. Am. Stat. Assoc. 67 (1959) 1–15. | MR | Zbl
[36] , Product line design for consumer durables: an integrated marketing and engineering approach. J. Marketing Res. 48 (2011) 128–139. | DOI
[37] , , and , Optimisation of product family design with consideration of supply risk and discount. Res. Eng. Design 27 (2016) 37–54. | DOI
[38] , and , Trends towards distributed manufacturing systems and modern forms for their design. Proc. CIRP 33 (2015) 185–190. | DOI
[39] and , Management of product configuration conflicts to increase the sustainability of mass customization. Sustainability 12 (2020) 1–14. | DOI
[40] , and , The influence of mass customization strategy on configuration complexity of assembly systems. Proc. CIRP 33 (2015) 538–543. | DOI
[41] , and , Forecast of individual customer’s demand from a large and noisy dataset. Comput. Ind. Eng. 118 (2018) 33–43. | DOI
[42] and , Forecasting stock market price by using fuzzified choquet integral based fuzzy measures with genetic algorithm for parameter optimization. RAIRO-Oper. Res. 54 (2020) 597–614. | MR | Zbl | Numdam | DOI
[43] and , Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall Inc, New Jersey, USA (1982). | MR | Zbl
[44] and , From mass production to mass customization: Hindrance factors, structural inertia and transition hazard. Prod. Oper. Manage. 17 (2010) 385–396. | DOI
[45] and , Imperialistic competitive algorithm for solving a dynamic cell formation problem with production planning. In: Advanced Intelligent Computing Theories and Applications, edited by , , and . International Conference on Advanced Intelligent Computing Theories & Applications: Intelligent Computing. Springer, Berlin, Heidelberg (2010) 266–276. | DOI
[46] , , , and , Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism. Expert Syst. App. 42 (2014) 2323–2335. | DOI
[47] , , , , , , and , Social manufacturing for high-end apparel customization. IEEE-CAA J. Autom. Sinica 5 (2018) 489–500. | DOI
[48] , , and , Mass customization model in food industry using industry 4.0 standard with fuzzy-based multi-criteria decision making methodology. Adv. Mech. Eng. 10 (2018) 113–145. | DOI
[49] and , Determining the optimal customization levels, lead times, and inventory positioning in vertical product differentiation. Int. J. Prod. Econ. 221 (2020) 1–20. | DOI
[50] and , Product line design with vertical and horizontal consumer heterogeneity: the effect of distribution channel structure on the optimal quality and customization levels. Eur. J. Market. 55 (2020) 95–131. | DOI
[51] , and , Particle swarm optimization for optimal product line design. Int. J. Res. Market. 28 (2011) 13–22. | DOI
[52] , , and , 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. Ind. Marketing Manage. 42 (2013) 496–506. | DOI
[53] , , and , Fuzzy self-tuning differential evolution for optimal product line design. Eur. J. Oper. Res. 287 (2020) 1–9. | MR | Zbl | DOI
[54] , , and , Fuzzy self-tuning differential evolution for optimal product line design. Eur. J. Oper. Res. 287 (2020) 1161–1169. | MR | Zbl | DOI
[55] , , , and , Amalgamation of mass customisation and agile manufacturing concepts: the theory and implementation study in an electronics switches manufacturing company. Int. J. Prod. Res. 48 (2010) 2141–2164. | DOI
[56] , , and , Industry 4.0: A way from mass customization to mass personalization production. Adv. Manuf. 5 (2017) 311–320. | DOI
[57] , and , A hybrid simulated annealing for scheduling in dual resource cellular manufacturing system considering worker movement. Automatika 60 (2019) 172–180. | DOI
[58] , , , and , Social manufacturing: a paradigm shift for smart prosumers in the era of societies 5.0. IEEE Trans. Comput. Soc. Syst. 6 (2019) 822–829. | DOI
[59] , and , A hybrid probabilistic multiobjective evolutionary algorithm for commercial recommendation systems. IEEE Trans. Comput. Soc. Syst. 8 (2021) 589–598. | DOI
[60] , , , , , , and , From mind to products: towards social manufacturing and service. IEEE-CAA J. Autom. Sinica 5 (2018) 47–57. | DOI
[61] , , , , and , A quantitative model of universalization, serialization and modularization on equipment systems. Phys. A Stat. Mech. App. 508 (2018) 359–366. | DOI
[62] , and , Multiqueue scheduling of heterogeneous tasks with bounded response time in hybrid green IaaS clouds. IEEE Trans. Ind. Inf. 15 (2019) 5404–5412. | DOI
[63] , , and , Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds. IEEE-CAA J. Autom. Sinica 7 (2020) 1380–1393. | DOI
[64] , , , and , State-of-the-art review of customer to business (C2B) model. Comput. Ind. Eng. 132 (2019) 207–222. | DOI
[65] , and , Improved imperialist competitive algorithms for rebalancing multi-objective two-sided assembly lines with space and resource constraints. Int. J. Prod. Res. 58 (2020) 3589–3617. | DOI
Cité par Sources :
