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Dynamic product planning for online service platform coordinating with service agents and operations resource providers: a three-level optimization approach

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Abstract

Product planning or product line design is critical for online service platform operations nowadays. There are limited studies on systematic optimization of service product planning (SPP) in light of emerging trend of online service platform that leverages upon multiple service agents and numerous service operations resource providers through outsourcing. This may be partially due to difficulties in quantitative optimization of service operations and complexity of service product fulfillment that are very different from those physical products in the manufacturing sector. The fulfillment of service products involves a complicated planning process that entails a dynamic interactive optimization problem involving multiple decision-makers at multiple levels of abstraction. This paper proposes a quantitative decision-making method with three-level leader-follower structure to deal with the dynamic SPP problem for online service platform to coordinate with service agents and their resource providers. The proposed solution framework includes designing a reasonable optimization structure, defining decision variables of three-level decision-makers, and establishing a 0–1 mixed three-level optimization (TLO) model for coordinated optimization of three decision-makers. A case study of applying the dynamic SPP method to the tourism industry is reported to illustrate the feasibility and potential.

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Abbreviations

\({U}_{kl}\) :

The part-worth utility of the l-th attribute level of the k-th attribute, \(l=1,\dots ,{L}_{k};k=1,\dots ,K\)

\({W}_{jk}\) :

Utility weight among attributes, j \(=1,\dots ,J;k=1,\dots ,K\)

\({Q}_{i}\) :

Market share of the i-th product line. i \(=1,\dots ,I\)

\({d}_{ij}\) :

The demand of the j-th service product of the i-th service product line for the online service platform

\({d}_{hf{g}^{E}}\) :

The product demand of the \({g}^{E}\)-th existing service product of the f-th existing service product family of the h-th service agent

\({d}_{hj}\) :

Product demand of the j-th new service product of the h-th service agent

\(\gamma\) :

The commission rate of online service platform

\(\vartheta\) :

Price coefficient of existing service products for service agents

\(\omega\) :

Price coefficient of new service products for service agents

\({\lambda }_{h}\) :

Development prospect coefficient of the h-th service agent. h \(=1,\dots ,H\)

\({D}_{ij}\) :

The demand of the j-th service product of the i-th service product line for service agents

\(\mu\) :

A positive scaling parameter of the probability choice model

\({\sigma }_{j}\) :

Parameter indicates whether the service products of online service platform can be matched by the existing service products of service agents

\({\beta }_{hf{g}^{E}}\) :

Sensitivity of demand to price changes for \({g}^{E}\)-th existing service product of the f-th product family of the h-th service agent. \(h=1,\dots ,H;f=1,\dots ,{F}_{h};{g}^{E}=1,\dots ,{G}_{hf}^{E}\)

\({\beta }_{hj}\) :

Sensitivity of demand to price changes for the j-th new service product of the h-th service agent. \(h=1,\dots ,H;j=1,\dots ,\widehat{J}=\left\{j|{\sigma }_{j}=1,j=1,\dots ,J\right\}\)

\({\beta }_{hsmn}\) :

Sensitivity of demand to price changes for the n-th basic resource module instance of the m-th basic resource module of the s-th resource provider of the h-th service agent. \(h=1,\dots ,H; s=1,\dots ,{S}_{h}; m={\sum }_{h=1}^{H}{M}_{h}^{E}+1,\dots , M;n={1,\dots , N}_{m}\)

\({c}_{hmn}\) :

Unit cost for the n-th basic resource module instance of the m-th basic resource module of the h-th service agent. \(h=1,\dots ,H;m=1,\dots ,{M}_{hf}^{E};n=1,\dots ,{N}_{m}^{E}\)

\({\tau }_{kl}^{hf{g}^{E}}\) :

Attribute configuration for the existing service products of service agents. \({\tau }_{kl}^{hf{g}^{E}}=1\) indicates that the existing service product \({P}_{hf{g}^{E}}^{S}\) contains the l-th attribute level of the k-th attribute, and \({\tau }_{kl}^{hf{g}^{E}}=0\) otherwise. \(h=1,\dots ,H;f=1,\dots ,{F}_{h};{g}^{E}=1,\)…\(,{G}_{hf}^{E};k=1,\dots ,K;l=1,\dots ,{L}_{k}\)

\({\zeta }_{mn}^{hf{g}^{E}r}\) :

Resource module configuration for the existing service products of service agents. \({\varepsilon }_{mn}^{hf{g}^{E}r}=1\) indicates that the r-th compound resource module of the \({g}^{E}\)-th existing resource combination at the f-th existing resource family for the h-th service agent contains the n-th basic resource module instance of the m-th basic resource module, and \({\varepsilon }_{mn}^{hf{g}^{E}r}=0\) otherwise. \(h=1,\dots ,H;f=1,\dots ,{F}_{h};{g}^{E}=1,\)…\(,{G}_{hf}^{E};r=1,\dots ,{R}_{hf}^{E};m=1,\dots ,{M}_{hf}^{E};n=1,\dots ,{N}_{m}^{E}\)

\({\xi }_{mn}^{hf{r}^{C}}\) :

\({\xi }_{mn}^{hf{r}^{C}}=1\) indicates that the rc th common resource platform of the f-th existing resource family for the h-th service agent contains the n-th basic resource module instance of the m-th basic resource module, and \({\xi }_{mn}^{hf{r}^{C}}=0\) otherwise. \(h=1,\dots ,H;f=1,\dots ,{F}_{h};{r}^{C}=1,\)…\(,{R}_{hf}^{C};m=1,\dots ,{M}_{hf}^{E};n=1,\dots ,{N}_{m}^{E}\). The number of base modules contained in the common resource platform of the existing resource family is \({M}^{hf{r}^{C}}\)

\({\theta }_{hmn}^{kl}\) :

The supporting relationship between service attributes and basic resource module instances, in which \({\theta }_{hmn}^{kl}=1\) indicates that the l-th attribute level of the k-th attribute is supported by the n-th basic resource module instance of the m-th basic resource module of the h-th service agent, and \({\theta }_{hmn}^{kl}=0\) otherwise.\(h=1,\dots ,H;m=1,\dots ,{\sum }_{h=1}^{H}{\sum }_{f=1}^{{F}_{h}}{M}_{hf}^{E}\dots , M;n={1,\dots ,N}_{m};k=1,\dots ,K;l=1,\dots ,{L}_{k}\)

\({d}_{hsmn}\) :

The demand of basic resource modules of resource providers

\({c}_{hsmn}\) :

Unit cost for the n-th basic resource module instance of the m-th basic resource module of the s-th resource provider of the h-th service agent.\(h=1,\dots ,H;s=1,\dots ,S;m={\sum }_{h=1}^{H}{\sum }_{f=1}^{{F}_{h}}{M}_{hf}^{E}+1,\dots , M;n={1,\dots , N}_{m}\)

References

  • Alguacil N, Delgadillo A, Arroyo JM (2014) A trilevel programming approach for electric grid defense planning. Comput Oper Res 41:282–290

    MathSciNet  Google Scholar 

  • Anandalingam G (1988) A mathematical programming model of decentralized multi-level systems. J Op Res Soc 39(11):1021–1033

    Google Scholar 

  • Aydin R, Kwong CK, Ji P (2016) Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products. J Cleaner Prod 114:286–298

    Google Scholar 

  • Bard JF (1984) An investigation of the linear three level programming problem. IEEE Trans Syst Man Cybern 5:711–717

    MathSciNet  Google Scholar 

  • Bard JF (1998) Practical bilevel optimization: applications and algorithms in series: nonconvex optimization and its application. Springer

    Google Scholar 

  • Bracken J, McGill JT (1974) A method for solving mathematical programs with nonlinear programs in the constraints. Op Res 22(5):1097–1101

    MathSciNet  Google Scholar 

  • Cao K, Yang Z (2016) A study of e-commerce adoption by tourism websites in China. J Destin Mark Manag 5(3):283–289

    Google Scholar 

  • Cao Y, Luo XG, Kwong CK, Tang JF, Zhou W (2012) Joint optimization of product family design and supplier selection under multinomial logit consumer choice rule. Concurr Eng 20(4):335–347

    Google Scholar 

  • Chen X, Luo Z, Wang X (2019) Commission pricing strategy on online retail platforms: power and dependence in triad. IEEE Trans Eng Manage 45:1–12

    Google Scholar 

  • Deng S, Aydin R, Kwong CK, Huang Y (2014) Integrated product line design and supplier selection: a multi-objective optimization paradigm. Comput Ind Eng 70:150–158

    Google Scholar 

  • Du G, Zhang Y, Liu X, Jiao RJ, Xia Y, Li Y (2019) A review of leader-follower joint optimization problems and mathematical models for product design and development. Int J Adv Manuf Technol 103(9–12):3405–3424

    Google Scholar 

  • Esmaeili M, Gamchi NS, Asgharizadeh E (2014) Three-level warranty service contract among manufacturer, agent and customer: A game-theoretical approach. Eur J Oper Res 239(1):177–186

    MathSciNet  Google Scholar 

  • Goswami M, Daultani Y, Tiwari MK (2017) An integrated framework for product line design for modular products: product attribute and functionality-driven perspective. Int J Prod Res 55(13):3862–3885

    Google Scholar 

  • Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Mark 54(4):3–19

    Google Scholar 

  • Han J, Zhang G, Hu Y, Lu J (2016) A solution to bi/tri-level programming problems using particle swarm optimization. Inf Sci 370:519–537

    Google Scholar 

  • He P, He Y, Xu H, Zhou L (2019) Online selling mode choice and pricing in an o2o tourism supply chain considering corporate social responsibility. Electron Commer Res Appl 38:100894

    Google Scholar 

  • Imran S, van Husen C, Haeberle D (2018) A service design framework for the initial phase of service development. Procedia CIRP 73:120–123

    Google Scholar 

  • Jiao J, Zhang Y (2005) Product portfolio planning with customer-engineering interaction. IIE Trans 37(9):801–814

    Google Scholar 

  • Jiao J, Ma Q, Tseng MM (2003) Towards high value-added products and services: mass customization and beyond. Technovation 23(10):809–821

    Google Scholar 

  • Kaul A, Rao VR (1995) Research for product positioning and design decisions: an integrative review. Int J Res Mark 12(4):293–320

    Google Scholar 

  • Lee JY, Fang E, Kim JJ, Li X, Palmatier RW (2018) The effect of online shopping platform strategies on search, display, and membership revenues. J Retail 94(3):247–264

    Google Scholar 

  • Li Y, Chu F, Chu C, Zhu Z (2019) An efficient three-level heuristic for the large-scaled multi-product production routing problem with outsourcing. Eur J Oper Res 272(3):914–927

    MathSciNet  Google Scholar 

  • Ling L, Guo X, Yang C (2014) Opening the online marketplace: An examination of hotel pricing and travel agency on-line distribution of rooms. Tour Manage 45:234–243

    Google Scholar 

  • Liu W, Yan X, Wei W, Xie D (2019) Pricing decisions for service platform with provider’s threshold participating quantity, value-added service and matching ability. Transp Res Part E: Logistics Transp Rev 122:410–432

    Google Scholar 

  • Liu X, Du G, Jiao RJ (2017) Bilevel joint optimization for product family architecting considering make-or-buy decisions. Int J Prod Res 55(20):5916–5941

    Google Scholar 

  • Liu Y, Zhang X, Zhang H, Zha X (2021) Competing tourism service provider introduction strategy for an online travel platform with demand information sharing. Electron Commer Res Appl 49:101084

    Google Scholar 

  • Long Y, Shi P (2017) Pricing strategies of tour operator and online travel agency based on cooperation to achieve O2O model. Tour Manage 62:302–311

    Google Scholar 

  • Lu J, Han J, Hu Y, Zhang G (2016) Multilevel decision-making: A survey. Inf Sci 346:463–487

    MathSciNet  Google Scholar 

  • Luo XG, Kwong CK, Tang JF, Deng SF, Gong J (2011) Integrating supplier selection in optimal product family design. Int J Prod Res 49(14):4195–4222

    Google Scholar 

  • Lv X, Li N, Xu X, Yang Y (2020) Understanding the emergence and development of online travel agencies: a dynamic evaluation and simulation approach. Internet Res 30(6):1783–1810

    Google Scholar 

  • Ma S, Dong P, Cai X (2020) Hierarchical optimization for green product line design. IEEE Trans Eng Manage 56:1–9

    Google Scholar 

  • McKercher B (2016) Towards a taxonomy of tourism products. Tour Manage 54:196–208

    Google Scholar 

  • Miao C, Du G, Jiao RJ, Zhang T (2017) Coordinated optimisation of platform-driven product line planning by bilevel programming. Int J Prod Res 55(13):3808–3831

    Google Scholar 

  • Min SR, Lee SM (2020) A study on the behavior of the user according to the distribution development of online travel agency. J Distrib Sci 18(6):25–35

    Google Scholar 

  • Moon SK, Shu J, Simpson TW, Kumara SR (2010) A module-based service model for mass customization: service family design. IIE Trans 43(3):153–163

    Google Scholar 

  • Niu B, Li Q, Mu Z, Chen L, Ji P (2021) Platform logistics or self-logistics? Restaurants’ cooperation with online food-delivery platform considering profitability and sustainability. Int J Prod Econ 234:108064

    Google Scholar 

  • Oliveto PS, He J, Yao X (2007) Time complexity of evolutionary algorithms for combinatorial optimization: a decade of results. Int J Autom Comput 4(3):281–293

    Google Scholar 

  • Qin X, Liu Z, Tian L (2020) The strategic analysis of logistics service sharing in an e-commerce platform. Omega 92:102153

    Google Scholar 

  • Si B, Gao Z (2007) Optimal model for passenger transport pricing under the condition of market competition. J Transp Syst Eng Inf Technol 7(1):72–78

    Google Scholar 

  • Steiner WJ, Hruschka H (2002) A probabilistic one-step approach to the optimal product line design problem using conjoint and cost data. Rev Market Sci 87:441

    Google Scholar 

  • Thakur LS, Nair SK, Wen KW, Tarasewich P (2000) A new model and solution method for product line design with pricing. J Op Res Soc 51(1):90–101

    Google Scholar 

  • Wang D, Du G, Jiao RJ, Wu R, Yu J, Yang D (2016) A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration. Int J Prod Econ 172:1–18

    Google Scholar 

  • White DJ (1997) Penalty function approach to linear trilevel programming. J Optim Theory Appl 93(1):183–197

    MathSciNet  Google Scholar 

  • Wu J, Du G, Jiao RJ (2021) Optimal postponement contracting decisions in crowdsourced manufacturing: a three-level game-theoretic model for product family architecting considering subcontracting. Eur J Op Res 291(2):722–737

    MathSciNet  Google Scholar 

  • Yang D, Jiao RJ, Ji Y et al (2015) Joint optimization for coordinated configuration of product families and supply chains by a leader-follower Stackelberg game. Eur J Op Res 246(1):263–280

    MathSciNet  Google Scholar 

  • Yang S, Huang GQ, Song H, Liang L (2009) Game-theoretic approach to competition dynamics in tourism supply chains. J Travel Res 47(4):425–439

    Google Scholar 

  • Yu E, Sangiorgi D (2018) Service design as an approach to implement the value cocreation perspective in new service development. J Serv Res 21(1):40–58

    Google Scholar 

  • Zha Y, Zhang J, Yue X, Hua Z (2015) Service supply chain coordination with platform effort-induced demand. Ann Op Res 235(1):785–806

    MathSciNet  Google Scholar 

  • Zhang G, Lu J, Montero J, Zeng Y (2010) Model, solution concept, and Kth-best algorithm for linear trilevel programming. Inf Sci 180(4):481–492

    Google Scholar 

  • Zhang X, Li G, Liu M, Sethi SP (2021a) Online platform service investment: A bane or a boon for supplier encroachment. Int J Prod Econ 235:108079

    Google Scholar 

  • Zhang X, Liu Y, Dan B (2021b) Cooperation strategy for an online travel platform with value-added service provision under demand uncertainty. Int Trans Oper Res 28(6):3416–3436

    MathSciNet  Google Scholar 

  • Zhao X, Lin W, Cen S, Zhu H, Duan M, Li W, Zhu S (2021) The online-to-offline (O2O) food delivery industry and its recent development in China. Eur J Clin Nutr 75(2):232–237

    Google Scholar 

  • Zhao Y, Alfandari L (2020) Design of diversified package tours for the digital travel industry: a branch-cut-and-price approach. Eur J Oper Res 285(3):825–843

    MathSciNet  Google Scholar 

  • Zhao Y, Song P, Feng F (2019) What are the revenue implications of mobile channel visits? Evidence from the online travel agency industry. Electron Commer Res Appl 36(1):100865

    Google Scholar 

  • Zhou K, Du G, Jiao RJ (2022) Personalized service product family design optimization considering crowdsourced service operations. Comput Ind Eng 166:107973

    Google Scholar 

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Authors and Affiliations

  1. College of Management and Economics, Tianjin University, Tianjin, China

    Kaihong Zhou & Gang Du

  2. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Roger J. Jiao

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  1. Kaihong Zhou
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  2. Gang Du
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  3. Roger J. Jiao
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Correspondence to Gang Du or Roger J. Jiao.

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This research is partially supported by National Natural Science Foundation of China under Project Number 71371132.

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Zhou, K., Du, G. & Jiao, R.J. Dynamic product planning for online service platform coordinating with service agents and operations resource providers: a three-level optimization approach. Flex Serv Manuf J 36, 36–70 (2024). https://doi.org/10.1007/s10696-022-09470-x

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