Optimasi Tarif Kereta Bandara Soekarno-Hatta dengan Model Permintaan Berbasis Discrete Choice Experiment
DOI:
https://doi.org/10.5614/jts.2021.28.1.9Keywords:
Bayesian, Cannibalization, Discrete-choice experiment, Randomized first choiceAbstract
AbstrakPenelitian ini bertujuan menentukan tarif optimal Kereta Bandara Soekarno Hatta dengan fungsi permintaan yang diturunkan dengan pendekatan discrete-choice experiment. Fungsi permintaan diperoleh dengan memprediksi pilihan setiap individu pada beberapa tingkat harga yang berbeda, mengagregasikannya, dan kemudian menginterpolasikannya sehingga diperoleh fungsi yang kontinyu dan differentiable. Pilihan setiap individu diprediksi dari data utilitas individual menggunakan simulasi randomized first choice, sementara interpolasi fungsi permintaan dilakukan menggunakan cubic spline. Utilitas individual diestimasi dari data stated-preference berbentuk choice menggunakan pendekatan Bayesian. Dengan membatasi dua kelas tarif, harga ditentukan dengan mempertimbangkan kanibalisasi antar kelas tarif dan memperhatikan profitabilitas jangka panjang. Formulasi masalah optimasi yang diperoleh berbentuk nonlinear integer programming dengan fungsi tujuan polinomial orde empat yang parameternya dipengaruhi oleh nilai variabel keputusan. Ruang solusi yang tidak terlalu luas memungkinkan untuk memperoleh solusi dengan enumerasi, di mana diperoleh tarif optimal sebesar Rp70.000 untuk kelas tarif 1 di mana layanan Kereta Bandara dibundle dengan diskon angkutan taksi berbasis aplikasi, dan Rp67.000 untuk kelas tarif 2 yang berupa layanan Kereta Bandara saja. Dengan tarif tersebut diperkirakan akan diperoleh kontribusi total sebesar Rp375,27 milyar per tahun.
Kata-kata Kunci: Bayesian, kanicalisasi, discrete-choice experiment, randomized first choice
AbstractThis research aims to determine optimal prices for the Soekarno-Hatta Airport Shuttle Train services in which the demand function is derived using the discrete-choice experiment approach. The demand function is obtained by predicting and aggregating individual choices at several price levels, followed by interpolating the results to obtain a continuous and differentiable function. Individual choices are predicted from individual utility data using the randomized first choice simulation, while the interpolation is conducted using cubic splines. Inidividual utilities are estimated choice stated-preference data using Bayesian approach. By assuming two fare-classes, optimal prices are determined by considering cannibalization between fare-classes and operator?s long-term profitability. The resulted optimization formulation is a nonlinear integer programming problem with quartic polynomial objective function whose coefficients depend on the value of the decision variables. Since the solution space is relatively small, optimal prices can be obtained using enumeration. The optimal prices are Rp70,000 for fare-class 1 where the sevice is bundled with a discount on the app-based taxi service, and Rp67,000 for fare-class 2 which provides shuttle train only. The annual total contribution from such pricing policy is estimated to be Rp375.27 billion.
Keywords: Bayesian, cannibalization, discrete-choice experiment, randomized first choice.
References
Ahmadi, A., Olshevsky, A., Parrilo, P.A., Tsitsiklis, J.N., 2013, NP-Hardness of Deciding Convexity of Quartic Polynomials and Related Problems, Mathematical Programming, Vol. 137, No. 1-2, 453-376.
Chuang, H.M., Chu, C.P., Niu, W.F., 2010, A Study on Revenue Management of Taiwan High Speed Railway, Proceedings of the International Conference on Industrial Engineering and Engineering Management (IEEM), Macau, China, 7-10 Desember 2010.
Cirillo, C., 2011, Passenger Demand Model for Railway Revenue Management, Final Report, The Thomas D. Larson Pennsylvania Transportation Institute, USA: The Pennsylvania State University.
Hair Jr., J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2014, Multivariate Data Analysis, Pearson Education Limited.
Hayes, B.E., 2008, Measuring Customer Satisfaction and Loyalty: Survey Design, Use, and Statistical Analysis Methods, ASQ Quality Press.
Hetrakul, P., Cirillo, C., 2014, A Latent Class Choice Based Model System for Railway Optimal Pricing and Seat Allocation, Transportation Research Part E, Vol. 61, 68-83.
Huber, J., Orme, B.K., Miller, R., 2007, Dealing with Product Similarity in Conjoint Simulations. Dalam: Gustafsson, A., Herrmann, A., Huber, F. (Editor), Conjoint Measurement: Methods and Applications, Springer.
Orme, B.K., 2020, Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. Research Publishers.
Orme, B. K., Chrzan, K., 2017, Becoming an Expert in Conjoint Analysis: Choice Modeling for Pros, Sawtooth Software, Inc.
Phillips, R.L., 2005, Pricing and Revenue Optimization, Stanford University Press.
Qian, B-Y., Shuai, B., 2014, Revenue Management for Dedicated Passenger Line Based on Passenger Preference Order, Information Technology Journal, Vol. 13, No. 13, 2152-2158.
Sato, K., Sawaki, K., 2012, Dynamic Pricing of High-Speed Rail with Transport Competition, Journal of Revenue & Pricing Management, Vol. 11, No. 5, 548-559.
Train, K.E., 2003, Discrete Choice Methods with Simulation, Cambridge University Press.
Wang, X., Wang, H., Zhang, X., 2016, Stochastic Seat Allocation Models for Passenger Rail Transportation Under Customer Choice, Transportation Research Part E: Logistics and Transportation Review, Vol. 96, 95-112.
Wang Y., Lan B.X., Zhang L., 2012, A Revenue Management Model for High-Speed Railway. Dalam: Ni, Y.Q., Ye, X.W. (Editor), Proceedings of the 1st International Workshop on High-Speed and Intercity Railways, Lecture Notes in Electrical Engineering, Vol 147.
Wolberg, G., Alfy, I., 2002, An Energy-Minimization Framework for Monotonic Cubic Spline Interpolation, Journal of Computational and Applied Mathematics, Vol. 143, 145-188.
Xiaoqiang, Z., Lang, M., Jin, Z., 2017, Dynamic Pricing for Passenger Groups of High-Speed Rail Transportation, Journal of Rail Transport Planning & Management, Vol. 6, No. 4, 346-356.
Yuan, W., Nie, L., 2020, Optimization of Seat Allocation with Fixed Prices: An Application of Railway Revenue Management in China, PLOS ONE, Vol. 15, No. 4: e0231706. https://doi.org/10.1371/journal.pone.0231706. Diakses 25 Juli 2020.
