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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Almira Nurchawilah
Universitas Informatika Dan Bisnis Indonesia
Computer Science & IT

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REINFORCEMENT LEARNING-BASED DYNAMIC PRICING IN A STOCHASTIC DEMAND–SUPPLY ENVIRONMENT Nur Alamsyah; Budiman; Almira Nurchawilah; Wala Erpurini
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8286

Abstract

Dynamic pricing in ride-sharing platforms must balance revenue generation with stable pricing decisions under changing demand and supply. This study aims to develop and evaluate a reinforcement learning-based dynamic pricing policy that maximizes expected revenue while reducing abrupt policy-level price adjustments. A stochastic contextual environment was constructed from 1,000 historical ride records and evaluated using a leakage-safe 70/15/15 train-validation-test split. The agent was trained with Proximal Policy Optimization (PPO) using five discrete price adjustments from -10% to +10%. Expected revenue was combined with a multiplier-based stability penalty, where stability was measured from changes in the price multiplier rather than nominal price variation across heterogeneous rides. Across 30 paired test episodes, the PPO policy achieved a cumulative reward of 103,316.25 +/- 3,243.99 and expected revenue of 103,449.18 +/- 3,241.27, significantly exceeding static pricing (p < 0.001). Relative to rule-based surge pricing, PPO produced statistically indistinguishable cumulative reward (p = 0.808) while reducing multiplier volatility by 24.83%, mean absolute multiplier change by 24.21%, and action switch rate by 10.97% (all p < 0.001). These results indicate that PPO can preserve near-surge revenue while producing smoother dynamic pricing decisions within the simulated environment.
Co-Authors Budiman Nur Alamsyah Wala Erpurini