1.
Groeneveld, J., Herrmann, J., Mollenhauer, N., Dreessen, L., Bessin, N., Schulze-Tast, J., Kastius, A., Huegle, J., Schlosser, R.: Self-Learning Agents for Recommerce Markets. Business & Information Systems Engineering 66 (4). 441–463 (2024).
2.
Schlosser, R., Gönsch, J.: Risk-Averse Dynamic Pricing using Mean-Semivariance Optimization. European Journal of Operational Research. 310 (1), 1151–1163 (2023).
3.
Schlosser, R., Chenavaz, R.: Joint Dynamic Pricing and Marketing-Mix Strategies for Revenue Management Applications with Stochastic Demand. International Transactions in Operational Research, accepted. (2023).
4.
Kastius, A., Schlosser, R.: Towards Transfer Learning for Revenue and Pricing Management. Operations Research Proceedings, OR2021. bll. 361–366 (2022).
5.
Kastius, A., Schlosser, R.: Dynamic Pricing under Competition using Reinforcement Learning. Journal of Revenue and Pricing Management. 21, 50–63 (2022).
6.
Schlosser, R.: Heuristic Mean Variance Optimization in Markov Decision Processes using State-Dependent Risk Aversion. IMA Journal of Management Mathematics. 33 (2), 181–199 (2022).
7.
Chenavaz, R., Klibi, W., Schlosser, R.: Dynamic Pricing with Reference Price Effects in Integrated Online and Offline Retailing. International Journal of Production Research. 60, 5854–5875 (2022).
8.
Schlosser, R., Kastius, A.: Stochastic Dynamic Pricing under Duopoly Competition with Mutual Strategy Adjustments. Operations Research Proceedings (OR 2021). bll. 367–372 (2022).
9.
Schlosser, R., Westphal, J., Pörschke, M., Maltenberger, T., Kaminsky, Y.: Self-Adaptive Agents in a Dynamic Pricing Duopoly: Competition, Collusion, and Risk Considerations. Springer Nature Computer Science. 3 (3), 1–17 (2022).
10.
Figge, F., Dimitrov, S., Schlosser, R., Chenavaz, R.: Does the circular economy fuel the throwaway society? The role of opportunity costs for products that lose value over time. Journal of Cleaner Production. 368 (133207), (2022).
11.
Schlosser, R., Kastius, A.: A Conceptual Framework for Studying Self-Learning Agents in Recommerce Markets. Operations Research Proceedings (OR 2022), to appear (2022).
12.
Kastius, A., Schlosser, R.: Multi-Agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information. Operations Research Proceedings (OR2022), to appear (2022).
13.
Schlosser, R., Chenavaz, R., Dimitrov, S.: Circular Economy: Joint Dynamic Pricing and Recycling Investments. International Journal of Production Economics. 108117, 1–13 (2021).
14.
Kaminsky, Y., Maltenberger, T., Pörschke, M., Westphal, J., Schlosser, R.: Pricing Competition in a Duopoly with Self-Adapting Strategies. 10th International Conference on Operations Research and Enterprise Systems (ICORES 2021). bll. 60–71 (2021).
15.
Schlosser, R.: Scalable Relaxation Techniques to Solve Stochastic Dynamic Multi-Product Pricing Problems with Substitution Effects. Journal of Revenue and Pricing Management. 20 (1), 54–65 (2021).
16.
Schlosser, R.: Risk-Sensitive Control of Markov Decision Processes: A Moment-Based Approach with Target Distributions. Computers and Operations Research. 123 (104997), 1–15 (2020).
17.
Schlosser, R.: Stochastic Dynamic Pricing with Waiting and Forward-Looking Consumers. Communications in Computer and Information Science (CCIS), Vol. 1162. bll. 47–69. Springer (2020).
18.
Schlosser, R., Richly, K.: Dynamic Pricing Competition with Unobservable Inventory Levels: A Hidden Markov Model Approach. Communications in Computer and Information Science. bll. 15–36. Springer (2019).
19.
Schlosser, R.: Data-Driven Stochastic Dynamic Pricing and Ordering. Operations Research Proceedings 2018. bll. 397–403 (2019).
20.
Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Automated Repricing and Ordering Strategies in Competitive Markets. AI Communications. 32, 15–29 (2019).
21.
Schlosser, R.: Stochastic Dynamic Pricing with Strategic Customers and Reference Price Effects. 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019. bll. 179–188 (2019).
22.
Schlosser, R., Richly, K.: Dynamic Pricing under Competition with Data-Driven Price Anticipations and Endogenous Reference Price Effects. Journal of Revenue & Pricing Management. 18, 451–464 (2019).
23.
Schlosser, R., Boissier, M.: Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach. KDD ’18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. bll. 705–714 (2018).
24.
Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Data-Driven Inventory Management and Dynamic Pricing Competition on Online Marketplaces. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2018). bll. 5856–5858 (2018).
25.
Schlosser, R., Richly, K.: Dynamic Pricing Strategies in a Finite Horizon Duopoly with Partial Information. 7th International Conference on Operations Research and Enterprise Systems, ICORES 2018. bll. 21–30 (2018).
26.
Schlosser, R.: Stochastic Dynamic Multi-Product Pricing under Competition. Operations Research Proceedings 2017. bll. 527–533 (2018).
27.
Schlosser, R., Boissier, M.: Optimal Repricing Strategies in a Stochastic Infinite Horizon Duopoly. Communications in Computer and Information Science (CCIS). bll. 129–150. Springer (2018).
28.
Schlosser, R., Boissier, M.: Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations. Computers and Operations Research. 100, 26–42 (2018).
29.
Schlosser, R., Boissier, M.: Optimal Price Reaction Strategies in the Presence of Active and Passive Competitors. Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES), Porto, Portugal. bll. 47–56 (2017).
30.
Schlosser, R.: Stochastic Dynamic Pricing and Advertising in Isoelastic Oligopoly Models. European Journal of Operational Research. 259, 1144–1155 (2017).
31.
Boissier, M., Schlosser, R., Podlesny, N., Serth, S., Bornstein, M., Latt, J., Lindemann, J., Selke, J., Uflacker, M.: Data-Driven Repricing Strategies in Competitive Markets: An Interactive Simulation Platform. Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17). bll. 355–357. ACM, New York, NY, USA (2017).
32.
Serth, S., Podlesny, N., Bornstein, M., Lindemann, J., Latt, J., Selke, J., Schlosser, R., Boissier, M., Uflacker, M.: An Interactive Platform to Simulate Dynamic Pricing Competition on Online Marketplaces. 21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017, Quebec City, QC, Canada, October 10-13, 2017. bll. 61–66. IEEE (2017).
33.
Schlosser, R.: Stochastic Dynamic Multi-Product Pricing with Dynamic Advertising and Adoption Effects. Journal of Revenue and Pricing Management. 15, 153–169 (2016).
34.
Schlosser, R., Boissier, M., Schober, A., Uflacker, M.: How To Survive Dynamic Pricing Competition in E-commerce. Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016 (2016).
35.
Schlosser, R.: Joint Stochastic Dynamic Pricing and Advertising with Time-Dependent Demand. Journal of Economic Dynamics and Control. 73, 439–452 (2016).
36.
Schlosser, R.: Dynamic Pricing with Time-Dependent Elasticities. Journal of Revenue and Pricing Management. 14, 365–383 (2015).
37.
Schlosser, R.: A Stochastic Dynamic Pricing and Advertising Model under Risk Aversion. Journal of Revenue and Pricing Management. 14, 451–468 (2015).
38.
Schlosser, R.: Dynamic Pricing and Advertising Models with Inventory Holding Costs. Journal of Economic Dynamics and Control. 57, 163–181 (2015).
39.
Helmes, K., Schlosser, R.: Dynamic Advertising and Pricing with Constant Demand Elasticities. Journal of Economic Dynamics and Control. 37, 2814–2832 (2013).