Learning market prices in real-time supply chain management

Burke, David A.; Brown, Kenneth N.; Tarim, Ş. Armağan; Hnich, Brahim

doi:10.1016/j.cor.2007.01.021

Cited by 12 publications

(7 citation statements)

References 17 publications

(15 reference statements)

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“…The reason is that prices in the TAC SCM game tend to be quite stable for long periods, especially if the start and end game conditions are excluded. 6 One of the primary reasons we use this as a baseline is that we believe it is reflective of the "default" heuristic methods used by many of the agents to make decisions (especially in the early years of the competition). In particular, it only makes use of local information about prices in the current game, and does not attempt to project changes in price levels over time.…”

Section: Discussionmentioning

confidence: 99%

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Forecasting market prices in a supply chain game

Kiekintveld

Miller

Jordan

et al. 2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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Future market conditions can be a pivotal factor in making business decisions. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the Trading Agent Competition Supply Chain Management Game. As a guiding principle we seek to exploit as many sources of available information as possible to inform predictions. Since information comes in several different forms, we integrate well-known methods in a novel way to make predictions. The core of our predictor is a nearest-neighbors machine learning algorithm that identifies historical instances with similar economic indicators. We augment this with an online learning procedure that transforms the predictions by optimizing a scoring rule. This allows us to select more relevant historical contexts using additional information available during individual games. We also explore the advantages of two different representations for predicting price distributions. One uses absolute prices, and the other uses statistics of price time series to exploit local stability. We evaluate these methods using both data from the 2005 tournament final round and additional simulations. We compare several variations of our predictor to one another and a baseline predictor similar to those used by many other tournament agents. We show substantial improvements over the baseline predictor, and demonstrate that each element of our predictor contributes to improved performance.

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Section: Discussionmentioning

confidence: 99%

“…Many of these approaches use only price information from the current game instance to predict prices, and do not attempt to predict changes in prices from the current levels [3,4,6,14]. Pardoe and Stone [20] explored a variety of machine learning techniques to estimating the probability of winning the current customer auctions.…”

Section: Related Workmentioning

confidence: 99%

Forecasting market prices in a supply chain game

Kiekintveld

Miller

Jordan

et al. 2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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“…The methods used by the teams include fuzzy reasoning inference mechanisms [27], additive regression with decision stumps [42], linear regression [3], linear cumulative density function (CDF) [5], reverse CDF [32], continuous knapsack problem [4], dynamic pricing [8,29], and k-nearest neighbors [13,34]. The comparison analysis of various predicting algorithms [43] demonstrates superior performance of machine learning algorithms over traditional statistical approaches.…”

Section: Related Workmentioning

confidence: 99%

Learning approaches for developing successful seller strategies in dynamic supply chain management

Fasli

Kovalchuk

2011

Information Sciences

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“…A dynamic pricing model is proposed in [7], where market conditions are represented as a set of bid prices which have a certain probability of being accepted. The approximations of these prices are learned using both online and historical information and updated after each day of trading.…”

Section: Learning Applications In Simulations Of Supply Chainsmentioning

confidence: 99%

Learning Framework for Multi-Agent Simulation of Supply Chain Systems

Jiang

Sheng

2008

2008 International Conference on Computer Science and Information Technology

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Multi-agent System (MAS) is becoming a popularand useful method applied to solve problems in supply chains under dynamic environment. The learning ability is one of the most important components for "intelligent" agents. It helps agents adjust to dynamic environment more quickly and gain experience for making decisions. This paper proposes a learning framework for multi-agent simulation of supply chain systems. And this framework is explained step by step in 4 levels: individual, department, organization and supply chain system. The validity of such framework is established by simulating an example learning method using the simplified version of the framework. Furthermore, the application of such framework is not limited for SC, but also for community, government etc.

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Learning market prices in real-time supply chain management

Cited by 12 publications

References 17 publications

Forecasting market prices in a supply chain game

Forecasting market prices in a supply chain game

Learning approaches for developing successful seller strategies in dynamic supply chain management

Learning Framework for Multi-Agent Simulation of Supply Chain Systems

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