TAC SCM Research Articles

Agent-assisted supply chain management: Analysis and lessons learned

This work explores “big data” analysis in the context of supply chain management. Specifically we propose the use of agent-based competitive simulation as a tool to develop complex decision making strategies and to stress test them under a variety of market conditions. We propose an extensive set of business key performance indicators (KPIs) and apply them to analyze market dynamics. We present these results through statistics and visualizations. Our testbed is a competitive simulation, the Trading Agent Competition for Supply-Chain Management (TAC SCM), which simulates a one-year product life-cycle where six autonomous agents compete to procure component parts and sell finished products to customers. The paper provides analysis techniques and insights applicable to other supply chain environments.

A dynamic programming approach for agent's bidding strategy in TAC-SCM game

Intelligent agents have been developed for a number of e-commerce applications including supply chain management. In trading agent competition for supply chain management (TAC SCM), several manufacturer agents compete in a reverse auction in order to sell assembled computers to customers. The manufacturer agent’s tasks include acquiring supplies, selling products and managing its local manufacturing process. The agent decide whether to accept an arriving bid in order to maximise its long-term expected profit. In this paper, we use dynamic programming to provide a pricing strategy for the TAC SCM. We consider a competition between an individual manufacturer agent and other automated agents in TAC SCM. The experiment results show that this strategy improves the agent’s revenue significantly comparing to several other heuristics in the current practice. This approach can also be applied to similar bidding problems in other e-commerce applications.

Pushing the Limits of Rational Agents: The Trading Agent Competition for Supply Chain Management

Over the years, competitions have been important catalysts for progress in artificial intelligence. We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted more than 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries.

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of today’s global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon University’s 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.

Flexible decision control in an autonomous trading agent

Modern electronic commerce creates significant challenges for decision-makers. The trading agent competition for supply-chain management (TAC SCM) is an annual competition among fully-autonomous trading agents designed by teams around the world. Agents attempt to maximize profits in a supply-chain scenario that requires them to coordinate Procurement, Production, and Sales activities in competitive markets. An agent for TAC SCM is a complex piece of software that must operate in a competitive economic environment. We report on results of an informal survey of agent design approaches among the competitors in TAC SCM, and then we describe and evaluate the design of our MinneTAC trading agent. We focus on the use of evaluators – configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports Sales and Procurement decisions, and how those decision process can be modified in useful ways by changing evaluator configurations.

CMieux

Existing supply chain management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The Supply Chain Trading Agent Competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading. TAC SCM pits against one another trading agents developed by teams from around the world. This paper presents Carnegie Mellon University's 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach to coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance.

Botticelli

The paper describes the design of the agent BOTTICELLI, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, BOTTICELLI competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to complete its orders. We formalize subproblems that dictate BOTTICELLI's behavior. Stochastic programming approaches to bidding and scheduling are developed in attempt to solve these problems optimally. In addition, we describe greedy methods that yield useful approximations. Test results compare the performance and computational effciency of these two techniques.

TacTex-03

This paper introduces TacTex-03, an agent designed to participate in the Trading Agent Competition Supply Chain Management Scenario (TAC SCM). As specified by this scenario, TacTex-03 acts as a simulated computer manufacturer in charge of buying components such as chips and motherboards, manufacturing different types of computers, and selling them to customers. TacTex-03 was the top scorer in two of the preliminary rounds of the 2003 TAC SCM competition, and finished in 3rd place in the finals.