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.     

Agent Mertacor: A robust design for dealing with uncertainty and variation in SCM environments

Supply Chain Management (SCM) has recently entered a new era, where the old-fashioned static, long-term relationships between involved actors are being replaced by new, dynamic negotiating schemas, established over virtual organizations and trading marketplaces. SCM environments now operate under strict policies that all interested parties (suppliers, manufacturers, customers) have to abide by, in order to participate. And, though such dynamic markets provide greater profit potential, they also conceal greater risks, since competition is tougher and request and demand may vary significantly in the quest for maximum benefit. The need for efficient SCM actors is thus implied, actors that may handle the deluge of (either complete or incomplete) information generated, perceive variations and exploit the full potential of the environments they inhabit. In this context, we introduce Mertacor, an agent that employs robust mechanisms for dealing with all SCM facets and for trading within dynamic and competitive SCM environments. Its efficiency has been extensively tested in one of the most challenging SCM environments, the Trading Agent Competition (TAC) SCM game. This paper provides an extensive analysis of Mertacor and its main architectural primitives, provides an overview of the TAC SCM environment, and thoroughly discusses Mertacor’s performance.     

Forecasting market prices in a supply chain game

Predicting the uncertain and dynamic future of market conditions on the supply chain, as reflected in prices, is an essential component of effective operational decision-making. We present and evaluate methods used by our agent, Deep Maize, to forecast market prices in the trading agent competition supply chain management game (TAC/SCM). We employ a variety of machine learning and representational techniques to exploit as many types of information as possible, integrating well-known methods in novel ways. We evaluate these techniques through controlled experiments as well as performance in both the main TAC/SCM tournament and supplementary Prediction Challenge. Our prediction methods demonstrate strong performance in controlled experiments and achieved the best overall score in the Prediction Challenge.     

Trading agents competing: performance, progress, and market effectiveness

The annual trading agent competition offers agent designers a forum for evaluating programmed trading techniques in a challenging market scenario. TAC aims to spur research by enabling researchers to compare techniques on a common problem and build on each other's ideas. A fixed set of assumptions and environment settings facilitates communication of methods and results. As a multiyear event, TAC lets researchers observe trading agents' progress over time, in effect accelerating the evolution of an adapted population of traders. Given all the participant effort invested, it is incumbent on us to learn as much from the experience as possible. After three years of TAC, we're ready to examine there we stand. To do this, we used data from actual TAC tournaments and some post-competition experimentation. We based our analysis almost entirely on outcomes (profits and allocations), with very little direct accounting for specific agent techniques.     

STRATEGIC INTERACTIONS IN A SUPPLY CHAIN GAME

The TAC 2003 supply-chain game presented automated trading agents with a challenging strategic problem. Embedded within a high-dimensional stochastic environment was a pivotal strategic decision about initial procurement of components. Early evidence suggested that the entrant field was headed toward a self-destructive, mutually unprofitable equilibrium. Our agent, Deep Maize, introduced a preemptive strategy designed to neutralize aggressive procurement, perturbing the field to a more profitable equilibrium; it worked. Not only did preemption improve Deep Maize's profitability, it improved profitability for the whole field. Whereas it is perhaps counterintuitive that action designed to prevent others from achieving their goals actually helps them, strategic analysis employing an empirical game-theoretic methodology verifies and provides insight about this outcome.     

Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system

Reinforcement learning (RL) appeals to many researchers in recent years because of its generality. It is an approach to machine intelligence that learns to achieve the given goal by trial-and-error iterations with its environment. This paper proposes a case-based reinforcement learning algorithm (CRL) for dynamic inventory control in a multi-agent supply-chain system. Traditional time-triggered and event-triggered ordering policies remain popular because they are easy to implement. But in the dynamic environment, the results of them may become inaccurate causing excessive inventory (cost) or shortage. Under the condition of nonstationary customer demand, the S value of (T, S) and (Q, S) inventory review method is learnt using the proposed algorithm for satisfying target service level, respectively. Multi-agent simulation of a simplified two-echelon supply chain, where proposed algorithm is implemented, is run for a few times. The results show the effectiveness of CRL in both review methods. We also consider a framework for general learning method based on proposed one, which may be helpful in all aspects of supply-chain management (SCM). Hence, it is suggested that well-designed ‘‘connections” are necessary to be built between CRL, multi-agent system (MAS) and SCM.     

Data-Mining-Enhanced Agents in Dynamic Supply-Chain-Management Environments

In modern supply chains, stakeholders with varying degrees of autonomy and intelligence compete against each other in a constant effort to establish beneficiary contracts and maximize their own revenue. In such competitive environments, entities—software agents being a typical programming paradigm—interact in a dynamic and versatile manner, so each action can cause ripple reactions and affect the overall result. In this article, the authors argue that the utilization of data mining primitives could prove beneficial in order to analyze the supply-chain model and identify pivotal factors. They elaborate on the benefits of data mining analysis on a well-established agent supply-chain management network, both at a macro and micro level. They also analyze the results and discuss specific design choices in the context of agent performance improvement.     

Analysis of effectiveness and benefits of collaboration modes with information- and knowledge-sharing

This study addresses an empirical analysis of information- and knowledge-sharing among collaborative decision makers. In this research, each decision maker represents one of three distributed centers in ERP systems: Sales, Production, and Procurement Centers. Their respective objectives and decision policies are defined to design collaborative decision-making environments. MICSS-LAN, an ERP simulation tool, was applied for the experiments, generating explicit numerical results that enable the measurement of the effectiveness and benefits by collaborative decision making. The experimental results indicate that total profit and due date performance have been improved by information- and knowledge-sharing. It implies that better design and utilization of ERP structures can be achieved by a certain level of information- and knowledge-sharing.     

Designing a successful trading agent:A fuzzy set approach

Software agents are increasingly being used to represent humans in online auctions. Such agents have the advantages of being able to systematically monitor a wide variety of auctions and then make rapid decisions about what bids to place in what auctions. They can do this continuously and repetitively without losing concentration. To provide a means of evaluating and comparing (benchmarking) research methods in this area the trading agent competition (TAC) was established. This competition involves a number of agents bidding against one another in a number of related auctions (operating different protocols) to purchase travel packages for customers. Against this background, this paper describes the design, implementation and evaluation of SouthamptonTAC, one of the most successful participants in both the Second and the Third International Competitions. Our agent uses fuzzy techniques at the heart of its decision making: to make bidding decisions in the face of uncertainty, to make predictions about the likely outcomes of auctions, and to alter the agent's bidding strategy in response to the prevailing market conditions.     

Autonomous bidding agents in the trading agent competition

Designing agents that can bid in online simultaneous auctions is a complex task. The authors describe task-specific details and strategies of agents in a trading agent competition. More specifically, the article describes the task-specific details of and the general motivations behind, the four top-scoring agents. First, we discuss general strategies used by most of the participating agents. We then report on the strategies of the four top-placing agents. We conclude with suggestions for improving the design of future trading agent competitions     

A multi-formalism architecture for agent-based,order-centric supply chain simulation

Agent-based simulation models can effectively represent decentralized systems. However, many supply-chains are order-driven, and agent modeling cannot effectively represent the order life-cycle. We present a conceptual architecture that combines simulation formalisms, allowing an agent representation of the supply-chain infrastructure while enabling a process-oriented approach to representing orders. This architecture allows for a natural, realistic representation of different supply-chain constructs and subsystems while following a consistent overall viewpoint. Our approach provides for excellent representation of supply-chain operations, allows for very detailed operational data to be gathered, and provides efficient representation of concurrent supply-chain activities in a manner that avoids preemption.     

Resource-sharing among autonomous agents

We study a scenario for cloud services based on autonomous resource management agents in situations of competition for limited resources. In the scenario, autonomous agents make independent decisions on resource consumption in a competitive environment. Altruistic and selfish strategies for agent behaviour are simulated and compared with respect to whether they lead to successful resource management in the overall system, and how much information exchange is needed among the agents for the strategies to work. Our results imply that local agent information could be sufficient for global optimisation. Also, the selfish strategy proved stable compared to uninformed altruistic behaviour.     

When educational agents meet surrogate competition: Impacts of competitive educational agents on students' motivation and performance

The design of educational agents increasingly attracts researchers' attention recently. One of major reasons is that educational agents could enhance student learning in various aspects. Although research in this area has mushroomed, such research mainly emphasizes on students in higher education. It is still unclear how educational agents influence young student learning. In addition, competition is a significant element, but fewer studies take competition into account while designing educational agents. Although some studies have indicated that educational agents in competitive environments has positive effects on students' perception and attribution, its impacts on students' motivation and performance are unclear. Thus, the study develops an integrative agent that combines educational and competitive elements for young students, and further examines its influences, in terms of motivation and learning performance. The results revealed that such competitive educational agents could enhance students' motivation and learning performance.     

Presenting a hybrid model in social networks recommendation system architecture development

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity.

     

An expansion matching method to improve transaction effectiveness in the double auction market

The double auction is an important transaction mechanism in electronic commerce. Buyers and sellers can interact and be matched with each other in a double auction e-market. Consequently, enhancing the effectiveness of the double auction market to help traders successfully complete their transactions is an important issue. In this research study, Trading Agent Competition (TAC) data were collected to examine double auction market mechanisms. The TAC is a worldwide, renowned competition in which intelligent agents are employed to simulate business/market operations, and the TAC Market Design (CAT) tournament is an individual TAC competition that focuses on the double auction market. Thus, we conducted simulation experiments on the CAT competition platform, and the transaction data were analyzed to identify the impact of market design strategies on market performance, such as market share, market profit and transaction success rate. Based on these results, we developed an expansion matching method to enhance market performance, and we conducted verification experiments to evaluate our method. The results show that our expansion matching method promotes improved performance of market policies in the double auction market.     

Electronic multilateral trade of electricity

This paper presents a technical approach for electronic multilateral trade of electricity in competitive power industries. The trade involves strategic sharing of data among agents in an attempt to provide the opportunity to intelligently discover competitive behavior of peer suppliers. A trading logic is implemented as a specialized software module within the agent. The logic mimics intelligence of the human strategic trade. A time-bounded trade protocol has been introduced as a trading basis among rivalry trade agents in the market. The protocol limits the trade rounds in order to bind the trading process to specific deadlines. The protocol is coded as part of the automated trade server. The results of a generic 3-bus test system show that the electronic multilateral trade logic presented in this paper better distributes market sales, lowers prices and consequently provides higher social welfare compared to the standard Cournot economic model that may be used by the human decision-maker for market trading. Based on a set of test cases with different load profiles, it is noted that the electronic multilateral trade drives the market price closer to the marginal cost of generation supply and far away from the estimated Cournot price.     

Optimal Search and One-Way Trading Online Algorithms

This paper is concerned with the time series search and one-way trading problems. In the (time series) search problem a player is searching for the maximum (or minimum) price in a sequence that unfolds sequentially, one price at a time. Once during this game the player can decide to accept the current price p in which case the game ends and the player's payoff is p . In the one-way trading problem a trader is given the task of trading dollars to yen. Each day, a new exchange rate is announced and the trader must decide how many dollars to convert to yen according to the current rate. The game ends when the trader trades his entire dollar wealth to yen and his payoff is the number of yen acquired. The search and one-way trading are intimately related. Any (deterministic or randomized) one-way trading algorithm can be viewed as a randomized search algorithm. Using the competitive ratio as a performance measure we determine the optimal competitive performance for several variants of these problems. In particular, we show that a simple threat-based strategy is optimal and we determine its competitive ratio which yields, for realistic values of the problem parameters, surprisingly low competitive ratios. We also consider and analyze a one-way trading game played against an adversary called Nature where the online player knows the probability distribution of the maximum exchange rate and that distribution has been chosen by Nature. Finally, we consider some applications for a special case of portfolio selection called two-way trading in which the trader may trade back and forth between cash and one asset. Received October 19, 1998; revised August 12, 1999.     

Coordination and price competition in a duopoly common retailer supply chain

This paper analyzes the coordination and competition issues in a two-stage supply-chain distribution system where two vendors compete to sell differentiated products through a common retailer in the same market. The demand of a product not only depends on its own price, but also on the price of the other. Mathematical models have been developed to analyze the coordination issues under three different contexts: (i) price competition without channel coordination; (ii) price competition with channel coordination; and (iii) global coordination. It has been shown that under certain conditions, price competition through the dynamic process of price adjustment reaches the Nash–Bertrand equilibrium. Conditions have been derived for the Nash–Bertrand equilibrium to be dynamically stable. Further, it has been shown that duopoly competition can make consumers better-off or worse-off depending on the degree of product differentiation and the type of the product; while coordination enhances overall supply-chain profitability. The model is illustrated with suitable numerical examples.     

A CSP approach for the network of product lifecycle constraints consistency in a collaborative design context

In this paper, we are considering that the design process can be modelled in the form of a constraint satisfaction problem (CSP). CSP modelling or resolution has proved its efficiency within the framework of single-designer design. We propose to extend the functions of CSP to the context of multi-concept design of the same artefact. We define CoCSP as cooperative constraint satisfaction problem including the actors of the design problem. We are presenting the operating principles of an algorithm for the real-time management of design decisions, based on a model described in the form of a CoCSP for the integration of supply-chain constraints. This algorithm enables the number of design decisions rejected at a given moment in design to be kept to a minimum. The algorithm forms the core of a prototype for an unsupervised, generic constraint-based collaborative design system. Our aim is to produce a platform centred on the notion of constraints that will enable a product design problem to be modelled and solved by integrating supply-chain constraints as far upstream as possible.