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Trading Agents Competition Supply Chain Management Scenario. Wednesday, 17 March, Ross 201 HUJI’s Runner Team {Leo Dorrendorf, Raphael Boussidan, Zweig Alon, Yaad Blum, Joyce Vogel}. Background and Motivation.
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Trading Agents Competition Supply Chain Management Scenario Wednesday, 17 March, Ross 201 HUJI’s Runner Team {Leo Dorrendorf, Raphael Boussidan, Zweig Alon, Yaad Blum, Joyce Vogel}
Background and Motivation • Supply Chain – The entire network of suppliers, factories, warehouses, distribution centers and retailers, that participate in the process from raw materials to finish products • Supply chain management is concerned with planning and coordinating the activities of organizations across the supply chain • While today’s supply chains are essentially static, relying on long-term relationships among key trading partners, more flexible and dynamic practices offer the prospect of better matches between suppliers and customers as market conditions change.
Background and Motivation • The TAC annual contest has been designed jointly by a team of researchers from the e-Supply Chain Management Lab at Carnegie Mellon University and the Swedish Institute of Computer Science (SICS). This year, there will be two games in the competition: • TAC-SCM, the Trading Agents Competition in a Supply Chain Management scenario, was designed to capture many of the challenges involved in supporting dynamic supply chain practices. • TAC Classic - The software agents will represent travel coordinators whose goal is to arrange travel packages (flights, hotel rooms, and tickets to entertainment events) for clients.
Background and Motivation • It is challenging in that it requires agents to concurrently compete in multiple markets (markets for different components on the supply side and markets for different products on the customer side) with interdependencies and incomplete information. • The final rounds of the competition will be held during AAMAS (20-22 July), preceded by qualifying and seeding rounds in June and July • Last year, Jeff's students, Nadav Wainshal and Nir Sharony, built an automated, learning agent that participated in TAC Classic. NNN placed sixth in the field of nine finalists, the scores of the automated agents were quite clustered
Game Overview • In TAC SCM, 6 software agents compete in a simulated sector of a market economy, specifically the personal computer (PC) manufacturing sector • Each agent can manufacture 16 different types of computers • Building each PC requires a different combination of components. 4 types of components are represented in the game: CPUs, Motherboards, Memory, and Hard drives. It features a variety of components of each type (e.g. 300GB H.D and 500GB H.D. etc’).
Game Overview • Components are acquired from a common pool of suppliers at costs that vary as a function of demand. • After assembly, each agent can sell its PCs to a common pool of customers by underbidding the other agents. • The agents are ranked based on their profits over 220 days, each of which lasts 15 seconds. • To succeed, agents will have to: • React to variations in customer demand and availability of supplies • Strategize (e.g. specializing in particular types of products, stocking up components that are in low supply) • Adapt to the strategies adopted by other competing agents
A TAC Day • Each competing agent needs to bid for customer orders, negotiate supply contracts, and manage assembly activities - all of which on daily basis. • Bid for customer orders • customers send a set of requests for quotes (RFQs) to the agents. • RFQ contains a SKU, a quantity, a due date, a penaltyrate(max 5 days), and a reserve price • Agents sends an offer to each customer for each RFQ, bidding to satisfy entire order (date & quantity) • Customer selects the agent with the lowest-priced offer and awards that agent with a purchase order
A TAC Day • Negotiate supply contracts • Agents send RFQs to suppliers, requesting a specific quantity of a component to arrive on a particular day • Suppliers respond the next day with offers • If the supplier can satisfy the order entirely, a full offer is sent • A partial offeris generated if the supplier can deliver only part of the requested quantity on the due date specified in the RFQ • An earliest complete offeris generated to reflect the earliest day (if any) that the supplier can deliver the entire quantity requested
A TAC Day Supplier 20 CPU’s available, d + 5 50 CPU’s available, d + 6 Partial Offer: 5 CPU’s, d+5 Earliest Complete Offer: 15 CPU’s, d+6 Supplier’s process order is crucial Offer: 15 CPU’s, d+5 RFQ: 15 CPU’s, d+5 RFQ: 15 CPU’s, d+5 Agent 1 Agent 2
A TAC Day • Manage assembly activities • Each agent converts the components it acquired from suppliers into SKUs according to a production schedule it generates for its factory, that decides which PC orders to assemble first. • Agents also report a delivery schedule assigning the PCs in its inventory to customer orders. • Each agent is endowed with an identical PC factory containing a bounded assembly cell (2000 ac/d) capable of assembling any type of PC, and an inventory storing both components and finished PCs, at a given storage cost (15%-25% annually, fixed).
Suppliers’ Agent Preference • In order to probe, and not swamp the supplier, each day an agent is allowed to send a maximum of 10 RFQs to each supplier, that are bundled to an order list RFQBundle, with the highest priority RFQ at the start. • The supplier selects an RFQ bundle and considers the next unprocessed RFQ in that bundle. Then the supplier moves to another bundle, and continue in a loop until all RFQ are processed • Let us denote Sa supplier in the game, and Aa competing agent. Define QuantityRequested(A,S) as the sum of the quantities in all RFQs A sent to S thus far, and QuantityPurchased(A,S)is the total quantity that A has purchased from the S thus far .
Suppliers’ Agent Preference • Agents should not be punished for requesting same components from both suppliers • Likelihood(A,S) = 1 at the beginning of the game • Supplier selects the RFQBundle with highest likelihood. Encourage genuinely interested agents
Suppliers Daily Production • Cp(S,d) - The production capacity on day d, is determined by a random walk with a lower bound • Cnominal(S) - The nominal capacity (500 C/D) is the expected capacity of supplier S. • Delivery only of entire quantity. No inventory cost charged by S for production of multiple day orders • If Cp(S,d)< Cnominal(S), missed orders are scheduled for next possible day. • Excess capacity used to satisfy future orders, however no early shipments are made.
Suppliers Available Capacity • Suppliers operate in a make to order basis • Cfree(S,d) and Cordered(S,d) denote the free and ordered capacity on day d. • On day d the available capacity forecast on d+i is some of all free capacity between d and d+i • At day d, an order for delivery on day d+i for n components will be responded with full offers, partial offers and earliest complete offers according to available capacity forecast
Supplier Pricing • In day d , P(d,d+i) is the offer price for component to be delivered in d+I • Pbase(Component) – Base price of component • 0.5– Discount factor • qty – quantity requested by the order • Ccurernt(S,d) – the supplier capacity on day d • C’avilable is like Cavailable but calculated using Ccurernt not Cnominal • Lower prices in case of high margin between demand and quantity, immediate orders, low production capacity
Customers • Customer demand is expressed as RFQs RFQ ::= <Id,PC,Quantity,DueDate,Penalty,ReservePrice> • PC is randomly selected from available types • Quantity is chosen U[1,20] • DueDateis chosen U[3,12] from today • ReservePrice is chosen U[75%,125%] from base price • Daily penalty is chosen U[5%,15%] of ReservePrice
Customers • Customers are classified into three market segments: High range, Mid range, and Low range • The number of RFQ issued per day in each market segment is a random variable NRFQwith a distribution: NRFQ = Poisson (average number of RFQ per day) • Average number of RFQ per day will be varied using a trend that is updated by a random walk:RFQavg(d) = RFQavg(d-1)*Trend(d), bounded in [25,100]Trend(d)=Trend(d-1)+rnd(-0.01,0.01) bound [0.95,1/0.95] • For Mid range bounded in [30,120]
Bank • Agents start the game with no money in the account • A fixed annual interest rate is chosen uniformly in the range [10% , 20%] at the start of game, and is either charged if the balance is in debt or credited if the balance is positive. • Customer will pay for PCs only on the agreed DueDate. However, agents may ship an order to a customer earlier than the agreed DueDate and save inventory cost
Periodic Reports of Market State • All agents will be informed about the price interval (min, max) for each type of PC ordered the previous day. • Component supply reports are made available every 20 TAC days: • Aggregate quantities produced by all suppliers • Aggregate quantities sold by all suppliers Insight into supply procuring practices of other agents! • Customer demand report • Average sold PC price of each PC type requested • Average sold volume of each PC type requested Detect lack of availability of a particular PC type
TAC-SCM-03 Participants • RedAgent is based on a distributed architecture: it is composed of simple agents, with markets serving as means of communication and exchange among them • DeepMaize is based on distributed architecture, but relies on feedback control for coordination • TacTex and Botticelli are similar, traditional TAC agent designs: build models of the environment and attempt to optimize with respect to those models
RedAgent-2003Average Score: 11.61 M (1st) Precup D., Keller P., Wahab M., Duguay, F.-O. Reasoning and Learning Laboratory McGill University's, CANADA
RedAgent Architecture • Redagent is centered around the idea of using an internal market in order to determine what products to focus on and how to allocate the existing resources • Create a highly distributed architecture, in which simple, heuristic-based agents are assigned to deal with individual aspects of the game • Agents communicate through a market mechanism in order to determine, collectively, which components to purchase, which types of PCs to produce, how to allocate the available components and production cycles, and what offers to send to customers. • Resulted in an efficient and flexible decision making
RedAgent Architecture • Uses 5 types of agents: • Order Agents (OA) • Obtain PCs needed to fill orders and delivers them • 10 Component Agents (CA) • Purchase components from suppliers • Production Agent (PA) • Provide production cycles available to Assemblers • 16 Assembler Agents(AA) • Assemble components into PCs • Buy from PAs & CAs and sells to OAs • Bidder • Bid on customer RFQs
RedAgent Architecture • Communication and exchanges through Markets • A market for each component type • A market for each PC type • Production cycles market • Markets help determine: • Which orders to fill with available SKUs • What to build with available components • What components to buy • How much components and systems are worth
RedAgent Markets • Sealed-bid double auction variant • Secret bids from buyers and sellers • Order bids according to price to obtain offer/demand profiles • Match up heads of both profiles • Market price: midpoint between highest offer & lowest demand • Resource markets hold several rounds of auctions each day, in order to allow the participating agents to adjust their valuations, based on current prices and on availability of other necessary resources
RedAgent Markets • Same price for all transactions • Lowest demand bids and highest offer bids not exchanged, though they could have been • Goal: Maximize overall gain in utility. That is justified since in the internal market agents share a common goal.
Buy-to-Build / Build-to-Order • buy-to-build strategy, an agent stocks up on components, and starts producing PCs without necessarily having orders for all the production • build-to-order strategy, the first concern of a PC maker is to secure orders from customers; then, PCs are mostly built in order to deliver these existing orders • Redagent adopted buy-to-build because of the absence of inventory cost in TAC-SCM-03. • Maintained a large stock, and obtained high profit margin when other competing manufacturers had low stocks • On the other hand, in a low-demand market, profits may not be high enough to cover the cost of the unsold PCs.
Order Agent Heuristics • One agent per order • Greedy - Always tries to buy enough to fill order • Always offers all inventory for sale • Same price for purchase or sale • Never ships early, always ships on/after due date when possible
Order Agent Heuristics • Consequences for SKU Markets: • If SKU are plentiful • OAs buy early at low price (- Greedy) • Market closing price ~ sum cost of components • If backlog • Order agents buy later (Everybody’s greedy) • Market closing price increases • Bidder bids higher • Remember: market prices used by bidder
Assembler Agents • One for each SKU • AA Must determine • Offer prices for SKU market • Offer prices for production cycles market • Demand prices for component & production cycles markets • Construct bids with the goal of maintaining a target inventory, a “buffer”,to avoid spikes • Buffer is estimated as number of PCs needed for 10 days of operation, according to last 20 days average • Also, generate production schedule & liquidation
Assembler Offers • Base price,Ba, is sum of estimated component and production costs • PC inventory is divided into three batches: • PC’s in batch-1 priced linearly betweenBaand1.3Ba • PC’s in batch-2 priced linearly between0.7BaandBa • PC’s in batch-3 priced 0.7Ba • Higher prices if inventory is low – selling buffer
Component market • Recall: sealed double auction • Sequential auctions, known order: • 1. Production cycles: usually the most contention • 2. CPUs: most expensive • 3. Motherboards • 4. Memory • 5. Hard drives • Repeated N times each day • Used to find good resource allocation between assemblers
Assembler Demand • First, determine total bid for an assembled SKU: • Use Demand profile from last SKU auction • Modify demand profile: • Compensate for highest bids with inventory • Add “fake” demand in order to build cheap SKUs for the future • Now determine bids for each component/cycle: • Use last relative prices from all the markets • Multiply relative price by prices in demand profile to obtain individual bids • Use the N auctions to try and match quantity of all components, lowering risk in each round
Component Agent • One for each component • Determines • purchases (e.g H.D) • offer price of components (from probing) • Based on • Inventory • Expected component deliveries • Average demand of components by AA • Goal: always keep “buffer” of components • Size depends on day of game and avg. demand, estimated from the components market
Bidder • Adaptive margin bidder • price offers are running average of the closing price from the PC market + a margin for each necessary production cycle • margin is decreased when a lot of production cycles are available, and increased if production is at capacity • maximize the profit per cycle since production cycles are fixed and form the main bottleneck in the system.
RedAgent • More details on : • Assembler agents • Component agents • Production agent • Learning Bidder agent • Delivery scheduale Can be found in the submitted article: KELLER, P. W., DUGUAY, F.-O., AND PRECUP, D. 2004. Redagent-2003: An autonomous, market-based supply-chain management agent. In Submitted.
