An Agent-Based Computational Economics Approach to Forecasting Freight Flows for the Chicago Region

Transcription

1 An Agent-Based Computational Economics Approach to Forecasting Freight Flows for the Chicago Region J. Gliebe, J. Doyle, K. Wies, C. Smith, K. Shabani, M. Outwater TRB Innovations in Travel Modeling -- Baltimore April 29, 2014

2 Acknowledgments David Wu, Lehigh University Sunil Chopra, Northwestern University Matt Roorda, University of Toronto Craig Heither, CMAP Jeff Keller, 2

3 Overview Motivation, questions, objectives Theoretical underpinnings Linkages between industries Model structure and elements Procurement market game Numerical results What s next 3

4 Motivation Tool needed to address economic development questions on freight policy and investments Limitations of commodity flow data Future looks much like today Inability to respond to economic stimuli Fuel prices Productivity improvements Trade policies, tariffs, etc. Insensitive to changes in capacity investments or behavioral policies 4

5 Questions a traditional freight forecast method can t answer How will the freight industry s business model change if: Chicago s CREATE program to address a national rail bottleneck is completed? A new multi-modal terminal is built in Iowa? The deep-water port in Prince Rupert, BC is expanded? U.S. trade policies with China are changed? 5

6 Modeling performance objectives High-level scenario analysis of assumptions affecting the formation of supply chains in a global economy Endogenous modeling of origin-destination flows Theoretically plausible shifts in the magnitude and direction of flows and modal usage in response to both regional and extra-regional stimuli Circumstances (scenario assumptions) under which various outcomes are likely to occur Predict emergent patterns of response that the analyst might not expect, but should consider 6

7 Theoretical drivers Individuals make supply chain decisions Imperfect information, culture/custom, affinity, imitation, satisficing Everyone wants to make/save money, but mathematical optimization is rare Variation in value systems across markets Cost savings, time responsiveness, need for centralized control Variation in market mechanisms Ad hoc bi-lateral agreements, auctions/bidding, collusion, oligopolies 7

8 Agent based computational economics (Tesfatsion et al 2006) Agents interact in simulated world following simple rules, often cooperation games Repeated encounters lead to learning, adaptive strategies Emergent behavior Used to study many adaptive, complex systems Source: 8

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10 Economic drivers 10

11 Supply chain networks: linkages between industries, firms, individuals (U.S. BEA input-output accounts) Consumer centric view Producer centric view 11

12 Multi-factor productivity (U.S. BEA Index) I n d e x V a l u e s NAICS Computer and peripheral equipment 2002 = '100' Multifactor productivity Labor productivity Capital productivity Intermediate purchases productivity Output I n d e x V a l u e s NAICS Seafood product preparation and packaging 2002 = '100' Multifactor productivity Labor productivity Capital productivity Intermediate purchases productivity Output 12

13 Sourcing: the heart of the supply chain 13

14 Initializing agents Buyer Attributes NAICS Size (# employees) Freight zone Output commodity Input commodity Input commodity requirements ($ annual purchase) demand B S Seller Attributes NAICS Size (# employees) Freight zone Output commodity Production level ($ annual output) capacity Buyer Preferences Min. cost vs. Max. response Risk spreading Centralized distribution Vertical Integration Buyer-Seller Matching Game Seller Cost-Service Bundle Shipment sizes Average shipping times Distribution centers Mode Cost 14

15 Procurement market game Inspired by trade network game (McFadzean et al, 2001) Iterative pairwise encounters (solicitation bid) B Decision Matrix Buyer Seller Decision Payoffs Decision Payoffs Yes Utility of Transaction Yes Revenue of Transaction No Expected Utility if another supplier must be chosen No Expected Revenue of holding out for other (more lucrative) buyers S Actual payoffs revealed at end of round after other potential pairwise games are played Agents rate their experiences with other agents 15

16 Agent payoffs, memory and learning Expected values formed dynamically through game play Cooperate This is a favored trading partner Defect Other trading partners are preferred B Payoff Matrix Seller Buyer Cooperate Defect Cooperate Defect Bi-lateral agreement game form (others possible) Payoff factors are applied to calculated utilities/revenues Defect payoffs discount what might have been or could be Each buyer agent remembers outcomes of pairwise games with each seller agent, and vice versa S 16

17 Constraints Sellers assumed to be production constrained May not be able to supply 100% of buyer s needs Downgraded commensurately by buyer Buyers may be assumed to be risk averse Max. fraction to be purchased from any one source Max. fraction to be shipped through any one port Will try to find multiple sources Implications of many buyers and few sellers May settle for non-favored sellers May relax max. fraction constraints 17

18 Examples of game chatter from log file Buyer buy from Seller quantity: 2316 Buyer buy from Seller quantity: 776 Adjusting BuyerTradePayoff < quantityrequested: 1751 ; quantitydelivered: 1685 fractionalsatisfaction: Suspending maxfrac for buyer diminishing offer from for to rejecting offer from for 263 Buyer asks Seller for 33 Seller OVEREXTENDED: capacity shortfall Buyer Tag CONSTRAINT 0 (30610) reducing supplemental offer from to 95 Buyer asks Seller for ADDITIONAL 95 18

19 Example NAICS Commodity 2122A0 Iron, gold, silver, and other metal ore mining Top consumers from I-O tables NAICS Paint & Coating Manufacturing 19

20 Consumer locations and characteristics for iron, gold, silver, and other metal ore mining 18,762 20

21 Producer locations and characteristics for iron, gold, silver, and other metal ore mining

22 Comparison of Chicago area producer and consumer locations for iron, gold, silver, and other metal ore mining (Lots of imports) 22

23 Consumer locations and consumption of iron, gold, silver, and other metal ore mining 23

24 Producer locations and production of iron, gold, silver, and other metal ore mining 24

25 All attempted trades and final trades with one seller Fontana Metal Works 25

26 All attempted trades and final trades for one Chicago buyer Cicero Paints & Coatings 26

27 Distance distributions for all pairs, trading pairs, final iteration pairs 27

28 What s next Now through June 2014 Address computational issues Sampling and/or representative agents to reduce combinatorics, developing closure criteria Adding representative agents for foreign trade Complete working model July 2014 June 2015 Improve resolution on non-transport prices, tariffs Sensitivity testing and fine tuning of parameters Comparisons with commodity flow and other data Scenario testing 28

29 Questions and Answers

30 Contacts John Gliebe, PhD Senior Consultant, Kermit Wies, PhD Deputy Executive Director for Research and Analysis, CMAP

31 Reserve slides to follow if needed for Q&A 31

32 Cost function Ordering Cost Transport and Handling Cost Damage Cost Inventory in-transit cost Carrying Cost Safety Stock Cost (Source: Cambridge Systematics, 2011) 32

33 Levels of response sensitivity in forecasting Stimulus: Change in price of an input commodity A 1. Switch suppliers for input commodity A? Part of model s basic response set-pmg 2. Switch suppliers for input commodity B? Requires budget-awareness. Would need to be sub-problem within PMG. Perhaps too complex defer to future Supply chain activity network propagation 3. Pass cost change along in price of output commodity? Change I-O coefficients? Change output levels? Change in final demand? 4. Cost is global and changes factors of production? 33