Abstract
With the tremendous increase in the globalization of trade the corresponding supply chains supporting the manufacture, distribution and supply of goods has become extremely complex. Intelligent agents can help with the problem of effective management of these complex supply chains. In this paper we introduce the design, implementation and testing of an intelligent agent for handling procurement, customer sales, and scheduling of production in a stylized supply chain environment. The supply chain environment used in this paper is modeled after the trading agent competition that is held annually to choose the best agent for managing a supply chain. Our supply chain agent, which we call SCMaster, uses dynamic inventory control and various reinforcement learning techniques like Q-learning, Softmax, ε-greedy, and sliding window protocol to make our agent adapt dynamically to the changing environment created by competing agents. A multi-agent simulation environment is developed in Java to test the efficacy of our agent design. Two competing agents are created modeled after the winners of past trading agent competitions and are tested against our agent in various experimental designs. Results of simulations show that our agent has better performance compared to the other agents.
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Appendices
Appendix 1: Meta-learning algorithm
Appendix 2: Procurement RFQ creation algorithm
Appendix 3: Component catalog
Components | Base price | Supplier | Description |
---|---|---|---|
100 | 1000 | Pintel | Pintel CPU, 2.0 GHz |
101 | 1500 | Pintel | Pintel CPU, 5.0 GHz |
110 | 1000 | IMD | IMD CPU, 2.0 GHz |
111 | 1500 | IMD | IMD CPU, 5.0 GHz |
200 | 250 | Basus, Macrostar | Pintel motherboard |
210 | 250 | Basus, Macrostar | IMD motherboard |
300 | 100 | MEC, Queenmax | Memory, 1 GB |
301 | 200 | MEC, Queenmax | Memory, 2 GB |
400 | 300 | Watergate, Mintor | Hard disk, 300 GB |
401 | 400 | Watergate, Mintor | Hard disk, 500 GB |
Appendix 4: Bill of materials
SKU | Components | Cycles required for assembly |
---|---|---|
1 | 100, 200, 300, 400 | 4 |
2 | 100, 200, 300, 401 | 5 |
3 | 100, 200, 301, 400 | 5 |
4 | 100, 200, 301, 401 | 6 |
5 | 101, 200, 300, 400 | 5 |
6 | 101, 200, 300, 401 | 6 |
7 | 101, 200, 301, 400 | 6 |
8 | 101, 200, 301, 401 | 7 |
9 | 110, 210, 300, 400 | 4 |
10 | 110, 210, 300, 401 | 5 |
11 | 110, 210, 301, 400 | 5 |
12 | 110, 210, 301, 401 | 6 |
13 | 111, 210, 300, 400 | 5 |
14 | 111, 210, 300, 401 | 6 |
15 | 111, 210, 301, 400 | 6 |
16 | 111, 210, 301, 401 | 7 |
Appendix 5: Parameters for the simulation
Parameter | Simulation setting |
---|---|
Length of simulation | 250 days |
Agent assembly cell capacity | 2000 cycles/day |
Nominal capacity of supplier assembly lines | 550 components/day |
Acceptable purchase ratio for single-source suppliers | 0.75 |
Acceptable purchase ratio for two-source suppliers | 0.45 |
Average number of customer RFQs per product on a day | 13 |
Average number of demand per product on a day | 200 |
Range of lead time (due date) for customer RFQs | 3–12 days from the day the RFQ is received |
Range of penalties for customer RFQs | 10% of the customer reserve price annually |
Customer reserve price | 75–125% of nominal price of the PC components |
Annual storage cost rate | 37.5% of nominal price of components |
The number of RFQs to a supplier per component | 5 |
Appendix 6: Bidding performance analysis
SKU (product type) | SCMaster | Agent-D | Agent-T | |||
---|---|---|---|---|---|---|
Avg. order price | Difference with Agent-D | Avg. order price | Difference with Agent-T | Avg. order price | Difference with SCMaster | |
1 | 979.56 | 26.30 | 953.27 | 40.17 | 913.09 | − 66.47 |
2 | 1020.98 | 190.98 | 830.00 | − 129.53 | 959.53 | − 61.45 |
3 | 1017.12 | 31.34 | 985.78 | 26.37 | 959.41 | − 57.71 |
4 | 1053.77 | 47.99 | 1005.79 | 2.83 | 1002.96 | − 50.81 |
5 | 1256.23 | 374.69 | 881.54 | − 292.55 | 1174.09 | − 82.14 |
6 | 1292.17 | 462.17 | 830.00 | − 390.96 | 1220.96 | − 71.20 |
7 | 1284.33 | 91.14 | 1193.19 | − 29.53 | 1222.72 | − 61.61 |
8 | 1323.93 | 86.22 | 1237.71 | − 27.68 | 1265.40 | − 58.53 |
9 | 979.82 | 23.67 | 956.16 | 44.81 | 911.34 | − 68.48 |
10 | 1020.46 | 190.46 | 830.00 | − 130.87 | 960.87 | − 59.59 |
11 | 1017.59 | 32.53 | 985.06 | 30.12 | 954.93 | − 62.66 |
12 | 1042.88 | 152.38 | 890.50 | − 101.70 | 992.20 | − 50.68 |
13 | 1230.90 | 80.12 | 1150.78 | − 5.79 | 1156.57 | − 74.33 |
14 | 1280.64 | 78.42 | 1202.22 | − 10.88 | 1213.09 | − 67.54 |
15 | 1278.90 | 77.19 | 1201.70 | − 7.70 | 1209.40 | − 69.50 |
16 | 1326.45 | 85.67 | 1240.78 | − 27.44 | 1268.23 | − 58.22 |
Order success rate (order/offer) | 3472/3566 = 0.97 | 3885/8632 = 0.45 | 7660/10,920 = 0.70 |
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Lee, Y.S., Sikora, R. Application of adaptive strategy for supply chain agent. Inf Syst E-Bus Manage 17, 117–157 (2019). https://doi.org/10.1007/s10257-018-0378-y
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DOI: https://doi.org/10.1007/s10257-018-0378-y