simpy-stockout

About

This is a replication, in Python, of the discrete event simulation given by Law and Kelton (2000) for a single-product inventory system (s,S), previously written in FORTRAN (p. 66) and in C (p. 73).

Note that Law and Kelton run a single replication, as does this Python replication, and thus the output will differ some between the simulations due, for instance, to the use of different random number streams.

Results given in Law and Kelton (2000)

Inventory Policy	Average total cost	Average ordering cost	Average holding cost	Average shortage cost
( 20, 40)	126.61	99.26	9.25	18.10
( 20, 60)	122.74	90.52	17.39	14.83
( 20, 80)	123.86	87.36	26.24	10.26
( 20,100)	125.32	81.37	36.00	7.95
( 40, 60)	126.37	98.43	25.99	1.95
( 40, 80)	125.46	88.40	35.92	1.14
( 40,100)	132.34	84.62	46.42	1.30
( 60, 80)	150.02	105.69	44.02	0.31
( 60,100)	143.20	89.05	53.91	0.24

Results from replication in Python (using SimPy DES library)

Inventory Policy	Average total cost	Average ordering cost	Average holding cost	Average shortage cost
( 20, 40)	126.87	97.36	8.61	20.90
( 20, 60)	124.72	92.13	15.87	16.71
( 20, 80)	128.44	90.36	24.19	13.89
( 20,100)	126.37	81.82	37.24	7.31
( 40, 60)	125.92	99.18	25.16	1.57
( 40, 80)	120.65	85.70	34.55	0.39
( 40,100)	131.16	85.11	45.76	0.29
( 60, 80)	138.88	92.85	45.96	0.07
( 60,100)	145.83	88.98	56.85	0.00