INBOUND SHIPMENT COORDINATION SENIOR DESIGN FINAL PRESENTATION DECEMBER 12, 2012

Transcription

1 INBOUND SHIPMENT COORDINATION SENIOR DESIGN FINAL PRESENTATION DECEMBER 12, 2012 ADVISOR: DR. XIAOMING HUO CONTACTS: CHAD HERRING DAN GROSS SHASHANK BHARADWAJ MARYBETH BLACK LYNN BLAU NAKUL CHITALIA MARIA GUZMAN CAITLIN HOGAN JOHN MILLER SIDDHARTHA PENAKALAPATI Disclaimer: This presentation has been created in the framework of a student design project and the Georgia Institute of Technology does not officially sanction its content.

2 PROJECT OVERVIEW Problem Design Strategy Deliverable < 2% of shipments are part of multi-stop pickups Integer programming Excel tool of 1,233 vendor groups Project Value 14,923 multi-stop pickup opportunities $9.1M reduction in transportation costs 2

3 INBOUND TRANSPORTATION NETWORK s s 18 Rapid Deployment Centers (s) & 633 s 3

4 PROBLEM CURRENT STATE i j FUTURE STATE i j i j i j k l m k l m 4

5 PROJECT FLOW Individual Shipments Multi-Stop Shipments Groups 5

6 DATA Bid Rate Mode of Transportation Shipment Date Total Cost Volume Weight Bid Rate from Boston to Los Angeles 6

7 DESIGN STRATEGY Objective Maximize cost savings via multi-stop pickup coordination Pre-processing Create all feasible pairs and triplets Optimization Integer Programming via FICO Xpress Optimization Suite Post-processing Create vendor groups from combined shipments 7

8 MATLAB PRE-PROCESSING Shipment cannot be paired with itself Same destination Originally a single stop Distance limitation Feasible Pairs & Triplets Same shipment week Volume limitation Weight limitation 8

9 COST MODEL i j i d i,j j k d k,l l d l,m m Total Cost (i) Total Cost (j) d ø,i d j,ø d ø,k d m,ø Current Model Two-Stop Model Three-Stop Model 9

10 OPTIMIZATION MODEL & CONSTRAINTS 10

11 OPTIMIZATION OUTPUT SAMPLE Independent Routes Combined Routes Total Distance: 3, miles Total Cost: $2, Total Distance: 1, miles Total Cost: $1, Savings: $

12 POST PROCESSING OUTPUT SAMPLE Five Most Frequently Combined Groups for all s 12

13 ANALYSIS Number of Shipments Total Shipment Reduction Per Min ( 5087) Average Max ( 5085) Pre-Optimization Post-Optimization Number of Multi-Stop Pickups Per 2-Stops 3-Stops Minimum Average Maximum

14 ANALYSIS Savings (in Thousands) $900 $800 $700 $600 $500 $400 $300 $200 $100 $- Total Savings Per Min ( 5087) Average Max ( 5085) Three-Stop Shipments Two-Stop Shipments Group Sizes Per Groups of 3 Groups of 4 Groups of 5 Average Total

15 DELIVERABLE State: MO : MO001 :

16 SUMMARY Problem: < 2% of shipments are part of multi-stops Design Strategy: IP optimization model Deliverable: Excel tool of 1,233 vendor groups Project Value: 14% of shipments are part of multi-stops $9.1M in transportation cost savings 16

17 QUESTIONS 17

18 APPENDIX 18

19 MATLAB DATA PRE-PROCESSING 1. The distance between vendors that are combined is limited to 225 miles. a) For a two-stop from i to j: distance i,j < 225 b) For a three-stop from i to j and j to k: distance i,j + distance j,k < The maximum weight that a 53-foot container can withstand is 42,000 pounds. a) For a two-stop combining shipments i and j: weight i + weight j 42,000 b) For a three-stop combining shipments i, j, and k: weight i + weight j + weight k 42, The maximum volume that a 53-foot container can withstand is 3,000 cubic feet. a) For a two-stop combining shipments i and j: volume i + volume j 3,000 b) For a three-stop combining shipments i, j, and k: volume i + volume j + volume k 3, The shipment date can move within the same calendar week as it is currently scheduled. a) For a two-stop combining shipments i and j: i. week i = week j ii. year i = year j b) For a three-stop combining shipments i, j, and k: i. week i = week j = week k ii. year i = year j = year k 19

20 OPTIMIZATION VARIABLES Data parameters i = j = k = l = m = {shipments} Ø = {} s i,j : cost savings per truck if shipments i and j are combined s k,l,m : cost savings per truck if shipments k, l, and m are combined ORM i,j : distance ø,j + distance i,j distance ø,i ORM k,l,m : distance Ø,m + distance l,m + distance k,l distance Ø,k Decision variables x i,j = 1, if a truck drives directly from shipment i to shipment j 0, otherwise x k,l,m = 1, if a truck drives from the origin of shipment k to shipment m to shipment l 0, otherwise 20

21 ANALYSIS Total Shipments Per Number of Shipments Pre-Optimization Post-Optimization

22 ANALYSIS Total Number of Multi-Stop Shipments Per Number of Shipments Three-Stop Shipments Two-Stop Shipments 0 22

23 ANALYSIS $900 Total Savings Per Savings (in Thousands) $800 $700 $600 $500 $400 $300 $200 $100 $- Three-Stop Shipments Two-Stop Shipments 23

24 ANALYSIS 120 Group Sizes per Number of Groups Five Four Three 0 24

25 HIGH FREQUENCY OPTIMIZATION MODEL 25