Improving Efficiency in Transportation Networks using Python
summary
Shows three concrete Python applications for transportation logistics: using Google's OR-Tools to compute cost-minimizing driver-to-load matches, using NetworkX for macro-level network optimization recommendations, and using scikit-fuzzy (skfuzzy) to support accept/reject work decisions with fuzzy logic. Covers graph analytics and data-science techniques applied to reduce costs and improve decision-making in dynamic freight distribution networks.
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chapters (18)
- 0:00Start
- 4:25The Carrier Network
- 5:41Graph Analytics
- 7:35Improving Connectivity
- 9:51Collaborative Decision Making
- 11:00Graphs for the Win
- 11:36Finding The Right Order
- 12:29The Challenges
- 13:27Factors, Components & Ratings
- 14:05Factors & Components
- 15:26Rating Mechanism
- 15:54Fuzzy Logic
- 18:21Making Decisions Easier
- 19:16Matching Powers to Loads
- 21:22Cost Function
- 22:29Minimizing The Cost Matrix
- 24:34Takeaways
- 25:46Questions & Resources.
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