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A Minimum Expected Response Model - Formulation, Heuristic Solution and Application by Dr. Cem Saydam and Dr. Hari Rajagopalan. Bjarne Berg UNC-Charlotte. What We’ll Cover …. Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions
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A Minimum Expected Response Model- Formulation, Heuristic Solution and Applicationby Dr. Cem Saydam and Dr. Hari Rajagopalan Bjarne BergUNC-Charlotte
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
Introduction • As The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
The Models – MERLP-1 • Variables: Conversly The
The Models – MERLP-1 • Minimize: • Subject to: The
The Models – MERLP-2 • Variables: The
The Models – MERLP-2 • Minimize: • Subject to: The
Reactive Tabu Search procedure sdf The
Greedy Search Algorithm sdf The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
Solution Algorithm • As The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
Average Distance Travelled • As The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
Conclusions • As The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home
Some Observations • As The
What We’ll Cover … Introduction The Models The Solution Algorithm Application to Charlotte Mecklenburg County data Conclusions Some Observations 7 Key Points to Take Home