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Intro; Contents; About the Editors; 1 Introduction; 1.1 Part I: High-Performance Computational Tools Dedicated to Computational Intelligence; 1.2 Part II: Simulating the Possibilities and Getting Ready for Real Applications; 1.3 Part III: The Social Science Behind the Cities of the Future; 1.4 Part IV: Emerging Cities' Services and Systems; Part I High-Performance Computational Tools Dedicated to Computational Intelligence; 2 Urban Mobility in Multi-Modal Networks Using Multi-Objective Algorithms; 2.1 Introduction; 2.2 Basic Concepts; 2.3 Related Works; 2.4 Implementations
2.4.1 Deterministic Approach2.4.2 Heuristic Approach; 2.4.3 Encoding; 2.4.4 Initialization; 2.4.5 Genetic Operators; 2.5 Computational Experiments; 2.6 Conclusion; References; 3 Urban Transport and Traffic Systems: An Approachto the Shortest Path Problem and Network FlowThrough Colored Graphs; 3.1 Introduction; 3.2 Brief Bibliographic Review; 3.3 Shortest Path Problem in Colored Graphs; 3.3.1 Algorithm 1; 3.3.2 Examples; 3.3.2.1 Instance 1; 3.3.2.2 Instance 2; 3.3.2.3 Instance 3; 3.3.2.4 Instance 4; 3.4 Minimum Cost Flow Problem in Colored Graphs; 3.4.1 Algorithm 2; 3.4.2 Examples
3.4.2.1 Instance 13.4.2.2 Instance 2; 3.4.2.3 Instance 3; 3.5 Final Considerations; References; 4 An Adaptive Large Neighborhood Search Heuristic to Solve the Crew Scheduling Problem; 4.1 Introduction; 4.2 Bibliographic Review; 4.3 Adaptive Large Neighborhood Search Applied to the CSP; 4.3.1 Solution Representation; 4.3.2 Initial Solution; 4.3.3 Objective Function (OF); 4.3.4 Destructive Heuristics; 4.3.4.1 Random Removal; 4.3.4.2 Removal of Worst Position; 4.3.4.3 Shaw Removal; 4.3.4.4 Average Removal; 4.3.5 Constructive Heuristics; 4.3.5.1 Greedy Insertion; 4.3.5.2 Regret Insertion
4.3.6 Choosing the Heuristics of Removal and Insertion4.3.7 Weights Adaptive Adjustment; 4.3.8 Acceptance Criteria; 4.3.9 Method to Minimize the Quantity of Duties; 4.4 Computational Experiments; 4.4.1 Parameters Calibration; 4.4.2 Results; 4.5 Conclusions; References; 5 Proposal for Analysis of Location of Popular ResidentialUsing the p-Median; 5.1 Introduction; 5.2 My Home My Life Program; 5.3 Methodology and Literature Review; 5.3.1 Step 1: Data Collection; 5.3.2 Step 2: Network Vertex Marking; 5.3.3 Step 3: Vertex Demand Calculation; 5.3.4 Step 4: Application of the p-Median Model
5.3.5 Step 5: Comparing Results with Market Launches5.4 Results; 5.5 Discussion of Results; 5.6 Conclusions; References; 6 An Ant Colony System Metaheuristic Applied to a Cooperative of Recyclable Materials of Sorocaba: A Case Study; 6.1 Introduction; 6.2 Mathematical Formulation; 6.2.1 Vehicle Routing Problem with Fuel Consumption Minimization Objective; 6.2.2 Ant Colony System (ACS); 6.3 Methodology; 6.3.1 Materials; 6.3.2 Methods; 6.3.2.1 Case Study; 6.4 Results and Discussion; 6.5 Conclusion; References
2.4.1 Deterministic Approach2.4.2 Heuristic Approach; 2.4.3 Encoding; 2.4.4 Initialization; 2.4.5 Genetic Operators; 2.5 Computational Experiments; 2.6 Conclusion; References; 3 Urban Transport and Traffic Systems: An Approachto the Shortest Path Problem and Network FlowThrough Colored Graphs; 3.1 Introduction; 3.2 Brief Bibliographic Review; 3.3 Shortest Path Problem in Colored Graphs; 3.3.1 Algorithm 1; 3.3.2 Examples; 3.3.2.1 Instance 1; 3.3.2.2 Instance 2; 3.3.2.3 Instance 3; 3.3.2.4 Instance 4; 3.4 Minimum Cost Flow Problem in Colored Graphs; 3.4.1 Algorithm 2; 3.4.2 Examples
3.4.2.1 Instance 13.4.2.2 Instance 2; 3.4.2.3 Instance 3; 3.5 Final Considerations; References; 4 An Adaptive Large Neighborhood Search Heuristic to Solve the Crew Scheduling Problem; 4.1 Introduction; 4.2 Bibliographic Review; 4.3 Adaptive Large Neighborhood Search Applied to the CSP; 4.3.1 Solution Representation; 4.3.2 Initial Solution; 4.3.3 Objective Function (OF); 4.3.4 Destructive Heuristics; 4.3.4.1 Random Removal; 4.3.4.2 Removal of Worst Position; 4.3.4.3 Shaw Removal; 4.3.4.4 Average Removal; 4.3.5 Constructive Heuristics; 4.3.5.1 Greedy Insertion; 4.3.5.2 Regret Insertion
4.3.6 Choosing the Heuristics of Removal and Insertion4.3.7 Weights Adaptive Adjustment; 4.3.8 Acceptance Criteria; 4.3.9 Method to Minimize the Quantity of Duties; 4.4 Computational Experiments; 4.4.1 Parameters Calibration; 4.4.2 Results; 4.5 Conclusions; References; 5 Proposal for Analysis of Location of Popular ResidentialUsing the p-Median; 5.1 Introduction; 5.2 My Home My Life Program; 5.3 Methodology and Literature Review; 5.3.1 Step 1: Data Collection; 5.3.2 Step 2: Network Vertex Marking; 5.3.3 Step 3: Vertex Demand Calculation; 5.3.4 Step 4: Application of the p-Median Model
5.3.5 Step 5: Comparing Results with Market Launches5.4 Results; 5.5 Discussion of Results; 5.6 Conclusions; References; 6 An Ant Colony System Metaheuristic Applied to a Cooperative of Recyclable Materials of Sorocaba: A Case Study; 6.1 Introduction; 6.2 Mathematical Formulation; 6.2.1 Vehicle Routing Problem with Fuel Consumption Minimization Objective; 6.2.2 Ant Colony System (ACS); 6.3 Methodology; 6.3.1 Materials; 6.3.2 Methods; 6.3.2.1 Case Study; 6.4 Results and Discussion; 6.5 Conclusion; References