Ground Vehicle Assignment 2 Operations

  • Vehicle allowances
    1. Employees must consistently drive an average of at least 300 miles per month on City business to qualify for vehicle allowances. Only one form of reimbursement is available for any employee for in-town travel; either a car allowance or mileage reimbursement, but not both. Anyone who receives a car allowance may not drive a City vehicle without approval from the department director. Mileage reimbursement is available for out-of-town travel for any employee (including those receiving car allowances) for any pre-approved out-of-town travel in a private vehicle.
    2. Employees must annually submit a Request for Vehicle Allowance (Form D) to receive a vehicle allowance. This form must be approved by the department director and submitted to ARA Payroll Services Division for processing. This form should be accompanied by a Form PD201. Vehicle allowances are not retroactive.
    3. Employees must maintain a log of trips on the Department Vehicle Use Report-Car Allowances (Form CA). This form shall be submitted to the department director or designee for internal departmental monitoring, review, and quarterly report preparation. Employees who fail to submit mileage reports on this form shall have their vehicle allowances revoked and shall not be reimbursed at the per mile rate for occasional travel.
    4. Department directors are required to review and adjust where necessary all vehicle allowances semi-annually to reflect the actual average miles reported by employees on the quarterly reports.
    5. Vehicle allowances shall be published by the ARA Department. These rates shall be re-evaluated and adjusted periodically without otherwise requiring revision to this A.P.
  • Monthly Mileage*Monthly Allowance*
    300-450$151.88
    451-600$212.83
    601-750$273.58
    Over 750$354.58

    *Amounts are current as of the revision date of this A.P. Employees should check with the Finance Department for updates.

  • Executive Vehicle Assignment/Allowance
    1. The Office of the Mayor shall determine the executive vehicle allowance rate and approve vehicle assignments or allowances for all executive staff.
    2. Department directors shall have the option of receiving a vehicle allowance or being assigned a home storage vehicle.
    3. Deputy directors shall receive a vehicle allowance or be assigned a home storage vehicle on the basis of job duties when it is beneficial to the City.
    4. Home storage vehicles for all other executive staff, including Police and Fire, shall be assigned on the basis of job duties where it is beneficial to the City. The executive vehicle allowance is not available to these employees.
  • Information detailing occasional use mileage for privately owned vehicles can be found in Administrative Procedure 2-5.
  • arking fees are reimbursable for City or privately owned vehicles if the fees are incurred while on official City business and accompanied by a validated receipt. Both employees who receive vehicle allowances and those who use their privately owned vehicle for City business shall be reimbursed for authorized parking expenses. Parking for local travel may be reimbursed out of the Petty Cash fund in accordance with the Petty Cash Fund Policy & Procedures (A.P. 5- 3 Revised) or reported on the Travel Expenses Summary Report & Log.
    1. All City vehicles must have a meter receipt or placard issued by the Administrative and Regulatory Affairs Department in the window to park at a City meter.
    2. Parking methods can be set up through ARA Parking Management to allow employees on City business to park at City meters and charge the department directly. These charges will be charged back to the department on a quarterly basis.
    3. Parking citations issued to City vehicles will be charged back to the department on a quarterly basis if not paid by the employee.
  • Reporting and Enforcement
    1. Employees are responsible for reporting the mileage incurred while on City business on Form CA. Employees must submit a request for vehicle mileage reimbursement no later than 60 days from the date the mileage was logged in order to receive payment.
    2. Copies of all vehicle allowance requests, approvals, mileage reports, and Petty Cash transactions shall be maintained by the Department Director or designee and shall be audited periodically for compliance.
    3. Department directors shall complete and maintain quarterly reports for all employees receiving a vehicle allowance. Quarterly reports shall be prepared in April, July, October, and January of each year for the preceding quarter.
    4. Department directors are required to review and adjust where necessary all vehicle allowances semi-annually to reflect the actual average miles reported by employees on the quarterly reports.
    5. Department directors are responsible for revoking and adjusting vehicle allowances in cases where employees have failed to adhere to the policies and procedures outlined herein.
  • About the contributors xiii

    Acknowledgements xix

    1 Introduction 1

    1.1 Introduction 1

    1.2 Background and Scope 3

    1.3 About the Chapters 4

    References 6

    2 The In‐Transit Vigilant Covering Tour Problem for Routing Unmanned Ground Vehicles 7

    2.1 Introduction 7

    2.2 Background 8

    2.3 CTP for UGV Coverage 9

    2.4 The In‐Transit Vigilant Covering Tour Problem 9

    2.5 Mathematical Formulation 11

    2.6 Extensions to Multiple Vehicles 14

    2.7 Empirical Study 15

    2.8 Analysis of Results 21

    2.9 Other Extensions 24

    2.10 Conclusions 25

    Author Statement 25

    References 25

    3 Near‐Optimal Assignment of UAVs to Targets Using a Market‐Based Approach 27

    3.1 Introduction 27

    3.2 Problem Formulation 29

    3.2.1 Inputs 29

    3.2.2 Various Objective Functions 29

    3.2.3 Outputs 31

    3.3 Literature 31

    3.3.1 Solutions to the MDVRP Variants 31

    3.3.2 Market‐Based Techniques 33

    3.4 The Market‐Based Solution 34

    3.4.1 The Basic Market Solution 36

    3.4.2 The Hierarchical Market 37

    3.4.2.1 Motivation and Rationale 37

    3.4.2.2 Algorithm Details 40

    3.4.3 Adaptations for the Max‐Pro Case 41

    3.4.4 Summary 41

    3.5 Results 42

    3.5.1 Optimizing for Fuel‐Consumption (Min‐Sum) 43

    3.5.2 Optimizing for Time (Min‐Max) 44

    3.5.3 Optimizing for Prioritized Targets (Max‐Pro) 47

    3.6 Recommendations for Implementation 51

    3.7 Conclusions 52

    Appendix 3.A A Mixed Integer Linear Programming (MILP) Formulation 53

    3.A.1 Sub-tour Elimination Constraints 54

    References 55

    4 Considering Mine Countermeasures Exploratory Operations Conducted by Autonomous Underwater Vehicles 59

    4.1 Background 59

    4.2 Assumptions 61

    4.3 Measures of Performance 62

    4.4 Preliminary Results 64

    4.5 Concepts of Operations 64

    4.5.1 Gaps in Coverage 64

    4.5.2 Aspect Angle Degradation 64

    4.6 Optimality with Two Different Angular Observations 65

    4.7 Optimality with N Different Angular Observations 66

    4.8 Modeling and Algorithms 67

    4.8.1 Monte Carlo Simulation 67

    4.8.2 Deterministic Model 67

    4.9 Random Search Formula Adapted to AUVs 68

    4.10 Mine Countermeasures Exploratory Operations 70

    4.11 Numerical Results 71

    4.12 Non‐uniform Mine Density Distributions 72

    4.13 Conclusion 74

    Appendix 4.A Optimal Observation Angle between Two AUV Legs 75

    Appendix 4.B Probabilities of Detection 78

    References 79

    5 Optical Search by Unmanned Aerial Vehicles: Fauna Detection Case Study 81

    5.1 Introduction 81

    5.2 Search Planning for Unmanned Sensing Operations 82

    5.2.1 Preliminary Flight Analysis 84

    5.2.2 Flight Geometry Control 85

    5.2.3 Images and Mosaics 86

    5.2.4 Digital Analysis and Identification of Elements 88

    5.3 Results 91

    5.4 Conclusions 92

    Acknowledgments 94

    References 94

    6 A Flight Time Approximation Model for Unmanned Aerial Vehicles: Estimating the Effects of Path Variations and Wind 95

    Nomenclature 95

    6.1 Introduction 96

    6.2 Problem Statement 97

    6.3 Literature Review 97

    6.3.1 Flight Time Approximation Models 97

    6.3.2 Additional Task Types to Consider 98

    6.3.3 Wind Effects 99

    6.4 Flight Time Approximation Model Development 99

    6.4.1 Required Mathematical Calculations 100

    6.4.2 Model Comparisons 101

    6.4.3 Encountered Problems and Solutions 102

    6.5 Additional Task Types 103

    6.5.1 Radius of Sight Task 103

    6.5.2 Loitering Task 105

    6.6 Adding Wind Effects 108

    6.6.1 Implementing the Fuel Burn Rate Model 110

    6.7 Computational Expense of the Final Model 111

    6.7.1 Model Runtime Analysis 111

    6.7.2 Actual versus Expected Flight Times 113

    6.8 Conclusions and Future Work 115

    Acknowledgments 117

    References 117

    7 Impacts of Unmanned Ground Vehicles on Combined Arms Team Performance 119

    7.1 Introduction 119

    7.2 Study Problem 120

    7.2.1 Terrain 120

    7.2.2 Vehicle Options 122

    7.2.3 Forces 122

    7.2.3.1 Experimental Force 123

    7.2.3.2 Opposition Force 123

    7.2.3.3 Civilian Elements 123

    7.2.4 Mission 124

    7.3 Study Methods 125

    7.3.1 Closed‐Loop Simulation 125

    7.3.2 Study Measures 126

    7.3.3 System Comparison Approach 128

    7.4 Study Results 128

    7.4.1 Basic Casualty Results 128

    7.4.1.1 Low Density Urban Terrain Casualty Only Results 128

    7.4.1.2 Dense Urban Terrain Casualty‐Only Results 130

    7.4.2 Complete Measures Results 131

    7.4.2.1 Low Density Urban Terrain Results 131

    7.4.2.2 Dense Urban Terrain Results 132

    7.4.2.3 Comparison of Low and High Density Urban Results 133

    7.4.3 Casualty versus Full Measures Comparison 135

    7.5 Discussion 136

    References 137

    8 Processing, Exploitation and Dissemination: When is Aided/Automated Target Recognition “Good Enough” for Operational Use? 139

    8.1 Introduction 139

    8.2 Background 140

    8.2.1 Operational Context and Technical Issues 140

    8.2.2 Previous Investigations 141

    8.3 Analysis 143

    8.3.1 Modeling the Mission 144

    8.3.2 Modeling the Specific Concept of Operations 145

    8.3.3 Probability of Acquiring the Target under the Concept of Operations 146

    8.3.4 Rational Selection between Aided/Automated Target Recognition and Extended Human Sensing 147

    8.3.5 Finding the Threshold at which Automation is Rational 148

    8.3.6 Example 148

    8.4 Conclusion 149

    Acknowledgments 151

    Appendix 8.A 151

    Ensuring [Q ] * decreases as ζ* increases 152

    References 152

    9 Analyzing a Design Continuum for Automated Military Convoy Operations 155

    9.1 Introduction 155

    9.2 Definition Development 156

    9.2.1 Human Input Proportion (H) 156

    9.2.2 Interaction Frequency 157

    9.2.3 Complexity of Instructions/Tasks 157

    9.2.4 Robotic Decision‐Making Ability (R) 157

    9.3 Automation Continuum 157

    9.3.1 Status Quo (SQ) 158

    9.3.2 Remote Control (RC) 158

    9.3.3 Tele‐Operation (TO) 158

    9.3.4 Driver Warning (DW) 158

    9.3.5 Driver Assist (DA) 158

    9.3.6 Leader‐Follower (LF) 159

    9.3.6.1 Tethered Leader‐Follower (LF1) 159

    9.3.6.2 Un‐tethered Leader‐Follower (LF2) 159

    9.3.6.3 Un‐tethered/Unmanned/Pre‐driven Leader‐Follower (LF3) 159

    9.3.6.4 Un‐tethered/Unmanned/Uploaded Leader‐Follower (LF4) 159

    9.3.7 Waypoint (WA) 159

    9.3.7.1 Pre‐recorded “Breadcrumb” Waypoint (WA1) 160

    9.3.7.2 Uploaded “Breadcrumb” Waypoint (WA2) 160

    9.3.8 Full Automation (FA) 160

    9.3.8.1 Uploaded “Breadcrumbs” with Route Suggestion Full Automation (FA1) 160

    9.3.8.2 Self‐Determining Full Automation (FA2) 160

    9.4 Mathematically Modeling Human Input Proportion (H) versus System Configuration 161

    9.4.1 Modeling H versus System Configuration Methodology 161

    9.4.2 Analyzing the Results of Modeling H versus System Configuration 165

    9.4.3 Partitioning the Automation Continuum for H versus System Configuration into Regimes and Analyzing the Results 168

    9.5 Mathematically Modeling Robotic Decision‐Making Ability (R) versus System Configuration 169

    9.5.1 Modeling R versus System Configuration Methodology 169

    9.5.2 Mathematically Modeling R versus System Configuration When Weighted by H 171

    9.5.3 Partitioning the Automation Continuum for R (Weighted by H) versus System Configuration into Regimes 175

    9.5.4 Summarizing the Results of Modeling H versus System Configuration and R versus System Configuration When Weighted by H 177

    9.6 Mathematically Modeling H and R 178

    9.6.1 Analyzing the Results of Modeling H versus R 178

    9.7 Conclusion 180

    9.A System Configurations 180

    10 Experimental Design for Unmanned Aerial Systems Analysis: Bringing Statistical Rigor to UAS Testing 187

    10.1 Introduction 187

    10.2 Some UAS History 188

    10.3 Statistical Background for Experimental Planning 189

    10.4 Planning the UAS Experiment 192

    10.4.1 General Planning Guidelines 192

    10.4.2 Planning Guidelines for UAS Testing 193

    10.4.2.1 Determine Specific Questions to Answer 194

    10.4.2.2 Determine Role of the Human Operator 194

    10.4.2.3 Define and Delineate Factors of Concern for the Study 195

    10.4.2.4 Determine and Correlate Response Data 196

    10.4.2.5 Select an Appropriate Design 196

    10.4.2.6 Define the Test Execution Strategy 198

    10.5 Applications of the UAS Planning Guidelines 199

    10.5.1 Determine the Specific Research Questions 199

    10.5.2 Determining the Role of Human Operators 199

    10.5.3 Determine the Response Data 200

    10.5.4 Define the Experimental Factors 200

    10.5.5 Establishing the Experimental Protocol 201

    10.5.6 Select the Appropriate Design 202

    10.5.6.1 Verifying Feasibility and Practicality of Factor Levels 202

    10.5.6.2 Factorial Experimentation 202

    10.5.6.3 The First Validation Experiment 203

    10.5.6.4 Analysis: Developing a Regression Model 204

    10.5.6.5 Software Comparison 204

    10.6 Conclusion 205

    Acknowledgments 205

    Disclaimer 205

    References 205

    11 Total Cost of Ownership (TOC): An Approach for Estimating UMAS Costs 207

    11.1 Introduction 207

    11.2 Life Cycle Models 208

    11.2.1 DoD 5000 Acquisition Life Cycle 208

    11.2.2 ISO 15288 Life Cycle 208

    11.3 Cost Estimation Methods 210

    11.3.1 Case Study and Analogy 210

    11.3.2 Bottom‐Up and Activity Based 211

    11.3.3 Parametric Modeling 212

    11.4 UMAS Product Breakdown Structure 212

    11.4.1 Special Considerations 212

    11.4.1.1 Mission Requirements 214

    11.4.2 System Capabilities 214

    11.4.3 Payloads 214

    11.5 Cost Drivers and Parametric Cost Models 215

    11.5.1 Cost Drivers for Estimating Development Costs 215

    11.5.1.1 Hardware 215

    11.5.1.2 Software 218

    11.5.1.3 Systems Engineering and Project Management 218

    11.5.1.4 Performance‐Based Cost Estimating Relationship 220

    11.5.1.5 Weight‐Based Cost Estimating Relationship 223

    11.5.2 Proposed Cost Drivers for DoD 5000.02 Phase Operations and Support 224

    11.5.2.1 Logistics – Transition from Contractor Life Support (CLS) to Organic Capabilities 224

    11.5.2.2 Training 224

    11.5.2.3 Operations – Manned Unmanned Systems Teaming (MUM‐T) 225

    11.6 Considerations for Estimating Unmanned Ground Vehicle Costs 225

    11.7 Additional Considerations for UMAS Cost Estimation 230

    11.7.1 Test and Evaluation 230

    11.7.2 Demonstration 230

    11.8 Conclusion 230

    Acknowledgments 231

    References 231

    12 Logistics Support for Unmanned Systems 233

    12.1 Introduction 233

    12.2 Appreciating Logistics Support for Unmanned Systems 233

    12.2.1 Logistics 234

    12.2.2 Operations Research and Logistics 236

    12.2.3 Unmanned Systems 240

    12.3 Challenges to Logistics Support for Unmanned Systems 242

    12.3.1 Immediate Challenges 242

    12.3.2 Future Challenges 242

    12.4 Grouping the Logistics Challenges for Analysis and Development 243

    12.4.1 Group A – No Change to Logistics Support 243

    12.4.2 Group B – Unmanned Systems Replacing Manned Systems and Their Logistics Support Frameworks 244

    12.4.3 Group C – Major Changes to Unmanned Systems Logistics 247

    12.5 Further Considerations 248

    12.6 Conclusions 251

    References 251

    13 Organizing for Improved Effectiveness in Networked Operations 255

    13.1 Introduction 255

    13.2 Understanding the IACM 256

    13.3 An Agent‐Based Simulation Representation of the IACM 259

    13.4 Structure of the Experiment 260

    13.5 Initial Experiment 264

    13.6 Expanding the Experiment 265

    13.7 Conclusion 269

    Disclaimer 270

    References 270

    14 An Exploration of Performance Distributions in Collectives 271

    14.1 Introduction 271

    14.2 Who Shoots How Many? 272

    14.3 Baseball Plays as Individual and Networked Performance 273

    14.4 Analytical Questions 275

    14.5 Imparity Statistics in Major League Baseball Data 277

    14.5.1 Individual Performance in Major League Baseball 278

    14.5.2 Interconnected Performance in Major League Baseball 281

    14.6 Conclusions 285

    Acknowledgments 286

    References 286

    15 Distributed Combat Power: The Application of Salvo Theory to Unmanned Systems 287

    15.1 Introduction 287

    15.2 Salvo Theory 288

    15.2.1 The Salvo Equations 288

    15.2.2 Interpreting Damage 289

    15.3 Salvo Warfare with Unmanned Systems 290

    15.4 The Salvo Exchange Set and Combat Entropy 291

    15.5 Tactical Considerations 292

    15.6 Conclusion 293

    References 294

    Index 295

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