Dedication	6
	Preface	8
	Contents	12
	Chapter 1: Train Scheduling	13
	1.1 Introduction and Background	13
	1.2 Role of Trains in the Railroad Operations Research Landscape	14
	1.3 Types of Trains and Related Definitions	17
	1.4 Specifying Road Trains	19
	1.5 OR Challenges: Designing the Road Train Plan	22
	1.5.1 Road Train Design Problem	23
	1.5.2 Single Versus Multi-Block Trains	23
	1.6 Train Routing/Block-to-Train Assignment Problems	25
	1.6.1 Example Problem	29
	1.6.2 Feasible Solution	31
	1.7 Train Scheduling (Timing) Problem	34
	1.7.1 Key Assumptions	35
	1.7.2 Scheduling Variables	36
	1.7.3 Scheduling Constraints	36
	1.7.4 Cost Parameters	37
	1.7.5 Observations on Solution Strategies	38
	1.7.6 Special Cases	39
	1.7.7 Problem Examples	41
	1.8 Specifying Unit Trains	45
	1.9 Local Service Specification Strategies	46
	1.10 Train Plan Design Versus Real-Time Operations	49
	1.11 Opportunities	52
	References	53
	Chapter 2: Locomotive Scheduling Problem	55
	2.1 Introduction	55
	2.2 Background on Locomotive Scheduling	56
	2.2.1 Hard Constraints	57
	2.2.2 Soft Constraints	58
	2.2.3 Objective Function	58
	2.3 Mathematical Models for Locomotive Scheduling	58
	2.3.1 Space–Time Network Construction	58
	2.3.2 Problem Size and Stage-Wise Solution Approach	59
	2.3.3 Consist Flow Formulation for the LPP	61
	2.3.3.1 Notation	62
	2.3.3.2 Decision Variables	62
	2.3.3.3 Objective Function	63
	2.3.3.4 Constraints	63
	2.4 Incorporating Practical Requirements	64
	2.4.1 Cab-Signal Requirements	64
	2.4.2 Foreign Power Requirements	65
	2.5 Applications of the Model	66
	2.5.1 Quantifying the Impact of Varying Minimum Connection Time	66
	2.5.2 Quantifying the Effect of Changing Transport Volume on Key Performance Characteristics	67
	References	68
	Chapter 3: Simulation of Line of Road Operations	69
	3.1 Introduction	69
	3.2 Fundamental Elements for a Dispatching Algorithm	76
	3.3 Developing a Dispatching Algorithm	78
	3.3.1 Overview	78
	3.3.2 Example	79
	3.3.3 Simplified Assumptions	89
	3.4 Future Directions	90
	Chapter 4: Car Scheduling/Trip Planning	91
	4.1 Introduction and Background	91
	4.2 Car Scheduling/Trip Planning Systems in Context	93
	4.3 Plan Compliance and the Value of Trip Plans	95
	4.4 Current Industry Practices: Basic Car Scheduling/ Trip Planning Concepts	96
	4.4.1 Current Industry Practices: Block Selection Logic	101
	4.4.2 Current Industry Practices: Train Selection Logic	101
	4.4.3 Current Industry Practices: Other Special Considerations	104
	4.5 OR Challenge: Typical Reasons of Trip Plan Failures	106
	4.6 Trip Plan Output Usages	107
	4.7 OR Challenges: Alternate Approaches to Car Scheduling and Special Cases	108
	4.8 Capacitation and Reservations	113
	4.8.1 Specifying Capacities	114
	4.8.2 Managing Reservations	118
	4.9 Planning and Optimization	119
	4.10 Time-Space Network Solutions	121
	4.10.1 Dynamic Car Scheduling	123
	4.11 Opportunities	127
	References	129
	Chapter 5: Railway Blocking Process	131
	5.1 Introduction and Background	131
	5.1.1 Impact of Blocking on System Efficiency and Service	132
	5.1.2 Specifying the Blocking Plan	134
	5.1.3 Plan Complexity	135
	5.2 Current Industry Practices: The Blocking Rules Concept	136
	5.2.1 Yard-Blocks, Train-Blocks, Class Codes, and Block Swaps	139
	5.2.2 Local Service	141
	5.3 The Table-Based Blocking Systems OR Challenge	142
	5.4 Algorithmic Blocking	144
	5.5 Examples of Areas Presenting OR Challenges	146
	5.6 Semi-manual Blocking Plan Design Techniques	148
	5.6.1 Incremental Blocking Plan Design Techniques	148
	5.6.2 Tuning an Existing Plan	148
	5.6.3 Checking Circuity and Excessive Handlings	150
	5.6.4 Change Traffic Volume at a Yard	150
	5.6.5 Designing Blocking Plans Using a Clean-Sheet Approach	151
	5.6.6 Tuning Table-Based, Traffic Destination Attribute Rules Using Relaxation	152
	5.6.7 Additional Methods for Testing Plans	155
	5.6.8 Triplet Analysis for Blocking Plan Comparisons	155
	5.6.9 Tree View Analysis	157
	5.7 Specialized Blocking Situations	157
	5.8 Blocking Plan Optimization	161
	5.8.1 Considerations That Automated Blocking Optimization Techniques Should Consider	162
	5.8.2 Mathematical Representation of the Block Design Optimization Problem	163
	5.8.2.1 Data	164
	5.8.2.2 Variables	164
	5.8.2.3 Constraints	165
	5.8.2.4 Objective	166
	Optimization Techniques	166
	Heuristic Approach	167
	Initial Blocking Plan	167
	Iteratively Improve the Plan	168
	Resequencing Quickly	168
	Finding Global Optimum	169
	Changing Yard Penalties	169
	Advanced Mathematical Programming	169
	5.9 Additional Considerations	171
	5.10 Opportunities	172
	References	173
	Chapter 6: Crew Scheduling Problem	175
	6.1 Introduction	175
	6.2 Background on Crew Scheduling	176
	6.2.1 Terminology	176
	6.2.2 Regulatory and Contractual Requirements	178
	6.3 Mathematical Models for Crew Scheduling	179
	6.3.1 Model Inputs	179
	6.3.2 Space–Time Network Construction	179
	6.3.3 Mathematical Formulation	181
	6.3.4 Solution Methods	183
	6.3.4.1 Successive Constraint Generation (SCG)	183
	6.3.4.2 Quadratic Cost-Perturbation (QCP) Algorithm	183
	6.4 Applications of the Model	185
	6.4.1 Tactical Benefits	185
	6.4.2 Planning Benefits	186
	6.4.3 Strategic Benefits	186
	References	187
	Chapter 7: Empty Railcar Distribution	188
	7.1 Introduction	188
	7.2 Background on Empty Railcar Distribution	189
	7.2.1 Local Distribution and Shipper Pools	189
	7.2.2 Rules-Based Transaction Processing Systems	189
	7.2.3 Nonintegrated Optimization Systems	190
	7.3 Current Day Integrated Real-Time Optimization Systems	190
	7.3.1 Model Inputs	190
	7.3.1.1 Car Supply: Actual and Predicted	191
	7.3.1.2 Car Orders: Actual and Predicted	191
	7.3.1.3 Shipper Preferences	191
	7.3.1.4 Cost Parameters	191
	7.3.1.5 Operational Information	192
	7.3.2 Model Framework	192
	7.3.2.1 Model Preprocessing	192
	7.3.2.2 Model Formulation	192
	7.3.3 Model Output Post Processing	194
	7.3.4 Systems Integration	194
	7.3.4.1 Optimization Engine: Customer Car Order System	195
	7.3.4.2 Optimization Engine-Transactional Equipment Distribution System	195
	7.3.4.3 Transactional Equipment Distribution System: Car Movement Management and Tracking System	195
	7.3.4.4 Optimization Model: Operational Systems: Decision Making Process Integration	196
	7.3.5 Reported Benefits	197
	7.3.6 Other Implementation Considerations	197
	7.3.6.1 User Acceptance	197
	7.3.6.2 Model Thrashing	197
	7.3.7 Other Modeling Considerations	198
	7.3.7.1 Endogenizing Stochasticity	198
	7.3.7.2 Including Blocking Costs in Empty Car Assignment	198
	7.3.8 Other Areas of Application in Rail	198
	References	199
	Chapter 8: Network Analysis and Simulation	201
	8.1 Introduction and Background	201
	8.1.1 Planning and Simulation	202
	8.1.2 Other Types of Simulations	203
	8.2 Types of Network Level Simulations	203
	8.2.1 Uncapacitated Deterministic Simulations with Fixed Plans	204
	8.2.2 Uncapacitated Deterministic Simulations with Probabilistic Connections	205
	8.2.3 Capacitated Simulations with Fixed Plans	205
	8.2.4 Capacitated Simulations with Dynamic Plan Elements	207
	8.2.5 Full Monte-Carlo Capacitated Simulations	208
	8.3 Resource Estimation	208
	8.3.1 Estimation of Crews	209
	8.3.2 Estimation of Locomotives	210
	8.3.3 Estimation of Railcar Requirements	211
	8.3.4 Estimation of Yard Workloads	213
	8.4 Roles of Network Simulation	214
	8.4.1 Mergers	214
	8.4.2 Network Modifications	215
	8.4.3 Emergency Situations or Special Circumstances	216
	8.5 Average Day Analysis	216
	8.5.1 Uncapacitated Average Day Analysis	217
	8.5.2 Capacitated Average Day Analysis	218
	8.5.2.1 Achieving a Robust Train Volume Formulation	222
	8.5.2.2 Train-Block Prioritization	223
	8.5.2.3 Fill Blocks	224
	8.5.2.4 Capacitation by Length and Gross Weight	224
	8.6 Future Directions and Opportunities	225
	References	226
	Chapter 9: Simulation of Yard and Terminal Operations	228
	9.1 Introduction	228
	9.2 Reasons to Simulate	229
	9.3 The Problem	231
	9.3.1 Train Arrival	231
	9.3.2 Handling the Inbound Crew and Power	232
	9.3.3 Inbound Car Inspection	232
	9.3.4 Switch (Classify) Cars	232
	9.3.5 Train Assembly	234
	9.3.6 Final Train Assembly	234
	9.3.7 Train Departure	235
	9.4 Matching the Analytic Approach with Study Requirements	235
	9.5 Building a Yard Simulation	237
	9.5.1 Conceptual Design	237
	9.5.1.1 Simulation Engine	238
	9.5.1.2 Decision Engine	239
	9.5.1.3 Inbound Process	240
	9.5.1.4 Switching Process	240
	9.5.1.5 Train Assembly Process	241
	9.5.1.6 Departure Process	241
	9.5.2 Data for Simulation	245
	9.5.3 Other Issues to Be Resolved	246
	9.6 Recent Past to Current State of the Art	247
	9.7 Future Directions	249
	Chapter 10: Operations Research in Rail Pricing and Revenue Management	252
	10.1 Introduction	252
	10.1.1 U.S. Freight Rail Pricing History	252
	10.1.2 Revenue Management for Rail: Importance	253
	10.1.3 Revenue Management for Rail: Challenges	253
	10.1.4 Revenue Management for Rail: Recent Opportunities	254
	10.2 Analytical Techniques in Freight Revenue Management	255
	10.3 Characterizing Customer Behavior: Estimating Product Demand	255
	10.3.1 Forecasting Demand Levels	256
	10.3.2 Predicting Customer Price Sensitivity	257
	10.4 Research in Revenue Management Models	258
	10.4.1 Train and Block-Based Capacity Approaches	258
	10.4.2 Service-Based Pricing Strategies	260
	10.4.3 Container-Centric Yield Management	261
	10.5 Future Directions and Opportunities for Revenue Management and Freight Rail	262
	References	262
	Chapter 11: Intermodal Rail	264
	11.1 Introduction and Background Information	264
	11.1.1 Definition of Intermodal	264
	11.1.2 Brief History of Intermodal	265
	11.1.3 Equipment Variations	267
	11.1.4 Role of Railroads and IMCs	268
	11.1.5 Chassis Pools, Both Domestic and International	268
	11.2 Examples of Decisions to Be Made Where OR Models Can Be Used	269
	11.2.1 Pricing	269
	11.2.2 Container Fleet Sizing	270
	11.2.3 Demand Forecasting	270
	11.2.4 Assignment of Equipment to Customers	271
	11.2.5 Chassis Fleet Sizing and Positioning	271
	11.3 Detailed Examples of Actual Model Implementations	272
	11.3.1 Empty Container Repositioning	272
	11.3.1.1 Background on Problem	272
	11.3.1.2 Typical Decision Making Approach	272
	11.3.1.3 Optimization Approach	273
	11.3.1.4 Network Construction	274
	11.3.1.5 Objective Function Components	275
	11.3.1.6 Constraints	275
	11.3.1.7 Solution Approach	276
	11.3.1.8 Results	276
	11.3.2 Chassis Pool Sizing	277
	11.3.2.1 Overview	277
	11.3.2.2 Approach	277
	11.3.3 Container Selection Process	278
	11.3.3.1 Background	278
	11.3.3.2 Approach	279
	11.3.3.3 Supply and Demand Forecasting	279
	11.3.3.4 Capacity Valuation	280
	11.3.3.5 Fleet Inventory Targeting	280
	11.3.3.6 Load Accept Optimization (LAO)	281
	11.3.3.7 Load Routing Optimization	281
	11.3.3.8 Results	282
	11.4 Opportunities	282
	11.4.1 Forecasting	282
	11.4.2 Tactical Equipment Matching	283
	Index	284