The contributions included in the volume are drawn from presentations at ODS2019 – International Conference on Optimization and Decision Science, which was the 49th annual meeting of the Italian Operations Research Society (AIRO) held at Genoa, Italy, on 4-7 September 2019. This book presents very recent results in the field of Optimization and Decision Science. While the book is addressed primarily to the Operations Research (OR) community, the interdisciplinary contents ensure that it will also be of very high interest for scholars and researchers from many scientific disciplines, including computer sciences, economics, mathematics, and engineering. Operations Research is known as the discipline of optimization applied to real-world problems and to complex decision-making fields. The focus is on mathematical and quantitative methods aimed at determining optimal or near-optimal solutions in acceptable computation times. This volume not only presents theoretical results but also covers real industrial applications, making it interesting for practitioners facing decision problems in logistics, manufacturing production, and services. Readers will accordingly find innovative ideas from both a methodological and an applied perspective.
Massimo Paolucci received a PhD in electronic and computer science in 1990. He is Associate Professor in Operations Research at the Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS) of the University of Genoa. His research activities are focused on metaheuristic and matheuristic algorithms for combinatorial optimization problems, planning and scheduling, decision support systems, and multi-criteria methods. Reference fields of application are intermodal logistics and shipping, and manufacturing.
Anna Sciomachen is Full Professor of Operations Research at the Department of Economics and Business Studies, University of Genoa, where she is Coordinator of the Master of Science in Management of Maritime and Port Enterprises and teaches Optimization and simulation methods for logistics. She is a past President of the Italian Society of Operations Research. Her main research fields are: optimization models and heuristic methods in distributive logistics and multimodal transportation networks, liner problems, stowage planning, simulation techniques for performance analysis, and location-routing problems.
Pierpaolo Uberti received his PhD in Mathematics for Financial Markets from the University of Milano-Bicocca in 2010 for a dissertation on "Higher Moments Asset Allocation". Since 2011 he has been a researcher at the University of Genoa. His research interests cover the fields of quantitative finance, optimization, portfolio selection, and risk measures.
Author(s): Massimo Paolucci, Anna Sciomachen, Pierpaolo Uberti
Series: AIRO Springer Series
Edition: 1
Publisher: Springer
Year: 2019
Language: English
Pages: XI, 503
Preface......Page 6
Contents......Page 8
About the Editors......Page 12
1 Introduction......Page 13
2 Dynamic Model of the Terminal......Page 15
3 Receding Horizon Berth Allocation......Page 17
4 Simulation Results......Page 19
References......Page 23
1 Introduction......Page 25
2 Problem Statement......Page 27
3 Problem Formulation......Page 29
4 Computational Experiments......Page 32
5 Conclusions......Page 33
References......Page 34
1 Introduction......Page 35
2 Optimal Transport......Page 36
3 The Maximum Nearby Flow Problem......Page 38
4 Computational Results......Page 42
References......Page 44
1 Introduction......Page 46
2 Problem and Literature Models......Page 48
3 Real Features......Page 50
4 Computational Experiments......Page 52
References......Page 55
Portfolio Leverage in Asset Allocation Problems......Page 57
1 Introduction......Page 58
2 The Theoretical Proposal......Page 60
3 Empirical Evidence......Page 63
References......Page 66
1 Introduction......Page 67
2 Problem Definition......Page 69
2.1 Overview of the Solution Approach......Page 71
3.1 Constructive Heuristic......Page 72
3.2 Multi-Start Randomized Constructive Heuristic......Page 73
3.3 Genetic Algorithm (GA)......Page 74
4 Computational Experiments......Page 75
References......Page 77
1 Introduction......Page 79
2 The Basic ELM Paradigm......Page 81
3 Gradient Boosting for Optimization with the ELM......Page 82
4 Application to Optimization Problems......Page 83
4.1 Optimal Control......Page 84
4.2 Multistage Stochastic Optimization......Page 85
4.3 Maximum Likelihood Estimation......Page 87
References......Page 88
1 Introduction......Page 90
2 Problem Description......Page 92
3 A Time-Indexed MILP Formulation for the Problem......Page 93
4 Preliminary Computational Results......Page 101
5 Conclusion......Page 102
References......Page 103
1 Introduction......Page 104
2 Extreme Learning Machine......Page 106
3 Infinite Kernel......Page 108
4 Infinite Kernel Extreme Learning Machine......Page 110
5 Conclusions......Page 113
References......Page 114
1 Introduction......Page 115
2 Lagrangian Mechanics......Page 117
3 Generalization to Time-Dependent Potential......Page 120
4 Conclusions......Page 121
References......Page 122
1 Introduction......Page 123
2 Problem Description......Page 125
3 Data-Driven Scheduling......Page 127
4 Experimental Analysis......Page 130
References......Page 133
1 Introduction......Page 134
2.1 Step (1): AS/RS Descriptive Model......Page 135
2.2 Step (2): Model Validation and Bottleneck Analysis......Page 138
2.3 Step (3): Optimizing Bottleneck......Page 140
2.4 Step (4): Experimental Evaluation......Page 142
References......Page 144
1 Introduction......Page 145
2 Modeling......Page 146
3 Algorithms......Page 148
4 Experimental Evaluation......Page 150
References......Page 155
1 The Automated Warehouse......Page 156
2.1 Problem Variations with Capacity 1 (1/l/o/s)......Page 158
2.3.2 Two Dimensions (2/2/P/F)......Page 159
2.3.4 Deliveries (2/1/D/F)......Page 160
2.3.6 Variable Sites (2/1/P/V)......Page 161
2.4.3 Capacity and Variable Sites (q/1/P/V)......Page 162
2.5 Three Complicating Features......Page 163
References......Page 164
1 Introduction and Problem Statement......Page 165
2.2 Objective Functions......Page 168
2.3 Neighborhood Search Algorithm......Page 170
3 Case Study: Hypothetical Multiple MCI......Page 171
4 Results and Discussion......Page 172
References......Page 175
1 Introduction......Page 177
2 The Game Theory Model......Page 178
3 Lagrange Multipliers and Nash Equilibria......Page 182
4 A Numerical Example......Page 184
References......Page 187
1 Introduction......Page 189
2 The Mathematical Model......Page 190
3 Numerical Examples......Page 194
References......Page 198
1 Introduction......Page 200
2 Mathematical Formulations......Page 202
2.2 KKT Reformulation......Page 203
2.3 Dual Reformulation......Page 204
3 Cut Generation Algorithm......Page 205
4 Computational Experiments......Page 206
References......Page 209
1 Introduction......Page 210
2 Preliminary Investigations......Page 211
3 Local Search Algorithms......Page 213
4 Experimental Results......Page 214
5 Conclusion and Perspectives......Page 219
References......Page 220
1 Introduction......Page 221
2 Course Timetabling Formulation......Page 222
2.1 Notation......Page 223
2.2 The Formulation......Page 224
3.1 Case Study: Master's Degree in Mathematics......Page 229
3.2 Data from the Literature......Page 230
References......Page 232
On the Sizing of Security Personnel Staff While Accounting for Overtime Pay......Page 234
1 Introduction......Page 235
2 Staff Size Optimization for a Fixed Workload......Page 237
3 A 0/1 Linear Programming Model for Staff Scheduling......Page 239
4 Application to a Real Case Study......Page 241
6 Conclusions......Page 243
References......Page 244
1 Introduction......Page 245
2 Related Literature......Page 246
3 Presentation of the Problem......Page 247
4 Simulation-Optimization Approach......Page 248
5 Results on a Case Study......Page 250
References......Page 252
Swap Minimization in Nearest Neighbour Quantum Circuits: An ILP Formulation......Page 254
1 Introduction......Page 255
2 Background and Problem Definition......Page 257
3 ILP Formulation for the MNS-NNC Problem......Page 259
4 Computational Results and Work Perspectives......Page 262
References......Page 263
1 Introduction......Page 265
2 Traffic Network Equilibrium and Efficiency Measure......Page 266
3 Investment Optimization Model......Page 268
4 Numerical Experiments......Page 269
5 Conclusions and Further Perspectives......Page 272
Appendix......Page 273
References......Page 274
1 Introduction......Page 276
2 The Any-to-Any Interaction Model......Page 277
3 Steady-State Opinions......Page 283
4 Conclusions and Open Problems......Page 285
References......Page 286
1 Introduction......Page 288
2 Truck Scheduling Models......Page 289
2.1 Time-Indexed Formulation......Page 291
2.2 Position-Indexed Formulation......Page 292
2.3 Strengthening of the PI Formulation......Page 294
3 Computational Results......Page 295
4 Conclusions......Page 297
References......Page 298
1 Introduction......Page 299
2 A Model for Traffic on Road Networks......Page 300
3 Optimal Coefficients for Traffic Dynamics......Page 302
4 Simulations......Page 303
References......Page 306
1 Introduction......Page 307
2 Problem Formulation......Page 308
3 Metaheuristic Procedure......Page 311
4 Computational Results......Page 313
References......Page 317
Dealing with the Stochastic Home Energy Management Problem......Page 319
1 Introduction......Page 320
2 Problem Definition and Mathematical Formulation......Page 321
3 Computational Experiments......Page 325
References......Page 329
1 Introduction......Page 331
2 Problem Definition......Page 333
3.1 Modeling......Page 334
3.3 Adaptive Large Neighborhood Search......Page 335
4 Experimental Results......Page 336
References......Page 344
1 Introduction......Page 346
2 Optimizing Activities to Make Intermodal Transport Competitive......Page 349
2.2 Rail Activities in a Maritime Terminal......Page 350
3 Port Rail Shunting Scheduling and Re-scheduling Problem......Page 351
3.1 Port Rail Shunting Re-scheduling Problem......Page 352
3.2 (Re-)scheduling Problems Literature Overview......Page 354
3.3 A Time-Space Network Model for Scheduling Shunting Operations......Page 355
3.4 Preliminary Results......Page 359
4 Conclusions......Page 360
References......Page 361
1 Introduction......Page 362
2 A Brief Taxonomy of Shortest Path Problems with Edge Constraints......Page 365
3 Mathematical Model......Page 366
4 Branch and Bound......Page 367
5 Experimental Results......Page 368
References......Page 370
1 Introduction......Page 372
2 Related Work......Page 374
3 Problem Description and System Implementation......Page 375
4 Results......Page 378
5 Conclusions......Page 379
References......Page 380
1 Introduction......Page 381
2 Presentation of the Problem......Page 383
3 Solution Method......Page 385
3.2 Build Routes......Page 386
3.3 Assign Vehicles to Delivery/Collection Routes......Page 387
4 Experiments......Page 388
References......Page 390
1 Introduction......Page 392
2 The Problem......Page 393
3.2 Mathematical Model (TPOS)......Page 395
4.1 Scenarios......Page 400
4.2 Resolution of the “Unlimited” Case......Page 401
4.3 Expansion Options......Page 402
References......Page 404
A Bi-objective Mixed Integer Model for the Single Link Inventory Routing Problem Using the ε-Constraint Method......Page 406
1 Introduction......Page 407
2 Description of the Problem and Mathematical Model......Page 409
3 Bi-objective Approach......Page 410
4 Computational Experiments......Page 411
References......Page 414
1 Introduction......Page 416
2 Problem Statement......Page 418
3 Computational Experiments......Page 422
4 Conclusion......Page 423
References......Page 425
Learning Inventory Control Rules for Perishable Items by Simulation-Based Optimization......Page 426
1 Introduction......Page 427
2 Problem Statement and System Dynamics......Page 428
3 Learning Ordering Rules by Simulation-Based Optimization......Page 430
4 Computational Experiments......Page 431
4.2 Experiment 1: Comparison of Alternative Search Strategies......Page 432
4.3 Experiment 2: Comparing Decision Rules......Page 433
4.4 Experiment 3: Is a More Complicated Policy Warranted?......Page 434
References......Page 435
1 Introduction......Page 437
3 Genetic Algorithm......Page 439
3.2 Initial Population......Page 440
3.5 Mutation......Page 441
4 Computational Experiments......Page 442
5 Considerations and Conclusions......Page 445
References......Page 446
1 Introduction......Page 448
2 The 4-OPT Neighborhood......Page 450
2.1 Reinsertion Schemes, Orbits and Moves......Page 451
3 Previous Approaches and Our Strategy......Page 453
3.2 Our ``Smart Force'' Approach......Page 454
4 Finding the Best 4OPT Move......Page 456
4.1 How to Find the Best Selection......Page 458
5 Computational Results and Conclusions......Page 459
References......Page 461
1 Introduction......Page 462
2 Definitions and SmartStart Algorithms......Page 464
3 Experimental Analysis......Page 465
3.1 Effect of α......Page 466
3.2 Lower Bound Comparison......Page 468
3.3 Realtime Applicability......Page 470
4 Conclusions......Page 471
References......Page 472
1 Introduction......Page 473
2 Related Work......Page 474
3 Modeling Aggregated Traffic Flows......Page 475
3.1 nD: Deterministic Case for a Group of Periodic Sensors......Page 477
4 Numerical Results......Page 479
References......Page 482
1 Introduction......Page 483
2 Problem Statement and Homogeneous Flows Case......Page 484
3 Heterogeneous Flows with Different Requirements......Page 488
4 Numerical Results......Page 489
5 Conclusions......Page 491
References......Page 492
