LEADER 08603nam 22004453 450 001 9910770271103321 005 20231218120806.0 010 $a3-031-49662-0 035 $a(MiAaPQ)EBC31020190 035 $a(Au-PeEL)EBL31020190 035 $a(EXLCZ)9929374290200041 100 $a20231218d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOperations Research and Enterprise Systems $e11th International Conference, ICORES 2022, Virtual Event, February 3-5, 2022, and 12th International Conference, ICORES 2023, Lisbon, Portugal, February 19-21, 2023, Revised Selected Papers 205 $a1st ed. 210 1$aCham :$cSpringer,$d2024. 210 4$d©2024. 215 $a1 online resource (276 pages) 225 1 $aCommunications in Computer and Information Science Series ;$vv.1985 311 08$aPrint version: Liberatore, Federico Operations Research and Enterprise Systems Cham : Springer,c2024 9783031496615 327 $aIntro -- Preface -- Organization -- Contents -- Methodologies and Technologies -- Multiple Heuristics with Reinforcement Learning to Solve the Safe Shortest Path Problem in a Warehouse -- 1 Introduction -- 2 Detailed Problem -- 2.1 A Warehouse and the Risk Induced by Current Activity -- 2.2 Our Problem: Searching for a Safe Shortest Path Inside the Warehouse -- 2.3 Some Structural Results -- 2.4 A Consequence: Risk Versus Distance Reformulation of the SSPP Model -- 2.5 Discussion About the Complexity -- 3 Solving the SSPP When the Path is Fixed -- 3.1 Generate Decisions with Three Methods -- 3.2 The Filtering Issue - Speeding up the Heuristic -- 4 Solving the SSPP - Proposed Algorithms -- 5 Speeding Algorithms Through Statistical Learning Techniques -- 5.1 Bounding the Number of States Generated -- 6 Numerical Experiments -- 7 Conclusion -- References -- Minimizing the Non-value Task Times: A Pickup and Delivery Problem with Two-Dimensional Bin-Packing -- 1 Introduction -- 2 Related Works -- 3 Case Study -- 3.1 Manufacturing Tool Repair Service -- 3.2 Solution Proposed -- 4 2D-BPP Model -- 5 Computational Results -- 5.1 Results -- 5.2 Model Efficiency -- 5.3 Integrated Solution with VRPSDPTW and 2D-BPP -- 6 Conclusions and Future Work -- References -- Predictive Maintenance Optimization Under Stochastic Production in Complex Systems -- 1 Introduction -- 2 Related Work -- 2.1 Maintenance Optimization in Complex Systems -- 2.2 Maintenance Optimization Methods -- 3 Problem Description and Formulation -- 3.1 DPMO and MILP Model -- 3.2 SPMO and Chance-constrained Programming Model -- 4 Approximations of the Chance-Constrained Model -- 4.1 Approximation Using Discrete Information -- 4.2 Approximation Using Continuous Information -- 4.3 Relationship Between the Approximation Approaches -- 5 Computational Experiments. 327 $a5.1 Parameter Settings of Instances, Uncertainty, and Indicators -- 5.2 Comparison of Our Approaches -- 5.3 Impact of Probabilities in Chance Constraints -- 6 Conclusions and Future Work -- References -- Automated City Segmentation for Pollution Threshold Attribution: The Example of New Cairo -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset Formation -- 3.2 Sorting Technique -- 3.3 Increasing the Boundaries -- 3.4 Plotting on Maps -- 3.5 Pollution Measurement -- 4 Results -- 4.1 Area of Test -- 4.2 Datasets Formation -- 4.3 Sorting Technique -- 4.4 Increasing the Boundaries -- 4.5 Plotting on Maps -- 4.6 Air Pollution -- 4.7 Noise Pollution -- 4.8 Comparison of Measurements and Threshold -- 5 Conclusion -- References -- Customer Satisfaction and Company Revenue: A Solution Approach for the Passenger Management on Buses -- 1 Introduction -- 2 Problem Description -- 3 Dynamic Programming Formulation -- 4 Linear Programming Approximation -- 5 Booking Limit Policy -- 6 Numerical Results -- 7 Conclusions -- References -- Reinforcement Learning Algorithms: Categorization and Structural Properties -- 1 Introduction -- 2 Classes of MDP Problems and Suitable RL Algorithms -- 2.1 Model-Free and Model-Based RL -- 2.2 Policy Optimization and Value Learning -- 2.3 Finite and Infinite Horizon Problems -- 2.4 Discrete and Continuous State Spaces -- 2.5 Discrete and Continuous Action Sets -- 2.6 Discrete and Continuous Time Problems -- 3 Further Properties of RL Algorithms -- 3.1 On-Policy and Off-Policy RL -- 3.2 Deterministic and Stochastic Policies -- 3.3 Bias-Variance Tradeoff -- 3.4 Exploration-Exploitation Tradeoff -- 3.5 Hyperparameter Tuning and Robustness -- 3.6 Learning Stability -- 4 Summary -- 5 Conclusion -- References. 327 $aComparison of Multi-objective Linear Programming Solutions Using Performance Metrics Based on Data Envelopment Analysis Models -- 1 Introduction -- 2 Applied Data Envelopment Analysis Models -- 2.1 Integer Slack-Based Measure Model (INT-SBM) -- 2.2 Super-Efficiency DEA Model (SE-DEA) -- 3 Performance Metrics Based on DEA Models -- 3.1 Cardinality Metric (CM) -- 3.2 Accuracy Metric (AC) -- 3.3 Diversity Metric (DM) -- 4 Analysis of MOLP Solutions Applying the Proposed DEA Metrics -- 4.1 Analysis of the Non-Convex Region of a Pareto Frontier -- 4.2 Performance Analysis of Pareto Frontiers for a Real Case Study -- 5 Conclusions -- References -- Comparing Power Flow Models in Tree Networks with Stochastic Load Demands -- 1 Introduction -- 2 Model Description -- 2.1 Queuing Model of EV-Charging -- 2.2 Distribution Network Model -- 2.3 Power Flow Equations -- 2.4 Summary -- 3 Analytical Results -- 3.1 Duality in Tree Networks -- 3.2 Inner Region of Feasible Region -- 4 Numerical Results -- 4.1 Line Network -- 4.2 Tree Network -- 5 Summary -- 6 Proofs -- 6.1 Proof of Lemma 1 -- 6.2 Proof of Lemma 2 -- References -- Robust Optimization for Operating Room Scheduling with Uncertain Surgical Durations: Impact of Risk-Aversion on Delay -- 1 Introduction -- 2 Literature Review -- 3 Operating Room Scheduling -- 3.1 Robust Optimization -- 3.2 Surgical Duration Uncertainty -- 4 Numerical Analysis -- 4.1 Data -- 4.2 Results -- 5 Conclusion -- References -- Matheuristic Local Search for the Placement of Analog Integrated Circuits -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 ILP Model and Extensions -- 4.1 Baseline Model -- 4.2 Improving the Performance of the Solver -- 5 Matheuristic as a Local Search -- 5.1 Intensification -- 5.2 Diversification -- 6 Experiments -- 6.1 Methodology and Data -- 6.2 Performance with Redundant Constraints. 327 $a6.3 Matheuristics on Synthetic Data -- 6.4 Improvement on Real Life Instances -- 7 Conclusion -- References -- Applications -- An Urban-Scale Application of the Problem of Designing Green Tourist Trips with Time Windows -- 1 Introduction -- 2 Literature Review -- 3 A Multi-criteria Method for the POI Evaluation -- 4 Mathematical Formulation -- 5 Case-Study Setting and Computational Results -- 6 Conclusion -- References -- Optimization of the Storage Location Assignment Problem Using Nested Annealing -- 1 Introduction -- 2 Literature Review -- 3 Simulated Annealing -- 4 Problem Formulation -- 4.1 Objective Function -- 4.2 Picking-Log Distance -- 4.3 Reassignment Distance -- 5 Optimization Algorithm -- 5.1 Assignment Sampling Using Markov Chain Monte Carlo (MCMC) and Hamming Distances -- 5.2 TSP Optimization and Cost Caching -- 5.3 Heatmap-Based Approximation -- 5.4 Nested Annealing -- 5.5 Restarts -- 6 Experiments -- 6.1 Overview -- 6.2 Parameters -- 6.3 Datasets -- 6.4 Experiment Results -- 7 Conclusion -- A Appendix -- References -- Inventory Management Optimization in Multi-Stage Supply Chains Under Uncertainty -- 1 Introduction -- 2 Mathematical Background -- 2.1 B-Spline Functions ch13DeB78 -- 2.2 The CRLS Problem -- 3 The MSSC Model -- 4 The DRMPC Approach -- 4.1 Local MMCOP for Ai -- 4.2 Some Remarks on the Cost Functional Ji,k -- 4.3 The Constraints u-i,k and u+i,k -- 5 Reformulation of the MMCOP -- 6 Feasibility and Stability of the DRMPC -- 7 Simulation Results -- 8 Concluding Remarks -- References -- Author Index. 410 0$aCommunications in Computer and Information Science Series 700 $aLiberatore$b Federico$01252586 701 $aWesolkowski$b Slawo$01460734 701 $aDemange$b Marc$01252587 701 $aParlier$b Greg H$01252585 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910770271103321 996 $aOperations Research and Enterprise Systems$93660702 997 $aUNINA