Days: Tuesday, June 4th Wednesday, June 5th Thursday, June 6th Friday, June 7th
View this program: with abstractssession overviewtalk overview
Meet in the "Compagnie des Indes" room on the ground floor.
50 Years of Metaheuristics
In this talk, we review the milestones in the development of heuristic methods for optimization over the last 50 years. We propose a critical analysis of the main findings and contributions. Starting with the roots of the area that can be traced back to the classical philosophers, we follow the historical path of heuristics and metaheuristics in the field of operations research and list the main milestones, up to the latest proposals to hybridize metaheuristics. We distinguish between well-established methodologies in our community as opposed to nature inspired methods in terms of their contribution to the field. We also comment on the evolution of the methodologies in the MIC conferences, and finish with the next generation of heuristics in the context of machine learning and quantum computing.
Surveying the literature, one could be excused for thinking that there exist hundreds or even thousands of different metaheuristics. We classify those metaheuristics according to the way in which they manipulate the solutions. Constructive heuristics, build solutions from their constituting elements. Local search heuristics perform small changes to a single solution iteratively to improve it as much as possible. Finally, Population-based heuristics combine solutions into new ones. These categories provide a natural way to approach heuristics both from a theoretical and a historical perspective. We do not try to cover all existing metaheuristic methodologies, but we limit ourselves to those that changed our way of thinking in problem solving, and in that sense may be considered the most relevant ones. This talks summarizes the research project carried out with Profs. Marc Sevaux and Kenneth Sörensen recently published in the European Journal of Operations Research (EJOR).
10:45 | Optimization Approaches for a General Class of Single-Machine Scheduling Problems (abstract) |
11:15 | Multi-Neighborhood Search for the Makespan Minimization Problem on Parallel Identical Machines with Conflicting Jobs (abstract) |
11:45 | Reinforcing local search with constraint propagation to solve scheduling problems in Hexaly Optimizer (abstract) |
10:45 | Quantum Optimization Approach for Feature Selection in Machine Learning (abstract) |
11:15 | Quantum heuristics for discrete optimization problems: An outline for three selected technologies. (abstract) |
11:45 | Solving Edge-weighted Maximum Clique problem with DCA warm-start Quantum Approximate Optimization Algorithm (abstract) |
10:45 | Matheuristic for feeder network design problem with optional paired demands and split delivery (abstract) |
11:15 | Area Coverage in Heterogeneous Multistatic Sonar Networks: a Simulated Annealing Approach (abstract) |
11:45 | A Biased Random Key Genetic Algorithm for solving the α-neighbor p-center problem (abstract) |
10:45 | A Large Neighborhood Search Metaheuristic for the Stochastic Mixed Model Assembly Line Balancing Problem with Walking Workers (abstract) |
11:15 | Large Neighborhood Search for the Capacitated P-Median Problem (abstract) |
11:45 | Investigating Large Neighbourhood Search for Bus Driver Scheduling (abstract) |
10:45 | Bi-Objective MRCPSP with Time-Dependent Resource Costs and Time-Varying Resource Capacities (abstract) |
11:15 | A primal matheuristic for multi-objective binary linear optimization problems (abstract) |
11:45 | Experiences using Julia for Implementing Multi-Objective Evolutionary Algorithms (abstract) |
Meet in the "Compagnie des Indes" room on the ground floor.
Meet in the "Compagnie des Indes" room on the ground floor.
16:30 | Two Examples for the Usefulness of STNWeb for Analyzing Optimization Algorithm Behavior (abstract) |
17:00 | Temporal Action Analysis in Metaheuristics: A Machine Learning Approach (abstract) |
17:30 | STNWeb for the Analysis of Optimization Algorithms: A Short Introduction (abstract) |
16:30 | Indirect Flow-Shop coding using rank: application to Indirect QAOA (abstract) |
17:00 | Addressing Machine Unavailability in Job Shop Scheduling: A Quantum Computing Approach (abstract) |
17:30 | Solving Quadratic knapsack problem with biased quantum state Optimization Algorithm (abstract) |
16:30 | A Basic Variable Neighborhood Search for the planar Obnoxious Facility Location Problem (abstract) |
17:00 | An efficient algorithm for the T-row facility layout problem (abstract) |
17:30 | Interpretability, Adaptability, and Scalability of Variable Neighborhood Search (abstract) |
16:30 | Deep Reinforcement Learning for smart restarts in Exploration-only Exploitation-only hybrid metaheuristics (abstract) |
17:00 | Extending CMSA With Reinforcement Learning: Application to Minimum Dominating Set (abstract) |
17:30 | Improved Golden Sine II in synergy with Non-Monopolized Local Search strategy (abstract) |
16:30 | Strategically Influencing Seat Selection in Low-cost Carriers: A GRASP Approach for Revenue Maximization (abstract) |
17:00 | A Continuous-GRASP Random-Key Optimizer (abstract) |
17:30 | Feedback on three topics related to GRASP (abstract) |
Meet in the "Compagnie des Indes" room on the ground floor.
View this program: with abstractssession overviewtalk overview
Solving large-scale vehicle routing problems ... and beyond !
Vehicle routing problems (VRPs) are at the core of distribution logistics optimization and huge research efforts were devoted to the development of effective heuristics for their solution.
In recent years, the explosion of urban logistics introduced new challenges in this domain, related to both the need of handling specific constraints and to a drastic increase in the size of the instances which characterise real world applications. Unfortunately, most of the existing heuristic methods for VRP are one the one hand very effective in achieving high quality solutions on many practically relevant variants of the VRP, but on the other hand do not scale very well with the growth of the size of the problem to be solved.
We developed a family of heuristics, originated by the FILO algorithm, that were designed to achieve a linear growth of the computing time with respect to the instance size while preserving state-of-the-art solution quality. The most recent implementations of FILO were capable of solving large-scale problems with additional constraints and classical variants with up to one million customers in a handful of minutes.
Meet in the "Compagnie des Indes" room on the ground floor.
10:15 | Breakout Local Search for Heaviest Subgraph Problem (abstract) |
10:45 | A matheuristic for a maintenance planning problem with non-convex objective function (abstract) |
11:15 | Fixed set search matheuristic applied to the min-Knapsack problem with compactness constraints and penalty values (abstract) |
11:45 | Matheuristic variants of DSATUR for the vertex coloring problem (abstract) |
10:15 | Hexaly Optimizer 12.5: A model & run solver based on heuristics and exact technics. (abstract) |
10:45 | Behaviour Analysis of Trajectory and Population-based Metaheuristics on Flexible Assembly Scheduling (abstract) |
11:15 | Solving an Integrated Bi-objective Container Terminal Integrated Planning with Transshipment Operations (abstract) |
11:45 | Job-Shop Scheduling with robot synchronization for transport operations (abstract) |
10:15 | A variable neighborhood search approach for the S-labeling problem (abstract) |
10:45 | Improving Biased Random Key Genetic Algorithm with Variable Neighborhood Search for the Weighted Total Domination Problem (abstract) |
11:15 | A Variable Formulation Search Approach for Three Graph Layout Problems (abstract) |
11:45 | VNS-based matheuristic approach to Group Steiner Tree with problem-specific node release strategy (abstract) |
10:15 | Reactive path relinking algorithm (abstract) |
10:45 | VNS with Path Relinking for the Profitable Close-Enough Arc Routing Problem (abstract) |
11:15 | A historical review of GRASP with Path Relinking (abstract) |
11:45 | Combining Neighborhood Search with Path Relinking: a statistical evaluation of path relinking mechanisms (abstract) |
10:15 | Tabu Search for Solving Covering Salesman Problem with Nodes and Segments (abstract) |
10:45 | Investigation of the Benefit of Extracting Patterns from Local Optima to solve a Bi-objective VRPTW (abstract) |
11:15 | A Memetic Algorithm for Large-Scale Real-World Vehicle Routing Problems with Simultaneous Pickup and Delivery with Time Windows (abstract) |
11:45 | Improving Hybrid-Genetic-Search for the Capacited Vehicle Routing Problem: a deep learning approach (abstract) |
Meet in the "Compagnie des Indes" room on the ground floor.
Variable Neighborhood Search: Application in Cross-Docking Optimization
This talk outlines the main variants of the Variable Neighborhood Search (VNS) metaheuristic proposed over the past three decades, emphasizing its application in optimizing cross-docking operations. Cross-docking, a collaborative distribution strategy, involves consolidating and transferring goods between vehicles at an intermediate point before their final distribution. This work considers a critical problem within this strategy: the efficient door assignment and scheduling of trucks to minimize the total processing time (makespan). Instead of traditional two-phase approaches, we propose an integrated mixed-integer linear programming model to simultaneously address both stages in the considered version of the Cross-Dock Door Assignment and Scheduling Problem (CDASP). Furthermore, we design practical variants of a Variable Neighborhood Search metaheuristic with Strategic Oscillation, which obtain solutions with an average gap of less than 0.04% compared to the optimal solutions.
Meet in the "Compagnie des Indes" room on the ground floor.
16:30 | Learning Sparse-lets for Interpretable Classification of Event-interval Sequences (abstract) |
17:00 | Extended set covering for time series segmentation (abstract) |
17:30 | A Chaotic Search Method by Using Adaptive Penalty Coefficient with Temperature Annealing for the Vehicle Routing Problem with Time Windows (abstract) |
16:30 | AdaptQUBO: A New Quantum-inspired Metaheuristic QUBO Solver (abstract) |
17:00 | Utilizing Graph Sparsification for Pre-processing in Max Cut QUBO Solver (abstract) |
17:30 | Comparing Integer Encodings in QUBO for Quantum and Digital Annealing: The Travelling Salesman Problem (abstract) |
16:30 | Optimization of fairness and accuracy on Logistic Regression models (abstract) |
17:00 | Exploring the integration of General Variable Neighborhood Search with exact procedures for the optimization of the Order Batching Problem (abstract) |
16:30 | A vertex weighting-based double-tabu search for the classical p-center problem (abstract) |
17:00 | An Empirical Analysis of Tabu Lists (abstract) |
17:30 | Multi-operator driven iterated tabu search for collaborative operating room scheduling (abstract) |
16:30 | Optimization models for fair horizontal collaboration in demand-responsive transportation (abstract) |
17:00 | A Matheuristic Multi-Start Algorithm for a Novel Static Repositioning Problem in Public Bike-Sharing Systems (abstract) |
17:30 | A decomposition-based metaheuristic to evaluate large-scale ride-sharing systems (abstract) |
18:00 | Optimization of A Last Mile Delivery Model with A Truck and A Drone Using Mathematical Formulation and A VNS Algorithm (abstract) |
View this program: with abstractssession overviewtalk overview
Meet in the "Compagnie des Indes" room on the ground floor.
10:15 | Modeling and Solving Polynomial Binary Programs: Going beyond QUBO (abstract) |
10:45 | A metaheuristic approach for solving the maximum disjoint dominating sets problem (abstract) |
11:15 | Adaptive Ant Colony Optimization Using Node Clustering with Simulated Annealing (abstract) |
11:45 | The Multiple Linear Ordering Problem (abstract) |
10:15 | A Dynamic Algorithm Configuration Framework Using Combinatorial Problem Features and Reinforcement Learning (abstract) |
10:45 | Automatic Configuration with crace (abstract) |
11:15 | Populations of hyperparametric solutions for the design of metaheuristic algorithms: An empirical analysis of performance in Particle Swarm Optimization (abstract) |
11:45 | Applying Learning to Rank for Algorithm Selection (abstract) |
10:15 | Solving the Integrated Patient-to-Room and Nurse-to-Patient Assignment by Simulated Annealing (abstract) |
10:45 | Enhancing Real-World Applicability in Home Healthcare: A Metaheuristic Approach for Advanced Routing and Scheduling (abstract) |
11:15 | Inverse biological radiotherapy treatment planning optimization using simulated annealing (abstract) |
11:45 | A Re-optimization Heuristic for a Dial-a-Ride Problem in the Transportation of Patients (abstract) |
10:15 | What characteristics define a Good Solution in Social Influence Minimization Problems? (abstract) |
10:45 | Optimizing Inventory Routing: A Data-Driven Perspective (abstract) |
11:15 | Data Analytics for Police Deployment Optimization in France (abstract) |
11:45 | Affinity-based distance to deal with categorical variables in a surrogate-based evolutionary algorithm (abstract) |
10:15 | The Multi-objective C2P Collaborative Network Vehicle Routing Problem for Urban Deliveries (abstract) |
10:45 | A rolling horizon framework for the time-dependent multi-visit dynamic safe street snow plowing problem (abstract) |
11:15 | An Adaptive Large Neighborhood Search Approach to Solve Large-Scale Two-Echelon Location Routing Problem in City Logistics (abstract) |
11:45 | Combining Heuristics and Constraint Programming for the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones (abstract) |
Meet in the "Compagnie des Indes" room on the ground floor.
Meet in the "Compagnie des Indes" room on the ground floor.
View this program: with abstractssession overviewtalk overview
Meet in the "Compagnie des Indes" room on the ground floor.
10:15 | Aspiration Probing for Binary Metaheuristic Optimization - Part 1, Simple Myopic Correction (abstract) |
10:45 | An Evolutionary Algorithm for the Rank Pricing Problem (abstract) |
11:15 | First-improvement or best-improvement? An in-depth computational study to elucidate a dominance claim (abstract) |
10:15 | SIRO: A deep learning-based next-generation optimizer for solving global optimization problems (abstract) |
10:45 | A simulated annealing algorithm to learn an RMP preference model (abstract) |
11:15 | A General-Purpose Neural Architecture Search Algorithm for building Deep Neural Networks (abstract) |
11:45 | The Use of Metaheuristics in the Evolution of Collaborative Filtering Recommender Systems: A review (abstract) |
10:15 | A hybrid biased-randomized heuristic for a home care problem with team scheme selection (abstract) |
10:45 | Extracting White-box Knowledge from Word Embedding: Modeling as an Optimization Problem (abstract) |
11:15 | Solving the Two-Stage Robust Elective Patient Surgery Planning under Uncertainties with Intensive Care Unit Beds Availability (abstract) |
11:45 | Greedy heuristics to static ambulance location: A real-life case study (abstract) |
10:15 | A Disjunctive Graph Solution Representation for the Continuous and Dynamic Berth Allocation Problem (abstract) |
10:45 | Modelling and solving a scheduling problem with hazardous products dynamic evolution (abstract) |
11:15 | Multi-Objective Iterated General Variable Neighborhood Search for the Online Flexible Job Shop Problem (abstract) |
11:45 | Advanced Algorithms for the Reclaimer Scheduling Problem with Sequence-Dependent Setup Times and Availability Constraints (abstract) |
10:15 | Optimization of Multi-modal Transportation for Offshore Wind Farm Maintenance: A Case Study from Norway (abstract) |
10:45 | A GRASP algorithm for the Meal Delivery Routing Problem (abstract) |
11:15 | Iterative Heuristic over Periods for the Inventory Routing Problem (abstract) |
Meet in the "Compagnie des Indes" room on the ground floor.