A5031762088- Optimization and Packing Problems
- Vehicle Routing Optimization Methods
- Advanced Manufacturing and Logistics Optimization
- Complexity and Algorithms in Graphs
- Advanced Graph Theory Research
- Optimization and Search Problems
- Scheduling and Timetabling Solutions
- Advanced Optimization Algorithms Research
- Scheduling and Optimization Algorithms
- Infrastructure Resilience and Vulnerability Analysis
- Constraint Satisfaction and Optimization
- Facility Location and Emergency Management
- Air Traffic Management and Optimization
- Aviation Industry Analysis and Trends
- Complex Network Analysis Techniques
- Risk and Portfolio Optimization
- Smart Grid Security and Resilience
- Advanced Control Systems Optimization
- Graph Labeling and Dimension Problems
- Formal Methods in Verification
- Maritime Ports and Logistics
- Railway Engineering and Dynamics
- Supply Chain and Inventory Management
- Advanced Measurement and Metrology Techniques
- International Law and Aviation
Sapienza University of Rome
2021-2024
University of Southampton
2024
Hamad bin Khalifa University
2024
Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti
2017-2022
Lamsade
2014-2021
Université Paris Dauphine-PSL
2014-2019
Centre National de la Recherche Scientifique
2013-2019
Université Paris Sciences et Lettres
2016-2019
Université Sorbonne Paris Nord
2012-2013
Laboratoire d'Informatique de Paris-Nord
2013
We propose a framework to model general guillotine restrictions in two-dimensional cutting problems formulated as mixed-integer linear programs (MIPs). The modeling requires pseudopolynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our cuts is the first one that, once it implemented within state-of-the-art MIP solver, tackle instances challenging size. mainly concentrate our analysis on knapsack problem (G2KP), model, an exact...
We propose a numerically exact algorithm for solving the Bin-Packing Problem (BPP) based on branch-price-and-cut framework combined with pattern-enumeration method. Key to is novel technique computation of safe dual bounds widely adopted set covering reformulation BPP (tightened additional valid inequalities) precision that higher than one general-purpose floating-point solvers. Our also relies an integer (fixed-point) label setting pricing problem associated tightened set-covering...
We consider a generalization of the 0–1 knapsack problem in which profit each item can take any value range characterized by minimum and maximum possible profit. A set specific profits is called scenario. Each feasible solution associated with scenario has regret, given difference between optimal for such considered solution. The interval min–max regret (MRKP) then to find that over all scenarios minimized. extremely challenging both from theoretical practical point view. Its decision...
We study a natural generalization of the knapsack problem, in which each item exists only for given time interval. One has to select subset items (as classical case), guaranteeing that instant, set existing selected total weight no larger than capacity. focus on exact solution noting prior our work, best method was straightforward application general-purpose solver integer linear programming formulation. Our results indicate much better can be obtained by using same tackle nonstandard...