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Parallel Tabu Search and Genetic Algorithm for the Job Shop Schedule Problem with Sequence Dependent Set Up Times
Repository of scripts and data for the "Robustness and resilience of complex networks" paper by Oriol Artime, Marco Grassia, Manlio De Domenico, James P. Gleeson, Hernán A. Makse, Giuseppe Mangioni, Matjaž Perc and Filippo Radicchi, published at Nature Review Physics (2024). https://doi.org/10.1038/s42254-023-00676-y
Repository of the paper "Machine learning dismantling and early-warning signals of disintegration in complex systems" by M. Grassia, M. De Domenico and G. Mangioni
[IEEE TKDE | TITS 2023] "Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling" | "Neural Airport Ground Handling"
A Python implementation of a branch-and-bound approach (plus a simple greedy heuristic) to solve a variation of the multiple knapsack problem where items have both individual and pairwise benefits.
Official Implementation of the NeurIPS'23 paper 'Maximum Independent Set: Self-Training through Dynamic Programming'.
Feasibility Intensive Genetic Algorithm (FIGA) for the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW)
My Edinburgh Napier University Honours Project: investigating the multi-objective Capacitated Vehicle Routing Problem with Time Windows (CVRPTW).
This repo encapsulates a Python implementation of the Simulated Annealing Algorithm to solve by means of a "minimum energy state" heuristic the NP-hard n-machines|no preemption|C_max job shop scheduling problem, considering n=2 machines and jobs having release dates. The code was designed and wrote by me. The whole heuristic design, complexity a…
Genetic algorithm implementation to solve the famous NP-hard problem - The Travelling Salesman
The ripple-spreading algorithm that determines all Pareto-optimal paths from one node to all other nodes for the multi-objective shortest path problem.
The ripple-spreading algorithm for the k-color shortest path problem
The ripple-spreading algorithm that determines all Pareto-optimal paths for the multi-category multi-objective path optimization problem.
Use local optimization and stochastic optimization algorithms for solving Igloo NP-Hard problem.
This is an algorithm that given a Boolean formula with n=3 distinct variables and 2 variables per clause (2-Satisfiability), determines the values of the variables that result in the formula being TRUE, or determines that there is no solution and the formula is never TRUE. Written in Python.
Solving Single Machine Schenduling problem with different approaches
Loynaz: Approximate Edge Dominating Set Solver
This repository contains a Python implementation of a color coding method for the problem of finding a subtree with k vertices in a given graph.
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