24 research outputs found
Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm
15+ MILLION TOP 1% MOST CITED SCIENTIST 12.2% AUTHORS AND EDITORS FROM TOP 500 UNIVERSITIES Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm
A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem
A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem
Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line
Modified DE Algorithms for Solving Multi-depot Vehicle Routing Problem with Multiple Pickup and Delivery Requests
Balancing Bicycle Sharing Systems: An Analysis of Path Relinking and Recombination within a GRASP Hybrid
The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review
Parameter estimation of MIMO bilinear systems using a Levy shuffled frog leaping algorithm
by Narendra Kawaria, Rohan Patidar and Nithin V. Georg
Path Relinking for a Constrained Simulation-Optimization Team Scheduling Problem Arising in Hydroinformatics
We apply Path Relinking to a real life constrained optimization problem concerning the scheduling of technicians due to activate on
site devices located on a water distribution network in case of a contamination event, in order to reduce the amount of consumed contaminated
water. Teams travel on the road network when moving from one device to the next, as in the Multiple Traveling Salesperson Problem. The objective, however, is not minimizing travel time but the minimization of consumed contaminated water. This is computed through a computationally demanding simulation given the devices activation times. We propose alternative Path Relinking search strategies exploiting time-based and precedence-based neighborhoods, and evaluate the improvement gained by coupling Path Relinking with state of the art, previously developed, hybrid Genetic Algorithms. Experimental results on a real network are provided to support the efficacy of the methodology