Heuristics for Optimization and Learning [Paperback]

This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: Recent Developments in Metaheuristics and Metaheuristics for Production Systems, books in Springer Series in Operations Research/Computer Science Interfaces.

The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. 

The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

Process Plan Generation for Recon
gurable Manufacturing Systems: Exact vs Evolutionary-Based Multi-Objective Approaches.- On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time.- A Variable Block Insertion Heuristic for the Energy-Ef
cient Permutation Flowshop Scheduling with Makespan Criterion.- Solving 0-1 Bi-Objective Multi-Dimensional Knapsack Problems using Binary Genetic Algorithm.- An asynchronous parallel evolutionary algorithm for solving large .instances of the multi-objective QAP.- Learning from Prior Designs for Facility Layout Optimization.- Single-objective Real-parameter Optimization: Enhanced LSHADE-SPACMA Algorithm.- Operations Research at Bulk Terminal: A Parallel Column Generation Approach.- Heuristic solutions for the (?,?)-k feature set problem.- Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization.- Dynamic Simulated Annealing with Adaptive Neighborhood using Hidden Markov Model.- Hl$