Montoya-Torres J.R., Juan A.A., Huatuco L.H., Faulin J., Rodriguez-Verjan G.L. (eds.) Hybrid Algorithms for Service, Computing and Manufacturing Systems. Routing and Scheduling Solutions

IGI Global, 2012. — 354 p.In a global and highly-competitive world, organizations face an increasingly difficult environment, with increasing economic pressure and customer demands for more complex products and services that are inexpensive and that can be provided at short notice. In relation to this, hybrid algorithms could play an important role in helping organizations achieve current imperative costs reduction and fast product development.
This book deals with the study of Hybrid Algorithms for Service, Computing and Manufacturing Systems. Solutions to current real-life problems, such as supply chain management require more than an individual algorithm. Hybrid algorithms take advantage of each individual algorithm and help find solutions which are stronger/faster or more efficient than those provided by individual algorithms. Recently, the use of hybrid algorithms has increased in popularity in different research areas and industries. One of the indicators of this situation is the number of sessions, workshops, and conferences dealing with the design and application of hybrid algorithms. In practice, hybrid algorithms have proved to be efficient in solving a wide range of complex real-life application problems in different domains, including: Logistics, Bioinformatics and Computational Biology, Engineering Design, Networking, Environmental Management, Transportation, Finance and Business.
This book aims at exploring state-of-the-art research developments and applications in these research areas from an interdisciplinary perspective that combines approaches from Operations Research, Computer Science, Artificial Intelligence and Applied Computational Mathematics.
The interest in hybrid algorithms has risen considerably among academics in order to improve both the behavior and the performance of meta-heuristics. Meta-heuristics are a branch of optimization in Computer Science, Operations Research and Applied Computational Mathematics that are related to algorithms and computational complexity theory. The past few years have witnessed the development of numerous meta-heuristics in various communities that sit at the intersection of several fields, including Artificial Intelligence, Computational Intelligence, Soft Computing, and Mathematical Programming. Most of the meta-heuristics mimic natural metaphors to solve complex optimization problems (e.g. evolution of species, annealing process, behavior of ant colonies, particle swarm, immune system, bee colony, wasp swarm, or bacterial behavior).Section 1 Hybrid Algorithms for Routing Problems
Matheuristics for Inventory Routing Problems
Vehicle Routing Models and Algorithms for Winter Road Spreading Operations
Routing Solutions for the Service Industry
A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective Vehicle Routing Problem with Time Windows
Strategies for an Integrated Distribution Problem
A Hybrid Algorithm Based on Monte-Carlo Simulation for the Vehicle Routing Problem with Route Length Restrictions
Section 2 Hybrid Algorithms for Scheduling Problems
A Hybrid Particle Swarm Algorithm for Resource-Constrained Project Scheduling
Marriage in Honeybee Optimization to Scheduling Problems
Global Bacteria Optimization Meta-Heuristic: Performance Analysis and Application to Shop Scheduling Problems
Hybrid Algorithms for Manufacturing Rescheduling: Customised vs. Commodity Production
Section 3 Other Applications of Hybrid Algorithms
HMIP Model for a Territory Design Problem with Capacity and Contiguity Constraints
Hybrid Heuristics for the Territory Alignment Problem
A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems