Hybrid Ant Colony Optimization For Fuzzy Unrelated Parallel Machine Scheduling Problems

This study extends the best hybrid ant colony optimization variant developed by Liao et al. (2014) for crisp unrelated parallel machine scheduling problems to solve fuzzy unrelated parallel machine scheduling problems in consideration of trapezoidal fuzzy processing times, trapezoidal fuzzy sequencing dependent setup times and trapezoidal fuzzy release times. The objective is to find the best schedule taking minimum fuzzy makespan in completing all jobs. In this study, fuzzy arithmetic is used to determine fuzzy completion times of jobs and defuzzification function is used to convert fuzzy numbers back to crisp numbers for ranking. Eight fuzzy ranking methods are tested to find the most feasible one to be employed in this study. The fuzzy arithmetic testing includes four different cases and each case with the following operations separately, i.e., addition, subtraction, multiplication and division, to investigate the spread of fuzziness as fuzzy numbers are subject to more and more number of operations. The effect of fuzzy ranking methods on hybrid ant colony optimization (hACO) is investigated. To prove the correctness of our methodology and coding, unrelated parallel machine scheduling with fuzzy numbers and crisp numbers are compared based on scheduling problems up to 15 machines and 200 jobs. Relative percentage deviation (RPD) is used to evaluate the performance of hACO in solving fuzzy unrelated parallel machine scheduling problems. A numerical study on large-scale scheduling problems up to 20 machines and 200 jobs is conducted to assess the performance of the hACO algorithm. For comparison, a discrete particle swarm optimization (dPSO) algorithm is implemented for fuzzy unrelated parallel machine scheduling problem as well. The results show that the hACO has better performance than dPSO not only in solution quality in terms of RPD value, but also in computational time

Preface: Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. Motivated by the capability of the biologically inspired algorithms the present book on ""Swarm Intelligence: Focus on Ant and Particle Swarm Optimization"" aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics

An improved ant colony algorithm for the job shop scheduling problem

Instances of static scheduling problems can be easily represented as graphs where each node represents a particular operation. This property makes the Ant Colony Algorithms well suited for different kinds of scheduling problems. In this paper we present an improved Ant System for solving the Job Shop Scheduling (JSS) Problem. After each cycle the Ant System applies a scheduler builder to each solution. The schedule builder is able to generate under a controlled manner different types of schedules (from non-delay to active). Any improvement achieved for a solution will affect the performance of the algorithm in the next cycles by changing accordingly the amount of pheromone on certain paths. Since the pheromone is the building block of an ant algorithm, it is expected that these changes guide the search towards more promising areas of the search space. The computational study involves a set of instances of different size and difficulty. The results are compared against the best solutions known so far and results reported from earlier studies of ant algorithms applied to the JSSP.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Unit Commitment or generator scheduling is one of complex combination issues aiming to obtain the cheapest generating power total costs. Ant Colony Optimization is proposed as a method to solve Unit Commitment issues because it has a better result convergence according to one of journals that reviews methods to solve Unit Commitment issues. Ant Colony Optimization modification into Nodal Ant Colony Optimization as well as addition of several elements are also conducted to overcome Ant Colony Optimization limitations in resolving Unit Commitment issues. Nodal Ant Colony Optimization simulations are then compared with Genetic Algorithm and Simulated Annealing methods which previously has similar simulations. Reliability index combination in a form of Loss of Load Probability and Expected Unserved Energy are also added as reliability constraints in the system. Comparison of three methods shows that Nodal Ant Colony Optimization is able to provide better results up to 0.08% cheaper than Genetic Algorithm or Simulated Annealing methods