Meta-heuristic based Construction Supply Chain Modelling and Optimization

Driven by the severe competition within the construction industry, the necessity of improving and optimizing the performance of construction supply chain has been aroused. This thesis proposes three problems with regard to the construction supply chain optimization from three perspectives, namely, deterministic single objective optimization, stochastic optimization and multi-objective optimization respectively. Mathematical models for each problem are constructed accordingly and meta-heuristic algorithms are developed and applied for resolving these three problems

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Copyright © 2015 Springer. This is a PDF file of an unedited manuscript that has been accepted for publication in The International Journal of Advanced Manufacturing Technology. The final publication is available at: http://link.springer.com/article/10.1007/s00170-015-7320-y. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions

Mixed-model parallel two-sided assembly line balancing problem: A flexible agent-based ant colony optimization approach

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Assembly lines are frequently used as a production method to assemble complex products. Two-sided assembly lines are utilized to assemble large-sized products (e.g., cars, buses, trucks). Locating two lines in parallel helps improve line efficiency by enabling collaboration between the line workers. This paper proposes a mixed-model parallel two-sided assembly line system that can be utilized to produce large-sized items in an inter-mixed sequence. The mixed-model parallel two-sided line balancing problem is defined and the advantages of utilizing multi-line stations across the lines are discussed. A flexible agent-based ant colony optimization algorithm is developed to solve the problem and a numerical example is given to explain the method systematically. The proposed algorithm builds flexible balancing solutions suitable for any model sequence launched. The dynamically changing workloads of workstations (based on specific product models during the production process) are also explored. A comprehensive experimental study is conducted and the results are statistically analyzed using the well-known paired sample t-test. The test results indicate that the mixed-model parallel two-sided assembly line system reduces the workforce need in comparison with separately balanced mixed-model two-sided lines. It is also shown that the proposed algorithm outperforms the tabu search algorithm and six heuristics often used in the assembly line balancing domain

Optimización metaheurística aplicada en la gestión de pavimentos asfálticos

Pavement engineering is a crossroads between geotechnical and transportation engineering with a sound base on construction materials. There are multiple applications of optimization algorithms in pavement engineering, emphasizing pavement management for its socioeconomic implications and back-calculation of layer properties for its complexity. A detailed literature review shows that optimization has been a permanent concern in pavement engineering. However, only in the last two decades, the increase in computational power allowed the implementation of metaheuristic optimization techniques with promising results in research and practice. Pavement management requires powerful optimization tools for multi-objective problems such as minimizing costs and maximizing the pavement state from network to project level with constrained budgets. A substantial amount of research focuses on genetic algorithms (GA), but new developments include particle intelligence (PSO, ACO, and ABC). The study must go beyond small-sized networks to improve the management of existing road infrastructure (pavement, bridges) based on mechanistic and reliability criteria.La ingeniería de pavimentos es una encrucijada entre la ingeniería geotécnica y la ingeniería de transporte con una sólida base en los materiales de construcción. Existen diferentes aplicaciones de los algoritmos de optimización en la ingeniería de pavimentos, las cuales enfatizan la gestión del pavimento por sus implicaciones socioeconómicas y el cálculo inverso de las propiedades de las capas por su complejidad. Una revisión detallada de la literatura muestra que la optimización ha sido una preocupación permanente en la ingeniería de pavimentos; sin embargo, solo en las últimas dos décadas, el incremento del poder computacional permitió la implementación de técnicas de optimización metaheurísticas con resultados prometedores en la investigación y en la práctica. La gestión del pavimento requiere poderosas herramientas de optimización para problemas con objetivos múltiples, como minimizar costos y maximizar el estado del pavimento desde el nivel de la red hasta el del proyecto con presupuestos limitados. Una cantidad sustancial de investigaciones se centra en los algoritmos genéticos (AG), pero los nuevos desarrollos incluyen inteligencia de partículas (PSO, ACO y ABC). El estudio debe ir más allá de las redes de pequeño tamaño para mejorar la gestión de la infraestructura vial existente (pavimento, puentes) con base en criterios mecanicistas y de confiabilidad

Dynamic programming with approximation function for nurse scheduling

Although dynamic programming could ideally solve any combinatorial optimization problem, the curse of dimensionality of the search space seriously limits its application to large optimization problems. For example, only few papers in the literature have reported the application of dynamic programming to workforce scheduling problems. This paper investigates approximate dynamic programming to tackle nurse scheduling problems of size that dynamic programming cannot tackle in practice. Nurse scheduling is one of the problems within workforce scheduling that has been tackled with a considerable number of algorithms particularly meta-heuristics. Experimental results indicate that approximate dynamic programming is a suitable method to solve this problem effectively

Mathematical Model Developed Using Meta-Initiative Optimization Algorithm for Production and Labor Planning

In todays competitive environment, production efficiency is a very important and key issue in success in the market. However, all decisions of the production unit are interdependent and it is necessary to use an integrated form which leads to finding a better approach for the management. Accordingly, in this research, the integration of three important fields in manufacturing companies has been addressed. These fields include production planning, maintenance, and labor scheduling. In this regard, a novel mathematical model with the aim of optimal use of labor and increasing production volume is presented. In this model of workers’ experience, machine utilization rate and machine failure rate are expressed using fuzzy numbers. To optimize this model, the ant colony optimization algorithm has been used. Numerical results obtained from the implementation of the mathematical model and solution method show that the used algorithm can provide solutions with the least possible error in a reasonable time. Moreover, the sensitivity analysis shows that the failure rate of the machine before and after maintenance has a great impact on the objective function of the mathematical model

Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators

This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators’ maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation

Knowledge‐Based Assignment Model for Allocation of Employees in Engineering‐to‐Order Production

In today’s rapidly changing business environment, it is necessary to react promptly in response to the product changes that happen constantly in an Engineering‐to‐Order production environment. Very often, there is not sufficient time to educate employees regarding new and necessary knowledge. If we insist on the standardization of a process execution, the process always requires appropriate knowledge from among available employees. In this chapter, an option for adjusting processes to available knowledge is studied. Following calculations, it was concluded that a partial corruption of a perfect process leads to a better knowledge alignment of employees. At first, with the corruption of a perfect process, its efficiency is decreased, but with better knowledge alignment, process efficiency is consequently increased to a level better than the original one. The optimization model presented in this chapter is based on a modified classic assignment problem and it includes a numerical example based on the data of ETO company. We proved our findings from the aspects of balance, employee capacity load and process efficiency