Multi-objective discrete particle swarm optimisation algorithm for integrated assembly sequence planning and assembly line balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

Using metaheuristic algorithms for solving a mixed model assembly line balancing problem considering express parallel line and learning effect

Mixed-model assembly line attracts many manufacturing centers' attentions, since it enables them to manufacture different models of one product in the same line. The present work proposes a new mathematical model to balancing mixed-model assembly two parallel lines, in which first one is a common line and the other is an express line due to more modern technology or operators with higher skills. Therefore, the cost of equipment and skilled labor in the express line is higher, and also, the learning effect on resource dependent task times and setup times is considered in the assemble-to-order environment. The aim of this study is to minimize the cycle time and the total operating cost and smoothness index by configuration of tasks in stations, according to their precedence diagrams. Also, assigning the assistants to some tasks in some stations and for some models is allowed. This problem is categorized as an NP-hard problem and for solving this multi-objective problem, non-dominated sorting genetic algorithm ІІ (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied. Finally, for comparing the proposed methods some numerical examples are implemented and the result show that MOPSO outperforms NSGAII

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Multi-Objective Discrete Particle Swarm Optimisation Algorithm for Integrated Assembly Sequence Planning and Assembly Line Balancing

In assembly optimisation, assembly sequence planning and assembly line balancing have been extensively studied because both activities are directly linked with assembly efficiency that influences the final assembly costs. Both activities are categorised as NP-hard and usually performed separately. Assembly sequence planning and assembly line balancing optimisation presents a good opportunity to be integrated, considering the benefits such as larger search space that leads to better solution quality, reduces error rate in planning and speeds up time-to-market for a product. In order to optimise an integrated assembly sequence planning and assembly line balancing, this work proposes a multi-objective discrete particle swarm optimisation algorithm that used discrete procedures to update its position and velocity in finding Pareto optimal solution. A computational experiment with 51 test problems at different difficulty levels was used to test the multi-objective discrete particle swarm optimisation performance compared with the existing algorithms. A statistical test of the algorithm performance indicates that the proposed multi-objective discrete particle swarm optimisation algorithm presents significant improvement in terms of the quality of the solution set towards the Pareto optimal set

A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers

This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm. This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorith

PID Tuning Of Process Plant Using Evolutionary Algorithm

PID controller is one of the most robust and well-implemented controller in today’s industry. The mature and stable performance of it had increased the usage of the PID controller in multiple fields such as process control, robotic and chemical plants. However, the advancement of technology has urged the industry to improve overall process in term of its overshoot, rise time, settling and other domains. In this project, Evolutionary algorithm (Particle Swarm Optimization) is implemented to optimize the controller parameters in order to improve the system performance of the real pressure plant. Simulation and experimental work are carried out side by side to prove the feasibility of the PSO method. The results show that PSO had successfully improved the overall system performance of the real pressure plant in term of percentage overshoot, rise time. There is always a trade-off for the system performance parameters (percentage overshoot, rise time and settling time) and it is depending on the type of applications

Worker Skills and Equipment Optimization in Assembly Line Balancing by a Genetic Approach

The Assembly Line Balancing Problem (ALBP) is to determine the optimal allocation of assembly operations to a set of workstations, with respect to precedence constraints. This paper proposes a multi-objective optimization to solve the ALBP using a Genetic Algorithm (GA) approach. The aim is to minimize, besides the number of workstations, two aspects, very important from an economic point of view, but poorly treated in literature: the number of high skilled workers needed to correctly accomplish the operations and the number of assembly equipment along the line. A case study was finally discussed in order to demonstrate the capability of the proposed method in finding optimized solutions in different scenarios

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione

Optimization of two sided assembly line balancing with resource constraint

Two-sided assembly line balancing (2S-ALB) problems are practically useful in improving the production of large-sized high-volume products. Many research has proposed various approaches to study and balance this well-known ALB problem. Although much attention has been given to solve and optimize 2S-ALB, the majority of the research assumed the workstation has similar capabilities. This research has been conducted in an automotive assembly line, where most of the equipment used in assembly is different from one workstation to another. The assumption that all workstation has similar capabilities lead to inefficient resource utilization in assembly line design. This research aims to model and optimize 2S-ALB with resource constraints. Besides optimizing the line balancing, the proposed model also will minimize the number of resources in the two-sided assembly line. The research begins with problem formulation by establishing four optimization objectives. The considered optimization objectives were to minimize the number of workstations, number of mated-workstation, total idle time, and number of resources. For optimization purpose, Particle Swarm Optimization is modified to find the best solution besides reducing the dependencies on a single best solution. This is conducted by replacing the best solution with the top three solutions in the reproduction process. A set of benchmark problems for 2S-ALB were used to test the proposed Modified Particle Swarm Optimization (MPSO) in the computational experiment. Later, the proposed 2S-ALB with resource constraint model and algorithm was validated using a case study problem. The computational experiment result using benchmark test problems indicated that the proposed MPSO was able to search for better solution in 91.6% of the benchmark problems. The good performance of MPSO is attributed to its ability to maintain particle diversity over the iteration. Meanwhile, the case study result indicated that the proposed 2S-ALB with resource constraint model and MPSO algorithm are able to be utilized for the real problem. In the future, the multiobjective optimization problem will be considered to be optimized for other types of general assembly lines