Yeo Keun Kim

A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling

This paper addresses the integrated problem of process planning and scheduling in job shop flexible manufacturing systems. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions of process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. For the performance improvement of the algorithm, it is important to enhance population diversity and search efficiency. We adopt the strategies of localized interactions, steady-state reproduction, and random symbiotic partner selection. Efficient genetic representations and operator schemes are also considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and an existing cooperative coevolutionary algorithm. The experimental results show that the proposed algorithm outperforms the compared algorithms.

Two-sided assembly line balancing to maximize work relatedness and slackness

This paper considers two-sided (left- and right-side) assembly lines that are often used in assembling large-sized products, such as trucks and buses. A large number of exact algorithms and heuristics have been proposed to balance one-sided assembly lines. However, little attention has been paid to balancing the two-sided lines. An efficient assignment procedure is developed for two-sided assembly line balancing problems. A special emphasis is placed on maximizing work relatedness and maximizing work slackness, which are of practical significance especially in two-sided lines. We first investigate the characteristics of two-sided lines and define new measures for the balancing. Then, a group assignment procedure, which assigns a group of tasks at a time rather than a unit task, is designed. Experiments are carried out to demonstrate the performance of the proposed method. The results show that our procedure is promising in the solution quality.

Two-sided assembly line balancing: A genetic algorithm approach

A two-sided assembly line balancing problem is typically found in plants producing large-sized high-volume products, e.g. buses and trucks. The features specific to the assembly line are described in this paper, which are associated with those of: (i) two-sided assembly lines; (ii) positional constraints; and (iii) balancing at the operational time. There exists a large amount of literature in the area of line balancing, whereby it has mostly dealt with one-sided assembly lines. A new genetic algorithm is developed to solve the problem, and its applicability and extensibility are discussed. A genetic encoding and decoding scheme, and genetic operators suitable for the problem are devised. This is particularly emphasized using problem-specific information to enhance the performance of the genetic algorithm (GA). The proposed GA has a strength that it is flexible in solving various types of assembly line balancing problems. An experiment is carried out to verify the performance of the GA, and the results are reported.

Genetic algorithms for assembly line balancing with various objectives

Abstract This article presents genetic algorithms (GAs) to solve assembly line balancing (ALB) problems with various objectives: 1. (1) minimizing number of workstations; 2. (2) minimizing cycle time; 3. (3) maximizing workload smoothness; 4. (4) maximizing work relatedness; and 5. (5) a multiple objective with (3) and (4). Some major aspects of the proposed GAs are discussed, with emphasis on representation, decoding and genetic operators. A repair method is newly developed so that the traditional GA approach is able to be flexibly adapted to various types of objectives in the ALB problems. An emphasis is placed on seeking a set of diverse Pareto optimal solutions for a multiple objective ALB problem. The results of extensive experiments are reported. The performance comparison between the proposed GAs and the known heuristic algorithms shows that our approach is promising.

An endosymbiotic evolutionary algorithm for the integration of balancing and sequencing in mixed-model U-lines

This paper proposes a new evolutionary approach to deal with both balancing and sequencing problems in mixed-model U-shaped lines. The use of U-shaped lines is an important element in Just-In-Time production. For an efficient operation of the lines, it is important to have a proper line balancing and model sequencing. A new genetic approach, called endosymbiotic evolutionary algorithm, is proposed to solve the two problems of line balancing and model sequencing at the same time. The algorithm imitates the natural evolution process of endosymbionts that is an extension of existing cooperative or symbiotic evolutionary algorithm. The distinguishing feature of the proposed algorithm is that it maintains endosymbionts that are a combination of an individual and its symbiotic partner. The existence of endosymbionts can accelerate the speed that individuals converge to good solutions. This enhanced capability of exploitation together with the parallel search capability of traditional symbiotic algorithms results in finding better quality solutions than existing hierarchical approaches and symbiotic algorithms. A set of experiments are carried out, and the results are reported.

A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.

Balancing and sequencing mixed-model U-lines with a co-evolutionary algorithm

A mixed-model production line is such a line where a variety of product models are produced. In U-lines used in the just-in-time production system, the strategy of mixing product models is often employed to provide various types of products to customers on time. Line balancing and model sequencing problems are important for an efficient use of mixed-model U-lines. Although the two problems are tightly interrelated with each other, prior research has considered them separately or sequentially. In this paper, a new approach using an artificial intelligence search technique, called co-evolutionary algorithm, is proposed to solve the two problems at the same time. To promote population diversity and search efficiency in the algorithm, we adopt strategies of localized evolution and steady-state reproduction, and develop methods of selecting environmental individuals and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. The experimental results demonstrate that the proposed algorithm outperforms existing approaches.

Sequencing in mixed model assembly lines: a genetic algorithm approach

The mixed model assembly lines are becoming increasingly popular in a wide area of industries. We consider the sequencing problem in mixed model assembly lines, which is critical for efficient utilization of the lines. We extend standard formulation of the problem to allow a hybrid assembly line, in which closed and open workstations are intermixed, and sequence-dependent setup time. A new approach using an artificial intelligence search technique, called genetic algorithm, is proposed. A genetic representation suitable for the problem is investigated, and genetic control parameters that yield good results are empirically found. A new genetic operator, Immediate Successor Relation Crossover (ISRX), is introduced and several existing ones are modified. An extensive experiment is carried out to determine a proper choice of the genetic operators. The performance of the genetic algorithm is compared with those of heuristic algorithm and of branch-and-bound method. The results show that our algorithm greatly reduces the computation time and its solution is very close to the optimal solution. We have identified the ISRX operator to play a significant role in improving the performance.

A heuristic-based genetic algorithm for workload smoothing in assembly lines

Workload smoothing in assembly lines has many beneficial features: it established the sense of equity among workers, and, more importantly, contributes to increasing the output. Although assembly line balancing has been studied extensively, workload smoothing as the objective has been relatively neglected in the literature. This study presents a new heuristic procedure based on genetic algorithm to balance an assembly line with the objective of maximizing workload smoothness. To improve the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Extensive computational experiments are performed for the algorithm. The advantages of incorporating problem-specific heuristic information into the algorithm are demonstrated. The performance comparison of our genetic algorithm with three existing heuristics and with an existing genetic algorithm is made. The experimental results show that our algorithm outperforms the existing heuristics and the compared genetic algorithm. In many cases, our algorithm also improves cycle time.

Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.