Jae Yun Kim

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.

An Endosymbiotic Evolutionary Algorithm for Optimization

This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.

An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling

This paper considers the integrated FMS (flexible manufacturing system) scheduling problem (IFSP) consisting of loading, routing, and sequencing subproblems that are interrelated to each other. In scheduling FMS, the decisions for the subproblems should be appropriately made to improve resource utilization. It is also important to fully exploit the potential of the inherent flexibility of FMS. In this paper, a symbiotic evolutionary algorithm, named asymmetric multileveled symbiotic evolutionary algorithm (AMSEA), is proposed to solve the IFSP. AMSEA imitates the natural process of symbiotic evolution and endosymbiotic evolution. Genetic representations and operators suitable for the subproblems are proposed. A neighborhood-based coevolutionary strategy is employed to maintain the population diversity. AMSEA has the strength to simultaneously solve subproblems for loading, routing, and sequencing and to easily handle a variety of FMS flexibilities. The extensive experiments are carried out to verify the performance of AMSEA, and the results are reported.

Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Recently, there has been an increasing effort to address integrated problems that are composed of multiple interrelated sub-problems. Many integrated problems in the real world have a multileveled structure. This paper proposes a new method of solving integrated and multileveled problems. The proposed method is named Multileveled Symbiotic Evolutionary Algorithm (MSEA). MSEA is an evolutionary algorithm that imitates the process of symbiotic evolution, including endosymbiotic evolution. It is designed to promote the balance of population diversity and population convergence. To verify its applicability, MSEA is applied to loading problems of flexible manufacturing systems with various flexibilities. Through computer experiments, the features of MSEA are shown and their effects on search capability are discussed. The proposed algorithm is also compared with existing ones in terms of solution quality. The experimental results confirm the effectiveness of our approach.

A Tournament-Based Competitive Coevolutionary Algorithm

For an efficient competitive coevolutionary algorithm, it is important that competing populations be capable of maintaining a coevolutionary balance and hence, continuing evolutionary arms race to increase the levels of complexity. We propose a competitive coevolutionary algorithm that combines the strategies of neighborhood-based evolution, entry fee exchange tournament competition (EFE-TC) and localized elitism. An emphasis is placed on analyzing the effects of these strategies on the performance of competitive coevolutionary algorithms. We have tested the proposed algorithm with two adversarial problems: sorting network and Nim game problems that have different characteristics. The experimental results show that the interacting effects of the strategies appear to promote a balanced evolution between host and parasite populations, which naturally leads them to keep on evolutionary arms race. Consequently, the proposed algorithm provides good quality solutions with a little computation time.

A Tabu search approach for designing a non-hierarchical video-on-demand network architecture

In this paper, we present a tabu search to design a non-hierarchical and decentralized video-on-demand (VOD) network architecture. To optimize the VOD network resource, we consider optimization of both video server locations and storage allocation subject to the tradeoffs among installation cost for video servers, program storage cost, and transmission (or communication) cost. In applying a tabu search technique to the problem, neighborhood structure and search strategy are elaborated to improve solution quality and to reduce computation time. We report the results of the computational experiments to demonstrate the performance of the proposed tabu search. A comparative study shows that our algorithm is promising.

Tournament Competition and its Merits for Coevolutionary Algorithms

In a competitive coevolutionary algorithm, the competition strategy, selecting opposing individuals, has a great influence on the performance of the algorithm. Therefore, a good competition strategy is crucial for an effective and efficient competitive coevolutionary algorithm. In this paper, we propose a new competition strategy called tournament competition. We investigate its characteristics and merits when applied to adversarial problems. To verify the performance of the new strategy, we first classify adversarial problems into two types: solution-test problems and game problems. We apply the strategy to both types. A set of experiments compares our strategy to several existing competition strategies and analyzes several aspects such as solution quality, evolution speed, and coevolutionary balance. The experimental results indicate that some of the existing competition strategies give different patterns according to problem types. The results also support that the proposed tournament strategy has the potential for finding good solutions, regardless of problem types.

OR/MS in Korea : The Review and Outlook

Department of Business Administration, Chonnam National UniversityThis paper reviews Operations Research (OR)/Management Science (MS) in Korea. After a brief survey of global OR/MS, this paper analyzes the research trend in Korea over the past forty years with respect to approach, methods, types of data, and so on. The OR/MS application and practice in Korea are also surveyed and the change of them with time is shown. To do this, we refer to four main OR/MS journals published in Korea. The future of OR/MS is discussed in practice, methods to problem-solving, and education.