Berna Haktanirlar Ulutas

A review of clonal selection algorithm and its applications

Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The Clonal Selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research.

An artificial immune system based algorithm to solve unequal area facility layout problem

This study introduces an artificial immune system (AIS) based algorithm to solve the unequal area facility layout problem (FLP) with flexible bay structure (FBS). The proposed clonal selection algorithm (CSA) has a new encoding and a novel procedure to cope with dummy departments that are introduced to fill the empty space in the facility area. The algorithm showed consistent performance for the 25 test problem cases studied. The problems with 100 and 125 were studied with FBS first time in the literature. CSA provided four new best FBS solutions and reached to sixteen best-so-far FBS solutions. Further, the two very large size test problems were solved first time using FBS representation, and results significantly improved the previous best known solutions. The overall results state that CSA with FBS representation was successful in 95.65% of the test problems when compared with the best-so-far FBS results and 90.90% compared with the best known solutions that have not used FBS representation.

A methodology to design virtual cellular manufacturing systems

Virtual Cellular Manufacturing System is a new type of cellular system that emerged as a promising alternative for designing cells in real time. The problem is complex and time consuming NP-hard problem in its nature. This study was conducted to design virtual cells that maximizes similarity index and minimizes lead time. A mathematical model and an effective solution procedure were proposed. Several randomly generated data sets were used along a case study to test the performance of the proposed approach. The effects of inherent parameters of virtual systems were investigated and compared by using particular performance measures.

Parameter setting for clonal selection algorithm in facility layout problems

The study introduces a Clonal Selection Algorithm (CSA), which depends on Artificial Immune System principles, for traditional facility layout problems. The CSA aims to minimize the total material handling cost between departments in a single manufacturing period. The determination of the optimum parameters for artificial intelligence algorithms is vital. Therefore a design of experiments study is made. The proposed algorithm is coded and tested by means test problems from literature based on the predefined parameters. The optimum solutions for small sized (5-8 department) layout problems are found. For larger (12, 15, 20, and 30 department) problems 1,077%, 5,703%, 1,126% and 3,671% improvements are obtained respectively. Better solutions are attained within shorter times compared with enumeration and CRAFT solutions.

Assessing hypermutation operators of a clonal selection algorithm for the unequal area facility layout problem

A mutation operator is critical for the performance of a clonal selection algorithm (CSA) since it diversifies the search directions and avoids early convergence to local optima. This article introduces a CSA approach for the unequal area facility layout problem (UAFLP) with flexible bay structure. A new encoding, the use of mutation types with different combinations, and different static and dynamic mutation application strategies are also proposed. In addition, a guideline in parameter optimization of the CSA is provided. An experimental study is performed on five cases of the UAFLP. It is concluded that the hypermutation types studied in this article, especially the inverse mutation followed by pairwise mutation, can be used to obtain good results within short computation times.

A novel attribute‐based dynamic content area layout for internet newspapers

Purpose – A layout problem may deal with the assignment and arrangement of buildings in a green field, location and/or relocation of machines/departments in manufacturing facilities, and so on. If multi‐periods are considered, the problem is called a dynamic layout problem in manufacturing environments. Designing web pages, especially internet newspaper layouts, might also be considered dynamic layout problems. This study aims to introduce a layout procedure for the front page of internet newspapers.Design/methodology/approach – The news contents are ranked and selected based on their characteristic attributes. Then they are assigned to locations on dynamic content area of the front page. Layout optimization is made by use of Clonal Selection Algorithm (CSA). Finally, an illustrative example is provided and concepts for real life applications are discussed. The proposed method is based on CSA, which is a nature‐inspired technique. The novel heuristic is applied to a simulated system to depict how the news...

A new methodology to cluster derivative product modules: an application

Companies are trying several ways to offer competitive and highly differentiated products. The goal for the product platform is to share elements for common functions and to differentiate each product in the family by satisfying different requirements as much as possible. This study focuses on the product variety and short product life cycles that result from the increase and diversification in consumer needs and expectations. Proposed methodology aims to maximise the use of common product modules by considering platform-based derivative products and modular product design approaches to minimise the planning complexity in supply chain, manufacturing and service for derivative products. Functional and technical features of the products are determined in the first step. Then, design structure matrix is formed. After defining product components, similarity matrix for derivative products is formed. A clustering algorithm based on Clonal Selection is used to generate critical product modules. Data from a home appliance manufacturer are used to assess three versions of a product by also considering the production process. The grouping enabled to shorten the release time of a new derivative product to the market.

Use of an eye-tracker to assess workers in ceramic tile surface defect detection

The inspection and classification of ceramic tiles in a production line can be done automatically by use of digital cameras and image processing algorithms. However, due to the investment and maintenance costs of these systems, there are still several firms that assign workers for ceramic tile surface defect detection. Also, human inspection system can effectively challenge this task to a good accuracy. This study aims to attract attention to the mental workload that results from high concentration during visual inspection. A mobile type eye-tracker is used to record the data for duration of fixation and number of fixations to determine fatigue that arises over a period of working time. Data are analyzed and comments are made for workers, type of the ceramic tile, and working period without rest. It is concluded that the eye tracking systems have a potential to identify human related problems during visual inspection.

Assessing the Performance of Two Bioinspired Algorithms to Solve Single-Row Layout Problem

The single-row layout problem (SRLP), also known as the one-dimensional layout problem, deals with arranging a number of rectangular machines/departments with equal or varying dimensions on a straight line. Since the problem is proved to be NP-hard, there are several heuristics developed to solve the problem. This study introduces both a Clonal Selection Algorithm (CSA) and a Bacterial Foraging Algorithm (BFA) for SRLP. The performance of the algorithms is assessed by using three (small, medium, and large sized) well known test problems available in the literature. The promising results illustrated that both algorithms had generated the best known solutions so far for most of the problems or provided better results for a number of problems.

Determining the parameters of MSG algorithm for multi period layout problem

Purpose – The facility layout problem aims to assign machines/departments to locations and modeled as a quadratic assignment problem (QAP). Multi period facility layout is a special case of this problem where the sum of material handling and re‐layout costs are minimized. Since the problem is proved to be NP‐hard, several exact and heuristic methods are proposed in the literature. The purpose of this paper is to solve the multi period layout problem by using the modified sub‐gradient (MSG) algorithm for the first time and to determine its parameters.Design/methodology/approach – The MSG algorithm can solve a large‐scale of optimization problems that also includes multi period facility layout. Since the performance of the algorithm depends on parameters, a design of experiment is made to determine the appropriate parameter values.Findings – The proposed method evaluates the parameters of the MSG algorithm and most suitable general algebraic modeling solvers. It is observed that the parameter α value and so...