The Japan (Toyota) industrial organization pattern in mixed-model-assembly-line

Mixed-model-assembly-lines (MMAL) are widely used in modern industries. There are two principal kinds of industrial organization patterns, which called the United States Pattern and the Japan Patter. The Japan (Toyota) Pattern will be studied in this article and the objective of this pattern is minimizing the stoppage and the idle time. Genetic algorithm is been used for solving the problem and more than 5000 examples have been designed. The results will shown the better parameters for the assembly line and the genetic algorithm

MODEL KESEIMBANGAN LINTAS PERAKITAN MENGGUNAKAN ALGORITMA VARIABLE NEIGHBORHOOD DESCENT DENGAN KRITERIA MINIMASI STASIUN KERJA

ABSTRAK Penelitian ini membahas masalah keseimbangan lintasan perakitan sederhana tipe I (Single Assembly Line Balancing Problem I (SALBP I)) menggunakan algoritmaVariable Neigborhood Descent (VND) dengan kriteria minimisasi jumlah stasiun kerja. Algoritma VND terdiri dari dua tahap, yaitu tahap pembangkitan solusi inisial dan tahap local search. Solusi awal diperoleh dengan mengaplikasikan algoritma region approach yang kemudian diperbaiki dengan menggunakan neighborhood/local-search seperti 1-0 insertion dan swap (1-1 interchange).Algoritma usulan diuji dengan menggunakan beberapa data set yang tedapat di literatur. skenario. Hasil pengujian menunjukkan bahwa algoritma usulan dapat menghasilkan solusi yang sama dengan solusi terbaik yang telah dipulikasikan. ABSTRACT This paper address the Single Assembly Line Balancing Problem I (SALBP I) using the Variable Neigborhood Descent (VND) with minimizing work station number criterion. The VND algorithm consist of two steps, the generation of the initial solution and the improvement step that using several neighborhoods/local searches. The initial solution is obtained by applying the region approach algorithm and then improved by using two neighborhoods/local searches, the 1-0 insertion and the 1-1 inter-change (swap). The proposed algorithm is tested using data sets from literatures. The result shows that the proposed algorithm prodeces similar results with the best known solution published. Pendahuluan Simple assembly line balancing problem(SALBP) merupakan permasalahan keseimbangan lintasan perakitan dengan model lintasan tunggal yang memproduksi satu jenis produk yang identik. SALBP dapat dibagi menjadi duayaituSALBP I dan SALBP II.SALBP I merupakan permasalahan penugasan sejumlah elemen kerja pada beberapa stasiun kerja untuk minimisasi jumlah stasiun kerja dengan waktu siklus yang telah diketahui. SALBP II merupakan pengalokasian sejumlah elemen kerja pada beberapa stasiun kerja dengan tujuan meminimisasi waktu siklus dengan jumlah stasiun kerja yang telah diketahui. Kedua masalahinitermasuk dalam masalah optimisasi kombinatorial atau dikenal sebagai permasalahan non-deterministik polynomial hard (NP-hard) (Gutjhar dan Nemhauser, 1967). Artinyapermasalahan SALBP memerlukanwaktukomputasi yang lamauntuk mendapatkan solusi yang optimal jika permasalahan bertambah kompleks, misalnya jumlah elemen kerja atau jumlah stasiun kerja bertambah besar

Heuristic procedures for solving the General Assembly Line Balancing Problem with Setups (GALBPS)

The General Assembly Line Balancing Problem with Setups (GALBPS) was recently defined in the literature. It adds sequence-dependent setup time considerations to the classical Simple Assembly Line Balancing Problem (SALBP) as follows: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time, thereby providing the task sequence inside each workstation. This paper proposes over 50 priority-rule-based heuristic procedures to solve GALBPS, many of which are an improvement upon heuristic procedures published to date

Heuristics and Lower Bounds for the Simple Assembly Line Balancing Problem Type 1: Overview, Computational Tests and Improvements

Assigning tasks to work stations is an essential problem which needs to be addressed in an assembly line design. The most basic model is called simple assembly line balancing problem type 1 (SALBP-1). We provide a survey on 12 heuristics and 9 lower bounds for this model and test them on a traditional and a lately-published benchmark dataset. The present paper focuses on algorithms published before 2011. We improve an already existing dynamic programming and a tabu search approach significantly. These two are also identified as the most effective heuristics; each with advantages for certain problem characteristics. Additionally we show that lower bounds for SALBP-1 can be distinctly sharpened when merging them and applying problem reduction techniques

A Comparative Study Of Ant Colony Optimization

Ant Colony Optimization (ACO) belongs to a class of biologically-motivated approaches to computing that includes such metaheuristics as artificial neural networks, evolutionary algorithms, and artificial immune systems, among others. Emulating to varying degrees the particular biological phenomena from which their inspiration is drawn, these alternative computational systems have succeeded in finding solutions to complex problems that had heretofore eluded more traditional techniques. Often, the resulting algorithm bears little resemblance to its biological progenitor, evolving instead into a mathematical abstraction of a singularly useful quality of the phenomenon. In such cases, these abstract computational models may be termed biological metaphors. Mindful that a fine line separates metaphor from distortion, this paper outlines an attempt to better understand the potential consequences an insufficient understanding of the underlying biological phenomenon may have on its transformation into mathematical metaphor. To that end, the author independently develops a rudimentary ACO, remaining as faithful as possible to the behavioral qualities of an ant colony. Subsequently, the performance of this new ACO is compared with that of a more established ACO in three categories: (1) the hybridization of evolutionary computing and ACO, (2) the efficacy of daemon actions, and (3) theoretical properties and convergence proofs. Ant Colony Optimization (ACO) belongs to a class of biologically-motivated approaches to computing that includes such metaheuristics as artificial neural networks, evolutionary algorithms, and artificial immune systems, among others. Emulating to varying degrees the particular biological phenomena from which their inspiration is drawn, these alternative computational systems have succeeded in finding solutions to complex problems that had heretofore eluded more traditional techniques. Often, the resulting algorithm bears little resemblance to its biological progenitor, evolving instead into a mathematical abstraction of a singularly useful quality of the phenomenon. In such cases, these abstract computational models may be termed biological metaphors. Mindful that a fine line separates metaphor from distortion, this paper outlines an attempt to better understand the potential consequences an insufficient understanding of the underlying biological phenomenon may have on its transformation into mathematical metaphor. To that end, the author independently develops a rudimentary ACO, remaining as faithful as possible to the behavioral qualities of an ant colony. Subsequently, the performance of this new ACO is compared with that of a more established ACO in three categories: (1) the hybridization of evolutionary computing and ACO, (2) the efficacy of daemon actions, and (3) theoretical properties and convergence proofs

Metaheuristic approach to solving U-shaped assembly line balancing problems using a rule-base coded genetic algorithm

Includes bibliographical references.2015 Summer.The need to achieve line balancing for a U-shaped production line to minimize production time and cost is a problem frequently encountered in industry. This research presents an efficient and quick algorithm to solve the U-shape line-balancing problem. Heuristic rules used to solve a straight line-balancing problem (LBP) were modified and adapted so they could be applied in a U-shape line-balancing problem model. By themselves, the heuristic rules, which were adapted from straight-line systems, can produce good solutions for the U-shape LBP, however, there is nothing that guarantees that this will be the case. One way to achieve improved solutions using heuristic rules can be accomplished by using a number of rules simultaneously to break ties during the task assignment process. In addition to the use of heuristic and simultaneous heuristic rules, basic genetic operations were used to further improve the performance of the assignment process and thus obtain better solutions. Two genetic algorithms are introduced in this research: a direct-coded and an indirect-coded model. The newly introduced algorithms were compared with well-known problems from literature and their performance as compared to other heuristic approaches showed that they perform well. The indirect-coded genetic algorithm uses the adapted heuristic rules from the LBP as genes to find the solutions to the problem. In the direct-coded algorithm, each gene represents an operation in the LBP and the position of the gene in the chromosome represents the order in which an operation, or task, will be assigned to a workstation. The indirect-coded genetic algorithm introduces sixteen heuristic rules adapted from the straight LBP for use in a U-shape LBP. Each heuristic rule was represented inside the chromosome as a gene. The rules were implemented in a way that precedence is preserved and at the same time, facilitate the use of genetic operations. Comparing the algorithm’s results with known results from literature, it obtained better solutions in 26% of the cases; it obtained an equivalent solution in 62% of the cases (not better, not worse); and a worse solution the remaining 12%. The direct-coded genetic algorithm introduces a new way to construct an ordered arrangement of the task assignation without violating any precedence. This method consists of creating a diagram that is isomorphic to the original precedence diagram to facilitate the construction of the chromosome. Also, crossover and mutation operations are conducted in a way that precedence relations are not violated. The direct-coded genetic algorithm was tested with the same set of problems as the indirect-coded algorithm. It obtained better solutions than the known solutions from literature in 22% of the cases; 72% of the problems had an equivalent solution; and 6% of the time it generated a solution less successful than the solution from literature. Something that had not been used in other genetic algorithm studies is a response surface methodology to optimize the levels for the parameters that are involved in the response model. The response surface methodology is used to find the best values for the parameters (% of children, % of mutations, number of genes, number of chromosomes) to produce good solutions for problems of different sizes (large, medium, small). This allows for the best solution to be obtained in a minimum amount of time, thus saving computational effort. Even though both algorithms produce good solutions, the direct-coded genetic algorithm option requires less computational effort. Knowing the capabilities of genetic algorithms, they were then tested in two real industry problems to improve assembly-line functions. This resulted in increased efficiency in both production lines