A genetic algorithm approach for balancing two-sided assembly lines with setups

Purpose This paper aims to discuss the sequence-dependent forward setup time (FST) and backward setup time (BST) consideration for the first time in two-sided assembly lines. Sequence-dependent FST and BST values must be considered to compute all of the operational times of each station. Thus, more realistic results can be obtained for real-life situations with this new two-sided assembly line balancing (ALB) problem with setups consideration. The goal is to obtain the most suitable solution with the least number of mated stations and total stations. Design/methodology/approach The complex structure it possesses has led to the use of certain assumptions in most of the studies in the ALB literature. In many of them, setup times have been neglected or considered superficially. In the real-life assembly process, potential setup configurations may exist between each successive task and between each successive cycle. When two tasks are in the same cycle, the setup time required (forward setup) may be different from the setup time required if the same two tasks are in consecutive cycles (backward setup). Findings Algorithm steps have been studied in detail on a sample solution. Using the proposed algorithm, the literature test problems are solved and the algorithm efficiency is revealed. The results of the experiments revealed that the proposed approach finds promising results. Originality/value The sequence-dependent FST and BST consideration is applied in a two-sided assembly line approach for the first time. A genetic algorithm (GA)-based algorithm with ten different heuristic rules was used in this proposed model

10. sınıf öğrencilerinin matematik problem çözme süreçlerinin incelemesi: bilgibilimsel inanç

Matematik hakkında öğrencilerin birçok bilgibilimsel inancı vardır. Bu çalışmanın amacı trigonometri, denklem ve geometri konularından hazırlanmış bir soru setinden elde edilen problem çözme süreçlerinde problem çözme hakkında ki bilgibilimsel inançların nasıl etkili olduğunun incelenmesidir. Bu çalışma pozitivist olmayan yorumlayıcı bir paradigmaya sahip olup, problem seti ve görüşmeler kullanıldığı için çoklu yöntem kullanılmıştır. Elde edilen veriler bağlamında ise nitel bir çalışmadır. Çalışma grubu amaca yönelik, uygun örneklem tekniği ile seçilen özel statülü bir devlet lisesinin 10. sınıf öğrencilerinden 47 kişidir. Soru setinin değerlendirilmesinde betimsel istatistik kullanılmıştır. Öğrencilerin matematik ile ilgili (bilgibilimsel) inançlarının problem çözüm süreçlerine yansıması farklı boyutlarda bulgularda gözlenmiştir. Öğrencilerin büyük çoğunluğu soruların sade ve kısa sonuçlu olduğuna inanmaktadırlar

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence

Stochastic two-sided U-Type assembly line balancing: A genetic algorithm approach

In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.In this paper, a novel stochastic two-sided U-type assembly line balancing (STUALB) procedure, an algorithm based on the genetic algorithm and a heuristic priority rule-based procedure to solve STUALB problem are proposed. With this new proposed assembly line design, all advantages of both two-sided assembly lines and U-type assembly lines are combined. Due to the variability of the real-life conditions, stochastic task times are also considered in the study. The proposed approach aims to minimise the number of positions (i.e. the U-type assembly line length) as the primary objective and to minimise the number of stations (i.e. the number of operators) as a secondary objective for a given cycle time. An example problem is solved to illustrate the proposed approach. In order to evaluate the efficiency of the proposed algorithm, test problems taken from the literature are used. The experimental results show that the proposed approach performs well.</p