Maximizing production rate and workload balancing in a two-sided assembly line using Harmony Search

Purnomo, Hindriyanto Dwi; Wee, Hui–Ming

doi:10.1016/j.cie.2014.07.010

Cited by 29 publications

(13 citation statements)

References 25 publications

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“…Purnomo et al [2] also proposed a genetic algorithm to handle the TALBP-II with assignment restrictions. Purnomo and Wee [7] proposed a harmony search algorithm to address multiobjective TALBP-II. More recently, Lei and Guo [8] proposed a variable neighborhood search and they developed a new method to determine the allocated sides of the tasks by utilizing a side string.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kim et al [1] focused on positional restrictions. Baykasoglu and Dereli [3],Özbakir and Tapkan [21], and Purnomo and Wee [7] considered zoning restrictions. Simaria and Vilarinho [4] considered the zoning and synchronism restrictions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To our knowledge, limited papers considered the TALBP-II [2,[6][7][8] even though the TALBP-II is of major importance in industry [5,7]. In addition, no paper introduces decoding schemes for the TALBP-II with assignment restrictions.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing the Cycle Time in Two-Sided Assembly Lines with Assignment Restrictions: Improvements and a Simple Algorithm

Li¹,

Tang

Zhang³

2016

Mathematical Problems in Engineering

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The two-sided assembly line balancing problem type-II (TALBP-II) is of major importance for the reconfiguration of the two-sided assembly lines which are widely utilized to assemble large-size high-volume products. The TALBP-II is NP-hard, and some assignment restrictions in real applications make this problem much more complex. This paper provides an integer programming model for solving the TALBP-II with assignment restrictions optimally and utilizes a simple and effective iterated greedy (IG) algorithm to address large-size problems. This algorithm utilizes a new local search by considering precedence relationships between tasks in order to reduce the computational time. In particular, a priority-based decoding scheme is developed to handle these assignment restrictions and reduce sequence-dependent idle times by adjusting the priority values. Experimental comparison among the proposed decoding scheme and other published ones demonstrates the efficiency of the priority-based decoding. A comprehensive computational comparison among the IG algorithm and other eight recent algorithms proves effectiveness of the proposed IG algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Minimizing the Cycle Time in Two-Sided Assembly Lines with Assignment Restrictions: Improvements and a Simple Algorithm

Li¹,

Tang

Zhang³

2016

Mathematical Problems in Engineering

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“…Workload balancing has been considered in decision planning problems related to vehicle routing problems [57][58][59][60] and machine scheduling problems [61]. Balancing the workload content of the supply chain is an important element when considering a vertical integrated company.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

et al. 2018

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This paper addresses a biobjective production-distribution planning problem. The problem is formulated as a mixed integer programming problem with two objectives. The objectives are to minimize the total costs and to balance the total workload of the supply chain, which consist of plants and depots, considering that it represents a company vertically integrated. In order to solve the model, we propose an adapted biobjective GRASP to obtain an approximation of the Pareto front. To evaluate the performance of the proposed algorithm, numerical experimentations are conducted over a set of instances used for similar problems. Results indicate that the proposed GRASP obtains a relatively small number of nondominated solutions for each tested instance in very short computational time. The approximated Pareto fronts are discontinuous and nonconvex. Moreover, the solutions clearly show the compromise between both objective functions.

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“…Moreover, one of the EAs i.e., harmony search (HS) algorithm was utilized to overcome problems of convergence rate at finding the global minimum of DE [15][16][17]. Subsequently, back propagation (BP) was used to speed up convergence known as a memetic approach.…”

Section: Introductionmentioning

confidence: 99%

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Saleh¹,

Shamsuddin²

2015

Int J Swarm Intel Evol Comput

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Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure. Unlike previous study mentioned earlier in Jin [14] and other single objective studies [18,19], this paper deals with an improved multi objective method to obtain simple and accurate ESNN. The proposed method evolves toward optimal values defined by several objectives with model accuracy and ESNN's structure to improve performance for classification problems. The remaining parts of this paper are organized as follows: Methods including: Evolving Spiking Neural Network, Multi Objective Differential Evolution are presented in section 2. In addition, section 3 clarifies the proposed method Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) used in this paper, Experimental design is discussed in section 4, section 5 explains in detail the results and discussion, and finally, section 6 concludes the paper with future works. MethodsThis section reviews the vital foundation of evolving spiking neural network (ESNN) and discusses the evolutionary algorithms that have been utilized for enhancement. In the first part, an introduction of ESNN, neuron coding, learning method and ESNN design, and its algorithm is presented. The second part focuses on the algorithms which are used for improvement of ESNN. The concepts and methods of multi objective optimization (MOO) are highlighted. After that, the literature focuses on the working of differential evolution, harmony search (HS) algorithm and memetic approach which is used to improve and enhance the performance of classification . Evolving spiking neural network (ESNN)Currently, several enhancements of SNN have been proposed. Wysoski improved one of these new models known as Evolving Spiking Neural Network (ESNN) [20]. Generally, ESNN used the principles of evolving connectionist systems (ECOS) where neurons are created incrementall...

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Maximizing production rate and workload balancing in a two-sided assembly line using Harmony Search

Cited by 29 publications

References 25 publications

Minimizing the Cycle Time in Two-Sided Assembly Lines with Assignment Restrictions: Improvements and a Simple Algorithm

Minimizing the Cycle Time in Two-Sided Assembly Lines with Assignment Restrictions: Improvements and a Simple Algorithm

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

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