Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm

Alvarado‐Iniesta, Alejandro; García-Alcaráz, Jorge Luis; Rodríguez-Borbón, Manuel Iván; Maldonado, Aide

doi:10.1016/j.eswa.2013.02.029

Cited by 39 publications

(20 citation statements)

References 21 publications

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“…However, when the ABC algorithm is applied into CVRP, this mechanism does not work efficiently. Many researchers have tried to adapt various operators in this procedure to improve the probability of finding better solutions, such as the swap operator, the reverse operator, and the insert operator and so on (Alvarado-Iniesta et al, 2013;Szeto et al, 2011;Xiao et al, 2012) as described in Fig. 3.…”

Section: Employed Bee Phasementioning

confidence: 99%

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Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

Zhang

Lee

Choy

et al. 2014

Transportation Research Part D: Transport and Environment

136

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a b s t r a c tThe vehicle routing problem (VRP) is a critical and vital problem in logistics for the design of an effective and efficient transportation network, within which the capacitated vehicle routing problem (CVRP) has been widely studied for several decades due to the practical relevance of logistics operation. However, CVRP with the objectives of minimizing the overall traveling distance or the traveling time cannot meet the latest requirements of green logistics, which concern more about the influence on the environment. This paper studies CVRP from an environmental perspective and introduces a new model called environmental vehicle routing problem (EVRP) with the aim of reducing the adverse effect on the environment caused by the routing of vehicles. In this research, the environmental influence is measured through the amount of the emission carbon dioxide, which is a widely acknowledged criteria and accounts for the major influence on environment. A hybrid artificial bee colony algorithm (ABC) is designed to solve the EVRP model, and the performance of the hybrid algorithm is evaluated through comparing with well-known CVRP instances. The computational results from numerical experiments suggest that the hybrid ABC algorithm outperforms the original ABC algorithm by 5% on average. The transformation from CVRP to EVRP can be recognized through the differentiation of their corresponding optimal solutions, which provides practical insights for operation management in green logistics.

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Section: Employed Bee Phasementioning

confidence: 99%

“…For each solution, x 2 X, let p(x) denote the total violation of capacity constraints (Alvarado-Iniesta et al, 2013). Referring to the previous notation, p(x) can be expressed as the following.…”

Section: Representation Scheme and Search Spacementioning

confidence: 99%

Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

Zhang

Lee

Choy

et al. 2014

Transportation Research Part D: Transport and Environment

136

View full text Add to dashboard Cite

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“…Artificial bee colony (ABC) is a new stochastic population-based algorithm inspired by the behavior of honey bees when searching for food sources. Recently, ABC has become one of the most popular algorithms for solving optimization problems [8]- [11]. But using ABC in data mining is a fairly new research area and has attracted a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

BeeMiner: A novel artificial bee colony algorithm for classification rule discovery

Talebi

Abadi

2014

2014 Iranian Conference on Intelligent Systems (ICIS)

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Artificial bee colony (ABC) is a new population-based algorithm that has shown promising results in the field of optimization. In this paper, we propose BeeMiner, a novel ABC algorithm for discovering classification rules. BeeMiner differs from the original ABC because it uses an information-theoretic heuristic function (IHF) to guide the bees to search across the most promising areas of the search space. We compare the performance of BeeMiner with those of J48, JRip, and PART on nine benchmark datasets from the UCI Machine Learning Repository. The results show that BeeMiner is competitive with J48, JRip, and PART in terms of the predictive accuracy.

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“…Based on the original, several improved versions of the ABC are suggested by the researchers [25][26][27]. Moreover, many variant ABC algorithms have been designed for solving many optimization problems, such as the job shop scheduling [28,29], the flow shop scheduling problem [30][31][32], the proportional integral derivative (PID) controller designing [33], the layer optimization problem [34], the analog filter design [35], the uncapacitated facility location problem [36], the clustering approach [37,38], the material supplying flow problem [39], the leaf-constrained minimum spanning tree problem [40], the dynamic deployment problem of wireless sensor networks [41], and the symbolic regression problem [42]. On the basis of the analysis, the ABC algorithm is an up-and-coming swarm intelligence algorithm that is competent in finding the near-optimum solutions of the NP-hard real-world combinational optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Key-node-based local search discrete artificial bee colony algorithm for obstacle-avoiding rectilinear Steiner tree construction

Zhang

Dong-yi

2014

Neural Comput & Applic

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The obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem is a fundamental problem in very large-scale integrated circuit physical design and can be reduced to the Steiner tree problem in graphs (GSTP), which can be solved by using three types of common methods: classic heuristics, local search algorithms, or computational intelligence algorithms. However, classic heuristics have poor solution qualities; local search algorithms easily fall into the problem of the local optimum; and the searching effects of the existing computational intelligence algorithms are poor for this problem. In order to improve the solution quality, we propose a novel discrete artificial bee colony algorithm for constructing an obstacle-avoiding rectilinear Steiner tree. We first generate the escape graph for the OARSMT problem. Then, we search for a near-optimal solution consisting of some edges of escape graph by using the discrete ABC algorithm. We apply a key-node neighborhood configuration for the local search strategy and introduce two local search operators. We then naturally use a key-node-based encoding scheme for representing the feasible solution and obtain a tight searching scope. We employ a modified classic heuristic as the encoder that can produce a feasible solution.Experiments conducted on both general GSTP and very large-scale integrated circuit instances reveal the superior performance of the proposed method in terms of the solution quality among the state-of-the-art algorithms.Keywords NP-complete problem Á Obstacle-avoiding rectilinear Steiner minimal tree problem Á Steiner tree problem in graphs Á Local search Á Artificial bee colony algorithm Á VLSI physical design 1 Introduction

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Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm

Cited by 39 publications

References 21 publications

Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem

BeeMiner: A novel artificial bee colony algorithm for classification rule discovery

Key-node-based local search discrete artificial bee colony algorithm for obstacle-avoiding rectilinear Steiner tree construction

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