Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach

Çil, Zeynel Abidin; Mete, Süleyman; Seri̇n, Faruk

doi:10.1016/j.apm.2020.05.006

Cited by 65 publications

(16 citation statements)

References 48 publications

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“…Since the 1970s, many studies on the path planning problem have been conducted. The path planning methods can be roughly divided into several groups: the grid search methods, such as A* algorithm [ 2 ], Depth-First Search (DFS) [ 3 ], Breadth-first Search (BFS) [ 4 ], and Dijkstra algorithm [ 5 ]; the sampling-based methods, such as Probabilistic Roadmap (PRM) [ 6 ] and Rapidly Exploring Random Tree (RRT) [ 7 ]; heuristic or swarm intelligence algorithms, such as Genetic Algorithm (GA) [ 8 ], Ant Colony Optimization (ACO) [ 9 ], Particle Swarm Optimization (PSO) [ 10 ], and neural network-based algorithms [ 11 ]; the potential field methods, such as Artificial Potential Field (APF) [ 12 ], optimal-control based method [ 13 ], and geometry-based method [ 14 ]. The listed algorithms have certain advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

Liu

et al. 2022

Sensors

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To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

Liu

et al. 2022

Sensors

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“…Wang et al [34] provide a genetic simulated annealing (SA) algorithm to achieve disassembly profit maximization as well as reach number of workstations and smoothness index minimization in a parallel DLBP. Çil et al [35] design an ant colony optimization method to minimize the cycle time in a robotic DLBP. Xiao et al [36] consider maximizing the total profit in a DLBP under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Liang

Gao

et al. 2021

Complex Intell. Syst.

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Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

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“…Effective solution approaches have been developed for problems that take into account the relevant situation such as stochastic programming [8,14,36,[44][45][46][47][48][49][50][51] and fuzzy programming [32,41,42,[52][53][54][55]. Other conditions are related to robotics [31,35,[56][57][58][59], green objectives, and sustainability [60][61][62][63][64][65][66].…”

Section: Introductionmentioning

confidence: 99%

Balancing Disassembly Line Under Hazardous Parts with Precise and Fuzzy Goals

Hezer

Kara

2021

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖzetDisassembly is one of the steps of the recovery activities. Since it includes expensive processes, disassembly shoul be performed with the system that provides efficient and effective outputs. A disassembly line is the most suitable system for disassembly of the returned products. A disassembly line balancing problem (DLBP) is assigning disassembly tasks to consecutive workstations by satisfying a series of constraints and optimizing one or more than one goal. In this paper, the DLBP with multiple conflicting goals which takes into account the negative zone (DLBP-Z) constraints has been proposed. Negative zone constraint is related to hazardous parts. If there are hazardous part/parts in the product and they need to be removed, they may damage the othetr parts and disassembly line. Therefore, these parts must be assigned to different stations from the other parts. Goal programming (GP) and fuzzy goal programming (FGP) approaches have been proposed in order to optimize three conflicting goals, namely total net recovery profit value, the number of parts to be removed for recycling and cycle time.Through a numerical example, the proposed approaches have been tested and goal programming formulations have been shown to be valid and useful. To the best of the authors knowledge, the proposed GP and FGP models are the first multi citeria decision making (MCDM) approaches for DLBP-Z.

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Robotic disassembly line balancing problem: A mathematical model and ant colony optimization approach

Cited by 65 publications

References 48 publications

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

An enhanced group teaching optimization algorithm for multi-product disassembly line balancing problems

Balancing Disassembly Line Under Hazardous Parts with Precise and Fuzzy Goals

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