Bio-inspired search algorithms to solve robotic assembly line balancing problems

Nilakantan, J. Mukund; Ponnambalam, S. G.; Jawahar, N.; Kanagaraj, G.

doi:10.1007/s00521-014-1811-x

Cited by 57 publications

(40 citation statements)

References 34 publications

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“…PSO algorithm does not involve the usage of genetic operators (mutation and crossover) which are commonly used for evolutionary algorithms. On top of that, PSO utilizes a minimum number of parameters to achieve the near-optimum solution in a faster computation time [31]. These factors have influenced the authors to choose PSO to solve the presented problem, where there is a requirement of generating a fast and feasible schedule in a real-time application.…”

Section: Application Of Pso For Uav Scheduling Systemmentioning

confidence: 99%

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Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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The application of unmanned aerial vehicle (UAV) in indoor environment is emerging nowadays due to the advancements in technology. UAV brings more space flexibility in an occupied or hardly accessible indoor environment, e.g. shop floor of manufacturing industry, greenhouse, and nuclear powerplant. UAV helps in creating an autonomous manufacturing system by executing tasks with less human intervention in a time-efficient manner. Consequently, a scheduler is an essential component to be focused on; yet the number of reported studies on UAV scheduling has been minimal. This work proposes a mathematical model of the problem and a heuristic-based methodology to solve it. To suit near real-time operations, a quick response towards uncertain events and a quick creation of new high-quality feasible schedule are needed. Hence, the proposed heuristic is incorporated with particle swarm optimization algorithm to find a near optimal schedule in a short computation time. This proposed methodology is implemented into a scheduler and tested on a few scales of datasets generated based on real flight demonstrations. Performance evaluation of scheduler is discussed in detail, and the best solution obtained from a selected set of parameters is reported.

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Section: Application Of Pso For Uav Scheduling Systemmentioning

confidence: 99%

“…After completing all iterations, a collection of position index pairs (for performing a transposition) is obtained. This procedure has been widely used in [31,36].…”

Section: Neural Computing and Applicationsmentioning

confidence: 99%

Task scheduling system for UAV operations in indoor environment

Khosiawan

Park

Moon

et al. 2018

Neural Comput & Applic

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“…In last few decades, heuristics and exact solution procedures have been widely applied to solve the ALB and RALB problems (Scholl & Voß, 1997). The advent of metaheuristics and its ability to arrive quickly at acceptable solutions to complex problems enabled the timely solution of the NP-hard assembly line balancing problems (Nilakantan et al, 2015). We likewise know from literature that the typical company must aggregate and batch (Eriksen & Nielsen, 2016) demand for products to achieve a stable input to their manufacturing system.…”

Section: Introductionmentioning

confidence: 99%

“…The literature has addressed two main types of RALB problems extensively. They are the type I RALB problem which deals mainly with minimizing the number of workstations for a given cycle time and the type II RALB problem dealing with minimizing the cycle time for a given number of workstations (Nilakantan et al, 2015). However, most of the focus has been on single-and mixed-model robotic assembly lines and the type II RALB problem (Rabbani, Mousavi, & Farrokhi-Asl, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The assembly of a product is divided into different tasks, and each of these tasks is executed on the available workstations (Sivasankaran & Shahabudeen, 2014) based on a predetermined precedence relationship. The simple assembly line balancing (ALB) problem primarily aims at allocating tasks equally to all workstations in such a manner that all tasks are completed without violating the precedence relationships Nilakantan, Ponnambalam, Jawahar, & Kanagaraj, 2015). An assembly line taxonomy has been proposed and classified according to five elements (Battaïa & Dolgui, 2013):…”

Section: Introductionmentioning

confidence: 99%

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Heuristics for solving a multi-model robotic assembly line balancing problem

Christensen

Janardhanan

Nielsen

2017

Production & Manufacturing Research

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a department of Mathematical sciences, aalborg university, aalborg, denmark; b department of Materials and Production, aalborg university, aalborg, denmark ABSTRACT Topic of balancing assembly lines is of great interest for researchers and industry practitioners due to the significant impact it has on increasing productivity and efficiency of manufacturing systems. Robots are widely applied in manufacturing industries for assembly processes. Wide literature has been reported on balancing of robotic assembly lines with single and mixed models. Researchers have extensively used heuristics and metaheuristics to solve these problems due to their NPhard nature. However, no work has been reported on how to balance a robotic assembly line with multiple models (MuRALB) with batch production. This problem is highly relevant for large-scale assembly of products found, e.g. the automotive industry. To authors' knowledge, this is the first attempt to solve this problem. This research proposes a novel heuristic to solve type II MuRALB problem. Type II problem deals with minimizing the cycle time for a fixed set of robots. Heuristic is implemented, and method for scheduling batched production with related setup times for a robotic assembly line is presented, and based on the analysis conducted, advantage of batching is presented. Proposed heuristic is tested on a set of new five datasets, and performance of this heuristic and batching is presented in detail.

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Nature-Inspired and Evolutionary Techniques for Automation

Gen

Lin

2023

Springer Handbooks

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Bio-inspired search algorithms to solve robotic assembly line balancing problems

Cited by 57 publications

References 34 publications

Task scheduling system for UAV operations in indoor environment

Task scheduling system for UAV operations in indoor environment

Heuristics for solving a multi-model robotic assembly line balancing problem

Nature-Inspired and Evolutionary Techniques for Automation

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