Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/31030
Title: A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0003-2978-2811
0000-0003-1744-3062
0000-0002-9220-7353
Yurtkuran, Alkın
Yağmahan, Betül
Emel, Erdal
AAH-1410-2021
B-5557-2017
N-8691-2014
26031880400
23487445600
6602919521
Keywords: Computer science
Workforce scheduling
Workforce balancing
Artificial bee colony
Unpaced assembly
Buffered feeder lines
Vehicle-routing problem
Manpower allocation
Differential evolution
Time windows
Optimization
Assembly
Assembly machines
Automobile manufacture
Integer programming
Particle swarm optimization (PSO)
Polynomial approximation
Scheduling
Artificial bee colonies
Artificial bee colony algorithms
Artificial bee colony algorithms (ABC)
Feeder line
Meta heuristic algorithm
Mixed integer programming model
Particle swarm optimisation
Workforce scheduling
Personnel
Issue Date: 11-Sep-2018
Publisher: Elsevier
Citation: Yurtkuran, A. vd. (2018). ''A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers''. Applied Soft Computing Journal, 73, 767-782.
Abstract: In this study, a workforce scheduling and balancing problem is solved in unpaced sub-assembly lines with buffers feeding the paced body assembly line of a car manufacturer. The goal is to determine the minimum workforce required to process split lots at sub-assembly stations to feed the paced line over a periodic time window. Limited by a given buffer capacity at each station but with flexible start times for each split lot, an efficient workforce scheduling is possible to prevent shortages in downstream stations. Therefore, a stock-continuity equation has been proposed yielding the size of those split lots. Next, a single-objective Mixed Integer Programming (MIP) model is formulated for the problem as a combination of two implicitly weighted goals to minimise the workforce and the unbalanced workloads. The problem is a variant of workforce scheduling and routing problem with time windows and negligible walking distances. Due to the non-deterministic similar to polyomial-time-hardness of the problem, we proposed an improved Artificial Bee Colony (ABC) algorithm named as discrete ABC with solution acceptance rule and multi-search (SAMSABC). The proposed algorithm is compared with different variants of ABC and other well-known metaheuristic algorithms such as Particle Swarm Optimisation and Differential Evolution on generated test cases. The computational results demonstrate the superiority of the proposed ABC algorithm and reveal that the SAMSABC can achieve accurate results within short computational times.
URI: https://doi.org/10.1016/j.asoc.2018.09.016
https://www.sciencedirect.com/science/article/pii/S1568494618305337
http://hdl.handle.net/11452/31030
ISSN: 1568-4946
1872-9681
Appears in Collections:Scopus
Web of Science

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