A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers

Yurtkuran A., Yağmahan B., Emel E.

APPLIED SOFT COMPUTING, vol.73, pp.767-782, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 73
  • Publication Date: 2018
  • Doi Number: 10.1016/j.asoc.2018.09.016
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.767-782
  • Keywords: Workforce scheduling, Workforce balancing, Artificial bee colony, Unpaced assembly, Buffered feeder lines, VEHICLE-ROUTING PROBLEM, MANPOWER ALLOCATION, DIFFERENTIAL EVOLUTION, TIME WINDOWS, OPTIMIZATION
  • Bursa Uludag University Affiliated: Yes


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. (C) 2018 Elsevier B.V. All rights reserved.