Rebalancing the assembly lines with total squared workload and total replacement distance objectives

Assembly line balancing is an important and well recognised operations research problem. The current line balance may not stay optimal, even feasible, due to the disruptions in one or more workstations. In this study, after the disruption, we aim to rebalance the assembly line by considering the tra...

Full description

Saved in:
Bibliographic Details
Published in:International journal of production research 2021-11, Vol.59 (22), p.6702-6720
Main Authors: Girit, Utku, Azizoğlu, Meral
Format: Article
Language:English
Subjects:
Algorithms
Assembly lines
Balancing
exact algorithm
Operations research
rebalancing
Search algorithms
Stability
Tabu search
Work stations
Workload
workload balancing
Workloads
Workstations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Assembly line balancing is an important and well recognised operations research problem. The current line balance may not stay optimal, even feasible, due to the disruptions in one or more workstations. In this study, after the disruption, we aim to rebalance the assembly line by considering the trade-off between workload balancing (fairness measure) and total replacement distance for the tasks assigned to the different workstations (stability measure). We try to generate all non-dominated objective function vectors for the defined fairness and stability measures. Two algorithms are developed: exact algorithm (classical approach) and tabu search algorithm. The results of the experiments have shown that the classical approach returns exact non-dominated objective vectors with up to 40 tasks and 7 workstations in one hour, and the tabu search algorithm returns approximate non-dominated objective vectors that are very close to their exact counterparts and can solve large sized instances with up to 94 tasks and 7 workstations in less than 10 s.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2020.1823027