A MATHEMATICAL PROGRAMMING MODEL FOR USING DYNAMICALLY-POSITIONED-REWORK STATIONS FOR PERFORMING PARALLEL TASKS IN ASSEMBLY LINE BALANCING

Authors

DOI:

https://doi.org/10.23055/ijietap.2021.28.2.6013

Keywords:

assembly line balancing, remedial actions, integer programming, nonlinear programming, linearization

Abstract

In this study, a mathematical programming model for using dynamically-positioned-rework stations for performing parallel tasks in assembly line balancing is proposed. We first introduce a nonlinear programming model, which is quadratic in constraints resulting from the modeling of the parallel task assignment and dynamic positioning of the rework station. We also establish some novel logical conditions in the model building process while deriving the proposed formulation. In the next step, we present appropriate variable transformations for linearization to take advantage of the algorithms for solving linear programs by noting that the quadratic expressions of the model are present as either the multiplications of binaries or binaries multiplied by continuous variables. After implementing the corresponding variable transformations, the model is transformed to a linear-mixed-integer program. A numerical example is then presented using the resulted linear model for illustration. We also perform some computational experiments using sample problems from the related literature to analyze the performance of the model.

Author Biographies

Fatih Cavdur, Bursa Uludag University

Industrial Engineering, Associate Professor

Asli Sebatli-Saglam, Bursa Uludag University

Industrial Engineering, Ph.D. Student

Elif Kaymaz, Bursa Uludag University

Industrial Engineering, Ph.D. Student

Published

2021-11-23 — Updated on 2021-11-24

Versions

How to Cite

Cavdur, F., Sebatli-Saglam, A., & Kaymaz, E. (2021). A MATHEMATICAL PROGRAMMING MODEL FOR USING DYNAMICALLY-POSITIONED-REWORK STATIONS FOR PERFORMING PARALLEL TASKS IN ASSEMBLY LINE BALANCING. International Journal of Industrial Engineering: Theory, Applications and Practice, 28(2). https://doi.org/10.23055/ijietap.2021.28.2.6013 (Original work published November 23, 2021)

Issue

Section

Operations Research