Sequencing mixed model assembly lines for a Just-In-Time production system

Jose Antonio Ventura, Sanjay Radhakrishnan

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This study is concerned with the mixed model assembly line sequencing problem for just-in-time production systems. Such a problem finds applications in flexible production lines where a uniform demand exists for N different item types and this demand needs to be satisfied in batches at a constant rate over a given planning horizon. Optimality properties are provided and used to develop a 0-1 integer linear programming formulation with three sets of constraints that considers varying batch processing times for different types of items. The first two sets of constraints are equivalent to the supply and demand constraints of an asymmetric assignment problem. The third set, which represents the process time non-overlap constraints, is relaxed to form a Lagrangian dual problem. The Lagrangian dual is then solved using a subgradient optimization technique. Some optimality conditions for the mixed model assembly line sequencing problem are provided. Efficient heuristics have been developed to yield an initial primal feasible solution and to convert a primal infeasible solution to a feasible solution. Computational results show that the average relative deviation from optimality for small size problems (up to 20 jobs) is 1.89%, for medium size problems (31-40 jobs) is 1.09%, and for large size problems (41-140 jobs) is 3.15%.

Original languageEnglish (US)
Pages (from-to)199-210
Number of pages12
JournalProduction Planning and Control
Volume13
Issue number2
DOIs
StatePublished - Mar 1 2002

Fingerprint

Just in time production
Linear programming
Planning
Mixed-model assembly line
Sequencing
Just-in-time production
Optimality

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

@article{d57d3df9bc614b0886f344e4076aa0d1,
title = "Sequencing mixed model assembly lines for a Just-In-Time production system",
abstract = "This study is concerned with the mixed model assembly line sequencing problem for just-in-time production systems. Such a problem finds applications in flexible production lines where a uniform demand exists for N different item types and this demand needs to be satisfied in batches at a constant rate over a given planning horizon. Optimality properties are provided and used to develop a 0-1 integer linear programming formulation with three sets of constraints that considers varying batch processing times for different types of items. The first two sets of constraints are equivalent to the supply and demand constraints of an asymmetric assignment problem. The third set, which represents the process time non-overlap constraints, is relaxed to form a Lagrangian dual problem. The Lagrangian dual is then solved using a subgradient optimization technique. Some optimality conditions for the mixed model assembly line sequencing problem are provided. Efficient heuristics have been developed to yield an initial primal feasible solution and to convert a primal infeasible solution to a feasible solution. Computational results show that the average relative deviation from optimality for small size problems (up to 20 jobs) is 1.89{\%}, for medium size problems (31-40 jobs) is 1.09{\%}, and for large size problems (41-140 jobs) is 3.15{\%}.",
author = "Ventura, {Jose Antonio} and Sanjay Radhakrishnan",
year = "2002",
month = "3",
day = "1",
doi = "10.1080/09537280110069775",
language = "English (US)",
volume = "13",
pages = "199--210",
journal = "Production Planning and Control",
issn = "0953-7287",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

Sequencing mixed model assembly lines for a Just-In-Time production system. / Ventura, Jose Antonio; Radhakrishnan, Sanjay.

In: Production Planning and Control, Vol. 13, No. 2, 01.03.2002, p. 199-210.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Sequencing mixed model assembly lines for a Just-In-Time production system

AU - Ventura, Jose Antonio

AU - Radhakrishnan, Sanjay

PY - 2002/3/1

Y1 - 2002/3/1

N2 - This study is concerned with the mixed model assembly line sequencing problem for just-in-time production systems. Such a problem finds applications in flexible production lines where a uniform demand exists for N different item types and this demand needs to be satisfied in batches at a constant rate over a given planning horizon. Optimality properties are provided and used to develop a 0-1 integer linear programming formulation with three sets of constraints that considers varying batch processing times for different types of items. The first two sets of constraints are equivalent to the supply and demand constraints of an asymmetric assignment problem. The third set, which represents the process time non-overlap constraints, is relaxed to form a Lagrangian dual problem. The Lagrangian dual is then solved using a subgradient optimization technique. Some optimality conditions for the mixed model assembly line sequencing problem are provided. Efficient heuristics have been developed to yield an initial primal feasible solution and to convert a primal infeasible solution to a feasible solution. Computational results show that the average relative deviation from optimality for small size problems (up to 20 jobs) is 1.89%, for medium size problems (31-40 jobs) is 1.09%, and for large size problems (41-140 jobs) is 3.15%.

AB - This study is concerned with the mixed model assembly line sequencing problem for just-in-time production systems. Such a problem finds applications in flexible production lines where a uniform demand exists for N different item types and this demand needs to be satisfied in batches at a constant rate over a given planning horizon. Optimality properties are provided and used to develop a 0-1 integer linear programming formulation with three sets of constraints that considers varying batch processing times for different types of items. The first two sets of constraints are equivalent to the supply and demand constraints of an asymmetric assignment problem. The third set, which represents the process time non-overlap constraints, is relaxed to form a Lagrangian dual problem. The Lagrangian dual is then solved using a subgradient optimization technique. Some optimality conditions for the mixed model assembly line sequencing problem are provided. Efficient heuristics have been developed to yield an initial primal feasible solution and to convert a primal infeasible solution to a feasible solution. Computational results show that the average relative deviation from optimality for small size problems (up to 20 jobs) is 1.89%, for medium size problems (31-40 jobs) is 1.09%, and for large size problems (41-140 jobs) is 3.15%.

UR - http://www.scopus.com/inward/record.url?scp=0036507643&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036507643&partnerID=8YFLogxK

U2 - 10.1080/09537280110069775

DO - 10.1080/09537280110069775

M3 - Article

AN - SCOPUS:0036507643

VL - 13

SP - 199

EP - 210

JO - Production Planning and Control

JF - Production Planning and Control

SN - 0953-7287

IS - 2

ER -