A heuristic method for two-stage hybrid flow shop with dedicated machines

Authors:
Shijin Wang;Ming Liu
Affiliations:
Department of Management Science and Engineering, School of Economics & Management, Tongji University, Shanghai 200092, China;Department of Management Science and Engineering, School of Economics & Management, Tongji University, Shanghai 200092, China
Venue:
Computers and Operations Research
Year:
2013

Citing 8
Cited 0

Hybrid flow shop scheduling: a survey

Computers and Industrial Engineering
Modeling realistic hybrid flexible flowshop scheduling problems

Computers and Operations Research
Scheduling of a two-stage differentiation flowshop to minimize weighted sum of machine completion times

Computers and Operations Research
Scheduling two-stage hybrid flow shop with availability constraints

Computers and Operations Research
Review: Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective

Computers and Operations Research
Minimizing total completion time in two-stage hybrid flow shop with dedicated machines

Computers and Operations Research
Total completion time minimization in a 2-stage differentiation flowshop with fixed sequences per job type

Information Processing Letters
Two-stage hybrid flow shop with precedence constraints and parallel machines at second stage

Computers and Operations Research

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper considers a two-stage hybrid flow shop scheduling problem with dedicated machines, in which the first stage contains a single common critical machine, and the second stage contains several dedicated machines. Each job must be first processed on the critical machine in stage one and depending on the job type, the job will be further processed on the dedicated machine of its type in stage two. The objective is to minimize the makespan. To solve the problem, a heuristic method based on branch and bound (B&B) algorithm is proposed. Several lower bounds are derived and four constructive heuristics are used to obtain initial upper bounds. Then, three dominance properties are employed to enhance the performance of the proposed heuristic method. Extensive computational experiments on two different problem categories each with various problem configurations are conducted. The results show that the proposed heuristic method can produce very close-to-optimal schedules for problems up to 100 jobs and five dedicated machines within 60s. The comparisons with solutions of two other meta-heuristic methods also prove the better performance of the proposed heuristic method.