Lagrangian relaxation algorithms for real-time hybrid flowshop scheduling with finite intermediate buffers

Lagrangian relaxation algorithms for real-time hybrid flowshop scheduling with finite intermediate buffers

0.00 Avg rating0 Votes
Article ID: iaor20071771
Country: United Kingdom
Volume: 57
Issue: 3
Start Page Number: 316
End Page Number: 324
Publication Date: Mar 2006
Journal: Journal of the Operational Research Society
Authors: ,
Keywords: markov processes, programming: dynamic
Abstract:

We investigate the problem of scheduling N jobs on parallel identical machines in J successive stages with finite buffer capacities between consecutive stages in a real-time environment. The objective is to find a schedule that minimizes the sum of weighted completion time of jobs. This problem has proven strongly NP-hard. In this paper, the scheduling problem is formulated as an integer programming model considering buffers as machines with zero processing time. Lagrangian relaxation algorithms are developed combined with a speed-up dynamic programming approach. The complication and time consumption of solving all the subproblems at each iteration in subgradient optimization motivate the development of the surrogate subgradient method, where only one subproblem is minimized at each iteration and an adaptive multiplier update scheme of Lagrangian multipliers is designed. Computational experiments with up to 100 jobs show that the designed surrogate subgradient algorithm provides a better performance as compared to the subgradient algorithm.

Reviews

Required fields are marked *. Your email address will not be published.