Production planning and scheduling integration through augmented Lagrangian optimization

TY - JOUR

T1 - Production planning and scheduling integration through augmented Lagrangian optimization

AU - Li, Zukui

N1 - Funding Information: The authors gratefully acknowledge financial support from the National Science Foundation under Grant CBET 0625515 .

PY - 2010/6/10

Y1 - 2010/6/10

N2 - To improve the quality of decision making in the process operations, it is essential to implement integrated planning and scheduling optimization. Major challenge for the integration lies in that the corresponding optimization problem is generally hard to solve because of the intractable model size. In this paper, augmented Lagrangian method is applied to solve the full-space integration problem which takes a block angular structure. To resolve the non-separability issue in the augmented Lagrangian relaxation, we study the traditional method which approximates the cross-product term through linearization and also propose a new decomposition strategy based on two-level optimization. The results from case study show that the augmented Lagrangian method is effective in solving the large integration problem and generating a feasible solution. Furthermore, the proposed decomposition strategy based on two-level optimization can get better feasible solution than the traditional linearization method.

AB - To improve the quality of decision making in the process operations, it is essential to implement integrated planning and scheduling optimization. Major challenge for the integration lies in that the corresponding optimization problem is generally hard to solve because of the intractable model size. In this paper, augmented Lagrangian method is applied to solve the full-space integration problem which takes a block angular structure. To resolve the non-separability issue in the augmented Lagrangian relaxation, we study the traditional method which approximates the cross-product term through linearization and also propose a new decomposition strategy based on two-level optimization. The results from case study show that the augmented Lagrangian method is effective in solving the large integration problem and generating a feasible solution. Furthermore, the proposed decomposition strategy based on two-level optimization can get better feasible solution than the traditional linearization method.

KW - Augmented Lagrangian relaxation

KW - Decomposition method

KW - Planning and scheduling integration

UR - https://www.scopus.com/pages/publications/77951098481

UR - https://www.scopus.com/pages/publications/77951098481#tab=citedBy

U2 - 10.1016/j.compchemeng.2009.11.016

DO - 10.1016/j.compchemeng.2009.11.016

M3 - Article

SN - 0098-1354

VL - 34

SP - 996

EP - 1006

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

IS - 6

ER -