scholarly journals Advanced Production Planning Models

2000 ◽  
Author(s):  
DEAN A. JONES ◽  
CRAIG R. LAWTON ◽  
EDWIN A. KJELDGAARD ◽  
STEPHEN TROY WRIGHT ◽  
MARK A. TURNQUIST ◽  
...  
Keyword(s):  
Production Planning ◽  
Planning Models ◽  
Advanced Production

Hierarchical production planning in forestry using price-directed decomposition

2007 ◽  
Vol 37 (10) ◽  
pp. 2010-2021 ◽  
Author(s):  
Samuel D. Pittman ◽  
B. Bruce Bare ◽  
David G. Briggs
Keyword(s):  
Production Planning ◽  
Net Present Value ◽  
Forest Planning ◽  
Planning Model ◽  
Data Set ◽  
Decomposition Approach ◽  
Hierarchical Production Planning ◽  
Production Models ◽  
Planning Models ◽  
Land Inventory

Forest planning models have increased in size and complexity as planners address a growing array of economic, ecological, and societal issues. Hierarchical production models offer a means of better managing these large and complex models. Hierarchical production planning models decompose large models into a set of smaller linked models. For example, in this paper, a Lagrangian relaxation formulation and a modified Dantzig–Wolfe decomposition – column generation routine are used to solve a hierarchical forest planning model that maximizes the net present value of harvest incomes while recognizing specific geographical units that are subject to harvest flow and green-up constraints. This allows the planning model to consider forest-wide constraints such as harvest flow, as well as address separate subproblems for each contiguous management zone for which detailed spatial plans are computed. The approach taken in this paper is different from past approaches in forest hierarchical planning because we start with a single model and derive a hierarchical model that addresses integer subproblems using Dantzig–Wolfe decomposition. The decomposition approach is demonstrated by analyzing a set of randomly generated planning problems constructed from a large forest and land inventory data set.

Simulation Testing of Aggregate Production Planning Models in an Implementation Methodology

1974 ◽  
Vol 20 (6) ◽  
pp. 903-911 ◽  
Author(s):  
William B. Lee ◽  
Basheer M. Khumawala
Keyword(s):  
Production Planning ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Planning Models ◽  
Simulation Testing ◽  
Implementation Methodology

Advanced production planning system instituted at chemical process machinery plants

1968 ◽  
Vol 4 (3) ◽  
pp. 257-261
Author(s):  
G. V. Ul'yanov ◽  
G. P. Sheremet'ev ◽  
A. A. Lazarev
Keyword(s):  
Production Planning ◽  
Chemical Process ◽  
Planning System ◽  
Process Machinery ◽  
Advanced Production

The Accuracy of Aggregate LP Production Planning Models

1993 ◽  
pp. 359-387 ◽  
Author(s):  
Harvey M. Wagner ◽  
Vincent A. Vargas ◽  
Narinder N. Kathuria
Keyword(s):  
Production Planning ◽  
Planning Models

scholarly journals Adaptive Model Building Framework for Production Planning in the Primary Wood Industry

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1256
Author(s):  
Matthias Kaltenbrunner ◽  
Maria Anna Huka ◽  
Manfred Gronalt
Keyword(s):  
Production Planning ◽  
Model Building ◽  
Adaptive Model ◽  
Wood Industry ◽  
Modelling Framework ◽  
Industrial Implementation ◽  
Planning Models ◽  
Wide Range ◽  
Adaptive Modelling ◽  
Modelling Approach

Production planning models for the primary wood industry have been proposed for several decades. However, the majority of the research to date is concentrated on individual cases. This paper presents an integrated adaptive modelling framework that combines the proposed approaches and identifies evolving planning situations. With this conceptual modelling approach, a wide range of planning issues can be addressed by using a solid model basis. A planning grid along the time and resource dimensions is developed and four illustrative and interdependent application cases are described. The respective mathematical programming models are also presented in the paper and the prerequisites for industrial implementation are shown.

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