2008Canadian Journal of Forest ResearchRequires access

Hierarchical forest management with anticipation: an application to tactical–operational planning integration

Daniel Beaudoin, Jean‐Marc Frayret, Luc LeBel

Open publisher page 38 citations

Abstract

This paper examines the problem of harvest capacity planning at a tactical level. Annual capacity planning allows planners to determine the number of contractors to hire per period throughout the year and to define the duration of their contracts. In practice, this process usually involves the analysis of historical data regarding the operational use of capacity and aggregated demand forecast, the output of which then serves to plan harvest operations. Although this form of hierarchical planning reduces the complexity of the task, the decomposition into subproblems that must be successively resolved can lead to infeasibility or poor use of harvesting capacity. The specific problem addressed here resides in how one can consider the operational impact of harvesting decisions taken at the tactical level to ensure a plan’s feasibility at the operational level. We present a tactical planning process based on Schneeweiss’ generic hierarchical modeling approach. A computational experiment demonstrates how a tactical planning process is influenced by the input of the operational level anticipation model. The anticipation approach we propose appears to be a valid method to better integrate key operational-level decisions into tactical plans.

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What this paper is about

This paper examines the problem of harvest capacity planning at a tactical level. Annual capacity planning allows planners to determine the number of contractors to hire per period throughout the year and to define the duration of their contracts. In practice, this process usually involves the analysis of historical data regarding the operational use of capacity and aggregated demand forecast, the output of which then serves to plan harvest operations. Although this form of hierarchical planning reduces the complexity of the task, the decomposition into subproblems that must be successively resolved can lead to infeasibility or poor use of harvesting capacity. The specific problem addressed here resides in how one can consider the operational impact of harvesting decisions taken at the tactical level to ensure a plan’s feasibility at the operational level. We present a tactical planning process based on Schneeweiss’ generic hierarchical modeling approach. A computational experiment demonstrates how a tactical planning process is influenced by the input of the operational level anticipation model. The anticipation approach we propose appears to be a valid method to better integrate key operational-level decisions into tactical plans.

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Available abstract

This paper examines the problem of harvest capacity planning at a tactical level. Annual capacity planning allows planners to determine the number of contractors to hire per period throughout the year and to define the duration of their contracts. In practice, this process usually involves the analysis of historical data regarding the operational use of capacity and aggregated demand forecast, the output of which then serves to plan harvest operations. Although this form of hierarchical planning reduces the complexity of the task, the decomposition into subproblems that must be successively resolved can lead to infeasibility or poor use of harvesting capacity. The specific problem addressed here resides in how one can consider the operational impact of harvesting decisions taken at the tactical level to ensure a plan’s feasibility at the operational level. We present a tactical planning process based on Schneeweiss’ generic hierarchical modeling approach. A computational experiment demonstrates how a tactical planning process is influenced by the input of the operational level anticipation model. The anticipation approach we propose appears to be a valid method to better integrate key operational-level decisions into tactical plans.

Key concepts: Anticipation (artificial intelligence), Operational planning, Operations research, Process (computing), Plan (archaeology), Computer science, Key (lock), Task (project management)

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