Search for a command to run...
📄 Overview The Forest Storage Yard Optimization System, developed for computational modeling and optimization of storage yard allocation in sustainable forest management (SFM). This version corresponds to the implementation described in a published scientific study, focusing on the application of the Covering Problem (Location Covering Problem - LCP) to minimize operational costs and environmental impact in timber extraction planning. ⚙️ Features Import and preprocessing of forest inventory datasets Generation of distance matrices between trees and candidate yards Application of covering problem optimization model Simulation of storage yard allocation scenarios Capacity-constrained allocation of timber volume Reduction of hauling distance and environmental impact Comparative analysis with empirical and p-median approaches 🏗️ Architecture The system follows a modular and computational optimization-oriented architecture: Data Layer Forest inventory datasets Preprocessing (distance filtering, candidate yard generation) Optimization Layer Mathematical modeling (Integer Linear Programming) Constraint handling (coverage and capacity) Solver integration (e.g., CPLEX / equivalent) Application Layer Scenario configuration Execution of optimization routines Result visualization and comparison Analysis Layer Statistical evaluation Performance metrics (time, iterations, convergence) 📦 Technical Stack Optimization Modeling (OPL / Linear Programming) IBM ILOG CPLEX (or compatible solver) Python / C# (possible integration layer) Data processing (CSV / structured datasets) Computational experiments environment 📚 Scientific Context This software is associated with the following published article: Title (English):"Covering Problem Applied for Allocation of Storage Yards in Sustainable Forest Management (SFM)" Authors:Dalvan Ribeiro de Almeida, Marcelo Otone Aguiar, Danilo de Paula e Silva Context:The system models the allocation of storage yards as a covering problem, where the goal is to ensure that all demand points (trees) are served within a maximum hauling distance while minimizing the number of facilities. The approach differs from traditional models (e.g., p-median) by not requiring a predefined number of facilities, enabling more flexible and realistic optimization. 🎯 Purpose of this Release This release aims to: Ensure reproducibility of the optimization model Provide a computational framework for forest planning Support decision-making in sustainable forest management Enable academic and engineering reuse of the model 📊 Key Contributions Adaptation of the Covering Problem to forest logistics Inclusion of capacity constraints per storage yard Reduction in number of required yards (~7.14% improvement) Support for large-scale datasets (hundreds of hectares) Improved balance between economic efficiency and environmental preservation 📌 Notes This release represents a stable snapshot of the optimization model The problem is NP-hard, and performance depends on dataset size Future versions may include: Heuristic/metaheuristic approaches Multi-objective optimization (cost vs distance) Scalability improvements for large forest areas