Optimize landscape fuel treatment locations to create control opportunities for future fires

2012 ◽  
Vol 42 (6) ◽  
pp. 1002-1014 ◽  
Author(s):  
Yu Wei
Keyword(s):  
National Parks ◽  
Spatial Information ◽  
Programming Model ◽  
Mixed Integer ◽  
Fire Intensity ◽  
Planning Problem ◽  
Fire Control ◽  
Fuel Treatments ◽  
Fuel Treatment ◽  
Ignition Probability

Fuel treatment can improve the efficiency of controlling future catastrophic fires. Selecting optimal fuel treatment locations across a landscape is a challenging strategic planning problem in wildland fire management. This research develops a new fuel treatment optimization model by extending a fire suppression model to simultaneously consider many future fires. Fire is ignited from every grid cell in a landscape and modeled for various durations in a mixed integer programming model. Fuel treatment in a cell decreases its fire intensity and makes future fire control effective in it. This model allocates fuel treatments to minimize the total landscape future fire loss. It was first tested on several artificial landscapes for model validation. Results show that it tends to allocate fuel treatments in contiguous areas following regular and intuitive spatial patterns. Spatial fuel treatment layouts vary according to the change of fire ignition probability distribution, the distribution of value to be protected from fire, and fire duration assumptions. Trade-off between protecting different parts of a landscape is a major driver in designing fuel treatment layouts. A test case in the Sequoia and Kings Canyon national parks demonstrates how this model assembles spatial information and helps study the effects of fuel treatments in a heterogeneous landscape. This model allows managers to assemble information from many possible future fires to make informative strategic-level fuel treatment decisions. A potential model extension and the limitations of this model are also discussed.

An optimization model for locating fuel treatments across a landscape to reduce expected fire losses

2008 ◽  
Vol 38 (4) ◽  
pp. 868-877 ◽  
Author(s):  
Yu Wei ◽  
Douglas Rideout ◽  
Andy Kirsch
Keyword(s):  
Spatial Information ◽  
Solution Process ◽  
Fire Risk ◽  
Simulation Models ◽  
Mixed Integer ◽  
Fire Intensity ◽  
Fuel Treatments ◽  
Fuel Treatment ◽  
Mip Model ◽  
Fire Ignition

Locating fuel treatments with scarce resources is an important consideration in landscape-level fuel management. This paper developed a mixed integer programming (MIP) model for allocating fuel treatments across a landscape based on spatial information for fire ignition risk, conditional probabilities of fire spread between raster cells, fire intensity levels, and values at risk. The fire ignition risk in each raster cell is defined as the probability of fire burning a cell because of the ignition within that cell. The conditional probability that fire would spread between adjacent cells A and B is defined as the probability of a fire spreading into cell B after burning in cell A. This model locates fuel treatments by using a fire risk distribution map calculated through fire simulation models. Fire risk is assumed to accumulate across a landscape following major wind directions and the MIP model locates fuel treatments to efficiently break this pattern of fire risk accumulation. Fuel treatment resources are scarce and such scarcity is introduced through a budget constraint. A test case is designed based on a portion of the landscape (15 552 ha) within the Southern Sierra fire planning unit to demonstrate the data requirements, solution process, and model results. Fuel treatment schedules, based upon single and dual wind directions, are compared.

scholarly journals The Airlift Planning Problem

2019 ◽  
Vol 53 (3) ◽  
pp. 773-795
Author(s):  
Dimitris Bertsimas ◽  
Allison Chang ◽  
Velibor V. Mišić ◽  
Nishanth Mundru
Keyword(s):  
Large Scale ◽  
Programming Model ◽  
Simulated Data ◽  
Mixed Integer ◽  
Planning Problem ◽  
Weight Restrictions ◽  
Total Delay ◽  
Data Set ◽  
Scale Optimization ◽  
Airlift Planning

The U.S. Transportation Command (USTRANSCOM) is responsible for planning and executing the transportation of U.S. military personnel and cargo by air, land, and sea. The airlift planning problem faced by the air component of USTRANSCOM is to decide how requirements (passengers and cargo) will be assigned to the available aircraft fleet and the sequence of pickups and drop-offs that each aircraft will perform to ensure that the requirements are delivered with minimal delay and with maximum utilization of the available aircraft. This problem is of significant interest to USTRANSCOM because of the highly time-sensitive nature of the requirements that are typically designated for delivery by airlift, as well as the very high cost of airlift operations. At the same time, the airlift planning problem is extremely difficult to solve because of the combinatorial nature of the problem and the numerous constraints present in the problem (such as weight restrictions and crew rest requirements). In this paper, we propose an approach for solving the airlift planning problem faced by USTRANSCOM based on modern, large-scale optimization. Our approach relies on solving a large-scale mixed-integer programming model that disentangles the assignment decision (which aircraft will pickup and deliver which requirement) from the sequencing decision (in what order the aircraft will pickup and deliver its assigned requirements), using a combination of heuristics and column generation. Through computational experiments with both a simulated data set and a planning data set provided by USTRANSCOM, we show that our approach leads to high-quality solutions for realistic instances (e.g., 100 aircraft and 100 requirements) within operationally feasible time frames. Compared with a baseline approach that emulates current practice at USTRANSCOM, our approach leads to reductions in total delay and aircraft time of 8%–12% in simulated data instances and 16%–40% in USTRANSCOM’s planning instances.

scholarly journals Application of Two-Phase Fuzzy Optimization Approach to Multiproduct Multistage Integrated Production Planning with Linguistic Preference under Uncertainty

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Shan Lu ◽  
Hongye Su ◽  
Lian Xiao ◽  
Li Zhu
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Service Level ◽  
Mixed Integer ◽  
Planning Problem ◽  
Optimization Approach ◽  
Two Phase ◽  
Steel Rolling ◽  
Solution Approach

This paper tackles the challenges for a production planning problem with linguistic preference on the objectives in an uncertain multiproduct multistage manufacturing environment. The uncertain sources are modelled by fuzzy sets and involve those induced by both the epistemic factors of process and external factors from customers and suppliers. A fuzzy multiobjective mixed integer programming model with different objective priorities is proposed to address the problem which attempts to simultaneously minimize the relevant operations cost and maximize the average safety stock holding level and the average service level. The epistemic and external uncertainty is simultaneously considered and formulated as flexible constraints. By defining the priority levels, a two-phase fuzzy optimization approach is used to manage the preference extent and convert the original model into an auxiliary crisp one. Then a novel interactive solution approach is proposed to solve this problem. An industrial case originating from a steel rolling plant is applied to implement the proposed approach. The numerical results demonstrate the efficiency and feasibility to handle the linguistic preference and provide a compromised solution in an uncertain environment.

scholarly journals Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

2021 ◽  
Vol 11 (20) ◽  
pp. 9687
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee ◽  
Bongjoo Jeong
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Programming Model ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Planning Problem ◽  
Manufacturing Plant ◽  
Production Lines ◽  
Multiple Production ◽  
Production Planning Problem

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented.

scholarly journals Effect of Railway Track Segmentation Method on the Optimal Solution of Tamping Planning Problem

2021 ◽  
Vol 7 (12) ◽  
pp. 1998-2010
Author(s):  
Mohammad Daddow ◽  
Xinglin Zhou ◽  
Hasan A.H. Naji ◽  
Mo'men Ayasrah
Keyword(s):  
Programming Model ◽  
Optimal Solution ◽  
Railway Track ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Planning Problem ◽  
Segmentation Method ◽  
Railway Network ◽  
Railway Infrastructure ◽  
Optimal Maintenance

The safety and continuality of the railway network are guaranteed by carrying out a lot of maintenance interventions on the railway track. One of the most important of these actions is tamping, where railway infrastructure managers focus on optimizing tamping activities in ballasted tracks to reduce the maintenance cost. To this end, this article presents a mixed integer linear programming model of the Tamping Planning Problem (TPP) and investigates the effect of track segmentation method on the optimal solution by three scenarios. It uses an opportunistic maintenance technique to plan tamping actions. This technique clusters many tamping works through a time period to reduce the track possession cost as much as possible. CPLEX 12.6.3 is used in order to solve the TPP instances exactly. The results show that the total number of machine preparations increases by increasing the number of track segments. It is also found that the total costs increase by 6.1% and 9.4% during scenarios 2 and 3, respectively. Moreover, it is better to consider the whole railway track as a single segment (as in scenarios 1) that consists of a set of sections during the tamping planning in order to obtain the optimal maintenance cost. Doi: 10.28991/cej-2021-03091774 Full Text: PDF

scholarly journals Developing a Master Production Schedule Using Fuzzy Mixed Integer Linear Programming

2021 ◽  
Vol 15 ◽  
pp. 8-13
Author(s):  
Mohamed K. Omar ◽  
Muzalna Mohd-Jusoh ◽  
Mohd Omar
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Mixed Integer ◽  
Programming Approach ◽  
Planning Problem ◽  
Production Schedule ◽  
Master Production Schedule

This paper considers the hierarchical production planning (HPP) concept to solve a production planning problem in the process industry in a fuzzy environment. The adopted fuzzy HPP consists of two levels in which a fuzzy aggregate production planning (FAPP) model is developed in the first level, and then a fuzzy disaggregate production planning (FDPP) model is developed at the second level. The FAPP was reported by Omar et al. [1] and therefore, this research paper discusses the FDPP model. We formulated the disaggregate model as a fuzzy mixed-integer linear programming model that aims to develop a master production schedule in which numbers of optimal batches are developed in presence of setup time. In addition, we evaluate the performance of the FMILP by comparing its results with a previously reported approach. The findings indicate that significant cost savings were achieved by adopting the fuzzy mathematical programming approach.

Emergency evacuation planning in natural disasters under diverse population and fleet characteristics

2018 ◽  
Vol 8 (4) ◽  
pp. 447-476 ◽  
Author(s):  
Evangelia Baou ◽  
Vasilis P. Koutras ◽  
Vasileios Zeimpekis ◽  
Ioannis Minis
Keyword(s):  
Programming Model ◽  
Emergency Evacuation ◽  
Disabled Persons ◽  
Mixed Integer ◽  
Evacuation Planning ◽  
Planning Problem ◽  
Practical Case ◽  
Evacuation Time ◽  
Content Type ◽  
Heterogeneous Fleet

PurposeThe purpose of this paper is to formulate and solve a new emergency evacuation planning problem. This problem addresses the needs of both able and disabled persons who are evacuated from multiple pick-up locations and transported using a heterogeneous fleet of vehicles.Design/methodology/approachThe problem is formulated using a mixed integer linear programming model and solved using a heuristic algorithm. The authors analyze the selected heuristic with respect to key parameters and use it to address theoretical and practical case studies.FindingsEvacuating people with disabilities has a significant impact on total evacuation time, due to increased loading/unloading times. Additionally, increasing the number of large capacity vehicles adapted to transport individuals with disabilities benefits total evacuation time.Research limitations/implicationsThe mathematical model is of high complexity and it is not possible to obtain exact solutions in reasonable computational times. The efficiency of the heuristic has not been analyzed with respect to optimality.Practical implicationsSolving the problem by a heuristic provides a fast solution, a requirement in emergency evacuation cases, especially when the state of the theater of the emergency changes dynamically. The parametric analysis of the heuristic provides valuable insights in improving an emergency evacuation system.Social implicationsEfficient population evacuation studied in this work may save lives. This is especially critical for disabled evacuees, the evacuation of whom requires longer operational times.Originality/valueThe authors consider a population that comprises able and disabled individuals, the latter with varying degrees of disability. The authors also consider a heterogeneous fleet of vehicles, which perform multiple trips during the evacuation process.

scholarly journals Integrated Production and Distribution Planning for the Iron Ore Concentrate

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wenbo Liu ◽  
Defeng Sun ◽  
Te Xu
Keyword(s):  
Distribution System ◽  
Iron Ore ◽  
Programming Model ◽  
Mixed Integer ◽  
Planning Problem ◽  
Distribution Centers ◽  
Distribution Planning ◽  
Production And Distribution ◽  
Production And Distribution Planning ◽  
Iron Ore Concentrate

This paper studies the production and distribution planning problem faced by the iron ore mining companies, which aims to minimize the total costs for the whole production and distribution system of the iron ore concentrate. The ores are first mined from multiple ore locations, and then sent to the corresponding dressing plant to produce ore concentrate, which will be sent to distribution centers and finally fulfill the customers' demands. This paper also tackles the difficulty of variable cut-off grade when making mining production planning decisions. A mixed-integer programming model is developed and then solved by a Lagrange relaxation (LR) procedure. Computational results indicate that the proposed solution method is more efficient than the standard solution software CPLEX.

FUZZY LINEAR PROGRAMMING FOR SUPPLY CHAIN PLANNING UNDER UNCERTAINTY

2010 ◽  
Vol 09 (03) ◽  
pp. 373-392 ◽  
Author(s):  
DAVID PEIDRO ◽  
JOSEFA MULA ◽  
RAÚL POLER
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Demand Uncertainty ◽  
Programming Model ◽  
Fuzzy Model ◽  
Fuzzy Linear Programming ◽  
Mixed Integer ◽  
Planning Problem ◽  
Planning Under Uncertainty ◽  
Supply Chain Planning

A new fuzzy mathematical programming model for supply chain planning under supply, process and demand uncertainty is proposed in this paper. A tactical supply chain planning problem has been formulated as a fuzzy mixed integer linear programming model where data are ill-known and modeled by fuzzy numbers with modified s-curve membership functions. The fuzzy model provides alternative decision plans to the decision maker (DM) for different degrees of satisfaction. Finally, the proposed model is tested by using data from a real automobile supply chain.

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