A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

scholarly journals Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints

2021 ◽  
Vol 11 (23) ◽  
pp. 11240
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee
Keyword(s):  
Waiting Time ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Heuristic Algorithms ◽  
Flow Shop ◽  
Mixed Integer ◽  
Time Constraints ◽  
Two Stage ◽  
Second Stage ◽  
Type Flow

This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the final product. In the flow shop, the assembly operations in the second stage should start within the limited waiting times after those components complete in the first stage. For this problem, a mixed-integer programming formulation is provided, and this formulation is used to find an optimal solution using a commercial optimization solver CPLEX. As this problem is proved to be NP-hard, various heuristic algorithms (priority rule-based list scheduling, constructive heuristic, and metaheuristic) are proposed to solve a large-scale problem within a short computation time. To evaluate the proposed algorithms, a series of computational experiments, including the calibration of the metaheuristics, were performed on randomly generated problem instances, and the results showed outperformance of the proposed iterated greedy algorithm and simulated annealing algorithm in small- and large-sized problems, respectively.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

A two-stage approach for assessment of distributional impacts in model-based delta planning: exploration of plausible inequality patterns and justice-based evaluation of policies

2020 ◽  
Author(s):  
Bramka Arga Jafino ◽  
Jan Kwakkel
Keyword(s):  
Large Scale ◽  
Mekong Delta ◽  
Adaptation Planning ◽  
Small Scale ◽  
Two Stage ◽  
Second Stage ◽  
Adaptation Policies ◽  
Small Scale Farmers ◽  
New Policies ◽  
Distributional Impacts

<p>Climate-related inequality can arise from the implementation of adaptation policies. As an example, the dike expansion policy for protecting rice farmers in the Vietnam Mekong Delta in the long run backfires to the small-scale farmers. The prevention of annual flooding reduces the supply of natural sediments, forcing farmers to apply more and more fertilizers to achieve the same yield. While large-scale farmers can afford this, small-scale farmers do not possess the required economics of scale and are thus harmed eventually. Together with climatic and socioeconomic uncertainties, the implementation of new policies can not only exacerbate existing inequalities, but also induce new inequalities. Hence, distributional impacts to affected stakeholders should be assessed in climate change adaptation planning.</p><p>In this study, we propose a two-stage approach to assess the distributional impacts of policies in model-based support for adaptation planning. The first stage is intended to explore potential inequality patterns that may emerge due to combination of new policies and the realization of exogenous scenarios. This stage comprises four steps: (i) disaggregation of performance indicators in the model in order to observe distributional impacts, (ii) performance of large-scale simulation experimentation to account for deep uncertainties, (iii) clustering of simulation results to identify distinctive inequality patterns, and (iv) application of scenario discovery tools, in particular classification and regression trees, to identify combinations of policies and uncertainties that lead to a specific inequality pattern.</p><p>In the second stage we attempt to asses which policies are morally preferable with respect to the inequality patterns they generate, rather than only descriptively explore the patterns which is the case in the previous stage. To perform a normative evaluation of the distributional impacts, we operationalize five alternative principles of justice: improvement of total welfare (utilitarianism), prioritization of worse-off actors (prioritarianism), reduction of welfare differences across actors (two derivations: absolute inequality and envy measure), and improvement of worst-off actor (Rawlsian difference). The different operationalization of each of these principles forms the so-called social welfare function with which the distributional impacts can be aggregated.</p><p>To test this approach, we use an agricultural planning case study in the upper Vietnam Mekong Delta. Specifically, we assess the distributional impacts of alternative adaptation policies in the upper Vietnam Mekong Delta by using an integrated assessment model. We consider six alternative policies as well as uncertainties related to upstream discharge, sediment supply, and land-use change. Through the first stage, we identify six potential inequality patterns among the 23 districts in the study area, as well as the combinations of policies and uncertainties that result in these types of patterns. From applying the second stage we obtain complete rankings of alternative policies, based on their performance with respect to distributional impacts, under different realizations of scenarios. The explorative stage allows policy-makers to identify potential actions to compensate worse-off actors while the normative stage helps them to easily rank alternative policies based on a preferred moral principle.</p>

scholarly journals Benchmarks for Current Linear and Mixed Integer Optimization Solvers

2015 ◽  
Vol 63 (6) ◽  
pp. 1923-1928 ◽  
Author(s):  
Josef Jablonský
Keyword(s):  
Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Academic Research ◽  
Mixed Integer ◽  
Important Class ◽  
Integer Optimization ◽  
Computational Performance ◽  
Optimization Solvers

Linear programming (LP) and mixed integer linear programming (MILP) problems belong among very important class of problems that find their applications in various managerial consequences. The aim of the paper is to discuss computational performance of current optimization packages for solving large scale LP and MILP optimization problems. Current market with LP and MILP solvers is quite extensive. Probably among the most powerful solvers GUROBI 6.0, IBM ILOG CPLEX 12.6.1, and XPRESS Optimizer 27.01 belong. Their attractiveness for academic research is given, except their computational performance, by their free availability for academic purposes. The solvers are tested on the set of selected problems from MIPLIB 2010 library that contains 361 test instances of different hardness (easy, hard, and not solved).

Analysis of Optimal Unit Numbers and Sizes for Microturbine Cogeneration Systems

2004 ◽  
Author(s):  
Satoshi Gamou ◽  
Koichi Ito ◽  
Ryohei Yokoyama
Keyword(s):  
Energy Demand ◽  
Large Scale ◽  
Maximum Energy ◽  
Mixed Integer ◽  
Small Scale ◽  
Optimization Approach ◽  
Operational Strategies ◽  
Energy Demands ◽  
Exhaust Heat ◽  
Annual Total

The relationships between unit numbers and capacities to be installed for microturbine cogeneration systems are analyzed from an economic viewpoint. In analyzing, an optimization approach is adopted. Namely, unit numbers and capacities are determined together with maximum contract demands of utilities such as electricity and natural gas so as to minimize the annual total cost in consideration of annual operational strategies corresponding to seasonal and hourly energy demand requirements. This optimization problem is formulated as a large-scale mixed-integer linear programming one. The suboptimal solution of this problem is obtained efficiently by solving several small-scale subproblems. Through numerical studies carried out on systems installed in hotels by changing the electrical generating/exhaust heat recovery efficiencies, the initial capital cost of the microturbine cogeneration unit and maximum energy demands as parameters, the influence of the parameters on the optimal numbers and capacities of the microturbine cogeneration units is clarified.

scholarly journals Partitioning and solving large-scale tactical harvest scheduling problems for industrial plantation forests

2020 ◽  
Vol 50 (8) ◽  
pp. 811-818
Author(s):  
Pedro Bellavenutte ◽  
Woodam Chung ◽  
Luis Diaz-Balteiro
Keyword(s):  
Large Scale ◽  
Optimization Procedure ◽  
Mixed Integer ◽  
Forest Planning ◽  
Planning Problem ◽  
Optimization Approach ◽  
Scheduling Problems ◽  
Harvest Scheduling ◽  
Plantation Forests ◽  
Industrial Plantation

Spatially explicit, tactical forest planning is a necessary but challenging task in the management of plantation forests. It involves harvest scheduling and planning for road access and log transportation over time and space. This combinatorial problem can be formulated into the fixed-charge transportation problem (FCTP), in which the sum of fixed and variable costs is minimized while meeting harvest volume requirements and allowing necessary road maintenance and log hauling activities. The problem can be solved using general optimization methods such as mixed-integer linear programming (MILP), but the computational efficiency of the MILP-based approach quickly drops as the size and complexity of the problem increases. We developed a new optimization procedure that partitions the large planning problem into smaller subproblems. We applied a hybrid optimization approach using both MILP and heuristic rules to efficiently solve the large FCTP that otherwise may not be solvable using traditional methods. We applied our approach to an industrial plantation forest in Brazil. Our applications demonstrate the performance of the new optimization procedure and the benefit of solving large forest planning problems that integrate harvest scheduling with road access and transportation.

Tight Second Stage Formulations in Two-Stage Stochastic Mixed Integer Programs

2018 ◽  
Vol 28 (1) ◽  
pp. 788-819 ◽  
Author(s):  
Manish Bansal ◽  
Kuo-Ling Huang ◽  
Sanjay Mehrotra
Keyword(s):  
Mixed Integer ◽  
Two Stage ◽  
Integer Programs ◽  
Mixed Integer Programs ◽  
Second Stage

scholarly journals Two-stage sector sampling for estimating small woodlot attributes

2011 ◽  
Vol 41 (9) ◽  
pp. 1819-1826 ◽  
Author(s):  
Piermaria Corona ◽  
Lorenzo Fattorini ◽  
Sara Franceschi
Keyword(s):  
Large Scale ◽  
Sampling Strategy ◽  
Sampling Variance ◽  
Sampling Strategies ◽  
Fixed Size ◽  
Two Stage ◽  
Sampling Schemes ◽  
Second Stage ◽  
Forest Inventories ◽  
Plot Sampling

A two-stage sampling strategy is proposed to assess small woodlots outside the forests scattered on extensive territories. The first stage is performed to select a sample of small woodlots using fixed-size sampling schemes, and the second stage is performed to sample trees within woodlots selected at first stage. Usually, fixed- or variable-area plots are adopted to sample trees. However, the use of plot sampling in small patches such as woodlots is likely to induce a relevant amount of bias owing to edge effects. In this framework, sector sampling proves to be particularly effective. The present paper investigates the statistical properties of two-stage sampling strategies for estimating forest attributes of woodlot populations when sector sampling is adopted at the second stage. A two-stage estimator of population totals is derived together with a conservative estimator of its sampling variance. By means of a simulation study, the performance of the proposed estimator is checked and compared with that achieved using traditional plot sampling with edge corrections. Simulation results prove the adequacy of sector sampling and provide some guidelines for the effective planning of the strategy. In some countries, the proposed strategy can be performed with few modifications within the framework of large-scale forest inventories.

scholarly journals Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

2020 ◽  
Author(s):  
Álinson S. Xavier ◽  
Feng Qiu ◽  
Shabbir Ahmed
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Large Scale ◽  
Unit Commitment ◽  
Energy Sector ◽  
Mixed Integer ◽  
Data Set ◽  
Computational Performance

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

scholarly journals An Efficient Two-Stage Sparse Representation Method

2015 ◽  
Vol 30 (01) ◽  
pp. 1651001 ◽  
Author(s):  
Chengyu Peng ◽  
Hong Cheng ◽  
Manchor Ko
Keyword(s):  
Sparse Representation ◽  
Large Scale ◽  
Optimization Problem ◽  
Two Stage ◽  
Linear Inverse Problems ◽  
Second Stage ◽  
Concrete Realization ◽  
Representation Method ◽  
Scale Optimization ◽  
Very High

There are a large number of methods for solving under-determined linear inverse problems. For large-scale optimization problem, many of them have very high time complexity. We propose a new method called two-stage sparse representation (TSSR) to tackle it. We decompose the representing space of signals into two parts”, the measurement dictionary and the sparsifying basis. The dictionary is designed to obey or nearly obey the sub-Gaussian distribution. The signals are then encoded on the dictionary to obtain the training and testing coefficients individually in the first stage. Then, we design the basis based on the training coefficients to approach an identity matrix, and we apply sparse coding to the testing coefficients over the basis in the second stage. We verify that the projection of testing coefficients onto the basis is a good approximation of the original signals onto the representing space. Since the projection is conducted on a much sparser space, the runtime is greatly reduced. For concrete realization, we provide an instance for the proposed TSSR. Experiments on four biometric databases show that TSSR is effective compared to several classical methods for solving linear inverse problem.

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