Using a genetic algorithm to solve the troops-to-tasks problem in military operations planning

2017 ◽  
Vol 14 (4) ◽  
pp. 439-446 ◽  
Author(s):  
Maria Fleischer Fauske
Keyword(s):  
Genetic Algorithm ◽  
Time Frame ◽  
Optimization Techniques ◽  
Operational Planning ◽  
Mixed Integer ◽  
Data Sets ◽  
Military Operations ◽  
Mixed Integer Linear Program ◽  
Problem Instances ◽  
Feasible Solutions

The troops-to-tasks analysis in military operational planning is the process where the military staff investigates who should do what, where, and when in the operation. In this paper, we describe a genetic algorithm for solving troops-to-tasks problems, which are typically solved manually. The study was motivated by a request from Norwegian military staff, who acknowledged the potential for solving the troops-to-tasks analysis more effectively by using optimization techniques. Also, NATO’s operational planning tool, TOPFAS, lacks an optimization module for the troops-to-tasks analysis. The troops-to-tasks problem generalizes the well-known resource-constrained project scheduling problem, and thus it is very difficult to solve. As the troops-to-tasks problem is particularly complex, the main purpose of our study was to develop an algorithm capable of solving real-sized problem instances. We developed a genetic algorithm with new features, which were crucial to finding good solutions. We tested the algorithm on two different data sets representing high-intensity military operations. We compared the performance of the algorithm to that of a mixed integer linear program solved by CPLEX. In contrast to CPLEX, the algorithm found feasible solutions within an acceptable time frame for all instances.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

Hybrid Evolutionary Optimization of Two-Stage Stochastic Integer Programming Problems: An Empirical Investigation

2009 ◽  
Vol 17 (4) ◽  
pp. 511-526 ◽  
Author(s):  
Thomas Tometzki ◽  
Sebastian Engell
Keyword(s):  
Evolutionary Algorithm ◽  
Empirical Investigation ◽  
Scale Up ◽  
Exact Algorithm ◽  
Integer Program ◽  
Mixed Integer ◽  
Two Stage ◽  
Integer Programming Problems ◽  
Problem Instances ◽  
Feasible Solutions

In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.

Hybridization of Biogeography-Based

2013 ◽  
Vol 2 (3) ◽  
pp. 86-101 ◽  
Author(s):  
Provas Kumar Roy ◽  
Dharmadas Mandal
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Population Based ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Swarm Optimization

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

scholarly journals Train-Scheduling Optimization Model for Railway Networks with Multiplatform Stations

2019 ◽  
Vol 12 (1) ◽  
pp. 257
Author(s):  
Gianmarco Garrisi ◽  
Cristina Cervelló-Pastor
Keyword(s):  
Mathematical Formulation ◽  
Computation Time ◽  
Mixed Integer ◽  
Initial Population ◽  
Mixed Integer Linear Program ◽  
Transit System ◽  
Train Scheduling ◽  
Railway Networks ◽  
Np Hard Problem ◽  
Problem Instances

This paper focuses on optimizing the schedule of trains on railway networks composed of busy complex stations. A mathematical formulation of this problem is provided as a Mixed Integer Linear Program (MILP). However, the creation of an optimal new timetable is an NP-hard problem; therefore, the MILP can be solved for easy cases, computation time being impractical for more complex examples. In these cases, a heuristic approach is provided that makes use of genetic algorithms to find a good solution jointly with heuristic techniques to generate an initial population. The algorithm was applied to a number of problem instances producing feasible, though not optimal, solutions in several seconds on a laptop, and compared to other proposals. Some improvements are suggested to obtain better results and further improve computation time. Rail transport is recognized as a sustainable and energy-efficient means of transport. Moreover, each freight train can take a large number of trucks off the roads, making them safer. Studies in this field can help to make railways more attractive to travelers by reducing operative cost, and increasing the number of services and their punctuality. To improve the transit system and service, it is necessary to build optimal train scheduling. There is an interest from the industry in automating the scheduling process. Fast computerized train scheduling, moreover, can be used to explore the effects of alternative draft timetables, operating policies, station layouts, and random delays or failures.

Multiperiod Highway Improvement and Construction Scheduling: Model Development and Application

1998 ◽  
Vol 1617 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Wael Eldessouki ◽  
Nagui Rouphail ◽  
Madalena Beja ◽  
S. Ranji Ranjithan
Keyword(s):  
Planning Process ◽  
Model Development ◽  
Mathematical Optimization ◽  
Planning Horizon ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Traffic Demand ◽  
Primary Model

A methodology is presented that emulates the transportation improvement planning process using mathematical optimization techniques. The scheduling problem is formulated as a mixed integer linear program (MILP) and can be considered as a multiperiod network design problem. The three primary model components are discussed: ( a) the input module in which the network, traffic demand, and pool of potential projects are identified over the planning horizon; ( b) the benefits estimation module using network travel time as the benefit criterion; and ( c) the schedule builder, an MILP that attempts to maximize the total benefits subject to annual resources and project precedence constraints. The proposed method is applied in a case-study context to the Lisbon metropolitan region’s network, a portion of Portugal’s highway network, and the results are discussed.

scholarly journals Solution procedures for block selection and sequencing in flat-bedded potash underground mines

OR Spectrum ◽  
2021 ◽  
Author(s):  
Cinna Seifi ◽  
Marco Schulze ◽  
Jürgen Zimmermann
Keyword(s):  
Crop Yields ◽  
Decisive Role ◽  
Planning Horizon ◽  
Underground Mines ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Constructive Heuristic ◽  
Processing Plants ◽  
Problem Instances ◽  
Feasible Solutions

AbstractPhosphates, and especially potash, play an essential role in the increase in crop yields. Potash is mined in Germany in underground mines using a conventional drill-and-blast technique. The most commercially valuable mineral contained in potash is the potassium chloride that is separated from the potash in aboveground processing plants. The processing plants perform economically best if the amount of potassium contained in the output is equal to a specific value, the so-called optimal operating point. Therefore, quality-oriented extraction plays a decisive role in reducing processing costs. In this paper, we mathematically formulate a block selection and sequencing problem with a quality-oriented objective function that aims at an even extraction of potash regarding the potassium content. We, thereby, have to observe some precedence relations, maximum and minimum limits of the output, and a quality tolerance range within a given planning horizon. We model the problem as a mixed-integer nonlinear program which is then linearized. We show that our problem is $${\mathcal {NP}}$$ NP -hard in the strong sense with the result that a MILP-solver cannot find feasible solutions for the most challenging problem instances at hand. Accordingly, we develop a problem-specific constructive heuristic that finds feasible solutions for each of our test instances. A comprehensive experimental performance analysis shows that a sophisticated combination of the proposed heuristic with the mathematical program improves the feasible solutions achieved by the heuristic, on average, by $$92.5\%$$ 92.5 % .

scholarly journals A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 434
Author(s):  
Nina Skorin-Kapov ◽  
Ricardo Santos ◽  
Hakim Ghazzai ◽  
Andreas Kassler
Keyword(s):  
Packet Loss ◽  
Mixed Integer ◽  
Greedy Heuristic ◽  
Mixed Integer Linear Program ◽  
Wireless Backhaul ◽  
Online Planning ◽  
Backhaul Networks ◽  
Problem Instances ◽  
Reduced Time ◽  
Number Of Iterations

In this paper, we consider the reconfiguration of wireless backhaul networks with mechanically steerable antennas in the presence of changing traffic demands. Reconfiguration requires the scheduling and coordination of several operations, including antenna alignment and link establishment/removal, with minimal disruption to existing user traffic. Previously, we proposed a Mixed Integer Linear Program (MILP) to orchestrate such reconfiguration with minimal packet loss. While the MILP solves the problem optimally for a limited number of discrete reconfiguration time slots, it does not scale well. In this paper, we propose an iterative randomized greedy algorithm to obtain suboptimal solutions in reduced time. The algorithm schedules the reconfiguration of wireless links by ranking them according to a set of attributes with associated weights and selecting them according to a randomized greedy function. Results on six different network scenarios indicate that the proposed algorithm can achieve good quality solutions in significantly less time. Furthermore, by extending the reconfiguration time beyond the maximum number of time slots solvable by the MILP, the proposed heuristic can obtain superior solutions for some problem instances. The number of iterations of the algorithm can be tuned for its applicability in both offline and online planning scenarios.

scholarly journals OPTIMIZATION OF CROSS-SECTIONAL AREAS OF REINFORCED CONCRETE COLUMNS SUBJECTED TO AXIAL FORCE AND BIAXIAL BENDING VIA GENETIC ALGORITHMS

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Rodrigo Oliveira Cruz ◽  
Afonso Celso de Castro Lemonge
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Compressive Force ◽  
Concrete Columns ◽  
Optimization Techniques ◽  
Biaxial Bending ◽  
Cross Sectional ◽  
Reinforced Concrete Columns ◽  
Feasible Solutions ◽  
Financial Losses

The sizing of reinforced concrete structures is influenced by high magnitude forces and, as a result of its calculations, some designs may present specifications that are not optimum. In this case, it can occur exaggerated dimensions and an oversized structure resulting in financial losses and material wastes. Thus, it can be applied to the sizing, optimization techniques to achieve the best solution regarding, as an example, efficiency and material costs. This paper presents the optimization of cross-sectional areas of reinforced concrete columns using a Genetic Algorithm (GA) and considering the structure subjected to an axially compressive force and biaxial bending. It was developed an algorithm using the formulation of Araújo (2014) for the sizing associated with Deb’s Genetic Algorithm (2001). The developed software present solutions as cross-sectional areas of a column regarding the minimization of its costs and in which its reinforcement steel positions and diameters are optimized. Its dimensions and concrete resistance may also be optimized as a choice of the designer/engineer. The algorithm was applied to an example and had its solutions compared with other authors. Its results had achieved feasible solutions and shown similar costs.

Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm

2005 ◽  
Vol 52 (5) ◽  
pp. 43-52 ◽  
Author(s):  
F. di Pierro ◽  
S. Djordjević ◽  
Z. Kapelan ◽  
S.-T. Khu ◽  
D. Savić ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Techniques ◽  
Urban Drainage ◽  
Network Modelling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sewer Network ◽  
Multi Objective Genetic Algorithm ◽  
Physically Based ◽  
Feasible Solutions

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.

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