Target Allocation Research Articles

Formation Transformation Based on Improved Genetic Algorithm and Distributed Model Predictive Control

In order to solve the problem of multiple aircraft formation transformation to a designated formation, a distributed formation transformation algorithm that decomposes the formation transformation problem into target-matching problems and trajectory-planning problems was studied. According to the actual formation transformation requirements, the target allocation index was proposed, and the improved genetic algorithm which is 23% better than other algorithms was used to achieve target matching. The adaptive cross-mutation probability was designed, and the population was propagated without duplicates by the hash algorithm. The multi-objective algorithm of distributed model predictive control was used to design smooth and conflict-free trajectories for the UAVs in formation transformation, and the trajectory-planning problem was transformed into a quadratic programming problem under inequality constraints. Finally, point-to-point collision-free offline trajectory planning was realized by simulation.

Rebalance Your Portfolio Without Selling

Summary How do you bring your assets as close as possible to your target allocation by only investing a fixed amount of additional funds, and not selling any assets? We look at two versions of this problem which have simple, closed form solutions revealed by basic calculus and algebra.

Bibliometric Analysis of Internationalization Strategies in Supporting MSME Business Growth in Indonesia

Research on MSME internationalization strategies is interesting and important in increasing global scale business growth. This study aims to analyze the five dimensions of internationalization strategy including market entry, target market strategy, time strategy, allocation strategy, and coordination strategy that supports MSME business growth in Indonesia. There have been no studies that have reviewed this topic using bibliometric analysis. Our analysis includes 180 journal articles published in Scopus, Google Scholar, and Crossref between 2018 – 2023. Researchers use VOSviewer software to classify and visualize research results. Research findings show that Indonesian MSME players combine several internationalization strategies such as market entry strategies, target market strategies, allocation strategies, and coordination strategies synergistically through e-commerce development so that they can help exploit existing competitive advantages or create new competitive advantages in the international market. Research on time strategies and allocation strategies has not been explored much, therefore this can be raised as a further research topic. The implications of this research present a bibliometric analysis of the dimensions of Indonesia's MSME internationalization strategy and propose future research directions.

Public Policy Analysis On Education Budget Allocation: Case Studys in Regencies/Municipalities East Java, Indonesia

The objective of the current study is to map regency and Municipality government budget allocations in East Java Province, Indonesia. The study uses quantitative and qualitative approaches. The quantitative approach is related to the use of numerical data on the percentage of education budget allocations. The qualitative approach is related to the use of interview data obtained from the head of the regency/Municipality Education Office in East Java Province. The study reveals that there are 5 (five) education budget allocation clusters in East Java, namely cluster A with the highest percentage of education budget allocation, covering Jember Regency and Nganjuk Regency, whereas Clusters B, C, D, and E are regencies/Municipalities with the medium, smallest, and lowest percentage of education budget allocations. Since there are only two regencies/Municipalities categorized into cluster A, it indicates that the 20% target allocation of the national education budget policy is still far from expectations, which might be caused by the planning problems, technical implementation guidelines, allocations, and different interests and perceptions among stakeholders.

Covariate-adjusted response-adaptive designs based on semiparametric approaches.

We consider theoretical and practical issues for innovatively using a large number of covariates in clinical trials to achieve various design objectives without model misspecification. Specifically, we propose a new family of semiparametric covariate-adjusted response-adaptive randomization (CARA) designs and we use the target maximum likelihood estimation (TMLE) to analyze the correlated data from CARA designs. Our approach can flexibly achieve multiple objectives and correctly incorporate the effect of a large number of covariates on the responses without model misspecification. We also obtain the consistency and asymptotic normality of the target parameters, allocation probabilities, and allocation proportions. Numerical studies demonstrate that our approach has advantages over existing approaches, even when the data-generating distribution iscomplicated.

A STUDY OF EFFECTIVE USE OF MARKETING STRATEGIES TO IMPROVE THE FINANCIAL PERFORMANCE WITH REFERENCE TO DIVINE INDUSTRIAL SOLUTIONS, JAYASINGPUR

The marketing strategy is not just evaluating the external and internal factors, but it also needs to be implemented efficiently to develop an attractive suitable product and distribution channel, to hire an effective sales team and to generate business support for the firm. The study of 150 customers located in Kolhapur (Maharashtra) which are manufacturer and traders also who purchase Divine Industrial Solutions products. The primary data is collected through Questionnaires, Personal interview, discussions, field survey from these 150 customers and the secondary data is collected from the companies previous records, files as well as review of financial statements, balance sheets etc. for the period of three year from 2019 to 2022. The marketing strategy of an enterprise is a complete and unbeatable plan or instrument specifically designed to achieve the set of marketing objectives that will influence and finally improves the financial performance of the company. The marketing strategies is a managerial process of developing and maintaining the relationship between the changing market opportunities, as well as enhancing the financial growth and performance of the company. Marketing strategies further predetermines the choice of target market segments, positioning, marketing mix and allocation of resources. This research will provide insights into the various marketing strategies to improve the financial performance of the organization and to maintain the growth of the organization. Keywords:- Marketing Strategies, Strategic Marketing, Financial Performance.

Three influential design quantities on the power of Wald-type tests for treatment comparisons in clinical trials

In clinical trials, efficient statistical inference is critical to the well-being of future patients. We therefore construct Wald-type tests for the hypothesis of treatment-by-covariate interaction when treatments are assigned to patients by an adaptive design and the true model is a generalized linear model. Our measure of efficiency is the power of the test while ethics of a trial or well-being of participating patients is measured by the success rate of treatments. We demonstrate that the power of the test depends on the target allocation proportion, the bias of the randomization procedure from the target, and the variability induced by the randomization process (design variability) for adaptive designs. We prove that these quantities influence the power when the trial involves two treatments and a single covariate. We also show that, in this case, as design variability decreases the power increases. Due to the complexity of the problem, we demonstrate by simulation that this result still holds when more than one covariate is present in the model. In simulation studies, we compare the measures of efficiency and ethics under response-adaptive (RA), covariate-adjusted responseadaptive (CARA), and completely randomized (CR) designs. The methods are applied to data from a clinical trial on stroke prevention in atrial fibrillation (SPAF).
 Journal of Statistical Research 2022, Vol. 56, No. 1, pp. 11-36

Optimal allocation of reliability improvement target based on multiple correlation failures and risk uncertainty

Optimal allocation of the reliability improvement target is essential for the system optimization design. In order to solve the problems that the optimization model is with loss of generality and the validity of the optimal solution is weakened, an optimal allocation method is proposed by considering multiple correlation failures and risk uncertainty in this paper. Two new concepts are presented, such as independent failure results in basic risk, and correlation failure leads to disturbance risk. A risk assessment machinery of “actual risk = basic risk + disturbance risk” is proposed. The action mechanisms of the three correlation failures are studied based on the cooperation game theory, and the generalized risk models are given under probability measure. Considering the improvement cost, the expectation and the variance of the reduction of system risk, a multi-objective optimal allocation model is developed, which is solved by using the PSO algorithm. Finally, the proposed optimal allocation is implemented at the 2-stage NGW planetary reducer, and the results show that it is more efficient and feasible for engineering practice.

Attack against layered defense

The ''attack-defense'' model is considered, in which the defense party at each point of defense has several lines and uses the target allocation of its forces. The problem of minimizing the mean quantity of attack forces breaking through the defense lines is solved in explicit formulae. Algorithm of finding a solution of the zero-sum damage game is developed.

Uncertain Sensor–Weapon–Target Allocation Problem Based on Uncertainty Theory

The sensor–weapon–target allocation (S-WTA) is a typical collaborative task allocation problem involved in network-centric warfare (NCW). The existing related studies have a limitation to the nature of cooperation and uncertainty in an air defense battle scenario, and most existing models have the assumption that they are determinate, i.e., the parameters in them are known certainly. For the actual battlefield environment, the asymmetric information in it could lead to the failure of the above assumption, and there are many uncertainties whose frequency can not be evaluated objectively. Based on uncertainty theory, this paper studied the S-WTA problem in an indeterminate battlefield environment. First, we analyze the uncertain factors existing in the actual battlefield environment and their influence on the S-WTA problem, and by considering the threat value of the target, the deviation parameters of the sensor tracking performance and weapon interception performance as uncertain variables, we then establish an uncertain S-WTA (USWTA) model, where the destruction value to targets is regarded as an objective function and four categories of typical constraints are set. Further, an equivalent transformation is presented to convert the unsolvable model into a determinate one by the expected value principle. To solve the proposed model efficiently, a permutation-based representation for the allocation scheme of the USWTA problem is introduced firstly, which can construct a feasible solution efficiently, and on this basis, a constructive heuristic algorithm based on maximum marginal return rule (MMRCH) is designed to construct a feasible solution with high quality. Additionally, a local search (LS) operation is proposed to explore for the better solution locally and further improve the quality of solution obtained by MMRCH. Finally, a set of instances are set to be solved by the designed algorithm, and the simulation experiment demonstrates the superiority of the designed algorithm and the feasibility of the proposed model.

Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration

Coordinating a team of autonomous agents to explore an environment can be done by partitioning the map of the environment into segments and allocating the segments as targets for the individual agents to visit. However, given an unknown environment, map segmentation must be conducted in a continuous and incremental manner. In this paper, we propose a novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment. Each cluster is then assigned with a cost-utility value based on the minimum cost possible for the agents to visit. In this way, map segmentation and target allocation can be performed continually in real-time to efficiently explore the environment. To evaluate our proposed model, we conduct extensive experiments on map segmentation and multi-agent exploration. The results show that the proposed method can produce more accurate and meaningful segments leading to a higher level of efficiency in exploring the environment. Furthermore, the robustness tests by adding noises to the environments were conducted to simulate the performance of our model in the real-world environment. The results demonstrate the robustness of our model in map segmentation and multi-agent environment exploration.

A multi-objective optimization framework with rule-based initialization for multi-stage missile target allocation.

This paper investigates a novel multi-objective optimization framework for the multi-stage missile target allocation (M-MTA) problem, which also widely exists in other real-world complex systems. Specifically, a constrained model of M-MTA is built with the trade-off between minimizing the survivability of targets and minimizing the cost consumption of missiles. Moreover, a multi-objective optimization algorithm (NSGA-MTA) is proposed for M-MTA, where the hybrid encoding mechanism establishes the expression of the model and algorithm. Furthermore, rule-based initialization is developed to enhance the quality and searchability of feasible solutions. An efficient non-dominated sorting method is introduced into the framework as an effective search strategy. Besides, the genetic operators with the greedy mechanism and random repair strategy are involved in handling the constraints with maintaining diversity. The results of numerical experiments demonstrate that NSGA-MTA performs better in diversity and convergence than the excellent current algorithms in metrics and Pareto front obtained in 15 scenarios. Taguchi method is also adopted to verify the contribution of proposed strategies, and the results show that these strategies are practical and promotive to performance improvement.

Multi-UCAV Cooperative Target Allocation Based on Energy-Reserved Chemical Reaction Algorithm (CNCRO)

Aimed at the disadvantages of constrained processing technology in the cooperative target allocation of multiple unmanned combat aircraft (UCAV), the energy-reserved chemical reaction algorithm (CNCRO) is proposed to solve the constrained optimization problems. On the one hand, convert multiple constraint conditions of the multi-UCAV target allocation into optimization targets to transform the constrained processing problem into a multiobjective optimization problem including allocation goals and constrained optimization goals. On the other hand, the energy-reserved chemical reaction algorithm (CNCRO) is proposed, which introduces the environmental energy-reserved variables buffer into the ineffective collision, combination or splitting reactions between single molecule and multimolecules of CNCRO, which is used to boost energy for the low kinetic energy molecules, so as to reduce its kinetic energy in case of invalid collision of molecules and control it to gradually stabilize and converge. At the same time, the splitting reaction is inspired, to greatly change the structure of molecules, promote its search for more solution space, and improve the splitting reaction ability and its global optimization ability. Finally, the simulation experiment is completed by using MATLAB software. The advantages of CNCRO in accuracy are verified by 8 standard test functions, the influence of the weight coefficients in the cooperative target allocation function of UCAV is studied, which are revenue, loss, and voyage, and the target allocation schemes of traditional constrained processing and unconstrained multiobjective optimization methods based on different attack target number C i and total target number N are investigated, to obtain the control law, which can be used to guide the given parameters with different emphases.

Air combat manoeuvre strategy algorithm based on two-layer game decision-making and the distributed MCTS method with double game trees

ABSTRACT In view of the huge strategy space and high real-time requirement for multi-fighter air combat maneouvre decisions, the target allocation and the manoeuvre decision model are established, respectively and the air combat strategy solving algorithm is proposed based on two-layer game decision-making and the distributed Monte Carlo strategy search method with double game trees. Moreover, the two-layer game decision-making method can precut the huge game tree strategy space, which improves the efficiency of strategy search. The distributed Monte Carlo strategy search method with double game trees can quickly search out the optimal decision scheme of an air combat game based on the opponent’s strategy. The experiment results show that the designed algorithm is effective and improves the efficiency of the decision compared with the Monte Carlo search algorithm of single-layer decision-making.

Bi-Level Fuzzy Expectation-Based Dynamic Anti-Missile Weapon Target Allocation in Rolling Horizons

The weapon target allocation (WTA) problem is a crucial issue in anti-missile command decisions. However, the current anti-missile weapon target allocation models ignore the dynamic complexity, cooperation, and uncertainty in the actual combat process, which results in the misclassification and omission of targets. Therefore, we propose a bi-level dynamic anti-missile weapon target allocation model based on rolling horizon optimization and marginal benefit reprogramming to achieve rapid impact on static and dynamic uncertainties in the battlefield environment. Further, we also propose an improved bi-level recursive BBO algorithm based on hybrid migration and variation to perform fast and efficient optimization of the model objective function. A simulation analysis demonstrate that the model is suitable for larger-scale, complex, dynamic anti-missile operations in uncertain environments, while the algorithm achieves better solution efficiency and solution time compared with the same type of heuristic algorithm, which meet the requirements of solution accuracy and timeliness. In addition, we obtain better rolling horizon parameters to further optimize its performance.

Offense-Defense Distributed Decision Making for Swarm vs. Swarm Confrontation While Attacking the Aircraft Carriers

The effectiveness of multiple UAVs at attacking the enemy target and defending against the attacking UAVs simultaneously gained researchers’ attention due to their cost efficiency and high mission success rates. This paper explores the UAV swarm vs. swarm offense/defense confrontation decision-making while attacking the aircraft carriers on open seas. The system is developed as a multi-agent system. Precisely, every UAV is modeled as an independent agent, which is free to make decisions based on the behavioral rules of the swarm, detection radius, and enemy location. A distributed auction-based algorithm with a limited data-sharing rate ability is formulated among swarm UAVs. A critical feature of the proposed strategy is that it empowers each UAV to make decisions in real-time during the mission in the presence of relatively limited communication hardware in terms of the update rate within the UAV communication system. The algorithm parameters are optimized to obtain an improved target allocation and elimination. Simulation results are presented to show the effectiveness of the proposed Offence-Defense UAV-based swarm Strategy.

Target allocation decision of incomplete information game based on Bayesian Nash equilibrium

Aiming at the incomplete information of AUV cooperative confrontation, the incomplete information game theory is used to study the confrontation behavior of AUVs. Taking the remaining survival probability and weapon consumption as the evaluation indicators, adding the position error factor, an AUV game confrontation target allocation model for incomplete information is established. In terms of the Bayesian Nash equilibrium theory, the prior probabilities of the offensive and defensive strategy types are set by the virtual participant "Nature" in advance. Then the types of AUVs to be allocated are selected, and the judgment on the types of the target assignment strategies adopted by the other party are modified through the posterior probability. An algorithm for solving incomplete information target assignment based on the multi-target discrete particle swarms is proposed, and the Bayesian Nash equilibrium target assignment strategies of the two sides are obtained, which provides strategic choice help for the commander's combat command.

Tactical asset allocation using the Kalman filter

ABSTRACT Tactical asset allocation (TAA) is a dynamic investment strategy which seeks actively to adjust fund allocation to a variety of asset classes by systematically exploiting inefficiencies and temporary imbalances in equilibrium values. This approach contrasts with strategic asset allocation (SAA) in which a long-term investment view target allocation is established using a combination of target return and risk tolerance. Asset returns are forecasted using the Capital Asset Pricing Model (CAPM), complemented with results obtained from the Kalman filter. Performance of TAA and SAA approaches are compared using several diagnostic metrics. The TAA approach outperforms its SAA counterpart for most of these metrics for the period under consideration, showing some potential benefits of using this approach.

Mathematical Model for Solving Problem of Target Allocation of Antiaircraft Fire Weapons in AD Contour of Surface Ship (in AD System of Group of Ships) in Case of Attack Threat and Repelling Attacks of Anti-Ship Cruise Missiles

The improvement of the algorithms of processing information and controlling of weapons systems in combination with their automation, which can significantly reduce time from the moment of detection of air targets to their destruction, is a promising direction in organizing of building and effective combat use of such complex systems as the contour of antiaircraft defense (AD) of a single surface ship and the AD system of a group of the surface ships. The transition to the creation and use of the precision-guided long-range munition, e.g. anti-ship missiles (AShM), allows to destroy ships from remote lines without entering the zone of the fighter aircraft action and reach of the antiaircraft missile system. When repelling attacks of anti-ship cruise missiles, a poor-quality solution of the problem of the target allocation of the antiaircraft fire weapons of a ship (group of ships) does not allow to realize the combat capabilities of the AD contour (system) as a whole, which leads to a loss of combat capability of the ship (ships). The main difference between the proposed mathematical model for solving the target allocation problem and those implemented in the combat information and controlling systems of the surface ships is the fact that it provides the possibility of evaluating own antiaircraft fire weapons and enemy’s AShM when developing an optimal target allocation plan based on a detailed account of the AShM detection range, coordination of the destruction zones, sectors of fire, lines of the implementation of the AShM tasks, mutual security measures, electromagnetic compatibility of the electronic means, i.e. taking into account the existing zones of the destruction of the antiaircraft fire weapons, as well as solving the problem of the target allocation in time. The solution of the task is carried out both directly during the antiaircraft combat, and in the case of the attack threat of the AShM. In its turn, the last one makes it possible to optimize building of the antiaircraft fire covering system and the AD system of both a single surface ship and the group of the surface ships as a whole, and to optimize the solution of the target allocation problem, in particular. The presented mathematical model and the algorithm and program developed on its basis can be recommended after appropriate modification for inclusion in both automated combat control systems and respectively combat information and control systems of connections of surface ships and single ships, that allows to solve the problem of the target allocation on a time scale close to real.

Multi-missile interception for multi-targets: Dynamic situation assessment, target allocation and cooperative interception in groups

Target allocation is a crucial point in the cooperative interception process. In this paper, a new algorithm of dynamic situation assessment, target allocation and cooperative interception in groups is proposed for multi-target interception with multiple missiles. An effective situation assessment function is proposed, according to which the targets of each intercepting missile are assigned to maximize their efficiency. The new situation assessment function evaluates the flight status and cooperation of missiles, which considers not only the relative position and velocity mentioned in traditional functions, but also relative normal velocity, normal acceleration, maneuverability and cooperation between multiple missiles. During the interception process, the flight status could be evaluated dynamically, thus the allocation of the missiles could be real-time adjusted. To optimize target allocation, a method based on genetic algorithm is applied, which uses the dynamic situation assessment function based proportional navigation guidance law, and we also use proportional navigation guidance law to realize the interception to maneuvering targets with multiple missiles. Simulations for target allocation and target interception validate the effectiveness of the algorithm. Potential adjustments and improvements are proposed according to the simulation results.