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Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

Introduction to stochastic programming

WHILE the introduction of statistical methods into the analysis of aeronautical experimental data, whether for quality control in production, for the interpretation of the results of structural and aerodynamic laboratory experiments, or for airline operation, has been brought about only in recent years, it may by now be fair to assert that their advantages and even their indispensability are no longer in dispute. Hitherto, investigations on these lines have usually involved, explicitly or implicitly, only the ‘normal curve of error’ and allied considerations; owing, it may be thought, to the controllability of the various manufacturing or laboratory techniques, but also perhaps to the scarcity of data hitherto available. It may well be, however, that with the accumulation of information arising out of investigations planned with particular reference to the statistical analysis of their results the whole range of the apparatus for statistical analysis, usually confined to such fields as those of biology or economics, will be called into full play.

The Statistical Analysis, of Experimental Data: A Fresh Approach Employing a Graphical Method for Investigating Non‐Normal Frequency Distributions

Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things

Cyber-physical social systems (CPSS) is an emerging complicated topic which is a combination of cyberspace, physical space, and social space Many problems in CPSS can be mathematically modelled as optimization problems, and some of them are multi-objective optimization (MOO) problems (MOPs) In general, the MOPs are difficult to solve by traditional mathematical programming methods High performance computing with much faster speed is required to address these issues In this paper, a kind of high performance computing approaches, evolutionary multi-objective optimization (EMO) algorithms, is used to deal with these MOPs A floorplanning case study is presented to demonstrate the feasibility of our proposed approach B*-tree and a multistep simulated annealing (MSA) algorithm are cooperatively used to solve this case As per experimental results for this case, the proposed method is well capable of searching for feasible floorplan solutions, and it can reach 7444 percent (268/360) success rates for floorplanning problems

High Performance Computing for Cyber Physical Social Systems by Using Evolutionary Multi-Objective Optimization Algorithm

The article deals with the hybrid Ant Colony Optimization algorithm and its application to the Multi-Depot Vehicle Routing Problem (MDVRP). The algorithm combines both probabilistic and exact techniques. The former implements the bio-inspired approach based on the behaviour of ants in the nature when searching for food together with simulated annealing principles. The latter complements the former. The algorithm explores the search space in a finite number of iterations. In each iteration, the deterministic local optimization process may be used to improve the current solution. Firstly, the key parts and features of the algorithm are presented, especially in connection with the exact optimization process. Next, the article deals with the results of experiments on MDVRP problems conducted to verify the quality of the algorithm; moreover, these results are compared to other state-of-the-art methods. As experiments, Cordreau’s benchmark instances were used. The experiments showed that the proposed algorithm overcomes the other methods as it has the smallest average error (the difference between the found solution and the best known solution) on the entire set of benchmark instances.

Hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem

This paper deals with the Tactical Decision Support System TDSS being developed at University of Defence, Brno, since 2006. TDSS is a command and control system designed for commanders of the Czech Army to support them in their decision-making processes on the tactical level. In the first part of the article, the basic characteristics and functions of the system are introduced. Next, advanced models of military tactics are presented including the model of optimal logistics on the battlefield, model of optimal cooperative reconnaissance by a fleet of unmanned aerial vehicles, and model of optimal cooperative reconnaissance by a fleet of ground elements. The last part of the article discusses the basic metaheuristic methods and approached used in our models. Finally, the paper summarizes some perspectives of our future work.

Tactical Decision Support System to Aid Commanders in Their Decision-Making

This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm—based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances—has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau’s benchmark instances are used.

https://www.mdpi.com/1999-4893/11/5/74/pdf

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem.

The multi-depot vehicle routing problem MDVRP is an extension of a classic vehicle routing problem VRP. There are many heuristic and metaheuristic algorithms e.g., tabu search, simulated annealing, genetic algorithms as this is an NP-hard problem and, therefore, exact methods are not feasible for more complex problems. Another possibility is to adapt the ant colony optimisation ACO algorithm to this problem. This article presents an original solution of authors to the MDVRP problem via ACO algorithm. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown.

Petr Stodola

Papers

Hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem

Tactical Decision Support System to Aid Commanders in Their Decision-Making

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem.

Applying the ant colony optimisation algorithm to the capacitated multi-depot vehicle routing problem