Zezwala się na korzystanie z artykułu na warunkach licencji Creative Commons Uznanie autorstwa 3.0 IntroductionMobile robots have been successfully applied in many areas such as medical and military applications, space exploration, public and domestic duties. They can perform difficult and hazardous tasks with complex requirements and often have to do so autonomously, without the aid of a human operator. To function in that manner they must be able to navigate the environment they are placed in.Collision-free path planning plays an important role in mobile robots navigation and is often a fundamental requirement for proper task execution. The main goal of such planning in an environment with static (stationary) and dynamic (moving) obstacles is to find a suitable movement path from a starting location to a destination, while avoiding the collision with any of these objects. This complex task poses many difficulties: computational complexity, adaptation to changing environment and determining a reasonable evaluation function for the generated path.Path planning is an active research area and many methods have been developed to deal with this problem. They can be classified into classical and heuristic based search algorithms [1], mainly discerned by the type of optimization techniques utilized.Recently some classical approaches, such as cell decomposition [2], potential field method [3][4][5], road map [6] and sub goal network have been presented in the field of mobile robotics. In a cell decomposition method a two-dimensional map is divided into several grids and the path is created in them. Another case of a classical approach is a potential field method in which the controlled robot is attracted by the destination while simultaneously being repelled by the obstacles.These path planning algorithms suffer from some drawbacks [1], e.g., a solution may not be optimal because the algorithm gets stuck in local minima or a new solution has to be generated again when the environment changes and therefore the original path can become infeasible.As a result, many heuristic based methods, such as fuzzy logic [7], artificial neural network [8], nature inspired algorithms [9-12] and hybrid algorithms were created. These methods can overcome drawbacks of the classical ones, but they do not guarantee to find the best solution. Still, the result can be sufficiently close to the optimal one. In this paper the authors used one of those methods -the Particle Swarm Optimization algorithm; it serves as a base solver for collision-free path planning problem.Particle Swarm Optimization (PSO) is a metaheuristic algorithm which is inspired by the social foraging behavior of some animals such as bird flocking and fish schooling. It was developed by Kennedy and Eberhart in 1995 and its description is presented in [13]. Since then, many approaches have been suggested by the researches to solve the collision-free path planning problem using the PSO algorithm [11,[14][15][16][17].Further sections of this paper are arranged as follows: Sect...

Abstract:The optimization of multi-objective problems is an area of important research. The importance attained by this type of problems has allowed the development of multiple algorithms. To determine which multi-objective algorithm has the best performance with respect to the problem of goods flow in the inventory, in this article an experimental comparison between two of the main multi-objective evolutionary algorithms is conducted: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The inventory model is optimized by taking into account two objectives: minimal cost of lost opportunities to make sales and minimal cost of used space in the inventory. The results obtained by both algorithms are compared and analysed based on hypervolume indicator that measures the volume of the dominated space.

Inventory optimization is critical in inventory control systems. The complexity of real-world inventory systems results in a challenging optimization problem, too complicated to solve by conventional mathematical programing methods. The aim of this work is to confront: a perpetual inventory system found in the literature and inventory system with PD control and Smith predictor proposed by the authors. To be precise, the two control systems for inventory management are analyzed with different shipping delays and compared. With regard to complexity of the proposed control system, we use a SPEA2 algorithm to solve optimization task for assumed scenario of the market demand. The objective is to minimize the inventory holding cost while avoiding shortages. A discrete-time, dynamic model of inventory system is assumed for the analysis. In order to compare the results of systems, Pareto fronts and signal responses are generated.

No abstract