Abstract The paper presents an approach to processing of measurement data obtained from ultrasonic system. The approach makes possible to simplify computing of object location. The important advantage of the proposed ...
Abstract The paper presents an approach to processing of measurement data obtained from ultrasonic system. The approach makes possible to simplify computing of object location. The important advantage of the proposed method is that it eliminates operations on float point numbers. Thus an algorithm based on this approach can be implemented using a simple microcontroller.
We propose a meta heuristic based on an evolutionary approach for a capacitated vehicle routing problem. The modifications concern a selection process and two new heuristics for crossover operators. The numerical resu...
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We propose a meta heuristic based on an evolutionary approach for a capacitated vehicle routing problem. The modifications concern a selection process and two new heuristics for crossover operators. The numerical results demonstrate the effectiveness of an adaptive selection evolutionary algorithm on the benchmark test problems. The main advantage is the possibility of arranging the proposed selection process and crossover operators in the space of feasible solutions. The presented results are very promising for solving bigger problems.
This paper is devoted to scheduling problems with the learning effect, which is understood as a process of acquiring experience that increases the efficiency of a processor. To bring closer the considered phenomenon, ...
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This paper is devoted to scheduling problems with the learning effect, which is understood as a process of acquiring experience that increases the efficiency of a processor. To bring closer the considered phenomenon, a short survey on results concerning scheduling problems with the learning effect is provided. In particular, the existing models of the experience are presented along with a discussion on different shapes of the learning curve. Some complexity results of scheduling problems with the learning effect are also presented. We also show that scheduling problems with the learning effect model such problems as a minimization of a total transmission cost of packets in a computer network that uses a reinforcement learning routing algorithm. We also derive properties that allow us to construct scheduling algorithms, which can be applied in the computer network to increase its effectiveness by the utilization of its learning ability.
This paper deals with resource allocation in multi-project manufacturing system design, where more than one shared renewable resource type may be required by the manufacturing operation and the availability of each ty...
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This paper deals with resource allocation in multi-project manufacturing system design, where more than one shared renewable resource type may be required by the manufacturing operation and the availability of each type is time limited. The aim of the paper is to present a knowledge-based and constraint programming-driven approach to resource allocation where that data can be imprecise. The presented design scheme is used as a framework for developing a task oriented decision support tool for project portfolio prototyping (DST4P 3 ). The tool provides a prompt and interactive service to a set of routine queries defined in terms of both direct and inverse resource allocation tasks.
Abstract The problems of scheduling of tasks described with dynamic models appear in the real-world situations, where management of the processes described with differential equations is needed. Possible applications ...
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Abstract The problems of scheduling of tasks described with dynamic models appear in the real-world situations, where management of the processes described with differential equations is needed. Possible applications contains e.g: refuelling of the feet of the boats in the given critical time, scheduling of tasks in the multiple computer systems and the forging process in the steel plants. The solution for such problems consists of two parts: continuous one (the allocation of the continuously divisible resource) and the discrete one (sequence of task subsets). The research has been done mostly for the former part so far, where the latter one was neglected. In the paper we recollect properties of the discrete part of the solution space and we prove some new properties. These new properties can be used to construct more efficient algorithms for the scheduling problems with the dynamic models of tasks.
Abstract Problem of scheduling n preemptive jobs on m identical parallel processors is studied, in which for each job a distinct due window is given in advance and an integer release date is specified. If a job is com...
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Abstract Problem of scheduling n preemptive jobs on m identical parallel processors is studied, in which for each job a distinct due window is given in advance and an integer release date is specified. If a job is completed within its due window, then it incurs no penalty. Otherwise, it incurs a job-dependent earliness or tardiness cost. The objective is to find a job schedule such that a maximum of job-dependent costs associated with earliness, tardiness and a time a job is in process is minimized. It is proved that optimal solutions to this problem can be found by a solving a polynomial number of instances of classical maximum flow problem.
Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. In practi...
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Abstract Problem of scheduling n preemptive jobs on a single processor is studied, in which for each job a distinct due window is given in advance. If a job is completed within its due window, then it incurs no penalt...
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Abstract Problem of scheduling n preemptive jobs on a single processor is studied, in which for each job a distinct due window is given in advance. If a job is completed within its due window, then it incurs no penalty. Otherwise, it incurs a job-independent earliness or tardiness cost. The objective is to find a job schedule such that a maximum of weighted costs associated with earliness and tardiness is minimized. Properties of optimal solutions of this problem are established and an algorithm based on them is presented. It is proved that the analysed problem is solvable in O ( n 2 ) time.
Making good operation decisions during abnormal power plant conditions represents in many cases the possibility to avoid a unit trip or having economical losses. This paper introduces AsistO, an intelligent assistant ...
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Making good operation decisions during abnormal power plant conditions represents in many cases the possibility to avoid a unit trip or having economical losses. This paper introduces AsistO, an intelligent assistant for the decision support based on decision theoretic planning techniques. It provides power plant operators with useful recommendations to (i) maintain a plant running under safe conditions, or (ii) deal with process transients when an unexpected event occurs. We present the formalism of Markov decision processes as the core of the intelligent assistant which uses a factored representation of plant states. We also show a very intutive algorithm to approximate decision models based on training data collected through random exploration routines in a simulated environment. We have tested our system in the steam generation system of a combined power plant to deal with load disturbances.
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