Models and optimisation approaches are developed for a flexible job shop scheduling problem with lot streaming and lot sizing of the variable sublots. A two-stage optimisation procedure is proposed. First, the makespan value is minimised with the smallest sublots defined for the problem instance. This makes it possible to shorten the makespan significantly, because each sublot is transferred separately to the next operation of a job. In the second stage, the sizes of the sublots are maximised without increasing the obtained makespan value. In this way, the quantity of sublots and transport activities is limited together with the related manufacturing cost. Two objectives are defined for the second stage. The first one is the maximisation of the sum of the sublot sizes of all operations, the second one is the maximisation of the number of the operations which do not need to be split at all. Mixed-integer linear programming, constraint programming and graph-based models are implemented for the problem. Two optimisation approaches are developed and compared in computational experiments for each stage and objective, one approach is based on a third-party solver, and the second one on an independent own implementation, namely a tabu search and a greedy constructive heuristic.

A new scheduling problem, the continuous flow flexible job shop (CF-FJS) is proposed. The formulation combines the well-known flexible job shop (FJS) problem and a dedicated continuous material flow model (MFM). In the MFM, operations are represented by material flow functions derived by integration of arbitrarily defined speed patterns. Two main concepts of the MFM formalism, i.e. variable speed of processing and continuous material flow, lead to position-dependent processing times and overlapping in operations which extend standard FJS formulation. Properties of the CF-FJS are investigated. A tabu search sched uling algorithm utilising these properties is proposed. Effective neighbourhood functions are defined based on elimination approaches. Two auxiliary procedures: search intensification level switching and fast feasibility detection are added to improve algorithm efficiency. The algorithm is verified using dedicated benchmark instances which comprise non-trivial representations of the CF-FJS specific features, i.e. machine efficiency patterns and minimum inter-operation buffers. The research is motivated by task scheduling in a fastener factory, but the presented results can be useful in many domains, such as production of granular goods, steel details, glass and fluids. The solution can be used in real-world applications. The published results can be helpful in testing new CF-FJS scheduling algorithms.

The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.

Simulation is a powerful process for perfectly planning and dimensioning web systems. However, a successful analysis using a simulation model usually requires variable load intensities. Furthermore, as the client’s behavior is subject to frequent changes in modern web systems, such models need to be adapted as well. Based on web systems observation, we come across the need for tools that allow flexible definitions of web systems load profiles. We propose Timed Colored Petri Nets (TCPN) event generators for web environments that could be used to drive simulations for performance evaluation. The article proposes the systematization of the generators for future development. The theoretical part focuses on a classification with a division into deterministic/stochastic and untimed/timed generators. Next, in this study, we investigate representative models of generators from different classes based on the formalism of TCPN. We perform model design and simulation processes using CPN Tools software. Finally, we present a case study involving workload analysis of a web system that processes requests from the designed generator.

A problem to determine a production schedule which minimises the cost of energy used for manufacturing is studied. The scenario assumes that each production task has assigned constant power consumption, price of power from conventional electrical grid system is defined by time-of-use tariffs, and a component of free of charge renewable energy is available for the manufacturing system. The objective is to find the most cost-efficient production plan, subject to constraints involving predefined precedence relationships between the tasks and a bounded makespan. Two independent optimisation approaches have been developed, based on significantly different paradigms, namely mixed-integer linear programming and tabu search metaheuristic. Both of them have been verified and compared in extensive computational experiments. The tabu search-based approach has turned out to be generally more efficient in the sense of the obtained objective function values, but advantages of the use of linear programming have also been identified. The results confirm that it is possible to develop efficient computational methods to optimise energy cost under circumstances typical of manufacturing companies. The set of numerous benchmark instances and their solutions have been archived and it can be reused in further research.