Integrated process planning and scheduling in holonic manufacturing systems

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

The Holonic Manufacturing Systems (HMS) Project is an international industrially driven project addressing systemization and standardization, research, pre-competitive development, deployment and support of architectures and technologies for open, distributed, intelligent, autonomous and co-operating systems on a global basis. During its ten-year program, the HMS Project has developed specifications of holonic architectures, a computer-aided environment for the encapsulation, reuse and integration of holonic systems technologies, and libraries of demonstrated, reusable technologies and tools for the construction of holonic manufacturing systems. These next-generation holonic systems are providing the flexibility, agility and robustness necessary for the rapid delivery of custom manufactured products in competitive global markets. By building on the results of Phase 1 of the HMS Project that emphasized the development of generic technologies and their demonstration in specific application areas, Phase 2 will achieve their integration and application at three levels of the manufacturing and supply chain enterprise: Holonic Control Devices, consisting of the technical manufacturing equipment; Holonic Production Sites and Physical Equipment, comprising the manufacturing work cells; Holonic Planning and Execution Systems, providing scheduling and control of the holonic manufacturing system and a virtual test bed environment for holonic system implementations; and Holonic Man-Machine and Emulation Systems, a virtual test-bed environment for holonic system implementations.

This paper deals with a real-time scheduling system of HMSs (Holonic Manufacturing Systems). A scheduling process based on the utility values have been proposed and applied to the realtime scheduling problems of the HMSs in the previous report. It was shown that the proposed methods generate suitable schedules from the view point of the objective functions of the individual holons in the HMSs. A new real-time scheduling method is proposed, in the paper, to consider both the objective functions of the individual holons and the whole HMSs. In this method, all the pareto optimal combinations of the resource holons and the job holons are generated based on the objective function values of the individual holons. Following this, a most suitable combination is selected from the pareto optimal ones, based on the objective functions of the whole HMSs, such as total make span and total tardiness. It is shown, through case studies, that the proposed method is effective to improve the production schedules from the viewpoint of the objective functions of the whole HMSs in comparison with the previous method.

Optimal load clipping with time of use rates

The genetic programming (GP) method is proposed as a new approach to perform texture classification based directly on raw pixel data. Two alternative genetic programming representations are used to perform classification. These are dynamic range selection (DRS) and static range selection (SRS). This preliminary study uses four brodatz textures to investigate the applicability of the genetic programming method for binary texture classifications and multi-texture classifications.Results indicate that the genetic programming method, based directly on raw pixel data, is able to accurately classify different textures. The results show that the DRS method is well suited to the task of texture classification. The classifiers generated in our experiments by DRS have good performance over a variety of texture data and offer GP as a promising alternative approach for the difficult problem of texture classification.KeywordsGenetic ProgrammingClass LabelTexture ClassificationTraining AccuracyDivision PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Industry 5.0 changes the paradigm of the current production model, with repercussions throughout the value chain, and opens up opportunities for new approaches that include reducing waste to optimize the use of the planet’s resources. This paper proposes a functional and executable model that implements a Holonic Manufacturing System (HMS) architecture inspired by the I5.0 guidelines. This architecture presents the factory floor as a service provider for the product to be built, intending to make the manufacturing process adaptable to changes. The model uses Reference nets as the modeling language, a high-level class of Petri nets, Java programming language as the annotation language, and free tool support. The model can be used to perform software-level simulations and can also be interconnected to existing physical devices using Internet of things technologies, enabling interactions between Cyber–Physical Systems (CPSs). It thus allows for the control of the shop floor and the reuse of the current machine park to make its adoption more sustainable. The model was used to generate several simulation results, which are presented and analyzed, thus demonstrating the model’s usefulness.

A context‐free genetic programming (GP) method is presented that simulated local scale daily extreme (maximum and minimum) temperatures based on large scale atmospheric variables. The method evolves simple and optimal models for downscaling daily temperature at a station. The advantage of the context‐free GP method is that both the variables and constants of the candidate models are optimized and consequently the selection of the optimal model. The method is applied to the Chute‐du‐Diable weather station in Northeastern Canada along with the National Center for Environmental Prediction (NCEP) reanalysis datasets. The performance of the GP based downscaling models is compared to benchmarks from a commonly used statistical downscaling model. The experiment results show that the models evolved by the GP are simpler and more efficient for downscaling daily extreme temperature than the common statistical method. The different model test results indicate that the GP approach significantly outperforms the statistical method for the downscaling of daily minimum temperature, while for the maximum temperature the two methods are almost equivalent. However, the GP method remains slightly more effective for maximum temperature downscaling than the statistical method.

This paper proposes a novel optimal configuration method of distributed generation and energy storage in microgrid. Firstly, K-means clustering method is introduced to formulate the typical days of the output power of renewable generation and load of the microgrid. Secondly, an optimal configuration model of distributed generation and energy storage in microgrid is established with the objective function of minimizing the annual total cost. Then, a hybrid solving strategy combing genetic algorithm, dynamic programming and primal-dual interior point method is proposed. Case studies are carried out on a real-world microgrid in China, and the results show the rationality of the model and the effectiveness of the solving algorithm.

Real time control of holonic manufacturing systems requires a radically different approach from that of traditional unit level regulatory control. Because they need to automatically adapt and reconfigure based on the ever changing requirements of the manufacturing system, control systems based on this approach are termed metamorphic control systems. The engineering of such software centric metamorphic control systems for dynamically reconfigurable distributed multi sensor based holonic systems is addressed. An integrated and uniform event driven control architecture is specified for various functional levels of metamorphic control system. This architecture utilizes the emerging International Electrotechnical Commission function block standard (IEC 1499) for industrial process measurement and control systems to specify the requisite behavior of distributed control software components (agents).

Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common scenario of evaluating a GP program on a set of input-output examples (fitness cases), the semantic approach characterizes program with a vector of outputs rather than a single scalar value (fitness). The past research on semantic GP has demonstrated that the additional information obtained in this way facilitates designing more effective search operators. In particular, exploiting the geometric properties of the resulting semantic space leads to search operators with attractive properties, which have provably better theoretical characteristics than conventional GP operators. This in turn leads to dramatic improvements in experimental comparisons. The aim of the tutorial is to give a comprehensive overview of semantic methods in genetic programming, illustrate in an accessible way a formal geometric framework for program semantics to design provably good mutation and crossover operators for traditional GP problem domains, and to analyze rigorously their performance (runtime analysis). A number of real-world applications of this framework will be also presented. Other promising emerging approaches to semantics in GP will be reviewed. In particular, the recent developments in the behavioral programming, which aims at characterizing the entire program behavior (and not only program outputs) will be covered as well. Current challenges and future trends in semantic GP will be identified and discussed. Selected methods and concepts will be accompanied with live software demonstrations. Also, efficient implementation of semantic search operators may be challenging. We will illustrate very efficient, concise and elegant implementations of these operators, which are available for download from the web.

The mathematical model of optimal water quantity allocation for a single main canal in a large-scale irrigation area was constructed that took the minimal sum of the squared deviation of water shortage for water receiving areas controlled by the single main canal in one given irrigation period as the study target, and the total irrigation quantity of the single main canal as a constraint condition. Taking the optimal allocation of water quantity of each branch canal as decision variables, and several branch canals under the irrigation sequence of the main canal as a state variable, this model was solved by the one-dimensional dynamic programming (DP) method, by which the minimal water shortage and corresponding optimal water quantity allocation of each branch canal was calculated. The proposed method could provide a decision-making reference for optimal water resources allocation of single main canal irrigation areas, and also provide the theoretical basis for optimal water quantity allocation of a main canal with rotation irrigation by strips or with segmented rotation irrigation mode in China’s large-scale irrigation areas. Taking Hengliu Main Canal of Zhouqiao Irrigation Area in Jiangsu Province as a study case, optimization results showed that in a medium drought year (p = 75%) and a special drought year (p = 95%), minimal water shortage for water receiving areas controlled by Hengliu Main Canal was respectively 2.57 × 104 m3 and 23.31 × 104 m3 during the ponding period of rice. The corresponding water quantity allocation for each branch canal has reflected a compellent model solution precision and efficiency.

In the dynamic programming paradigm the value of an optimal solution is recursively defined in terms of optimal solutions to subproblems. Such dynamic programming definitions can be very tricky and error-prone to specify. This paper presents a novel, elegant method based on tabled logic programming that simplifies the specification of such dynamic programming solutions. Our method introduces a new mode declaration for tabled predicates. The arguments of each tabled predicate are divided into indexed and non-indexed ones so that tabled predicates can be regarded as functions: indexed arguments represent input values and non-indexed arguments represent output values. The non-indexed arguments in a tabled predicate can be further declared to be aggregate, e.g., the minimum, so that while generating answers, the global table will dynamically maintain the smallest value for that argument. This mode declaration scheme, coupled with recursion, provides a considerably easy-to-use method for dynamic programming: there is no need to define the value of an optimal solution recursively, instead, defining a general solution suffices. The optimal value as well as its corresponding concrete solution can be derived implicitly and automatically using tabled logic programming systems. Experimental results are shown to indicate that the mode declaration improves both time and space performances in solving dynamic programming problems on tabled LP systems. Additionally, our mode declaration scheme provides an alternative implementation vehicle for preference logic programming.

In the dynamic programming paradigm the value of an optimal solution is recursively defined in terms of optimal solutions to subproblems. Such dynamic programming definitions can be tricky and error‐prone to specify. This paper presents an elegant method based on tabled logic programming (TLP) that simplifies the specification of such dynamic programming solutions. Our method introduces a new mode declaration for tabled predicates. The arguments of each tabled predicate are divided into indexed and non‐indexed arguments so that tabled predicates can be regarded as functions: indexed arguments represent input values and non‐indexed arguments represent output values. The non‐indexed arguments in a tabled predicate can be further declared to be aggregated, for example, the minimum, so that while generating answers, the global table will dynamically maintain the smallest value for that argument. This mode‐declaration scheme, coupled with recursion, provides an easy‐to‐use method for dynamic programming: there is no need to define the value of an optimal solution recursively, as the definition of a general solution suffices. The optimal value as well as its corresponding concrete solution can be derived implicitly and automatically using tabled logic programming systems. Our experimental results show that mode declarations improve performance in solving dynamic programming problems on TLP systems. Copyright © 2007 John Wiley & Sons, Ltd.

Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28%, 12.57%, and 15.62% in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.

The task of resource saving is relevant for all transport companies and many world communities of scientists and engineers are engaged in search for ways to solve it. World railway companies, and especially large ones such as Russian Railways, are large consumers of energy resources and the problem of saving is the most urgent for them. One of directions for solving this task can be the use of an optimal energy regulation system for traction of each train – an operational rationing system. Such a task can only be solved by modelling the process of movement through the dynamic programming method. In modern conditions of development of engineering and technology, it has become possible to develop such operational standardization systems endowed with important properties: high performance, multitasking, solution accuracy, ease of use and maintenance. These requirements impose certain restrictions on the architecture of the operational rationing system. Typical system architecture should be built around a centralized node, which will act as a solver and storage, nodes for input and output of information can be geographically separated. The method of dynamic programming can be improved by using it in the process of training artificial neural networks, which will form not only a priori estimates of energy consumption for traction, but also an a posteriori estimate of train control (by a train driver or auto-driving system). Also, the use of artificial neural networks will allow us to continuously improve the method due to training using the accumulated amount of data from real trips, which will allow us to clarify the norms of energy consumption and to plan our costs in the future. The prototype of the operational standardization system was developed at the department of traction rolling stock of Russian Open Academy of Transport of Russian University of Transport and the results obtained allow us to state that the chosen approach to solving the problem of energy saving has been chosen correctly.