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1
EN
In this paper, a parallel multi-path variant of the well-known TSAB algorithm for the job shop scheduling problem is proposed. Coarse-grained parallelization method is employed, which allows for great scalability of the algorithm with accordance to Gustafon’s law. The resulting P-TSAB algorithm is tested using 162 well-known literature benchmarks. Results indicate that P-TSAB algorithm with a running time of one minute on a modern PC provides solutions comparable to the ones provided by the newest literature approaches to the job shop scheduling problem. Moreover, on average P-TSAB achieves two times smaller percentage relative deviation from the best known solutions than the standard variant of TSAB. The use of parallelization also relieves the user from having to fine-tune the algorithm. The P-TSAB algorithm can thus beused as module in real-life production planning systems or as a local search procedure in other algorithms. It can also provide the upper bound of minimal cycle time for certain problems of cyclic scheduling.
EN
A comparison of two heuristic algorithms solving a bi-criteria joint location and scheduling (ScheLoc) problem is considered. In this strongly NP-hard problem the sum of job completion times and location investment costs are used to evaluate the solution. The first solution algorithm (EV) uses an evolutionary approach, and the second more time-efficient algorithm (SA) is based on Simulated Annealing.
4
Content available Optymalizacja procesu wysyłki cukru
PL
W artykule rozważany jest proces wysyłki cukru do klientów cukrowni. Podstawowym celem naszej pracy było określenie efektywności procesów logistycznych w fabryce oraz znalezienie i korekta wąskich gardeł. Zaprezentowano wybrane sposoby optymalizacji procesów logistycznych. W pracy, dla potrzeb harmonogramowania, prezentujemy zastosowanie metod heurystycznych oraz metod wykorzystujących programowanie z ograniczeniami. Praca przedstawia również dyskusję metod optymalizacji procesów logistycznych oraz omawia trudności z ich zastosowaniem.
EN
We consider the sugar dispatching process at a sugar mill. The main goal of our work was to check an efficiency of the logistics system in the mill, find and correct the bottlenecks. Some methods for logistics processes optimization are presented. We base on the heuristics and CLP techniques for solving the scheduling problems. Some additional remarks about possibility of using the optimization methods in scheduling and logistics optimizations are presented too.
EN
The paper presents the problem of determining the prioritisation of production orders. The proposed criterion function allows a comprehensive evaluation of various ways of prioritising taking into account both the income derived from the execution of production orders and the penalty for any delays which may occur. The criterion function was implemented in an algorithm based on the operation of a colony of bees. The experiments which have been carried out make it possible to evaluate the solutions obtained through the provided algorithm and compare them with the solutions obtained through the typical heuristic rules. The results show that the prioritisation obtained through the algorithm is characterized by the highest qualities of the criterion function and is definitely superior to that obtained through the simple heuristic rules.
PL
W pracy przedstawiono zagadnienia ustalania kolejności wprowadzania zleceń do produkcji. Zaproponowano zastosowanie kompleksowej funkcji kryterialnej do oceny różnorodnych uszeregowań. Funkcja ta uwzględnia zarówno przychód uzyskany z realizacji zleceń produkcyjnych, jak i ewentualne kary za opóźnienia w ich wykonaniu. Opracowano algorytm oparty na działaniu roju pszczół, w którym zaimplantowano proponowaną funkcję kryterialną. Wykonane eksperymenty pozwoliły na ocenę uszeregowań uzyskiwanych z użyciem algorytmu pszczelego oraz ich porównanie z rozwiązaniami dla typowych reguł heurystycznych. Analiza otrzymanych wyników pozwoliła na stwierdzenie, że uszeregowania uzyskiwane z zastosowaniem opracowanego algorytmu cechowały się największymi wartościami funkcji kryterialnej. Zdecydowanie przewyższały uszeregowania uzyskiwane z wykorzystaniem prostych reguł heurystycznych.
EN
The manufacturing industry has evolved over the past several decades in response to changing customer needs. Customers have become more demanding and want products that can meet their specific individual requirements. The standard products previously produced in large batches are not sufficient to meet this variety demanded. Given the increased competition, both locally and globally, companies must also now respond faster to get and keep customers. Enterprises were forced to unit and small-batch production. Currently, advanced planning systems are coming into use, however their cost exceeds the possibilities of small and medium enterprises and algorithms used often require great customization to industries' needs and conditions of unit and small-batch production. The paper has been drawn on the basis of research on overloads of moving bottlenecks in conditions of unit and small batch production in real conditions having a big number of resources and tasks. The methods used so far are not capable of finding the global optimum of such big data ranges. The author took on building a heuristic algorithm, which could find solution good enough and based on TOC (Theory of Constraints) assumptions and verification of assumptions using tests in real production systems. The above method found application to the industrial scale, as extension of the ERP class system.
EN
Since the production is aimed at fulfilling specific needs of demanding customers and not at filling warehouses, the production volume should reflect the volume of orders. In times of fight for the client every order has to be performed on time. What is more, in limes of fight for shortening the delivery cycle, meeting safe deadlines, that is distant in time, is not enough. Companies are forced to meet short deadlines with keeping the product price competitiveness condition. It is hardly possible without a proper, APS (Advanced Planning System) class, advanced planning support system. Currently, advanced planning systems arc coming into use, however (heir cost exceeds the possibilities of small and medium enterprises and algorithms used often require great customization to industries' needs and conditions of unit and small-hatch production. The paper has been drawn on the basis of research on overloads of moving bottlenecks in conditions of unit and small batch production in real conditions having a big number of resources and tasks. The methods used so far are not capable of finding the global optimum of such big data ranges. At present few working enterprises in conditions of unit and small batch production, especially in small and medium-sized enterprises (SME), are exploiting techniques of the production process optimization. For this reason computer tools for applying to the industrial scale arc needed. The above method basis on the data so far collected in computer systems. Results of preliminary research were introduced from applying the possibility of TOC (Theory of Constraints) to the industrial scale for reducing bottlenecks in unit and small batch production. The authors built a heuristic algorithm which could find solution good enough and based on TOC assumptions and verification of assumptions using tests in real production systems. The above method found application to the industrial scale, as extension of the ERP class system.
EN
Online scheduling (also known as dynamic scheduling) refers to a real-time coordination of operation processing while new operations are continuously arriving. Despite the attention this area received over last years, little effort has been spent on cases where multiple resources are required for an operation processed. The objective of this paper is the study of different online scheduling techniques specifically in a dynamic, stochastic job shop scheduling with multiple resources. We adopt algorithms for dispatching and optimizing schedulers to this case and provide a quantitative study of their performance under varying stochastic conditions. We also discuss the regret-based algorithm from Bent and Van Hentenryck. While this approach is superior in the case of scheduling single resource, it showed inferior performance in our experiments compared to its underlying optimizing scheduler.
9
Content available remote Intelligent modelling in manufacturing
EN
Purpose: Modeling of production systems is very important and makes optimization of complicated relation in production system possible. The purpose of this paper is introducing artificial techniques, like Genetic Algorithms in modelling and optimization of job shop scheduling in production environment and in programming of CNC machine tools. Design/methodology/approach: Conventional methods are not suitable for solving such complicated problems. Therefore Artificial Intelligent method was used. We apply Genetic Algorithm method. Genetic Algorithms are computation methods owing their power in particular to autonomous mechanisms in biological evolution, such as selection, "survival of the fittest" (competition), and recombination. Findings: In example solutions are developed for an optimization problem of job shop scheduling by natural selection. Thus no explicit knowledge was required about how to create a good solution: the evolutionary algorithm itself implicitly builds up knowledge about good solutions, and autonomously absorbs knowledge. CNC machining time was significant shorter by using GA method for NC programming. Research limitations/implications: The system was developed for PC and tested in simulation process. It needs to be tested more in detail in the real manufacturing environment. Practical implications: It is suitable for small and medium-sized companies. Human errors are avoid or at lower level. It is important for engineers in job - shops. Originality/value: The present paper is a contribution to more intelligent systems in production environment. It used genetic based methods to solve engineering problem.
PL
W pracy przedstawiono zadanie planowania produkcji małoseryjnej typu gniazdowo-otwartego. Do rozwiązania tej klasy zadań opracowano algorytm na podstawie procedury GRASP (Greedy Randomized Adaptive Search Procedure). Dla przedstawionego algorytmu opisano wyniki eksperymentu komputerowego.
EN
The paper presents task formulation job shop - open shop problem. The algorithm based on the on procedure GRASP, has been prepared for the class of mentioned task. The results of the computer experiment for the ałgorithm has been presented.
PL
W pracy przedstawiono jedną z klas gniazdowego problemu szeregowania zadań. Do rozwiązania tego problemu zaimplementowano algorytm symulowanego wyżarzania. Eksperymenty były porównane z zadaniami testowymi i otrzymanymi przy użyciu procedury GRASP.
EN
One class of problem, that presents general case of scheduling problem. The algorithm is presented for solving the flexible job shop problem. The algorithm is based on the simulated annealing metaheuristic modified appropriatelly. Experiments were compared with test problem and results obtained with GRASP procedure.
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