scholarly journals Análise de problemas de partição de instalações em sistemas job-shops por meio de modelos de redes de filas

2007 ◽  
Vol 27 (2) ◽  
pp. 333-356 ◽  
Author(s):  
Claudio Rogerio Negri da Silva ◽  
Reinaldo Morabito
Keyword(s):  
Job Shop ◽  
Job Shops

Este artigo estuda o problema de projeto de fábrica focalizada envolvendo a partição da instalação (planta) em subplantas e a alocação de capacidade em cada estação de trabalho das subplantas. O sistema de manufatura job-shop é representado por meio de uma rede de filas aberta genérica, e aproximações baseadas em métodos de decomposição são utilizadas para avaliar e otimizar o desempenho do sistema. O objetivo é reduzir a complexidade do sistema do ponto de vista da gestão do produto ou da gestão da estação, por exemplo, limitando-se a variância dos leadtimes dos produtos na rede. Apresenta-se um modelo de programação não-linear inteira para o problema e um algoritmo heurístico para resolvê-lo. Aplicando-se o algoritmo em alguns problemas testes, mostra-se que a partição da instalação em subplantas pode reduzir a variância dos leadtimes dos produtos na rede, sem necessidade de investimentos adicionais em capacidade. Além disso, algumas vezes é possível manter (ou até melhorar) o desempenho da rede, particionando-a em subplantas que necessitam de menos capacidade do que a configuração original da rede como uma planta única.

Deadlock Prevention in Manufacturing Systems With AGV Systems: Banker’s Algorithm Approach

1997 ◽  
Vol 119 (4B) ◽  
pp. 849-854 ◽  
Author(s):  
Chang Wan Kim ◽  
J. M. A. Tanchoco ◽  
Pyung-Hoi Koo
Keyword(s):  
Material Flow ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Deadlock Prevention ◽  
Job Shops ◽  
Vehicle Systems ◽  
Problems And Solutions ◽  
Handling Methods ◽  
Banker's Algorithm ◽  
Agv Systems

An important issue in the operational control of an automated job shop is the prevention and resolution of shop deadlocks. In this paper, we discuss the problems and solutions of deadlocks in manufacturing systems with automated guided vehicle systems, describe a banker’s algorithm for the control of material flow in job shops, and present the results of simulation experiments to compare the performance of several deadlock handling methods.

Genetic Programming for Evolving Due-Date Assignment Models in Job Shop Environments

2014 ◽  
Vol 22 (1) ◽  
pp. 105-138 ◽  
Author(s):  
Su Nguyen ◽  
Mengjie Zhang ◽  
Mark Johnston ◽  
Kay Chen Tan
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Experimental Results ◽  
Job Shops ◽  
Due Date ◽  
Dynamic Nature ◽  
Due Date Assignment ◽  
Delivery Performance ◽  
Aggregate Information ◽  
Assignment Models

Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.

A new approach for performance assessment of parallel and non-bottleneck machines in a dynamic job shop environment

2019 ◽  
Vol 13 (4) ◽  
pp. 787-803
Author(s):  
Maurizio Faccio ◽  
Mojtaba Nedaei ◽  
Francesco Pilati
Keyword(s):  
Completion Time ◽  
Job Shop ◽  
Primary Objective ◽  
Design Parameters ◽  
Job Shops ◽  
Objective Functions ◽  
New Approach ◽  
Content Type ◽  
Decision Variables ◽  
Dynamic Job Shop

Purpose The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops. Design/methodology/approach An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model. Findings Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s. Originality/value Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

Design and control of food job-shop processing systems

2017 ◽  
Vol 28 (3) ◽  
pp. 782-797 ◽  
Author(s):  
Stefano Penazzi ◽  
Riccardo Accorsi ◽  
Emilio Ferrari ◽  
Riccardo Manzini ◽  
Simon Dunstall
Keyword(s):  
Food Processing ◽  
Job Shop ◽  
Discrete Event ◽  
Cost Optimization ◽  
Job Shops ◽  
Content Type ◽  
Processing Equipment ◽  
Catering Industry ◽  
High Flexibility ◽  
Capacity Constrained

Purpose The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry. Design/methodology/approach The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective. Findings This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs. Originality/value The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.

scholarly journals Selecting a Mix of Dispatching Rules for a Job Shop by Using Artificial Neural Networks

2021 ◽  
Author(s):  
Pramit Shah
Keyword(s):  
Job Shop ◽  
Intelligent System ◽  
Past Research ◽  
Dispatching Rules ◽  
Test Results ◽  
Job Shops ◽  
The Past ◽  
Processing Times ◽  
Commercial Package ◽  
The One

Dispatching rules are a popular and commonly researched technique for scheduling tasks in job shops. Much of the past research has looked at the performance of various dispatching rules when a single rule is applied in common to all machines. However, better schedules can frequently be obtained if the machines are allowed to use different rules from one another. This research investigates an intelligent system that selects dispatching rules to use on each machine in the shop, based on a statistical description of the routings, processing times and mix of the jobs to be processed. Randomly generated problems are scheduled using permutations of three different dispatching rules on five machines. A neural network is then trained by using a commercial package to associate the statistical description of each problem with its best solution. Once trained, a network is able to recommend for new problems a dispatching rule to use on each machine. Two networks were trained separately for minimizing makespan and the total flowtime in the job shop. Test results showed that the combination of dispatching rules suggested by the trained networks produced better results for both objectives than the alternative of using the one identical rule on all machines.

An Automated Process Planning System Based on Genetic Algorithm and Simulated Annealing

2002 ◽  
Author(s):  
G. H. Ma ◽  
F. Zhang ◽  
Y. F. Zhang ◽  
A. Y. C. Nee
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Process Planning ◽  
Job Shop ◽  
Planning System ◽  
Job Shops ◽  
Computer Aided Process Planning ◽  
Optimization Criteria ◽  
Process Planning System ◽  
Automated Process

The paper presents the development of a computer-aided process planning (CAPP) system based on Genetic Algorithm (GA) and Simulated Annealing (SA). The system employs an optimization modeling method that generates all the feasible operation-method alternatives. It also provides flexible optimization criteria that will satisfy the various needs from different job-shops and/or job-batches. Two search algorithms based on GA and SA respectively have been developed to solve the problem effectively. Also, the system provides manufacturability analysis function, which gives designers and job shop operators helpful information about the manufacturing.

Workshop Layout Optimization Based on Genetic Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 169-173 ◽  
Author(s):  
Long Qiao ◽  
Hong Bin Yu ◽  
Jian Jun Sun
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Objective Function ◽  
Material Handling ◽  
Job Shop ◽  
Job Shops ◽  
Transfer Time ◽  
Set Up ◽  
Material Handling Costs ◽  
First Time

To shorten the transfer time of workpiece in job shop, it is necessary to optimize the equipment arrangement of job shops based on the technological process of workpiece. The objective function only considers the material handling costs, but it ignores the geometry of the workshop area utilization and so on factors. We propose and take an objective function that considers material handling costs and utilization proposed at the same time. And we set up an optimization model of facility layout is proposed and genetic algorithms is used to solve this mode1. The author brings forward the concept of carry quadrature for the first time. It is good to use this concept for the workshop in which many kinds of workpiece are produced. The result of optimal design is consonant with the desire of actual manufacture.

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

2020 ◽  
pp. 1748006X2094653
Author(s):  
Behzad Karimi ◽  
Seyed Taghi Akhavan Niaki ◽  
Amir Hossein Niknamfar ◽  
Mahsa Gareh Hassanlu
Keyword(s):  
Manufacturing Systems ◽  
Production Efficiency ◽  
Performance Metrics ◽  
Job Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Automated Guided Vehicles ◽  
Job Shops ◽  
Multi Objective

The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

scholarly journals Defining accurate delivery dates in make to order job-shops managed by workload control

2020 ◽  
Author(s):  
Davide Mezzogori ◽  
Giovanni Romagnoli ◽  
Francesco Zammori
Keyword(s):  
Job Shop ◽  
Industrial Applications ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Time Variability ◽  
Due Dates ◽  
Job Shops ◽  
Predictive Tool ◽  
Workload Control ◽  
Tardy Jobs

Abstract Workload control (WLC) is a lean oriented system that reduces queues and waiting times, by imposing a cap to the workload released to the shop floor. Unfortunately, WLC performance does not systematically outperform that of push operating systems, with undersaturated utilizations levels and optimized dispatching rules. To address this issue, many scientific works made use of complex job-release mechanisms and sophisticated dispatching rules, but this makes WLC too complicated for industrial applications. So, in this study, we propose a complementary approach. At first, to reduce queuing time variability, we introduce a simple WLC system; next we integrate it with a predictive tool that, based on the system state, can accurately forecast the total time needed to manufacture and deliver a job. Due to the non-linearity among dependent and independent variables, forecasts are made using a multi-layer-perceptron; yet, to have a comparison, the effectiveness of both linear and non-linear multi regression model has been tested too. Anyhow, if due dates are endogenous (i.e. set by the manufacturer), they can be directly bound to this internal estimate. Conversely, if they are exogenous (i.e. set by the customer), this approach may not be enough to minimize the percentage of tardy jobs. So, we also propose a negotiation scheme, which can be used to extend exogenous due dates considered too tight, with respect to the internal estimate. This is the main contribution of the paper, as it makes the forecasting approach truly useful in many industrial applications. To test our approach, we simulated a 6-machines job-shop controlled with WLC and equipped with the proposed forecasting system. Obtained performances, namely WIP levels, percentage of tardy jobs and negotiated due dates, were compared with those of a set classical benchmark, and demonstrated the robustness and the quality of our approach, which ensures minimal delays.

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