Resource Allocation and Job Dispatching for Unreliable Flexible Flow Shop Manufacturing System

2012 ◽  
Vol 445 ◽  
pp. 947-952
Author(s):  
Umar M. Al-Turki ◽  
Haitham Saleh ◽  
Tamer Deyab ◽  
Yasser Almoghathawi
Keyword(s):  
Resource Allocation ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Stochastic Systems ◽  
Flow Shop ◽  
Discrete Event ◽  
Batch Size ◽  
Objective Functions ◽  
Flexible Flow Shop

Resource allocation, product batching and production scheduling are three different problems in manufacturing systems of different structures such as flexible flow shop manufacturing systems. These problems are usually dealt with independently for a certain objective function related to production efficiency and effectiveness. Handling all of them in an integrated manner is a challenge facing many manufacturing systems in practice and that challenge increases for highly complicated and stochastic systems. Random arrival of products, machine setup time requirements, unexpected machine breakdowns, and multiple conflicting objective functions are some of the common complications in such systems. This research attempts to study the integrated problem under the mentioned complications with various objective functions. The decisions parameters are the batch size, the number of machines at each workstation, and the dispatching policy. Discrete event simulation is used as an optimization tools. The system is modeled using the ARENA software and different scenarios are tested for optimum parameter selection under different conditions.

A Production Scheduling Method for Bell-Type Batch Annealing Shop

2010 ◽  
Vol 97-101 ◽  
pp. 2436-2439
Author(s):  
Jun Zhao ◽  
Wei Wang ◽  
Yong Li Lin
Keyword(s):  
Production Scheduling ◽  
Production Efficiency ◽  
Discrete Event ◽  
Differential Evolution Algorithm ◽  
Discrete Problem ◽  
Event Simulation ◽  
Batch Annealing ◽  
Crossover Probability ◽  
Scheduling Method ◽  
Improved Differential Evolution Algorithm

A discrete event simulation approach based on heuristics is presented to emulate the production process of the bell-type batch annealing in this paper. For optimizing the scheduling objective of minimizing the resource free ratio, an improved Differential Evolution algorithm is proposed, which solves a class of discrete problem, and whose crossover probability is adaptively assigned in order to improve the search ability not only on local but also on global. The experiments using practical production data demonstrate that the proposed method has a great effectiveness, and increases the production efficiency of the bell-type annealing shop.

507 Study on Production Scheduling Considering Process Planning for Skip Flow-shop Manufacturing Systems

2011 ◽  
Vol 2011.60 (0) ◽  
pp. _507-1_-_507-2_
Author(s):  
Tatsuhiko SAKAGUCHI ◽  
Tatsurou MURAKAMI ◽  
Syohei FUJITA ◽  
Yoshiaki SHIMIZU ◽  
Keiichi SHIRASE
Keyword(s):  
Process Planning ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Flow Shop

Production Planning Models using Max-Plus Algebra

2013 ◽  
pp. 227-257 ◽  
Author(s):  
Arun N. Nambiar ◽  
Aleksey Imaev ◽  
Robert P. Judd ◽  
Hector J. Carlo
Keyword(s):  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Performance Metrics ◽  
Job Shop ◽  
Flow Shop ◽  
Linear Equations ◽  
Building Blocks ◽  
Buffer Allocation ◽  
Planning Models ◽  
Machining Operations

The chapter presents a novel building block approach to developing models of manufacturing systems. The approach is based on max-plus algebra. Within this algebra, manufacturing schedules are modeled as a set of coupled linear equations. These equations are solved to find performance metrics such as the make span. The chapter develops a generic modeling block with three inputs and three outputs. It is shown that this structure can model any manufacturing system. It is also shown that the structure is hierarchical, that is, a set of blocks can be reduced to a single block with the same three inputs and three output structure. Basic building blocks, like machining operations, assembly, and buffering are derived. Job shop, flow shop, and cellular system applications are given. Extensions of the theory to buffer allocation and stochastic systems are also outlined. Finally, several numerical examples are given throughout the development of the theory.

scholarly journals An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 302 ◽  
Author(s):  
Zhonghua Han ◽  
Quan Zhang ◽  
Haibo Shi ◽  
Jingyuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Flow Shop ◽  
Probability Model ◽  
Real Data ◽  
Flow Shop Scheduling ◽  
Dispatching Rules ◽  
Scheduling Problems ◽  
Flexible Flow Shop ◽  
Compact Genetic Algorithm

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.

scholarly journals Field-synchronized Digital Twin framework for production scheduling with uncertainty

2020 ◽  
Author(s):  
Elisa Negri ◽  
Vibhor Pandhare ◽  
Laura Cattaneo ◽  
Jaskaran Singh ◽  
Marco Macchi ◽  
...  
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Flow Shop ◽  
Health Management ◽  
Discrete Event ◽  
Operating Conditions ◽  
Sensor Data ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Digital Twin

Abstract Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

Resource Allocation and Job Dispatching for Unreliable Flexible Flow Shop Manufacturing System

2012 ◽  
Vol 445 ◽  
pp. 947-952
Author(s):  
Umar M. Al-Turki ◽  
Haitham Saleh ◽  
Tamer Deyab ◽  
Yasser Almoghathawi
Keyword(s):  
Resource Allocation ◽  
Manufacturing System ◽  
Flow Shop ◽  
Flexible Flow Shop ◽  
Job Dispatching

Mission reliability-oriented integrated scheduling optimization model by combining production planning and maintenance activities

2020 ◽  
pp. 095440542097108
Author(s):  
Changchao Gu ◽  
Yihai He ◽  
Zhaoxiang Chen ◽  
Xiao Han ◽  
Di Zhou ◽  
...  
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Integrated Scheduling ◽  
Scheduling Model ◽  
Production Practice ◽  
Maintenance Activities ◽  
Scheduling Plan

Machine utilization and production efficiency of manufacturing systems can be effectively improved through reasonable production scheduling. Traditionally, production scheduling and maintenance planning are considered as two independent issues, but it may lead to a suboptimal solution that is unable to maximize the productivity of the manufacturing system. Therefore, a mission reliability-oriented integrated scheduling model that considers production planning and maintenance activities is proposed. Firstly, the mission reliability that takes into account product type and equipment performance is defined to characterize production rhythm. Secondly, the maintenance strategy based on machine degradation cumulative failure and stochastic failure is proposed to guarantee the mission reliability of the machine effectively. Thirdly, an integrated scheduling model is established with the goal of minimizing total operational time, and Genetic algorithm is tailored to find the best production scheduling plan. Finally, a case study and comparative study of the cylinder head manufacturing system are presented to demonstrate the effectiveness of the proposed method. Results show that the proposed method is more suitable for production practice than the previous production scheduling strategy.

THE IMPACT OF INTELLIGENT TOOLS ON SIMULATION METHODOLOGY

1996 ◽  
Vol 06 (01) ◽  
pp. 135-158 ◽  
Author(s):  
PAUL A. SAVORY ◽  
GERALD T. MACKULAK
Keyword(s):  
Manufacturing Systems ◽  
Stochastic Systems ◽  
Simulation Analysis ◽  
Discrete Event ◽  
Computer Software ◽  
Simulation Environment ◽  
Simulation Methodology ◽  
Intelligent Simulation ◽  
Intelligent Tools ◽  
The Impact

Simulation is one of the most effective techniques for analyzing stochastic systems. Recent computer software and hardware advances have had an important impact on the traditional discrete-event simulation methodology. Intelligent simulation environments consisting of integrated sets of “intelligent” tools for performing simulation studies have emerged. These tools significantly impact the methodology of a simulation analysis. This paper defines these intelligent tools and discusses how they alter the simulation paradigm by illustrating the development of a simulation model using an intelligent simulation environment. Special emphasis is on how an intelligent simulation environment provides a responsive analysis technique for studying manufacturing systems.

An efficiency improvement of flexible flow shop scheduling in automotive part company

2020 ◽  
pp. 1-14
Author(s):  
Waraporn Fangrit ◽  
Hwa Jen Yap ◽  
Mukhtar Fatihu Hamza ◽  
Siow-Wee Chang ◽  
Keem Siah Yap ◽  
...  
Keyword(s):  
Tabu Search ◽  
Flow Shop ◽  
Simulation Software ◽  
Flow Shop Scheduling ◽  
Current Status ◽  
Flexible Flow Shop ◽  
Shop Scheduling ◽  
Scheduling Method ◽  
Automotive Part ◽  
Flexible Flow Shop Scheduling

Flexible flow shop is becoming more interested and applied in industries due to its impact from higher workloads. Flexible flow shop scheduling problem is focused to minimize the makespan. A metaheuristic model based on Hybrid Tabu Search is developed to overcome this problem. Firstly, Hybrid Tabu Search is modelled based on the factory data. The Earliest Due Date rule is used as the scheduling method for the current status. FlexSim simulation software is used to evaluate the Hybrid Tabu Search model. The outcome is validated with two different basic heuristic solutions; Campbell, Dudek and Smith’s and Gupta’s heuristics. It is found that the proposed model can improve the job sequence based on makespan criteria.

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