Optimization of POLCA-controlled production systems with a simulation-driven genetic algorithm

2013 ◽  
Vol 70 (1-4) ◽  
pp. 385-395 ◽  
Author(s):  
M. Braglia ◽  
D. Castellano ◽  
M. Frosolini
Keyword(s):  
Genetic Algorithm ◽  
Production Systems

PERFORMANCE EVALUATION OF GENETIC DISTRIBUTED FUZZY CONTROLLERS FOR MULTI-PART-TYPE PRODUCTION LINE

2007 ◽  
Vol 06 (02) ◽  
pp. 115-128
Author(s):  
SEYED MAHDI HOMAYOUNI ◽  
TANG SAI HONG ◽  
NAPSIAH ISMAIL
Keyword(s):  
Genetic Algorithm ◽  
Performance Evaluation ◽  
Objective Function ◽  
Production Rate ◽  
Production Line ◽  
Production Systems ◽  
Membership Functions ◽  
Part Type ◽  
High Penalty ◽  
Single Part

Genetic distributed fuzzy (GDF) controllers are proposed for multi-part-type production line. These production systems can produce more than one part type. For these systems, "production rate" and "priority of production" for each part type is determined by production controllers. The GDF controllers have already been applied to single-part-type production systems. The methodology is illustrated and evaluated using a two-part-type production line. For these controllers, genetic algorithm (GA) is used to tune the membership functions (MFs) of GDF. The objective function of the GDF controllers minimizes the surplus level in production line. The results show that GDF controllers can improve the performance of production systems. GDF controllers show their abilities in reducing the backlog level. In production systems in which the backlog has a high penalty or is not allowed, the implementation of GDF controllers is advisable.

Optimal Inspection Allocation for Workstations of Attribute Data with Multi-Characteristics in Multi-Station Systems

2010 ◽  
Vol 450 ◽  
pp. 397-400
Author(s):  
Hsin Rau ◽  
Kuo Hua Cho ◽  
Yi Hsiang Wang
Keyword(s):  
Genetic Algorithm ◽  
Production Systems ◽  
Computation Time ◽  
Optimization Method ◽  
Type I ◽  
Type Ii ◽  
Profit Model ◽  
Serial Production ◽  
Attribute Data ◽  
Type Ii Errors

. The study models multi-characteristics inspection for inspection allocation problems with workstations of attribute data in serial production systems. Either 100% or 0% inspection is performed and Type I and Type II errors are considered. In addition, this study considers three possibilities of treatment of detected nonconforming units, namely, repair, rework and scrap. With the above considerations, a profit model is developed for optimally allocating inspections. Moreover, a genetic algorithm is used to solve the problem and it is proved to have much less computation time, compared with an optimization method based on complete enumeration, especially when number of workstations and characteristics becomes more.

scholarly journals Development of Production Simulator for Buffer Size Decisions in Complex Production Systems Using Genetic Algorithm

2007 ◽  
Vol 1 (3) ◽  
pp. 418-429 ◽  
Author(s):  
Jaber ABU QUDEIRI ◽  
Hidehiko YAMAMOTO ◽  
Rizauddin RAMLI ◽  
Khalid R. Al-MOMANI
Keyword(s):  
Genetic Algorithm ◽  
Production Systems ◽  
Buffer Size

scholarly journals Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

Computers ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Eduardo Guzman ◽  
Beatriz Andres ◽  
Raul Poler
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Open Source ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Systems ◽  
Machine Scheduling ◽  
Efficient Solutions ◽  
Point Of View ◽  
Sequencing Problems

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.

Solving Lot-Sizing and Sequencing Integrated Optimization Problems in Mixed-Model Production Systems

2012 ◽  
Vol 472-475 ◽  
pp. 3335-3338 ◽  
Author(s):  
Bing Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
Mixed Model ◽  
Production Systems ◽  
Optimization Problems ◽  
Lot Sizing ◽  
Flow Line ◽  
Adaptive Genetic Algorithm ◽  
Integrated Optimization ◽  
Comparison Results ◽  
Mixed Model Assembly Line

This paper is concerned about the lot-sizing and sequencing integrated optimization problems in mixed-model production systems composed of one mixed-model assembly line and one fabrication flow line. The optimization objective is minimizing the total makespan cost in regular hour, the overtime makespan cost and the holding cost in the whole production system. The mathematic models are presented and an adaptive genetic algorithm is developed for solving this problem. A traditional genetic algorithm is also designed for testing the optimization performance of the adaptive genetic algorithm. Computational experiments are conducted and the optimization results are compared between the above two algorithms. The comparison results show that the adaptive genetic algorithm is a feasible and effective method for solving this problem.

scholarly journals Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Chen ◽  
Zhenyu Yang ◽  
Yuejin Tan ◽  
Renjie He
Keyword(s):  
Genetic Algorithm ◽  
Production Systems ◽  
Hamming Distance ◽  
Scheduling Problem ◽  
Order Acceptance ◽  
Solution Quality ◽  
Survival Selection ◽  
Benchmark Instances ◽  
Order Acceptance And Scheduling ◽  
Dependent Setup Times

Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA) is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.

Optimization of Production Systems Using Genetic Algorithms

2003 ◽  
Vol 03 (03) ◽  
pp. 233-248 ◽  
Author(s):  
Wael Mustafa
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production System ◽  
Execution Time ◽  
Production Systems ◽  
Matching Algorithm ◽  
Optimal Ordering ◽  
Number Of Iterations

This paper presents a Genetic Algorithm for Production Systems Optimization (GAPSO). The GAPSO finds an ordering of Condition Elements (CEs) in the rules of a Production System (PS) that results in a (near) optimal PS with respect to execution time. Finding such an ordering can be difficult since there is often a large number of ways to order CEs in the rules of a PS. Additionally, existing heuristics to order CEs in many cases conflict with each other. The GAPSO is applicable to PSs in general and no assumptions are made about the matching algorithm or the interpreter that executes the PS. The results of applying the GAPSO to some example PSs are presented. In all examples, the GAPSO found an optimal ordering of CEs in a small number of iterations.

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