scholarly journals A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

2021 ◽  
Vol 16 (4) ◽  
pp. 473-484
Author(s):  
A.S. Xanthopoulos ◽  
D.E. Koulouriotis
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Production Control ◽  
Control Strategies ◽  
Base Stock ◽  
Single Type ◽  
Hybrid Strategy ◽  
Product Manufacturing ◽  
Pull Control ◽  
Varying Demand

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

scholarly journals A comparison of HK-CONWIP and BK-CONWIP control strategies in a multi-product manufacturing system

2015 ◽  
Vol 2 ◽  
pp. 137-149 ◽  
Author(s):  
Chukwunonyelum Emmanuel Onyeocha ◽  
Jiayi Wang ◽  
Joseph Khoury ◽  
John Geraghty
Keyword(s):  
Manufacturing System ◽  
Control Strategies ◽  
Product Manufacturing

FORFMS: A FORTRAN Simulation and Analysis Model for Flexible Manufacturing Systems

1993 ◽  
Author(s):  
Chienann Hou ◽  
Milton Bennett
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Control Strategies ◽  
General Purpose ◽  
Time Interval ◽  
Analysis Model ◽  
Transfer Lines ◽  
High Production

Abstract The flexible manufacturing system (FMS) is designed to fill the gap between high-production transfer lines and low-production NC machines by optimized utilization of tools and machines. This paper describes a general purpose simulation and analysis model, FORFMS, for the study of flexible manufacturing system. The model is PC based and programmed in FORTRAN with fixed-increment time interval mechanism. A case study of a FMS with six workstations was conducted to evaluate the system performance and to examine control strategies.

On Designing Robust Kanban Production Control Strategies in Multiproduct Manufacturing Environments

2014 ◽  
pp. 68-88
Author(s):  
Oladipupo Olaitan ◽  
Anna Rotondo ◽  
Paul Young ◽  
John Geraghty
Keyword(s):  
Manufacturing System ◽  
Production Control ◽  
Control Strategies ◽  
Product System ◽  
Service Levels ◽  
Work In Progress ◽  
Demand Variability ◽  
Multiproduct Manufacturing ◽  
Manufacturing Environments ◽  
Different Levels

In this chapter, two Kanban Allocation Policies, Shared (S-KAP) and Dedicated (D-KAP), are analysed to understand how they would perform under different manufacturing scenarios, and the authors identify the merits and demerits of each. To evaluate the performance, a three-stage two product system was simulated under scenarios that provide for different levels of demand variability for each product. When operated under S-KAP, the system contained less Work In Progress (WIP); however, under D-KAP, the system provided more robust service levels as the variability increased. Based on the results from the model, guidelines on how to effectively combine these two policies to achieve the benefits of both in a multiproduct manufacturing system are developed. By partitioning the system at locations that would suit the transformation from one policy to another in a similar fashion to what is obtained in hybrid push-pull strategies, and deploying the policies that match the dominant characteristics at each segment, gives reduced WIP while maintaining service levels.

Hedging-point control policy for a failure-prone manufacturing system

2015 ◽  
Vol 231 (8) ◽  
pp. 1479-1487 ◽  
Author(s):  
Hong-Sen Yan ◽  
Tian-Hua Jiang ◽  
Xian-Gang Meng ◽  
Wen-Wu Shi
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Production Control ◽  
Learning Algorithm ◽  
Computation Time ◽  
Control Policy ◽  
Simulation Method ◽  
Iterative Learning ◽  
Point Control ◽  
Better Than

The production control of failure-prone manufacturing systems is notoriously difficult because such systems are uncertain and non-linear. Since the introduction of hedging-point policies, many researches have been done in this field. However, there are few literatures that consider the production control problem of tree-structured manufacturing systems. In this article, a hedging-point production control policy is proposed for a multi-machine, tree-structured failure-prone manufacturing system. To obtain the optimal hedging points, an iterative learning algorithm is developed by considering the system’s characteristics. A simulation method is embedded in the iterative learning algorithm to calculate the system cost. To estimate the performance of the proposed algorithm, comparisons are made between our algorithm, genetic algorithm and particle swarm optimization algorithm. The experimental results show that our algorithm works better than others in reducing the computation time and minimizing the production cost.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

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