scholarly journals Work In Process Control for a High Product Mix Manufacturing System

Procedia CIRP ◽  
2017 ◽  
Vol 63 ◽  
pp. 277-282 ◽  
Author(s):  
Oladipupo Olaitan ◽  
Quan Yu ◽  
Erlend Alfnes
Keyword(s):  
Process Control ◽  
Manufacturing System ◽  
Product Mix ◽  
Work In Process

Simultaneous Modular Product Scheduling and Manufacturing Cell Reconfiguration Using a Genetic Algorithm

2006 ◽  
Vol 128 (4) ◽  
pp. 984-995 ◽  
Author(s):  
Hegui Ye ◽  
Ming Liang
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing System ◽  
Product Variety ◽  
Manufacturing Cost ◽  
Successful Implementation ◽  
Strong Impact ◽  
Reconfigurable Manufacturing ◽  
Product Variant ◽  
Modular Product ◽  
Work In Process

Modular product design can facilitate the diversification of product variety at a low cost. Reconfigurable manufacturing, if planned properly, is able to deliver high productivity and quick responsiveness to market changes. Together, the two could provide an unprecedented competitive edge to a manufacturing company. The production of a family of modular products in a reconfigurable manufacturing system often requires reorganizing the manufacturing system in such a way that each configuration corresponds to one product variant in the same family. The successful implementation of this strategy lies in proper scheduling of the modular product operations and optimal selection of a configuration for producing each product variant. These two issues are closely related and have a strong impact on each other. Nevertheless, they have often been treated separately, rendering inefficient, infeasible, and conflicting decisions. As such, an integrated model is developed to address the two problems simultaneously. The objective is to minimize the sum of the manufacturing cost components that are affected by the two planning decisions. These include reconfiguration cost, machine idle cost, material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate the application of the integrated approach. Our computational experience shows that the proposed GA substantially outperforms a popular optimization software package, LINGO, in terms of both solution quality and computing efficiency.

System Reliability Estimation for a Manufacturing Network with Joint Buffers

2018 ◽  
Vol 25 (04) ◽  
pp. 1850020 ◽  
Author(s):  
Ping-Chen Chang ◽  
Chia-Chun Wu ◽  
Chin-Tan Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
System Reliability ◽  
Manufacturing System ◽  
Reliability Estimation ◽  
Experimental Result ◽  
Production Lines ◽  
Work In Process ◽  
Process Output ◽  
Manufacturing Network

This paper develops a Monte Carlo Simulation (MCS) approach to estimate the performance of a multistate manufacturing network (MMN) with joint buffers. In the MMN, products are allowed to be produced by two production lines with the same function to satisfy demand. A performance index, system reliability, is applied to estimate the probability that all workstations provide sufficient capacity to satisfy a specified demand and buffers possess adequate storage. The joint buffers with finite storage are considered in the MMN. That is, extra work-in-process output from different production lines can be stored in the same buffer. An MCS algorithm is proposed to generate the capacity state and to check the storage usage of buffers to evaluate whether the demand can be satisfied or not. System reliability of the MMN is estimated through this MCS algorithm. Besides, performability for demand pairs assigned to production lines can be obtained. A practical example of touch panel manufacturing system is used to demonstrate the applicability of the MCS approach. Experimental result shows that system reliability is overestimated when buffer storage is assumed to be infinite. Moreover, joint buffer for an MMN is more reliable than buffers are installed separately in different production lines.

Impact of Product Mix Variations on Material Handling for Cellular Manufacturing

2004 ◽  
Author(s):  
Amit Bhandwale ◽  
Thenkurussi Kesavadas
Keyword(s):  
Machine Part ◽  
Material Handling ◽  
Incidence Matrix ◽  
Manufacturing System ◽  
Cellular Manufacturing ◽  
Cell Formation ◽  
Product Mix ◽  
Part Families ◽  
Machine Groups ◽  
The Impact

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system. The primary input to cell formation algorithms is the machine-part incidence matrix, which is a binary matrix representing machining requirements of parts in various part families. One common assumption of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers, resulting in a highly volatile market. This has given rise to a class of products characterized by low volume and high variety, which presents engineers with lots of problems and decisions in the early stages of product development. This can have an adverse effect on manufacturing like high investment in new machinery and material handling equipment, long setup times, high tooling costs, increased intercellular movement and excessive scrap which increases the cost without adding any value to the parts. Any change to the product mix results in a change in the machine-part incidence matrix, which may change the part families and machine groups, which form the cells. The manufacturing system needs to be flexible in order to handle large product mix changes. This paper discusses the impact of product mix variations on cellular manufacturing and presents a methodology to incorporate these variations into an existing cellular manufacturing setup.

scholarly journals Effect of routing flexibility on the performance of manufacturing system

2019 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Mohammed Ali ◽  
Wasif Ullah Khan
Keyword(s):  
Manufacturing System ◽  
Initial Level ◽  
System Capacity ◽  
Routing Flexibility ◽  
Work In Process ◽  
Analysis Of Results ◽  
Part Sequencing ◽  
Four Levels ◽  
Time Resource ◽  
Load Conditions

This work presented in this paper is based on the simulation of the routing flexibility enabled manufacturing system. In this study four levels of each factor (i.e. routing flexibility, system load conditions, system capacity and four part sequencing rules) are considered for the investigation. The performance of the routing flexibility enabled manufacturing system (RFEMS) is evaluated using three performance measures like make-span time, resource utilization and work-in-process. The analysis of results shows that the performance of the manufacturing system may be improved by adding in routing flexibility at the initial level along with other factors. However, the benefit of this flexibility diminishes at higher levels of routing flexibilities.

scholarly journals Modeling and analysis of carousel-based mixed-model flexible manufacturing system using colored Petri net

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401988974
Author(s):  
Hafiz Zahid Nabi ◽  
Tauseef Aized
Keyword(s):  
Petri Net ◽  
System Performance ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Mixed Model ◽  
Colored Petri Net ◽  
Material Supply ◽  
Work In Process ◽  
Production Resources

This study aims to model, analyze, and evaluate performance of a flexible manufacturing system, constituting a carousel-based manufacturing and assembly cells layout, configured to produce mixed-model multiple products employing inter-/intra-cellular routing flexibility in which manufacturing and assembly resources are subject to working and failure modes. A hierarchical colored Petri net model is developed to analyze performance of the flexible manufacturing system. Colored Petri net modeling experiments have been conducted to evaluate the system performance for throughput, cycle time, and work-in-process. The system performance has been investigated in relation to material supply and handling system, process execution, and production resources reliability variables. Different input factors are considered for simulation modeling such as mean machining time, mean loading/unloading time, mean assembly time, buffer capacity, material supply inter-arrival time, number of operations between failures, and mean time to repair for production resources; a variation in input factors has shown a significant impact on system performance measures. The colored Petri net–based modeling, simulation, and analysis approach has been demonstrated as an efficient method for carousel-based mixed-model configured flexible manufacturing system.

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