Capacity Management in Reconfigurable Manufacturing Systems With Stochastic Market Demand

Manufacturing ◽  
2002 ◽  
Author(s):  
Farshid Maghami Asl ◽  
A. Galip Ulsoy
Keyword(s):  
Inventory Control ◽  
Manufacturing Systems ◽  
Optimal Solution ◽  
Capacity Management ◽  
Management Problem ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Market Demand ◽  
Management Scenario ◽  
Stochastic Market

An optimal solution, based on Markov Decision Theory, is presented for the capacity management problem in Reconfigurable Manufacturing Systems with stochastic market demand with a time delay between the time capacity change is ordered and the time it is delivered. The optimal policy in this paper is presented as optimal boundaries representing the optimal capacity expansion and reduction levels. The effects of change in the cost function parameters and the delay time on the optimal boundaries are presented for a capacity management scenario. The major differences between this research and the ones in inventory control lie in two folds. One is the fact that unlike inventory, capacity levels can be reduced according to the market demand. The other one is the novel approach presented in this paper to solve the delay problem which unlike the inventory control does not account for the cumulative unmet demand as a decision factor.

Performance analysis and measurement of reconfigurable manufacturing systems

2014 ◽  
Vol 25 (7) ◽  
pp. 934-957 ◽  
Author(s):  
Ibrahim H. Garbie
Keyword(s):  
Performance Analysis ◽  
Performance Measurement ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Relative Weight ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Market Demand ◽  
System Productivity ◽  
Content Type

Purpose – The purpose of this paper is to propose a new performance analysis and measurement regarding reconfigurable manufacturing systems (RMS) taken into consideration new circumstances which include changes in the market demand, changes in a product design, and/or introduction of a new product. As the reconfiguration process is applied to a manufacturing system to improve the system's performance due to new circumstances, the RMS process has potential quantitative and qualitative measures. Design/methodology/approach – The manufacturing system has a great impact on the performance measurement and the selection of the objectives to measure the performance is very important. These objectives include the critical requirements for a RMS and they are as follows: product cost, manufacturing response, system productivity, people behavior, inventory, and quality of the finished products. Because each criterion measure in a RMS is a potential source of evaluation, it should have a relative weight with respect to the other measures. First, each criterion will be measured individually. Second, these measures need to be evaluated through an aggregate quantitative metric because there is a lack of analytical techniques to analyze and evaluate both qualitative and quantitative measures. Findings – Performance evaluation of a RMS from one circumstance to another is highly desired by using the new quantitative metric regarding updating (upgrading) the system for the next period based on the previous one. The results show that the applicable of using this new technique in evaluating the RMS. The results also support the new quantitative metric. Originality/value – The suggestion of a new aggregate performance measurement metric including the all potential objectives is highly considered. This paper provides an insight into each objective individually to measure it. It is also used from 0 to 1 as range of measure to evaluate the potential and aggregate metrics toward next reconfiguration with respect to the existing one.

Optimal Capacity Management With Stochastic Market Demand and Imperfect Information

2003 ◽  
Author(s):  
Farshid Maghami Asl ◽  
A. Galip Ulsoy
Keyword(s):  
Imperfect Information ◽  
Capacity Management ◽  
Management Problem ◽  
Market Demand ◽  
Economic Stability ◽  
New Approach ◽  
Optimal Capacity ◽  
Markov Decision ◽  
Reconfigurable Capacity ◽  
Stochastic Market

Over-capacity has been a major problem in the world economy over the past decade. Reconfigurable capacity, and optimal capacity management policies, can contribute to increased economic stability. This research introduces a new approach to optimal capacity management for a firm faced with uncertainties and imperfect information of the market demand. It presents an optimal policy for the capacity management problem in a firm facing stochastic market demand, based on Markov decision theory. To make the approach more realistic, it is assumed that the firm has imperfect information of its stochastic market demand, and can only observe its previous sales. Optimal policies are presented as boundaries representing the optimal capacity expansion and reduction levels.

Optimal Part Family and Production Module Planning for Reconfigurable Manufacturing Systems

2006 ◽  
Author(s):  
Jian Liu ◽  
Derek M. Yip-Hoi ◽  
Wencai Wang ◽  
Li Tang
Keyword(s):  
Manufacturing Systems ◽  
Optimization Procedure ◽  
Cost Effective ◽  
System Level ◽  
Reconfigurable Manufacturing Systems ◽  
Reconfigurable Manufacturing ◽  
Market Demand ◽  
Individual Module ◽  
Reconfiguration Planning ◽  
And Performance

Manufactures are adopting Reconfigurable Manufacturing Systems (RMS) to better cope with frequently changing market conditions, which place tremendous demands on a system’s flexibility as well as its cost-effectiveness. Considerable efforts have been devoted to the development of necessary tools for the system level design and performance improvement, resulting in approaches to designing a single RMS. In this paper, a methodology for cost-effective reconfiguration planning for multi-module-multi-product RMS’s that best reflect the market demand changes is proposed. Formulated as an optimization procedure, reconfiguration planning is defined as the best reallocation of part families to production modules in an RMS and the best rebalancing of the whole system and each individual module to achieve minimum related cost and simultaneously satisfy the market demand. A Genetic Algorithm (GA) approach is proposed to overcome the computational difficulties caused by the problem complexity. Effectiveness of the proposed methodology is demonstrated with a case study.

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