Optimality of a hedging-point control policy for a failure-prone manufacturing system under a probabilistic cost criterion

2011 ◽  
Author(s):  
Amir Ahmadi-Javid ◽  
Roland Malhame
Keyword(s):  
Manufacturing System ◽  
Control Policy ◽  
Cost Criterion ◽  
Point Control

scholarly journals Integrated Control Policy for a Multiple Machines and Multiple Product Types Manufacturing System Production Process with Uncertain Fault

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 952
Author(s):  
Jia You ◽  
Ming Li ◽  
Kai Guo ◽  
Hao Li
Keyword(s):  
Production Cost ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Cost Control ◽  
Integrated Control ◽  
Control Policy ◽  
Point Control ◽  
Multiple Product ◽  
Production Cost Control ◽  
Multiple Machines

The optimization of production cost has always been a key issue in manufacturing systems; for the single product type manufacturing systems, lots of research studies have proved the validity of the hedging point control policy in production cost control. However, due to the complexity of the multiple machines and multiple product types manufacturing systems with uncertain fault, it is difficult to achieve a good control effect only by using the hedging point control policy. To optimize the total production cost under constantly changing demands, an integrated control policy that combines the prioritized hedging point (PHP) control policy with the production capacity planning during production is proposed, and the decision variables are obtained by a particle swarm optimization (PSO) algorithm. The simulation experiments show the effectiveness of the proposed integrated control policy in production cost control for the multiple machines and multiple product types manufacturing system.

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 Reusability Evaluation Models In Reverse Logistics

2021 ◽  
Author(s):  
Shahin Kouchekian-Sabour
Keyword(s):  
Reverse Logistics ◽  
Manufacturing System ◽  
Control Policy ◽  
Green Manufacturing ◽  
Fuzzy Analytical Hierarchy Process ◽  
Ahp Method ◽  
Inventory Control Policy ◽  
Decision Making Model ◽  
Second Category ◽  
Hierarchy Process

This study focuses on the problem of reusability evaluation in reverse logistics. To deal with it, products are categorized into two types: well established products, and products with fast innovations. An innovation reliability based model (model 1) is suggested to evaluate reusability of returns for the first category. For the second category, a fuzzy multiple participant-multiple criteria (MPMC) decision making model is presented, which is a modified combination of two previous researches: the disposal cause analysis matrix (Umeda et al., 2005), and the fuzzy analytical hierarchy process (AHP) method (van Laarhoven and Pedrycz, 1983). To present the application of model 1, a green manufacturing system with an (s, Q) inventory control policy is simulated using Arena. With the aid of it, the system is analyzed in two situations: with recovery operations, and without recovery operations to investigate the effects of both model 1 and recovery operations on the system parameters.

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