Robust production control policy for a single machine and single part-type manufacturing system with inaccurate observation of production surplus

2012 ◽  
Vol 44 (12) ◽  
pp. 1061-1082 ◽  
Author(s):  
Zheng Wang ◽  
Yugang Yu
Keyword(s):  
Single Machine ◽  
Manufacturing System ◽  
Production Control ◽  
Control Policy ◽  
Part Type ◽  
Single Part

scholarly journals A simulation based optimization approach to combine two hedging control policies in a transported degrading failure-prone manufacturing system

2018 ◽  
Author(s):  
Saeed Abdolmaleki ◽  
Sajjad Shokouhyar ◽  
Mohammadbagher Afshar-Bakeshloo
Keyword(s):  
Manufacturing System ◽  
Production Control ◽  
Control Policy ◽  
Joint Production ◽  
Optimization Approach ◽  
Setup Cost ◽  
Network Failure ◽  
Holding Costs ◽  
Repair Costs ◽  
Decision Variables

This paper deals with joint production and corrective maintenance problem of a transported material network failure-prone manufacturing system along which two aspects are supposed. First, each non-identical machine are subject to degradation with failure phenomena. When a failure occurs, system is either repaired or replaced with new one, repairing activity not only degrades machine operating state, but also increases with the next repair time. Second, optimality production control policy called Modified Hedging Point Policy (MHPP) and Modified Hedging Corridor Policy (MHCP) are applied for given network machine. The aim of this article is to find decision variables so as to minimize incurred cost function, including repair costs, stock related costs (i.e., holding costs, backlog and shortage) and setup cost, over an finite-horizon time. Simulation experimental approach with meta-heuristic algorithm is applied to obtain near-optimum decision variables.

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.

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.

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