Continuous improvement in quality is the most important mantra for success in today’s competitive market. Previous studies indicated that although quality is the most important factor in gaining competitive superiority, increasing the level of quality alone cannot meet customers’ needs. One complementary approach to improving the level of quality to meet customers’ expectations is to lower the production costs and price of finished products. The quality circle as a path to achieve greater customer satisfaction is formed to identify the cost of quality and thus reduce this cost, which is a significant cost portion of the entire life cycle of products. This paper presents a case study in an electromotor manufacturing company. First, we build a mathematical model to allocate inspection stations to manufacturing processes and propose a heuristic approach to optimize it. Next, we use Enterprise Dynamic software for simulation. Finally, we compare these methods.
PennMRavivT. A polynomial time algorithm for solving a quality control station configuration problem. Discrete Appl Math2008; 156(4): 412–419.
5.
LindsayGFBishopAB. Allocation of screening inspection effort: a dynamic programming approach. Manage Sci1965; 10(2): 342–352.
6.
AzadehASangariMSAmiriAS. A particle swarm algorithm for inspection optimization in serial multi-stage processes. Appl Math Model2012; 36(4): 1455–1464.
7.
VolsemSVDullaertWLaneghemHV. An evolutionary algorithm and discrete event simulation for optimizing strategies for multi-stage processes. Eur J Oper Res2007; 179(3): 621–633.
8.
AzadehASangariMS. A metaheuristic method for optimizing inspection strategies in serial multi-stage processes. Int J Prod Quality Manage2010; 6(3): 289–303.
9.
BaiDYunH. Optimal allocation of inspection effort in a serial multi-stage production system. Comput Ind Eng1987; 30(3): 387–396.
10.
MousaviSMMirdamadiSSiadatAet al. An intuitionistic fuzzy grey model for selection problems with an application to the inspection planning in manufacturing firms. Eng Appl Artif Intell2015; 39: 157–167.
11.
HanneTNickelS. A multi-objective evolutionary algorithm for scheduling and inspection planning in software development projects. Eur J Oper Res2005; 167(3): 663–678.
12.
ViswanadhamNSharmaSMTanejaM. Inspection allocation in manufacturing systems using stochastic search techniques. IEEE Trans Syst Man Cybern1996; 26 (2): 222–230.
13.
Garcia-DiazAFosterJWBonyuetM. Dynamic programming analysis of special multi-stage inspection systems. IIE Trans1984; 16(2): 115–125.
14.
ChakravartyAKShtubA. Strategic allocation of inspection effort in a serial, multi-product production system. IIE Trans1987; 19(1): 13–22.
15.
ShinWSHartSMLeeHF. Strategic allocation of inspection stations for a flow assembly line: a hybrid procedure. IIE Trans1995; 27(6): 707–715.
16.
AzadehASangariMSSangariEet al. A particle swarm algorithm for optimising inspection policies in serial multistage production processes with uncertain inspection costs. Int J Comp Integ M2015; 28(7): 766–780.
17.
ShiauYR. Inspection resource assignment in a multistage manufacturing system with inspection error model. Int J Prod Res2002; 40(8): 1787–1806.
18.
ShiauYR. Quick decision-making support for inspection allocation planning with rapidly changing customer requirements. Int J Adv Manuf Tech2003; 22(9–10): 633–640.
19.
ShiauYR. Inspection allocation planning for a multiple quality characteristic advanced manufacturing system. Int J Adv Manuf Tech2003a; 21(7): 494–500.
20.
BattiniDFaccioMPersonaAet al. Design of an integrated quality assurance strategy in production systems. Int J Prod Res2012; 50(6): 1682–1701.
21.
ShiauYRLinMHChuangWC. Concurrent process/inspection planning for a customized manufacturing system based on genetic algorithm. Int J Adv Manuf Tech2007; 33(7–8): 746–755.
22.
KorytkowskiP. A genetic algorithm with tournament selection for optimising inspection allocation in multiproduct multistage production systems. IJSPM2011; 6 (3): 238–244.
23.
TengSHHoSY. Failure mode and effects analysis: an integrated approach for product design and process control. IJQRM1996; 13(5): 8–26.
24.
MalekiHSaadatY. Comparison of failure mode and effects analysis by using AHP versus REMBRANDT system. Int J Ind Syst Eng2013; 14(1): 5–19.
25.
Noor-alsanaR. Statistical process control. Tehran: Iran Science and Technology Publication, 2009.
26.
MalekiHTaghaviFardMT. Evaluation of scheduling using triangular whitenization functions. J Manuf Tech Manage2015; 26(2): 297–310.
