Conclusion and gap analysis - inspection planning problem under uncertainty and breakdown

solution approach, namely an evolutionary algorithm (EA) to solve the model. They also used simulation to calculate the inspection costs for every candidate solution.

Korytkowski (2011) considered a multiproduct MPS in case of allocating inspection stations throughout the production system. In the proposed model, part types competed with each other for common production resources. In such environment, it is important to consider factors such as throughput time variability and to include the corresponding queuing aspects into the model. The author modeled each workstation as a GI/G/c queue. Finally, the optimal allocation was determined by using a genetic algorithm with tournament selection, one–point crossover and uniform mutation.

In a different work, Mousavi et al. (2015) studied the problem of selecting important quality characteristics to be inspected in order to minimize inspection cost while assuring a high level of quality for the final products. The authors considered uncertainty in their selection model by modifying the classical methods. To cope with uncertainty of the input parameters, this research introduced a distance-based decision model for the multi-attributes analysis by considering the concepts of intuitionistic fuzzy sets (IFSs), grey relations and compromise ratio approaches. The authors first developed a weighting method for the attributes based on a generalized version of the entropy and IFSs along with experts׳ judgments. Then, a new grey relational analysis was introduced to analyze the extent of connections between two potential scenarios by an intuitionistic fuzzy distance measurement. Finally, an intuitionistic fuzzy compromise ratio index to prioritize the scenarios was proposed by considering the weight of the strategy for the maximum group utility in intuitionistic fuzzy grey environment. Finally, the authors illustrated the feasibility and practicability of the proposed selection method by implementing it in a real case study to the inspection planning for the oil pump housing from Renault automobile manufacturing.

Table 2.1. Classification of literature based on the production system characteristics

Author Year Prod.

structure

Prod./Insp. flow Insp.

type

Insp.

strategy

Insp. errors Failure type and rate Nonconforming strategy

Serial Convergent Nonserial SingleProd./single Insp. SingleProd./batch Insp. MixedProd./single Insp. Mixed Prod./batch Insp. Conformity Monitoring No Insp. Full Insp. SamplingInsp. type I type II Error free Constantrate/single type Randomrate/single type Constantrate/multiple type Randomrate/multiple type No Scrap Scrapping some Scrapping all Probabilistic

Beightler and Mitten 1964 √ √ √ √ √ √ √

Lindsay and Bishop 1964 √ √ √ √ √ √ √

White 1965 √ √ √ √ √ √ √

Pruzan and Jackson 1967 √ √ √ √ √ √ √

Brown 1968 √ √ √ √ √ √ √

White 1969 √ √ √ √ √ √ √

Ercan 1972 √ √ √ √ √ √ √

Garey 1972 √ √ √ √ √ √ √

Woo and Metcalfe 1972 √ √ √ √ √ √ √

Britney 1972 √ √ √ √ √ √ √

Hurst 1973 √ √ √ √ √ √ √ √

Dietrich and Sanders 1974 √ √ √ √ √ √ √

Eppen 1974 √ √ √ √ √ √ √ √

Ercan et al. 1974 √ √ √ √ √ √ √

Trippi 1974 √ √ √ √ √ √ √

Enrick 1975 √ √ √ √ √ √ √ √

Trippi 1975 √ √ √ √ √ √ √

Yum and McDowell 1981 √ √ √ √ √ √ √ √

Ballou and Pazer 1982 √ √ √ √ √ √ √ √

Hsu 1984 √ √ √ √ √ √ √

Peters and Williams 1984 √ √ √ √ √ √ √

Table 2.1. Classification of literature based on the production system characteristics (continue)

Author Year Prod.

structure

Prod./Insp. flow Insp.

type

Insp.

strategy

Insp. errors Failure type and rate Nonconforming strategy

Garcia-Diaz et al. 1984 √ √ √ √ √ √ √

Ballou and Pazer 1985 √ √ √ √ √ √ √ √

Gunter and Swanson 1985 √ √ √ √ √ √ √

Bai and Yun 1986 √ √ √ √ √ √ √ √

Chakravarty and Shtub 1987 √ √ √ √ √ √ √

Lee and Rosenblatt 1987 √ √ √ √ √ √ √

Peters and Williams 1987 √ √ √ √ √ √ √

Yum and McDowell 1987 √ √ √ √ √ √ √ √

Tayi and Ballou 1988 √ √ √ √ √ √ √

Saxena et al. 1990 √ √ √ √ √ √

Barad 1990 √ √ √ √ √ √ √ √

Foster et al. 1990 √ √ √ √ √ √ √ √

Kang et al. 1990 √ √ √ √ √ √ √ √

Raz and Kaspi 1991 √ √ √ √ √ √ √ √

Tang 1991 √ √ √ √ √ √ √

Villalobos and Foster 1991 √ √ √ √ √ √ √ √

Villalobos et al. 1993 √ √ √ √ √ √ √ √

Taneja & Viswanadham 1994 √ √ √ √ √ √ √ √ √

Jewkes 1995 √ √ √ √ √ √ √

Rebello et al. 1995 √ √ √ √ √ √ √

Shin 1995 √ √ √ √ √ √ √

Table 2.1. Classification of literature based on the production system characteristics (continue)

Author Year Prod.

structure

Prod./Insp. flow Insp.

type

Insp.

strategy

Insp. errors Failure type and rate Nonconforming strategy

Deliman and Feldman 1996 √ √ √ √ √ √ √

Gurnani et al. 1996 √ √ √ √ √ √ √

Viswandham et al. 1996 √ √ √ √ √ √ √ √ √

Narahari and Khan 1996 √ √ √ √ √ √ √

Chevalier and Wein 1997 √ √ √ √ √ √ √ √

Rabinowitz and Emmons 1997 √ √ √ √ √ √ √

Chen et al. 1998 √ √ √ √ √ √ √

Lee and Unnikrishnan 1998 √ √ √ √ √ √ √ √

Yao and Zheng 1999a √ √ √ √ √ √ √

Yao and Zheng 1999b √ √ √ √ √ √ √

Chen and Thornton 1999 √ √ √ √ √ √ √

Hassan and Pham 2000 √ √ √ √ √ √ √ √

Veatch 2000 √ √ √ √ √ √ √ √ √

Zheng 2000 √ √ √ √ √ √ √

Verduzco et al. 2001 √ √ √ √ √ √ √ √

Zhou and Zhao 2002 √ √ √ √ √ √ √

Shiau 2002 √ √ √ √ √ √ √ √

Emmons and Rabinowitz 2002 √ √ √ √ √ √ √

Avinadav and Raz 2003 √ √ √ √ √ √ √ √

Oppermann et al. 2003 √ √ √ √ √ √ √ √ √

Van Volsem & Van Landeghem 2003 √ √ √ √ √ √ √

Table 2.1. Classification of literature based on the production system characteristics (continue)

Author Year Prod.

structure

Prod./Insp. flow Insp.

type

Insp.

strategy

Insp. errors Failure type and rate Nonconforming strategy

Shiau 2003a √ √ √ √ √ √ √ √

Shiau 2003b √ √ √ √ √ √ √ √

Kakade et al. 2004 √ √ √ √ √ √ √

Valenzuela et al. 2004 √ √ √ √ √ √ √

Rau and Chu 2005 √ √ √ √ √ √ √

Hanne and Nickel 2005 √ √ √ √ √ √ √

Feng and Kapur 2006 √ √ √ √ √ √ √ √ √

Shiau et al. 2007 √ √ √ √ √ √ √ √

Penn and Raviv 2007 √ √ √ √ √ √ √

Van Volsem et al. 2007 √ √ √ √ √ √ √

Penn and Raviv 2008 √ √ √ √ √ √ √

Vaghefi and Sarhangian 2009 √ √ √ √ √ √ √ √

Ferreira et al. 2009 √ √ √ √ √ √ √

Rau and Cho 2009 √ √ √ √ √ √ √

Azadeh and Sangari 2010 √ √ √ √ √ √ √

Van Volsem 2010 √ √ √ √ √ √ √ √

Korytkowski 2011 √ √ √ √ √ √ √

Rau and Cho 2011 √ √ √ √ √ √ √

Azadeh et al. 2012 √ √ √ √ √ √ √

Azadeh et al. 2014 √ √ √ √ √ √ √ √

Mousavi et al. 2015 √ √ √ √ √ √ √ √ √

Table 2.2. Classification of literature based on the methodology

Author Year Cost component Objective

Function

Constraint Solution Approach

Internal Failure

External Failure

Insp. Cost Production Cost Total/input Total/output Total/Conf. output Insp. time No. of Insp. Station Insp. No. of repeated Budget Dynamic Prog. Integer Prog. Nonlinear Prog. Metaheuristics Heuristics & Simulation Rework Replace Scrap Defect Dep. Defect Ind. Fixed Variable

Linear Nonlinear

Beightler and Mitten 1964 √ √ √ √ √

Lindsay and Bishop 1964 √ √ √ √

White 1965 √ √ √ √ √

Pruzan and Jackson 1967 √ √ √ √ √ √

Brown 1968 √ √ √ √ √

White 1969 √ √ √ √ √ √ √ √ √ √

Britney 1972 √ √ √ √ √

Ercan 1972 √ √ √ √ √ √ √

Garey 1972 √ √ √ √

Woo and Metcalfe 1972 √ √ √ √ √

Hurst 1973 √ √ √

Dietrich and Sanders 1974 √ √ √ √ √

Eppen 1974 √ √ √ √ √

Ercan et al. 1974 √ √ √ √ √ √

Trippi 1974 √ √ √ √ √ √ √ √

Enrick 1975 √ √ √ √ √ √ √

Trippi 1975 √ √ √ √ √ √

Yum and McDowell 1981 √ √ √ √ √ √

Ballou and Pazer 1982 √ √ √ √ √

Garcia-Diaz et al. 1984 √ √ √ √ √

Hsu 1984 √ √ √ √ √

Table 2.2. Classification of literature based on the methodology (continue)

Author Year Cost component Objective

Function

Constraint Solution Approach

Internal Failure

External Failure

Insp. Cost Production Cost Total/input Total/output Total/Conf. output Insp. time No. of Insp. Station No. of repeated Insp. Budget Dynamic Prog. Integer Prog. Nonlinear Prog. Metaheuristics Heuristics & Simulation Rework Replace Scrap Defect Dep. Defect Ind. Fixed Variable

Linear Nonlinear

Peters and Williams 1984 √ √ √ √ √ √ √ √

Ballou and Pazer 1985 √ √ √ √ √

Gunter and Swanson 1985 √ √ √ √ √ √

Bai and Yun 1986 √ √ √ √ √ √

Chakravarty and Shtub 1987 √ √ √ √ √ √ √ √ √

Lee and Rosenblatt 1987 √ √ √ √ √

Peters and Williams 1987 √ √ √ √ √ √

Yum and McDowell 1987 √ √ √ √ √ √ √

Tayi and Ballou 1988 √ √ √ √ √ √ √

Saxena et al. 1990 √ √ √ √ √

Barad 1990 √ √ √ √ √ √ √ √

Foster et al. 1990 √ √ √ √ √ √

Kang et al. 1990 √ √ √ √ √ √ √ √

Raz and Kaspi 1991 √ √ √ √ √ √ √

Tang 1991 √ √ √ √ √ √ √

Villalobos and Foster 1991 √ √ √ √ √

Villalobos et al. 1993 √ √ √ √ √ √

Taneja & Viswanadham 1994 √ √ √ √ √ √ √ √

Jewkes 1995 √ √ √ √ √ √

Rebello et al. 1995 √ √ √ √ √ √ √ √

Shin 1995 √ √ √

Table 2.2. Classification of literature based on the methodology (continue)

Author Year Cost component Objective

Function

Constraint Solution Approach

Internal Failure

External Failure

Linear Nonlinear

Deliman and Feldman 1996 √ √ √ √ √ √ √

Gurnani et al. 1996 √ √ √ √

Narahari and Khan 1996 √ √ √ √

Viswandham et al. 1996 √ √ √ √ √ √ √ √

Chevalier and Wein 1997 √ √ √ √ √

Rabinowitz and Emmons 1997 √ √ √

Chen et al. 1998 √ √ √ √ √

Lee and Unnikrishnan 1998 √ √ √ √ √ √ √

Chen and Thornton 1999 √ √ √ √ √

Yao and Zheng 1999a √ √ √ √ √

Yao and Zheng 1999b √ √ √ √ √

Hassan and Pham 2000 √ √ √ √ √ √ √

Veatch 2000 √ √ √ √ √ √

Zheng 2000 √ √ √ √ √

Zhou and Zhao 2002 √ √ √ √ √ √ √

Emmons and Rabinowitz 2002 √ √ √ √

Shiau 2002 √ √ √ √ √ √ √ √ √

Oppermann et al. 2003 √ √ √ √ √ √

Avinadav and Raz 2003 √ √ √ √ √

Van Volsem & Van Landeghem 2003 √ √ √ √ √ √

Shiau 2003a √ √ √ √ √ √ √ √

Table 2.2. Classification of literature based on the methodology (continue)

Author Year Cost component Objective

Function

Constraint Solution Approach

Internal Failure

External Failure

Linear Nonlinear

Shiau 2003b √ √ √ √ √ √ √ √

Kakade et al. 2004 √ √ √ √ √

Valenzuela et al. 2004 √ √ √

Rau and Chu 2005 √ √ √ √ √ √ √ √

Hanne and Nickel 2005 √ √ √ √ √ √ √

Feng and Kapur 2006 √ √ √ √ √

Shiau et al. 2007 √ √ √ √ √ √ √

Penn and Raviv 2007 √ √ √ √ √

Van Volsem et al. 2007 √ √ √ √ √ √

Penn and Raviv 2008 √ √ √ √ √

Vaghefi and Sarhangian 2009 √ √ √ √ √ √ √ √

Ferreira et al. 2009 √ √ √ √ √ √ √

Rau and Cho 2009 √ √ √ √ √ √ √

Azadeh and Sangari 2010 √ √ √ √

Van Volsem 2010 √ √ √ √ √ √ √ √

Korytkowski 2011 √ √ √ √ √

Rau and Cho 2011 √ √ √ √ √ √

Azadeh et al. 2012 √ √ √ √ √ √ √

Azadeh et al. 2014 √ √ √ √ √ √ √ √

Mousavi et al. 2015 √ √ √ √ √ √

Figure 2.3. Papers in category of production system characteristics 74

5 7

39 39

5 2

1 1

35 33 36

45 51

10 20

20 24 34

0 10 20 30 40 50 60 70 80 90

Number of Papers

Production System Characteristics Criteria

Figure 2.4. Papers in category of methodology 46

12 52

14 45

31 77

7 7 4 9

1 3

7 13 32

5 0

10 20 30 40 50 60 70 80 90

Number of Papers

Methodology criteria

 A few of papers have considered multi-product manufacturing system with different quality characteristics.

 Only three of papers have considered inspection tool selection, while this assumption makes the model more real and provides more flexible inspection plan. By this assumption, manufacturer can purchase inspection tools with higher precision and reduce non-detected items that reach customers and consequently increase customer satisfaction.

 To the best of our knowledge, there is no paper in the literature considering machine selection. By considering machine selection assumption, manufacturer can purchase machine with high capability to obtain high quality level for important design characteristics.

 No paper has design a multi-objective inspection planning model by considering other criteria to be optimized. Other objectives can be maximizing customer satisfaction as well as minimizing total manufacturing time.

 By considering time as an objective, one important issue that comes up is waiting time of items through the production system. Different items must wait before each machinery or inspection station to receive services. These waiting times should be analyzed and taken into account in the final decisions.

 To the best of our knowledge, no paper has considered the reliability of production system. Since production systems are stochastic in nature and are affected by different unpredictable environmental factors, machines and inspection tools are subjected to disruption. Any breakdown in the production system not only increases the manufacturing cost, but also significantly affects the quality of final products. Therefore, considering reliability issue of production system and investigate the effect of unreliable machines and inspection tools on the final inspection planning could be an interesting research direction.

 Almost all of the authors have ignored manufacturing constraint in their studies. Some of these constraints could be capacity of machines and inspection tools, an upper bound for total production time, low capital for initial investment and limited places for performing inspections and so on.

Considering these constraints provides more real and applicable inspection plans.

 Developing more efficient metaheuristic algorithms for solving inspection planning models could be also another gap in the literature.

No documento inspection planning problem under uncertainty and breakdown (páginas 65-77)