Inventory and multi-criteria: Systematic review

Abstract

Inventory management decisions have become increasingly complex as they are driven by multiple criteria both within and without the organizations; as a result of that, the multi-criteria analysis methods have been gaining importance as an alternative to support the decision making process. The present work endeavours to systematize the main articles that have approached MCDA and Inventory within the last 31 years (1987–2018), determining the multi-criteria methods with greater prominence in the scientific community discussions as well as the main problems addressed by them. The authors conducted a study about the criteria for decision making in inventory and its alignment with the specialized literature, initially 439 articles catalogued in the Scopus/Embase and Web of Science database were considered for the present revision; after careful analysis of their theoretical content, 47 of them were chosen as they held the information needed to complete the study. The authors observed that the majority of the selected articles refer to the utilization of the Analytic Hierarchy Process (AHP) method and its fuzzy extension whilst the main problems addressed are ranking and sorting.

Keywords

Inventory control multi-criteria analysis decision making

1. Introduction

MCDA – Multicriteria Decision Analysis – is a comprehensive term to describe a collection of formal approaches that seek to take into account several criteria to help individuals or groups to explore decisions that matter [1, 2].

One of the main objectives of the MCDA approaches is to help decision makers organize and synthesize information as to make them feel at ease and confident in making decisions, minimizing the potential for regrets after the decision as well as meeting all the criteria or factors duly analysed [1].

Such methods focus on the search for the optimal solution at the maximum value and/or minimum value. However, there is a great variety of decision problems [3]; thus, the ideal solution is not always strictly the maximum value and/or the minimum value, but rather a value (or set of values) that are intermediary.

In this sense, uncertainty is an important part of real-world decision-making problems, deriving from two areas: (1) uncertainty in subjective judgments (2) uncertainty due to incomplete data [4]. MCDA problematics are divided into four fields: (1) choice, (2) classification, (3) ordering and (4) description [5].

In general terms, three types of formal analysis can be employed to resolve decision making problems: The descriptive analysis is concerned with the problems the decision makers really solve; the prescriptive analysis considers the methods that the decision makers must use to improve their decisions; the normative analysis, on the other hand, focus on the problems the decision makers must idealize [5].

There are several studies that analyse the MCDA methodologies applied to inventory management, some studies focus on the inventory management with conventional multicriteria methods such as AHP [6, 7, 8], whilst others engage in the discussion of multicriteria with Fuzzy [9, 10] methodology. Furthermore, there are studies that work on methods combination [11, 12].

One of the inventory control techniques widely utilized on inventory management is the ABC analysis (Pareto Principle) which is useful for the planning process simplification as well as for the effectiveness of material control [13, 14, 15].

The ABC classification is, in practical terms, a method for the division of the inventory per order of importance: Class A (very important); class B (moderately important) and class C (little importance) in order to allocate resources such as work, time and capital into items according to their relative importance [16, 17].

The items classification on the A, B and C categories have been usually based on only one criterion, the monetary value of the item. That is, the traditional ABC methodology considers as unique criterion the unitary prices of the items utilized annually. The total cost criterion, however, does not reflect the complexities intrinsic to the procurement process [18].

Given the diversity of the items, there are other criteria that emphasize different considerations for inventory management – the reliability of suppliers, the obsolescence rate and the impact of an item break are but a few examples. Some of those criteria may outweigh the monetary value of the item as an essential condition. Hence, it is possible to argue that the traditional ABC analysis may often be an infective method for the appropriate classification of the stock items [19, 20].

MCDA becomes fundamental when the conflict level amongst the different criteria or the conflicts regarding the classification of importance and urgency of the criteria by different stakeholders reaches a level in which an intuitive decision-making process is no longer satisfactory [1].

As a result of that, the multicriteria inventory classification problem is increasingly attracting more interest from researchers, becoming a key aspect of inventory management research [21]. Hence, the present research endeavours to develop a systematization of the scientific works concerning multicriteria decision analysis in inventory management in order to determine the multicriteria methods most commonly found in scientific researches as well as to identify the key issues addressed by them.

2. Methodology

Scopus/Embase and Web of Science were analysed on August 29 ${}^{\text{th}}$ 2018 for articles in English without any date restriction. The following research terms were utilized “Multi-criteria” and “Inventory”. Titles and abstracts were revised independently by four researchers. Science Direct database was not analysed as it is indexed to Scopus. Google Academics was not considered due to the preference of the authors for peer reviewed databases.

During the full text revisions, some studies were excluded for not attending two key criteria: (1) application of a multicriteria method and (2) application on inventory management. The studies were not excluded based on the quality of their methodology. The final sample for analysis was 140 articles from a total of 439 studies. At the end of the selection process, 47 scientific articles remained to be fully revised. So far in the present research no revision article was found that approached the theme currently addressed in this paper. In Fig. 1 there is a representation of the analysis process taken to select the articles that presented relevant characteristics in accordance to the systematization interests of the present revision.

Figure 1.

Review development flow charter.

Figure 2.

Evolution of publication related to inventory and multi-criteria.

Figure 3.

Publications on inventory and multi-criteria per country.

Figure 4.

Keywords found in database search.

Figure 5.

Seven main journals with publications on the topic.

The database research catalogued the following indicators in the selected articles: year of publication, journal in which it was published, Scimago Journal Rank (SJR), Journal Citation Report (JCR), country in which the study was conducted, title, authors, keywords, article objectives, multicriteria method utilized, criteria utilized in the method, where it was applied, limitations. Based on the initial data, it was developed an analysis of the descriptive statistics elements and the graphic representations of that evaluation are present in the current study, as to numerically evaluate the conclusions obtained by the authors. The results are presented and discussed in Sections 3 and 4 respectively.

3. Results

3.1 Bibliometric review

Throughout the systematization of 31 years of scientific production related to inventory and multi-criteria, when taking into account the years the articles were published, it is possible to perceive that the number of qualified research on inventory and multi-criteria has substantially increased, as it is evident on Fig. 2.

The continents with larger number of relevant studies on the topic are Asia [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41] and America [42, 43, 44, 45, 46, 47, 48, 49, 50] as it is evident on Fig. 3. The former is responsible for 53.19% of the total number of publications whilst the latter only 23.40%. Furthermore, the number of publications on inventory and multi-criteria in Africa is rather small.

Table 1
Journal Citation Report and Scimago Journal Rank on the journals referred in the present review

Journals	JCR	SJR
Optimization Letters	1.01	1.01
Applied Soft Computing	3.91	1.20
Measurement	2.22	0.73
International Journal for Interactive Design and	–	0.49
Manufacturing
Computers and Operations Research	2.96	1.92
International Journal of Production Economics	4.41	2.40
Tehnički Vjesnik	–	0.31
Pesquisa Operacional	–	0.37
International Journal of Advanced Manufacturing	2.60	0.99
Technology
Expert Systems with Applications	3.77	1.27
European Journal of Operational Research	3.43	2.44
Computers and Industrial Engineering	3.20	1.46
Production and Manufacturing Research	–	0.56
Electronic Notes in Discrete Mathematics	–	0.26
International Journal of Industrial Engineering	–	0.54
Computations
Arabian Journal for Science and Engineering	1.09	0.30
Procedia Manufacturing	–	0.20
Journal of Industrial Engineering and Management	–	0.21
Advances in Mechanical Engineering	0.85	0.27
International Transactions in Operational Research	2.40	1.07
Procedia CIRP	–	0.67
Informatica	1.39	0.46
Procedia Computer Science	–	0.26
Mathematical and Computer Modelling	–	0.00
Journal of Quality in Maintenance Engineering	–	1.44
International Journal of Operations and Production	2.96	2.05
Management
IMA Journal of Management Mathematics	1.28	0.54
International Journal of Computational Intelligence	2.00	0.56
Systems

Figure 6.

Classification of the multicriteria methods within the articles selected as categories MADM and MODM.

Figure 7.

Classification of the multicriteria methods of the articles selected in the categories MADM and MODM.

Turkey is the country with more qualified scientific publication related to multi-criteria analysis and inventory; followed by China and USA. It is possible to perceive the representativeness of the emerging countries in publications related to inventory and multi-criteria, which raises the debate about the strategic interest seen by researchers in these countries in contributing to the development of studies that explore inventory management methodologies, thus perfecting tools and strategies to overcome logistical barriers and increase competitive advantage through inventory control [10, 22, 23, 25, 28, 30, 32, 34, 36, 38, 41].

Figure 4 represents the network diagram of keywords found during the research, the circumferences diameter represent the frequency in which the keywords were referred in the articles. The most referred keywords were multicriteria analysis, decision making and inventory control. It is also important to notice the frequency in which the descriptors Landslides and Landslide Susceptibility appeared in the initial research without filters. The Authors have investigated the interrelation between those descriptors and the keywords Inventory and multi-criteria and have identified that there are works that relate integrated inventory management to multi-criteria to conduct the historical records on landslides in specific locations.

On Fig. 5 it is possible to see the number of articles published by qualified journals. In total there are 28 distinct journals, however, only those that had more than one publication and another with a significant JCR ( $>$ 1) were presented. JCR is a key indicator that evaluates and compares through citations and publication data, the journal performances. As seen on Table 1, amongst all journals utilized, 15 bear a significant JCR ( $>$ 1).

Table 2

Criteria proposed by Flores et al. [6] referred by the articles in the review

Authors	Cost	Lead time	Obsolescence	Availability	Substitutability	Criticality
Bastian et al. [42]	X	X
Baykasoglu et al. [22]	X	X	X	X		X
Büyüközkan et al. [23]	X	X
Chen [25]	X	X
De La Vega et al. [43]	X	X
Durán [9]	X					X
Fontana et al. [44]	X
Fontana et al. [45]	X
Hadi-Vencheh et al. [26]	X	X
Hadi-Vencheh [27]	X	X
Hamdan et al. [24]	X	X
Hu et al. [28]	X	X				X
Iqbal et al. [46]	X	X
Iqbal et al. [47]	X	X
Kaabi et al. [58]	X	X
Kabir et al. [29]	X		X			X
Lee et al. [48]
Roy et al. [59]		X
İen et al. [30]	X	X				X
Szajubok et al. [49]	X	X		X
Teixeira et al. [60]	X	X
Torabi et al. [31]	X	X				X
Torkabadi et al. [12]		X
Tsou [11]	X
Zhang et al. [32]	X	X
Zhou et al. [33]	X	X				X
Chu et al. [33]		X				X
Li et al. [34]	X	X
Balaji et al. [7]	X
Kiriş [35]	X					X
Douissa et al. [61]	X	X
Flores et al. [6]	X	X				X
Cakir et al. [50]	X	X			X
Ghorabaee et al. [36]
Sarmah et al. [37]	X	X				X
Stoll et al. [62]		X
Soylu et al. [38]	X	X				X
Molenaers et al. [63]		X
Bacchetti et al. [64]		X				X
Guvenir et al. [16]	X	X	X		X
Park et al. [39]	X	X				X
Lolli et al. [65]	X	X				X
Yu [40]	X	X				X
Ishizaka et al. [51]	X
Ladhari et al. [66]	X	X				X
Zhang et al. [41]	X	X				X

3.2 Analysis scope

In order to facilitate the systematic research on MCDM, Hwang and Yoon suggested classifying MCDM problems into two main categories: Multiple Attributes Decision Making (MADM) and Multiple Objective Decision Making (MODM), based on different objectives as well as data types.

When decisions are met in continuous space the MODM techniques are utilized, on the other hand, when the space of decisions is narrow, the MADM techniques are employed. As it is possible to perceive on Fig. 6, within the studies analysed, there is a larger frequency of applications of the category MADM.

Figure 7 presents the most applied multicriteria methods and their respective problems: (1) Choice problem, which indicates that a decision must be taken; (2) Classification problem, which defends to accept or reject determined actions; (3) Ranking problem, which suggests a partial or total order of categories containing actions considered to be equivalent; and (4) Description problem, which formally and systematically describes actions and their consequences both in qualitative and quantitative terms [51, 52, 53].

Amongst the articles selected for this review, the main MCDA method applied was the Analytic Hierarchy Process (AHP) and its fuzzy extension. It should be noted that AHP and AHP-fuzzy were used in association with other methods; thus, through the application of AHP, the weights for each criterion and respective values of the alternatives considered in the problem were obtained. Hence, this information supported the classification of alternatives [22, 24, 26].

The AHP method was developed by Saaty (1980) and employs direct evaluation of all possible alternative pairs with respect to each criterion in order to determine the preference of the criterion pair according to Saaty’s fundamental scale [1].

Further to the AHP method, the TOPSIS, ELECTRE TRI, R Model and NG model were also used. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multicriteria method developed by Hwang and Yoon in 1981, which bears a simple understanding and application [54]. This method presents as a key feature the concept of ideal solution which in this context is represented as two artificial alternatives: (a) The positive ideal solution and (b) the negative ideal solution [52]. The positive ideal solution is composed of a value considered by the decision maker as ideal for each criterion, so that the negative ideal solution is its opposite [5].

Both R Model and Ng Model are models created specifically for inventory. The R Model is a model in which the weight of each criterion on each item is automatically generated when the model is run, generating an inventory score for each item. The objective function provides the optimal inventory score for the item. And the model is repeatedly solved for each one, changing the objective function [13]. Ng Model, on the other hand, can get stock item scores without a linear optimizer. When the model is solved, the objective function maximizes the score of each inventory item; thus, weights are automatically generated when the model is solved [55].

The ELECTRE multicriteria method is rather ubiquitous in decision-making problems [56]. Elimination Et Choix Traduisant la REalité (Elimination and Choice Expressing Reality-ELECTRE) was originally developed by Roy (1978) and consists of an Outranking method, so that dominance relationships are established between the alternatives [5, 57]. ELECTRE is an umbrella term for a family of methods, including ELECTRE III, whose main feature is minimizing the effect of compensation on decision-making issues [56, 57].

Flores and Whybark [18] were the pioneers on working with inventory management utilizing more than one criterion. According to these authors, there are many other important criteria to be taken into account in inventory management besides costs, such as lead time, obsolescence, availability, substitutability and criticality. Table 2 analyse the criteria utilized in the systematized studies according to authors mentioned above.

As it can be seen, only Keshavarz et al. [36] did not carry any of the established criteria due to the fact that their work is a literature review thus not carrying any practical application. Amongst the other studies analysed, it is evident the frequency of the criteria utilized related to costs, lead time and criticality. Besides the criteria defined by Flores and Whybark, other criteria were also applied such as item weight, storage space and item demand.

4. Conclusions

The present article has reached its aim to develop the systematization of works referring to the multicriteria decision analysis in inventory management. The research undertaken during the methodological process demonstrated that multicriteria analysis has been gaining space and its application may generate positive returns to inventory management.

In relation to the general aspects of the study, it was observed that different countries have produced relevant studies into inventory management; however, in numerical terms, Turkey, China and USA do stand out.

Furthermore, the most dominant journals on the subject are Computers & Industrial Engineering, International Journal of Production Economics, Expert Systems with Applications and European Journal of Operational Research. Those journals are in a prominent position in relation to the JCR indicator. The words that were most referred in the revised articles were: multicriteria analysis, decision making and inventory control.

During the systematization, it was verified that more than half of the articles addresses multiple attributes decision making (MADM), furthermore, the problem analysis indicates that the inventory management has a comprehensive problem solving application by Ranking. In regards to methods, it was patent the prevalence of AHP and Fuzzy AHP. Finally, the criteria often used in the methods were cost, lead time and criticality.

Thus, it is possible to identify based on the description and analysis of the data of this review that more than the multicriteria methods, the multicriteria decision model has been highlighted in the inventory management.

Finally, from the 47 articles listed in this review, it was possible to identify that 18 of them have applications based on numerical experiments or data replication, which comprises 38.3% of the total papers. This gap in real applications tends to create a deficiency of links with decision makers, resulting in application barriers related to the environment and criteria, which ultimately, delays potential improvements in the area. Furthermore, these applications have a deficit in the healthcare field [67], as the existing applicability comes from Reid’s replication data [68], found in Flores’s research [6].

References

Belton

Stewart

. Multiple Criteria Decision Analysis [Internet]. Vol. 1. Boston, MA: Springer US; 2002. Available from: https://link-springer-com.web.bisu.edu.cn/10.1007/978-1-4615-1495-4.

Bystrzanowska

Tobiszewski

. How can analysts use multicriteria decision analysis? TrAC – Trends Anal Chem [Internet]. 2018; 105: 98–105. Available from: doi: 10.1016/j.trac.2018.05.003.

Cabral EL dos S, Castro WRS, Florentino DR de M, Viana D de A, Costa Junior JF da, Souza RP de, et al. Response time in the emergency services. Systematic review. Acta Cir Bras [Internet]. 2018 Dec; 33(12): 1110–21. Available from: doi: 10.1590/s0102-865020180120000009.

Cables

Lamata

Verdegay

. RIM-reference ideal method in multicriteria decision making. Inf Sci (Ny). 2016; 337–338: 1–10.

Tzeng

G-H

Huang

J-J

. Multiple Attribute Decision Making: methods and applications. New York: Chapman and Hall/CRC; 2011.

Flores

Dorai

Olson

. Management of multicriteria inventory classification. Math Comput Model. 1992; 16(12): 71–82.

Partovi

Burton

. Using the analytic hierarchy process for ABC analysis. Int J Oper Prod Manag [Internet]. 1993; 13(9): 29–44. Available from: doi: 10.1108/01443579310043619.

Balaji

Kumar

VSS

. Multicriteria inventory ABC classification in an automobile rubber components manufacturing industry. Procedia CIRP [Internet]. 2014; 17: 463–8. Available from: doi: 10.1016/j.procir.2014.02.044.

Durán

. Spare parts criticality analysis using a fuzzy AHP approach. Teh Vjesn Gaz [Internet]. 2015 Aug 30; 22(4): 899–905. Available from: http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=211069.

10.

Kırış

. Multi-criteria inventory classification by using a fuzzy analytic network process (ANP). Informatıca. 2013; 24(2): 199–217.

11.

Tsou

. Multi-objective inventory planning using MOPSO and TOPSIS. Expert Syst Appl. 2008; 35(1–2): 136–42.

12.

Torkabadi

Mayorga

. Evaluation of pull production control strategies under uncertainty: An integrated fuzzy AHP-TOPSIS approach. J Ind Eng Manag [Internet]. 2018 Mar 19; 11(1): 161. Available from: http://www.jiem.org/index.php/jiem/article/view/2528.

13.

Ramanathan

. ABC inventory classification with multiple-criteria using weighted linear optimization. Comput Oper Res. 2006; 33(3): 695–700.

14.

Chu

Liang

Liao

. Controlling inventory by combining ABC analysis and fuzzy classification. Comput Ind Eng [Internet]. 2008; 55(4): 841–51. Available from: doi: 10.1016/j.cie.2008.03.006.

15.

Zimmerman

. The ABC’s of vilfredo pareto. Production and Inventory Management Journal. 1975; 16: 1–9.

16.

Altay Guvenir

Erel

. Multicriteria inventory classification using a genetic algorithm. Eur J Oper Res [Internet]. 1998; 105(97): 29–37. Available from: https://ac.els-cdn.com/S0377221797000398/1-s2.0-S0377221797000398-main.pdf?_tid=f4ecdefc-7392-48d4-abc7-94449dabddbc&acdnat=1530897957_092238e332a0c16822a390ee0634f09f.

17.

Medeiros

Ferreira

. Development of a purchasing portfolio model: An empirical study in a Brazilian hospital. Prod Plan Control [Internet]. 2018 May 19; 29(7): 571–85. Available from: doi: 10.1080/09537287.2018.1434912.

18.

Flores

Clay Whybark

. Multiple criteria ABC analysis. Int J Oper Prod Manag [Internet]. 1986 Mar; 6(3): 38–46. Available from: doi: 10.1108/eb054765.

19.

Huiskonen

. Maintenance spare parts logistics: Special characteristics and strategic choices. Int J Prod Econ [Internet]. 2001 May 19; 71(1–3): 125–33. Available from: http://www.app.pan.pl/article/item/app20110016.html.

20.

Partovi

Anandarajan

. Classifying inventory using an artificial neural network approach. Comput Ind Eng [Internet]. 2002 Feb; 41(4): 389–404. Available from: http://linkinghub.elsevier.com/retrieve/pii/S036083520100064X.

21.

Zhiyan

Tieying

. Hybrid predictive method for multicriteria inventory classification. Computer Integrated Manufacturing System-CIMS. 2004; 10(5): 594–599.

22.

Baykasoglu

Subulan

Karaslan

. A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification. Appl Soft Comput J. 2016; 42: 1–17.

23.

Büyüközkan

Karabulut

Arsenyan

. RFID service provider selection: An integrated fuzzy MCDM approach. Meas J Int Meas Confed. 2017; 112(January): 88–98.

24.

Hamdan

Cheaitou

. Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability. Comput Ind Eng [Internet]. 2017; 110: 573–89. Available from: doi: 10.1016/j.cie.2017.03.028.

25.

Chen

. Peer-estimation for multiple criteria ABC inventory classification. Comput Oper Res [Internet]. 2011; 38(12): 1784–91. Available from: doi: 10.1016/j.cor.2011.02.015.

26.

Hadi-Vencheh

Mohamadghasemi

. A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Syst Appl [Internet]. 2011; 38(4): 3346–52. Available from: doi: 10.1016/j.eswa.2010.08.119.

27.

Hadi-Vencheh

. An improvement to multiple criteria ABC inventory classification. Eur J Oper Res [Internet]. 2010; 201(3): 962–5. Available from: doi: 10.1016/j.ejor.2009.04.013.

28.

Chakhar

Siraj

Labib

. Spare parts classification in industrial manufacturing using the dominance-based rough set approach. Eur J Oper Res. 2017; 262(3): 1136–63.

29.

Kabir

Ahsan Akhtar Hasin

. Multiple criteria inventory classification using fuzzy analytic hierarchy process. Int J Ind Eng Comput. 2012; 3(2): 123–32.

30.

İsen

Boran

. A novel approach based on combining ANFIS, genetic algorithm and fuzzy c-means methods for multiple criteria inventory classification. Arab J Sci Eng. 2018; 43(6): 3229–39.

31.

Torabi

Hatefi

Saleck Pay

. ABC inventory classification in the presence of both quantitative and qualitative criteria. Comput Ind Eng [Internet]. 2012; 63(2): 530–7. Available from: doi: 10.1016/j.cie.2012.04.011.

32.

Zhang

Jiang

Hui

Zhu

. A distributed configuration scheme for warehouse product service system. Adv Mech Eng. 2017; 9(5): 1–13.

33.

Zhou

Fan

. A note on multi-criteria ABC inventory classification using weighted linear optimization. Eur J Oper Res [Internet]. 2007 Nov; 182(3): 1488–91. Available from: http://www.doiserbia.nb.rs/Article.aspx?ID=0354-02431002293R.

34.

Liu

Chen

. Multicriteria ABC inventory classification using acceptability analysis. Int Trans Oper Res. 2017; 00: 1–14.

35.

Kırış

. Multi-criteria inventory classification by using a fuzzy analytic network process (ANP). Informatıca. 2013; 24(2): 199–217.

36.

Keshavarz Ghorabaee

Zavadskas

Olfat

Turskis

. Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica [Internet]. 2015 Sep 25; 26(3): 435–51. Available from: http://www.mii.lt/Informatica/htm/INFO1070.htm.

37.

Sarmah

Moharana

. Multi-criteria classification of spare parts inventories – A web based approach. J Qual Maint Eng. 2015; 21(4): 456–77.

38.

Soylu

Akyol

. Multi-criteria inventory classification with reference items. Comput Ind Eng [Internet]. 2014; 69(1): 12–20. Available from: doi: 10.1016/j.cie.2013.12.011.

39.

Park

Bae

. Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification. Comput Ind Eng [Internet]. 2014 Oct; 76(1): 40–8. Available from: doi: 10.1016/j.cie.2014.07.020.

40.

. Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Syst Appl [Internet]. 2011; 38(4): 3416–21. Available from: doi: 10.1016/j.eswa.2010.08.127.

41.

Zhang

Zhao

. Integrating Replenishment Policy with GSAA-FCM Based Multi-Criteria Inventory Classification. 2018; 11: 248–55.

42.

Bastian

Griffin

Spero

Fulton

. Multi-criteria logistics modeling for military humanitarian assistance and disaster relief aerial delivery operations. Optim Lett [Internet]. 2016; 10(5): 921–53. Available from: doi: 10.1007/s11590-015-0888-1.

43.

De La Vega

Vieira

JGV

Toso

EAV

de Faria

. A decision on the truckload and less-than-truckload problem: An approach based on MCDA. Int J Prod Econ [Internet]. 2018 Jan; 195(August 2017): 132–45. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0925527317302979.

44.

Fontana

Cavalcante

CAV

. Electre tri method used to storage location assignment into categories. Pesqui Operacional [Internet]. 2013 Aug; 33(2): 283–303. Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382013000200009&lng=en&tlng=en.

45.

Fontana

Nepomuceno

. Multi-criteria approach for products classification and their storage location assignment. Int J Adv Manuf Technol [Internet]. 2017 Feb 16; 88(9–12): 3205–16. Available from: https://link-springer-com.web.bisu.edu.cn/10.1007/s00170-016-9040-3.

46.

Iqbal

Malzahn

. Evaluating discriminating power of single-criteria and multi-criteria models towards inventory classification. Comput Ind Eng [Internet]. 2017 Feb; 104: 219–23. Available from: doi: 10.1016/j.cie.2016.12.007.

47.

Iqbal

Malzahn

Whitman

. Statistical analysis of multi-criteria inventory classification models in the presence of forecast upsides. Prod Manuf Res [Internet]. 2017 Jan 24; 5(1): 15–39. Available from: doi: 10.1080/21693277.2017.1322544.

48.

Lee

Cho

Kim

. Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection. Expert Syst Appl [Internet]. 2015 Feb; 42(3): 1136–48. Available from: https://www-scopus-com-s.web.bisu.edu.cn/inward/record.uri?eid=2-s2.0-84908032209&doi=10.1016%2Fj.eswa.2014.08.041&partnerID=40&md5=24a77dbd2b37c705d5bb68a4b177f745.

49.

Szajubok

, Mota CM de M, Almeida AT de. Uso do método multicritério ELECTRE TRI para classificação de estoques na construção civil. Pesqui Operacional [Internet]. 2006 Dec; 26(3): 625–48. Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382006000300010&lng=pt&tlng=pt.

50.

Cakir

Canbolat

. A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Syst Appl [Internet]. 2008 Oct; 35(3): 1367–78. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0957417407003491.

51.

Ishizaka

Lolli

Balugani

Cavallieri

Gamberini

. DEASort: Assigning items with data envelopment analysis in ABC classes. Int J Prod Econ [Internet]. 2018 May; 199(February): 7–15. Available from: doi: 10.1016/j.ijpe.2018.02.007.

52.

Yoon

Kim

. The behavioral TOPSIS. Expert Syst Appl. 2017 Elsevier Ltd; 89: 266–272.

53.

Diaby

Goeree

. How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: A step-by-step guide. Expert Review of Pharmacoeconomics & Outcomes Research. 2014 Dec; 14(1): 81–99.

54.

Behzadian

Khanmohammadi Otaghsara

Yazdani

Ignatius

. A state-of the-art survey of TOPSIS applications. Expert Syst Appl [Internet]. 2012; 39(17): 13051–69. Available from: doi: 10.1016/j.eswa.2012.05.056.

55.

. A simple classifier for multiple criteria ABC analysis. Eur J Oper Res. 2007; 177(1): 344–53.

56.

Liao

Jiang

Lev

Fujita

. Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Appl Soft Comput J [Internet]. 2019; 80: 450–64. Available from: doi: 10.1016/j.asoc.2019.04.018.

57.

Chanvarasuth

Boongasame

Boonjing

. An ELECTRE III based CBR approach to combinatorial portfolio selection. Asia-Pacific J Financ Stud. 2019; 48(3): 386–409.

58.

Kaabi

Jabeur

Enneifar

. Learning criteria weights with TOPSIS method and continuous VNS for multi-criteria inventory classification. Electron Notes Discret Math [Internet]. 2015; 47: 197–204. Available from: doi: 10.1016/j.endm.2014.11.026.

59.

Lenard

Roy

. Multi-item inventory control: A multicriteria view. Eur J Oper Res [Internet]. 1995 Dec; 87(3): 685–92. Available from: http://linkinghub.elsevier.com/retrieve/pii/0377221795002391.

60.

Teixeira

Lopes

Figueiredo

. Multi-criteria classification for spare parts management: A case study. Procedia Manuf. 2017; 11(June): 1560–7.

61.

Douissa

Jabeur

. A new model for multi-criteria ABC inventory classification: PROAFTN method. Procedia Comput Sci [Internet]. 2016; 96: 550–9. Available from: doi: 10.1016/j.procs.2016.08.233.

62.

Stoll

Kopf

Schneider

Lanza

. Criticality analysis of spare parts management: A multi-criteria classification regarding a cross-plant central warehouse strategy. Prod Eng [Internet]. 2015 Apr 4; 9(2): 225–35. Available from: https://link-springer-com.web.bisu.edu.cn/10.1007/s11740-015-0602-2.

63.

Molenaers

Baets

Pintelon

Waeyenbergh

. Criticality classification of spare parts: A case study. Int J Prod Econ [Internet]. 2012 Dec; 140(2): 570–8. Available from: doi: 10.1016/j.ijpe.2011.08.013.

64.

Bacchetti

Plebani

Saccani

Syntetos

. Empirically-driven hierarchical classification of stock keeping units. Int J Prod Econ [Internet]. 2013 Jun; 143(2): 263–74. Available from: doi: 10.1016/j.ijpe.2012.06.010.

65.

Lolli

Ishizaka

Gamberini

. New AHP-based approaches for multi-criteria inventory classification. Int J Prod Econ [Internet]. 2014; 156: 62–74. Available from: doi: 10.1016/j.ijpe.2014.05.015.

66.

Ladhari

Babai

Lajili

. Multi-criteria inventory classification: New consensual procedures. IMA J Manag Math [Internet]. 2016 Apr; 27(2): 335–51. Available from: https://academic-oup-com-s.web.bisu.edu.cn/imaman/article-lookup/doi/10.1093/imaman/dpv003.

67.

Frazão

TDC

Camilo

DGG

Cabral

ELS

Souza

. Multicriteria decision analysis (MCDA) in health care: A systematic review of the main characteristics and methodological steps. BMC Med Inform Decis Mak. 2018; 18(1): 1–16.

68.

Reid

. The ABC method in hospital inventory management: A practical approach. Production and Inventory Management. 1987; 28(4): 67–70.