Evaluating vehicle painting plans in an automobile assembly plant using an integrated AHP‐PROMETHEE approach

• Author: Márcia Oliveira,Teresa Pereira,Dalila B. M. M. Fontes
• Published date: 01 July 2018
• DOI: http://doi.org/10.1111/itor.12179
• Date: 01 July 2018

Intl. Trans. in Op. Res. 25 (2018) 1383–1406

DOI: 10.1111/itor.12179

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Evaluating vehicle painting plans in an automobile assembly

plant using an integrated AHP-PROMETHEE approach

M´

arcia Oliveiraa, Dalila B. M. M. Fontesaand Teresa Pereirab, c

aFEP, Faculdade de Economia da Universidade do Porto, LIAAD/INESC TEC, Rua Dr.Roberto Frias, 4200-464, Porto,

Portugal

bIPP/ESEIG, Escola Superior de Estudos Industriais e de Gest˜

ao, Instituto Polit´

ecnico do Porto, CIEFGEI,

Rua D. Sancho I, 981, 4480-876 Vila do Conde, Portugal

cPortugaland Algoritmi Center, Universidade do Minho, 4800-058 Guimar˜

aes, Portugal

E-mail: mdbo@inescporto.pt [Oliveira];fontes@fep.up.pt [Fontes]; teresapereira@eseig.ipp.pt [Pereira]

Received 23 October 2013; receivedin revised form 3 March 2015; accepted 5 April 2015

Abstract

The painting activity is one of the most complex and important activities in automobile manufacturing. The

inherent complexity of the painting activity and the frequent need for repainting usually turn the painting

process into a bottleneck in automobile assembly plants, which is reflected in higher operating costs and

longer overall cycle times. One possible approach for optimizing the performance of the paint shop is to

improve the efficiency of the color planning. This can be accomplished by evaluating the relative merits of

a set of vehicle painting plans. Since this problem has a multicriteria nature, we resort to the multicriteria

decision analysis (MCDA) methodology to tackle it. A recenttrend in the MCDA field is the development of

hybrid approaches that are used to achieve operational synergies between different methods. Here we apply,

for the first time, an integratedapproach that combines the strengths of the analytic hierarchy process(AHP)

and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), aided by

Geometrical Analysis for Interactive Aid (GAIA), to the problem of assessing alternative vehicle painting

plans. The management of the assembly plant found the results of value and is currently using them in order

to schedule the painting activities such that an enhancement of the operational efficiency of the paint shop is

obtained. This efficiency gain has allowedthe management to bid for a new automobile model to be assembled

at this specific plant.

Keywords:AHP; automobile paint shop; GAIA; multicriteria decision analysis; PROMETHEE

1. Introduction

In recent years, the increasing competitiveness of the global market, as well as the eruption of

the so-called global financial crisis (also known as the 2008 financial crisis), forced companies

to rethink their processes in order to raise the levels of efficiency, responsiveness and flexibility.

C

2015 The Authors.

International Transactionsin Operational Research C

2015 International Federation of OperationalResearch Societies

Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA02148,

USA.

1384 M. Oliveira et al. / Intl. Trans.in Op. Res. 25 (2018) 1383–1406

In such contexts, resorting to multicriteria decision analysis (MCDA) to assist in both strategic and

operational decision problems can be a decisive step toward achieving these goals.

Over the years several MCDA methods have been proposed (Goodwin and Wright,2004; Turskis

and Zavadskas, 2011). Two of the most popular are the analytic hierarchy process (henceforthAHP;

Saaty, 1986, 1990) and the Preference Ranking Organization METHod for Enrichment Evaluations

(henceforth PROMETHEE; Brans, 1982; Brans and Vincke, 1985; Brans and Mareschal, 1994).

The former pertains to the normative (or American) school of thought, which is represented by

methods that perform preference aggregation through value functions, such as multiple attribute

utility theory (MAUT), multiple attribute value theory (MAVT), analytic network process (ANP),

and simple multi-attribute rating technique (SMART). The latter belongs to the European (or

French) school of thought, whose theoretical underpinnings rely on the concept of outranking. The

families of methods influenced by this school, such as ELECTRE (which stands for ELimination Et

Choix Traduisant la REalit´

e) and PROMETHEE,perform outranking so as to eliminate alternatives

that are dominated in a particular sense. Despite the popularity of AHP and PROMETHEE, none

of these methods is better than the other, with both having strengths and weaknesses.

Realizing that there is a lack of a universally recognized best method for MCDA that would suit

every type of multicriteria decision-making problem and is impervious to weaknesses(Chang et al.,

2013), scholars started to explore synergies between well-established methods in an attempt to boost

the strengths and mitigatethe weaknesses associated with each individual method. As a result, hybrid

approaches combining within a single framework two or more MCDA methods, or one MCDA

method with other methodologies,have emerged as a trend in the field (Keuneet al., 2013). Examples

of hybrid approachesinclude, among others, integrated AHP approaches(with, for instance, genetic

programming, PROMETHEE, and data envelopment analysis; Badr´

ı, 2001; Macharis et al., 2004;

Wang et al., 2008), integrated ANP approaches (with, forinstance, technique for order of preference

by similarity to ideal solution, decision making trial and evaluation laboratory and multiobjective

programming; Shyur and Shih, 2006; Yang et al., 2008; Demirtas and ¨

Ust¨

un, 2008), and integrated

fuzzy approaches (for example, fuzzy ANP with fuzzy TOPSIS and fuzzy ELECTRE; Kabaket al.,

2012). This paper follows this trend and proposes the use of a hybrid approach that integrates

the AHP and PROMETHEE to address a multicriteria decision problem in the paint shop of an

automobile assembly plant.

The automobile industry has been one of hardest hit by the 2008 financial crisis, which led to

a sharp fall in industry sales. One of the most complex and important activities in automobile

manufacturing is the painting activity (Geffen and Rothenberg, 2000; Li et al., 2007). According

to Leichtling (2002), color is an important marketing tool due to the psychological effects it can

trigger and its intrinsic power in attracting (or repulsing) potential customers. Given the influence

that color has on the customers’ purchasing behavior, automobile companies devote great efforts

to assure the quality and attractiveness of their products’ color appearance. However, the inherent

complexity of the painting activity and the frequent need for repainting usually turns the painting

activity into a bottleneck in automobile assembly plants. Thus, there is a great need for improving

the operational efficiency of the paint shop.

Previous research on paint shops (Ulgen and Gunal, 1998; Chung et al., 2001; Meunier and

Neveu, 2012), focuses on a very particular aspect of the problem (the number of color changes in

the incoming sequence of vehicles) and the solution involves optimizing a single objective function

(minimizing the length of the schedule for the given time horizon, which is equivalent to minimizing

C

2015 The Authors.

International Transactionsin Operational Research C

2015 International Federation of OperationalResearch Societies