Selection of projects for automotive assembly structures using a hybrid method composed of the group-input compatible, best-worst method for criteria weighting and TrBF-TOPSIS

Highlights

• An important application in automotive assembly project development, is presented.

• A consistent method of weighting considering the opinions of decision makers.

• The trapezoidal bipolar fuzzy TOPSIS method allows handling uncertainty.

• Subjective and objective criteria involving economic and technical factors are used.

• The hybrid method is a suitable tool for industrial projects and product development.

Abstract

The constant search for the optimal outcome, and its associated decision-making schemes within automotive assembly projects, can be greatly enhanced by utilizing informed decision methods that access relevant information. When simulated project alternatives are developed and benchmarks established, decision makers must select the appropriate project. When differences in opinion arise between decision makers, or when selection criteria are numerous, it can be difficult to be sure the correct choice was made. This highlights the need for a method that assists decision makers, when presented with such circumstances, so that they can be sure their choice is informed and optimal. Therefore, this paper proposes the use of a hybrid best-worst method (BWM) for weighting group decision making; and a Trapezoidal Bipolar Fuzzy TOPSIS (TrBF-TOPSIS) decision making strategy. The proposed method is used in ranking twenty-one design alternatives for automotive door assembly structures. The observed results indicate that the proposed method can be a relevant tool for variable selection in automotive assembly projects that involve multiple criteria and alternative designs.

Introduction

Automobile assembly processes seek to develop leaner production systems. The goal of these processes is to avoid rework while also speeding up assembly steps. In this way, times are reduced, and the use of labor and associated movements required for standardized procedures are optimized. Each assembly step must be critically analyzed in order to bring about an optimized, lean manufacturing process. Within each step, suboptimal quality issues have the potential to require rework. In this context, it is imperative to identify the points from which rework arise, and to focus on failure control. In the automotive industry, quality problems in manufacturing and assembly projects often occur as the result of poor equipment selection, disorganization of work space layout, and the inadequate balance of production lines (Khouja et al., 2000, Manassero et al., 2004).

The variability of decisions made along the assembly line model in different companies can impact the allocation of strategic materials and equipment, limit the movement of components, and affect the location of jobs. This variability is associated with the current need to serve customers who wish to place products on the market with greater frequency and speed. (Steimer, Cadet, Aurich, & Stephan, 2016). The ability to adapt to product alterations without the need to renew production assets is essential. As a result, robust optimization methods that allow for the selection of asset design options in a flexible manner, are particularly attractive to decision makers (Chica, Bautista, & de Armas, 2019).

One example of a critical point in the automobile manufacturing process is the assembly of automotive doors. This step is carried out after the carcass has been painted and consists of the assembly of all items that will eventually be included within the door structure. This includes glass paneling, an electrical system, the rear-view mirror, door handles, and other finishing items. The assembly of automotive doors occurs separately from the car body construction, which involves the installation of the seats, another electrical system, structural paneling, and miscellaneous finishing items. Upon completion of the door assemblage, it can subsequently be installed on the body.

In order for a lean execution of door assembly, concepts associated with workplace ergonomics (Duraccio, Elia, & Forcina, 2016), structural and aesthetic integrity of the bodywork, translocation potential, and automation capability must be considered prior to selecting the equipment responsible for supporting the doors. Optimal alternative selection depends on the level of importance (weights) assigned to these various criteria, and this can vary between decision makers. Different companies may assign different weighting schemes to different criteria as it fits their perception of lean assembly. The application of a Multi-Criteria Decision Making (MCDM) method thus becomes an important tool when balancing opposing demands from different criteria. In these methods a decision maker (DM) or a group of decision makers chooses the most appropriate alternative, which is selected from a finite number of proposed alternatives, each judged based on a set of finite performance criteria.

Kumar and Ray (2015) emphasize the fact that designers must know the performance parameters of selected materials in order to match them with the basic requirements of their final product. The importance of mathematically-grounded tools is verified as they often guide designers to decisions that result in successful projects (Ashby, Bréchet, Cebon, & Salvo, 2004). Jahan et al. (2010) structured a review that contributes to the field of material selection for projects. Undesirable costs and premature failures are examples of project outcomes that result from the selection of inadequate materials (Chatterjee et al., 2009, Chatterjee et al., 2011).

Among the various decision-aid methods, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach, proposed by Hwang and Yoon (1981), has been widely applied in several areas over decades with great success. According to Behzadian et al. (2012) various characteristics, such as the use of fuzzy numbers and the application of hybridization, make the TOPSIS method a decision-making tool well suited for material selection, mechanical design, medical applications, business decision making, and human resources. Du et al. (2014) used TOPSIS as a tool for reliability analysis in complex networks. Lourenzutti and Krohling (2016) applied the TOPSIS method to decision groups in environments with heterogeneous information. Cables et al. (2012) adapted the method and applied it to problems that require the use of linguistic variables.

Grabisch (1996) mentions that the advantage of using the Fuzzy TOPSIS method is the flexibility of the model as it accounts for interactions between criteria. Authors used the Fuzzy TOPSIS method to aid decision making in the selection of water resource solution projects (Afshar, Mariño, Saadatpour, & Afshar, 2011), the selection of the most suitable abrasives for the manufacture of grinding wheels in metal machining (Maity & Chakraborty, 2013), and the selection of long-term evolution cells to optimize the performance of networks (Hussein, Ali, Rasid, Sali, & Mansoor, 2016). Baccour (2018) also proposes the use of a hybrid method between TOPSIS and VIKOR, named ATOVIC, to classify alternatives. Dymova et al., 2013, Kuo, 2017 have proposed changes in the TOPSIS method, aiming at improvements in the ordering of alternatives. With regards to projects and product development, several studies have also utilized the TOPSIS method for the selection of coating materials (Athanasopoulos, Riba, & Athanasopoulou, 2009), to classify the comfort of car seats according to consumer preferences (Fazlollahtabar, 2010), to evaluate industrial robotic systems (Kahraman, Çevik, Ates, & Gülbay, 2007) and to select suitable materials based on design requirements (Maniya & Bhatt, 2010).

Another critical point in the decision-making process is the assignment of criteria weightings to indicate comparative relevance. Weighting methods can be classified as either objective or subjective. The entropy method developed by Shannon (1948) is a technique that depends only on the variability of the values in establishing the weights. This approach is considered an objective weighting method; and was applied in combination with Fuzzy TOPSIS by Mavi et al. (2016) in the context of Supply Chain risk management.

Subjective weighting methods are utilized when it is desirable to interfere with the decision maker’s opinion in the criteria weighting selection. Examples of subjective weighting methods include: the Direct Rating Method (Zardari, Ahmed, Shirazi, & Yusop, 2015), the Graphical Weighting Method (Hajkowicz, McDonald, & Smith, 2000), the Analytic Hierarchy Process (AHP) (Saaty, 1990), and the Best-Worst Method (BWM) (Rezaei, 2015). Among the methods mentioned, the BWM approach is advantageous in comparison to the AHP method. The combination number in the BWM method is equal to (2n − 3), which is smaller than that required in the AHP method, (n² - n)/2. The BWM method also offers more consistent weighting values (Rezaei, 2015, Rezaei, 2016).

In order to address the positive and negative outcomes of decisions, Zhang utilized bipolar fuzzy numbers (Zhang, 1994). Akram and Arshad (2019) applied trapezoidal bipolar fuzzy numbers in association with TOPSIS to aid in group decision making. They also verified and applied the method to medical diagnostics. (Akram, Shumaiza, & Arshad, 2020). In the same line of research, Akram, Shumaiza, & Al-Kenani (2020) worked with a multi-criteria group decision-making process for selection of green suppliers under bipolar fuzzy PROMETHEE processes. Shumaiza, Akram, Al-Kenani, & Alcantud, (2019) developed a VIKOR method with Trapezoidal Bipolar Fuzzy Information for group decisions. Shumaiza, Akram, & Al-Kenani (2019) developed a multiple-attribute decision making ELECTRE II method under the bipolar fuzzy model. Ali, Akram, & Alcantud (2020) developed an attribute reduction method with binary, fuzzy, or bipolar-valued fuzzy formats in order to remove unnecessary attributes in the search for the ideal solution.

Multi-criteria decision-making methods are tools to aid in the development of new products because they consider materials selection as well as project variables. The materials in mechanical designs can be selected, or even developed, in accordance with design strategies or process optimization (Brechet & Embury, 2013). Therefore, in order to reduce inconsistency in the selection of project variables, it is essential that strategies be adopted such that the product development is carried out successfully.

The objective of this study is to establish a strategy for the development of projects for door assembly structures through multi-criteria decision-making. This was accomplished by using a linguistic scale to analyze the following criteria: the maintainability and hardiness of the structure (associated to the analysis of quantitative criteria as the weight of the structure), the cost, and the properties of the materials remaining in contact with the product.