Conclusions

It is clear from the literature that there are a range of methods available for decision support. However, there is no clear indication which methods would be the most effective for solving decision problems in the context of WPD. The overall aim of this chapter was to answer the following questions:

RQ2: Which methods in the literature are the most commonly cited/applied for solving multi-criteria decision problems? Furthermore, which of these methods are most suitable for handling uncertainty?

RQ3: Which methods in the literature have been proposed or used for decision- making in process design?

Therefore the conclusions are presented in two sections, addressing each question in sequence.

2.5.1 Methods and Uncertainty

The most commonly reported methods in the literature for multi-criteria decision support can be classified into three groups; MOO methods, MA methods and outranking methods. Each group has its own advantages and limitations. This section presents a benchmark study of these groups (Table 2-3). The benchmark was created using the literature discussed and the comparative studies of Malczewski (1999) and Linkov et al. (2006).

Table 2-3 Benchmark study of Decision-Making Methods Groups

MOO Methods MA Methods Outranking Methods

Criteria defined by: Objectives Attributes Attributes

No. of Alternatives: Infinite Finite (1-15) Finite (1-15)

Decision Variables:

Quantitative only Quantitative & Qualitative

Quantitative & Qualitative

Results: Cardinal Value Cardinal Value Ordinal Rank

Results accuracy: High Moderate Moderate

Method

Complexity: High Moderate High

Modelling time: High Low Low

Ease of modelling

Uncertainty: Moderate High High

Ease of group decision-making:

Low Moderate Moderate

Relevant to Search / Design Evaluation / Choice Evaluation / Choice

The benchmark study shows that MOO methods differ from MA and Outranking methods in multiple ways. MOO methods utilise objective functions in search or design problems to explore a vast number of solutions. As a consequence, modelling is complex and demands time from the decision-maker. Furthermore, MOO methods are unable to handle qualitative information, this makes the modelling of uncertainty difficult, particularly when decision-makers have limited knowledge or understanding of a selection. Alternatively, MA and outranking methods utilise qualitative and quantitative attributes to evaluate decisions and recommend choices. Subsequently, they are more suited to handling uncertainty than MOO methods but their results accuracy is lower. The only difference between MA and outranking techniques in the benchmark study is that MA methods output numerical results while outranking methods output an ordinal rank.

It is evident that there is no best MOO method despite the fact that an array different techniques have been proposed including goal programming, simulated annealing, evolutionary algorithms and swarm techniques. As of a result, memetic approaches which combine different algorithms for global and local searches have become popular. Similarly to MOO, the literature suggests there is no best MA method even though AHP has clearly received the most academic and industrial interest. However other methods such as WSM have become popular due its straightforward

implementation. MA methods cannot be easily combined unlike MOO methods as the inputs required for each method differ. For example AHP requires pairwise comparisons while WSM requires direct decision variables. Therefore evaluating a single decision utilising different MA methods requires extended time and effort by the decision-maker. Outranking methods are limited to three method families with ORESTE receiving little interest in the literature. Of the two most commonly cited outranking methods, ELECTRE and PROMETHEE, Salminen et al. (1998) stated that ELECTRE III is more superior to PROMETHEE II due to its ability to model inprecise data using threshold values.

2.5.2 Methods used in Process Design

A number of decision-making methods have been proposed for application during product and process design. Three research groups have developed frameworks utilising MOO algorithms for optimising process design with environment, health and safety considerations incorporated. These frameworks have been proven to be useful for the specific case studies reported. However, the frameworks are complex and inflexible which may deter industry users from adopting them.

A number of researchers have reviewed and proposed the use of MA and outranking methods for management decision-making in the chemical-using industries. However, only three methods have been applied to real problems in the literature; Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) and Weighted Sum Method (WSM). Potentially this is due to the fact that they are easy to implement and/or software is readily available (Huang et al., 2011).

The following chapter identifies the industrial requirements for developing a decision-making framework for use during Whole Process Design (WPD). In the subsequent chapters, these requirements will be compared to the methods presented in this chapter to identify an effective solution for decision-making in WPD.

“Biology is now widely considered to be a foundation science of chemical

engineering. Will management be next?” Ka M. Ng (2004)

3 Industrial Requirement

3.1 Introduction

The previous chapter critically reviewed the academic literature to identify and discuss a range of methods available for decision support. This chapter aims to identify the industrial requirements for developing a decision-making solution for use in Whole Process Design (WPD) by considering the following three research questions:

RQ4: What techniques are currently being used for decision-making in industry?

RQ5: What are the most common decisions made in WPD and in what stage of development are they considered?

RQ6: What does industry require from a decision-making framework?

The approach adopted was to undertake a mixed methods practice. This involved carrying out two qualitative semi-structured interviews with senior industrial decision-makers. The goals of these interviews were to identify:

 the company’s decision-making processes.

 the company’s requirements for a decision-making framework.

The outcomes of the interviews identified further questions which were addressed through the circulation of two questionnaires to professionals within the chemical- using industries via Britest Ltd. The initial questionnaire focused on identifying the decision-making procedures used by professionals and determining the common problems faced in WPD. The goal of the second questionnaire was to identify the requirements for a decision-making framework. Together the data from the two methods generate complementary insights in accordance with the research questions.