Introduction

In today’s highly competitive business environment, the success of any business depends to a large extent on the efficiency of the supply chain. Competition has moved beyond firm-to firm rivalry to rivalry between supply chains. Traditionally, businesses have concentrated on improving the forward supply chain for their products (e.g., manufacturer-wholesalerretailer-consumer). However, another important and emerging dimension of successful SCM is the reverse logistics process (Marien, 1988). As the business environment becomes more competitive, it becomes increasingly important for them to concentrate on optimizing the backward loop, as well. Reverse supply chain practices have been in existence for a long time, especially in the automotive industry, where manufacturers try to recover value from recycling car parts. Reverse supply chains differ from forward supply chains in information flow, physical distribution flow and cash flow. To manage reverse supply chain, companies need sophisticated information systems. Some of the technology involved in reverse supply chain is similar while in some areas the technology used differs from that of traditional supply chain. Technology used in reverse supply chain such as real-time inventory tracking system (bar codes and sensors) are similar to that used in the forward supply chain. On the other hand, activities such as repair optimization, slow moving inventory optimization, warranty tracking and de-manufacturing of product is different. In designing a successful reverse supply chain, it is important to know what type of product will be returned at which point in time at which place and in which condition. Hence, importance of data is immense. Tightly integrated automatic data capture, system directed disposition support, unique receipt handling, credit processing, comprehensive and flexible reporting are some of the important functional capabilities in reverse supply chain which can help a company have a competitive edge over its competitors.Reverse supply chain deals with products that have reached their life cycle or the ones returned by the final users, and then aims at best extracting their remaining values by repair, refurbishing, remanufacturing, cannibalization, and recycling ( Thierry et al.,1995) or other activities in order to protect the environment. Furthermore, reverse supply chain management can reduce the negative environmental impacts of waste disposal and resource extraction, and reduce production costs. All these factors will be taken into consideration while designing the operational framework to handle product disposition strategies for effective reverse supply chains. There are few articles on disposition decisions in reverse supply chain (Agrawal et al., 2016a; Agrawal et al., 2016b). The proposed study contributes to the very few studies available on disposition decisions in reverse supply chain. In this article, we’ve defined the reverse supply chain process, the steps involved and how it can be used as a strategic business weapon for organizations by studying the product disposition strategies. Drivers for implementing reverse supply chains are also explored alongwith inherent challenges that a reverse supply chain facessuch as managing customer expectations on returns policies, partnering with other players in the supply chain and handling the mounting pressure from regulatory authorities.

Introduction

Selection of supplier is very critical aspect in SCM where firms expend minimum 60 percent of its total sales on purchasing items like parts, components and raw materials [1]. Further, manufacturers procure services and goods using upto 70 percent of product cost [2]. Supplier selection is a region of enormous significance and must be considered as a tactical aspect in effectual SCM. During 1990s, manufacturers attempted to develop strategic partnerships to improve their management’s preference and competitiveness [3]. Supplier selection and evaluation are complex tasks for decision makers as they required to consider various criteria. Dickson [4] recognized 23 criteria for the selection of supplier based on which Weber et al. [5] measured supplier performance considering the criteria of price, delivery, quality, location, technical capability, productive capability, industry position, reputation, financial stability, maintainability and history. Evans [6] studied various key criteria for the selection of supplier such as price, quality and delivery. Recently, the marketing management literature has been paid considerable attention to the supplier selection process. Few important criteria are: suppliers’ profitability, technological capabilities, relationship closeness, conflict resolution and performance quality. Lin and Chang [7] emphasized that reputation, customer responsiveness, communication, relationship closeness and industry position are necessary in vendor selection. Due to inexact, imprecise, vague and uncertain nature of data, modeling of many situations may be insufficient or inexact [8]. The one of the most important business function is strategic sourcing (SS). Under the expanded heading of logistics, now SS is an essential segment of the firm program to cover the purchasing schemes. Companies are interested to find out how they can provide facilities to the customers rapidly with reasonable pricing compared to their competitors. So, managers realized that they should work in a mutual system with the best corporation in their logistics networks containing warehouses, suppliers, customers undoubtedly,production units and the distribution centers. The long-term viability of the company is determined on the selection of supplier decision [9]. Liao and Kao [10] analyzed issues related to selection of supplier in SCM. Both qualitative and quantitative criteria make multi criteria for selection of supplier problem more complex [11]. Methods are developed for decision makers in a suitable way to deal with the problem related to the selection of supplier more effectively [12]. The applications of various type fuzzy models are explored in the context of decision making problems [13]. Liao and Kao [10] presented MCGP and fuzzy TOPSIS approaches simultaneously for selection of supplier problems using trapezoidal fuzzy number. Asamoah et al. [14] reported an AHP approach in a pharmaceutical manufacturing unit in Ghana for evaluation and selection of suppliers. Kumar and Roy [15] studied factors which are qualitatively important to obtain suitable suppliers. Wang et al. [16] presented preemptive goal programming (PGP) and analytic hierarchy process (AHP) jointly for selection of supplier.

1- Introduction

2- Literature Review

3- Criteria for Ship Management Firm Selection

4- Advanced Evaluation Method for SMF Selection

5- Application of Fuzzy-TOPSIS on Ship Management Firm Selection

6- Concluding Remarks

References

Abstract

Although extant literature has stressed importance of the use of ship management and identified the selection factors, relative evaluation of comprehensive selection factors is found to be lacking. This study aims to evaluate the overall performance and rank of the ship management firms by adopting an integrated model of Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) in order to support the critical decision making on SMF selection based on multiple criteria. This study contributes to enhancing the selection criteria of ship management firms by the ship-owners, and at the same time identifies the areas that the ship management firms require to improve their service standards. The result indicates that competency is the most important criterion, followed by cost, courtesy, organisation characteristics and image.

Introduction

Shipping is the backbone of global logistics and supply chain. Its nature is complex, so conducting shipping business requires expertise in a wide range of ship operations. Ship management industry is a well-established sector within and as an integral part of the value chain in the maritime industry has been operating in its own right for around half a century. Today, third party Ship Management Firms (hereafter SMF) account for 35% of the management of global shipping with SMF such as V-ships, Columbia Ship Management, Barber International, ASP Ship Management and Orient Ship Management as some of the leaders (Branch and Robarts, 2014). With the growing prevalence of SMF, there is a need for studying their competitive position which currently lacks in academic research (Mitroussi, 2004a). The growth of a SMF represents a revolutionary change in the concept and practice of shipping business (Mitroussi, 2003). Over the last few decades a large number of ship owners have separated their ownership and control of vessels. Originally, management of ships was considered integral with its ownership, but, increasingly this activity has been outsourced.

Over the past few years, the need for sustainable environmental management has increased rapidly and green management has emerged as an important tool for the same. The role of Green Human Resource Management (GHRM) practices in environmental management and green management is widely known but still lesser discussed in academic literature. Thus, realizing the importance of GHRM in environmental management by organizations, this study attempts to identify the important practices of GHRM and evaluate the performance of manufacturing organizations using GHRM practices. A three-phase methodology is used for the same. The first phase involves identification of GHRM practices in manufacturing organizations through literature review and expert opinion. The second phase involves ranking of GHRM practices using Best Worst Method (BWM) and third phase methodology involves evaluating manufacturing organizations on the basis of GHRM practices using Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This research can help managers to identify important practices of GHRM for their organization. This study also provides a framework for managers to evaluate their organization’s performance on the basis of GHRM practices.

Introduction

Increased manufacturing facilities have caused a transformative change in the economic condition of the developing countries, these changes are greatly influenced by resource constraints and environmental challenges (Marquis et al., 2015; Ren et al., 2017). Also, pressure from stakeholders has forced the modern-day organizations to introduce environment-friendly processes and activities (Molina-Azorín et al., 2009). Organizations commitment towards saving the environment is an indicator of its environmental performance, the performance depends on the following criteria: ability of the organization to control the pollution, lesser discharge of waste in the environment, implementation of recycling and reuse practices at the organization and implementation of systems like ISO 14001 at the organization. All these activities and systems require direct involvement of Human Resource Management (HRM) department (Lober, 1996; del Brío et al. 2007). The success of these pro-environmental strategies is ensured only when they are well aligned with organizations HRM practices (Collins and Clark, 2003). For any new strategy to succeed, organizations require competent manpower and resources that are well trained in performing that task (Jiang et al., 2012). Similarly, implementing green practices in the organization for environmental protection is an arduous task which is largely dependent on the availability of right workforce and managers. Thus, organizations need to develop a strong GHRM department that can recruit people with zeal towards environment protection and also train its current workforce to adopt and implement these activities through proper training programs or by luring them through rewards and special benefits (Mishra, 2017). Ren et al. (2017) have given a working definition of GHRM as “phenomena relevant to understanding relationships between organizational activities that impact the natural environment and the design, evolution, implementation, and influence of HRM systems”. GHRM although being a very important area for organizations is still less researched and most of the studies are done in western context (Masri and Jaaron, 2017; Ragas et al., 2017; Tang et al., 2017). Almost all of these studies are based on either literature review or are focusing on investigating the relationship between GHRM and some other constructs like organizational performance. No study has been done to rank the practices of GHRM.

Supply chain finance has received increasing attention. The combination of sustainable development and supply chain finance requires a deeper discussion to address the theoretical and managerial gaps. Thus, this study adopts the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to develop a sustainable supply chain finance model under uncertainty to identify the existing problems and deficiencies of financing patterns. Expert assessments were performed, and the results indicate that economic factors have a significant effect on other aspects and that delivery management policies are the most effective tools for reinforcing sustainable supply chain finance practices. Moreover, the findings provide a theoretical foundation that can reinforce the understanding of sustainable supply chain finance, and the managerial implications provide a precise guideline for firms to improve their performance.

Introduction

Vietnam’s textile industry has been experiencing rapid growth in recent years, and as a result, it has generated numerous employment opportunities nationwide. In addition, this industry plays an important role in the layout of the worldwide sustainable supply chain. To improve sustainable development (SD), Vietnamese textile firms have been aggressive in balancing the triple bottom line (TBL). Ahi and Searcy (2013) presented, the SD principles require significant political engagement due to the constant requirement for seeking equilibrium among the TBL aspects (i.e., economic, environmental and social aspects). The TBL offers a comprehensive method of evaluating these aspects (Lozano, 2012). For example, organizations can benefit from cost reductions, reputation improvements, and resource savings by solving environmental issues (Tseng et al., 2018). Hence, these issues have become important to firms because their stakeholders, such as regulatory authorities, customers, competitors, non-governmental organizations, and employees, are increasingly demanding that firms address issues pertaining to environmental and social sustainability in their business operations (Carter & Easton, 2011). However, previous studies of supply chain finance (SCF) focused on economic aspects have failed to address the social and environmental dimensions. Sustainable Supply Chain Finance (SSCF) helps to establish the connection and build equilibrium among the TBL aspects. To efficiently enhance SSCF, an analysis that can identify the relationships among the TBL parameters and the factors necessary for improvement is essential. In the literature, SD is defined as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” (Brundtland Commission, 1987). SD possesses the complexity, dynamism and uncertainty features that exist in the context of economic and human activities. This complexity is manifested via interactions between the social aspects, such as companies and projects, and the environmental aspects, each of which has evolving properties that together compose the world of SD (Roome, 2013; Rammel and van den Bergh, 2003). Furthermore, SD addresses the integrated TBL of activities that create value for the project and the parent organization and thus simultaneously contribute to the sustainable world and sustainability (Keeys and Huemann, 2017). On the one hand, SCF is one of the key categories of finance (He and Tang, 2012). Given the benefits of SCF, the decision to adopt and promote SCF increases the exposure and willingness of the suppliers’ executives to explore and adopt SCF (Wuttke et al., 2016).

Water is recognized as a commodity fundamental to health but scarce, and achieving adequate water delivery to the public can be an overwhelming task anywhere in the world, especially when characterized by increasing population, urbanization and industrialization, as in developing countries especially. Yoe 2002, described associated problems with urban water supplies in achieving sustainability as a wicked task, since no answer can exactly be termed right, hence a compromise situation or solution is usually opted for. This is because the problem of uncertainty and human rationality is encapsulated in all wicked problems (Khisty and Mohammadi 2001). The renaissance to non-conventional or alternative water supply options are recognized as ecofriendly and sustainable, and their integration globally such as, reclaimed water, grey water, black water, rain water harvesting (RWH), desalination, borehole water, water importation, into water supply schemes is intrinsic. Hence exploring these pathways usually borders on the ‘soft path’ approach which suggest a combination of supply options that is usually not technology driven and viable, especially in new urban areas where no central infrastructure pre-exist. These systems undoubtedly can limit demand for fresh water resources, diversify water source, boost reliability of access to resource, and minimize volume of waste water discharged into the environment. These supply options over time, have undergone and undertaken diverse research patterns (Ukpong and Agunwamba 2012; Mara 2004; Dolnicar 2009; Contard and Gouvello 2013; Chowdhury and Rahman 2008; Kanakoudis et al. 2011; Angelakis and Gikas 2014). However the numerous difficulty, rigor, variability of criteria, and uncertainty encountered/ interplay during the decision making process for the best or appropriate supply option endears our employ of multi-criteria decision making approach (MCDA), a decision support tool in overcoming these shortcomings. Fuzzy TOPSIS (technique for order preference by similarity to ideal solution), is a MCDA approach, employed in this study for the first time in alternative water supply option, with consideration of the socio-cultural input in its analysis, a fundamental aspect of sustainability, to evaluate the preferred water supply alternative in the eastern Niger delta of Nigeria, based on economic, technical, environmental and socio-cultural criteria. This region is endowed with water resources including over 2200 mm of annual rainfall, but yet faced with gross inadequacies in water supply, evidenced by the proliferation of water vendors, a clear indication that supply are below acceptable levels. This condition is prevalent in most other parts of the country (Sule et al. 2010). (Okoli et al. 2010) identified major alternative sources of water in the region to be boreholes, wells and ground water.

An increase in the population, rapid urbanisation and a rise in community living standards have significantly accelerated municipal solid waste (MSW) generation (Song et al., 2015), and so a sustainable and efficient waste management strategy is needed to balance the need for development, the quality of human life and the environment (Tan et al., 2014). The management of this growing and complex problem requires approaches that deliver more consistent and robust results for decision-making. This study has two objectives: 1. To select the best MSW treatment methods to be applied for the amount of waste generated in a specific territory. It should be based on the interests and wishes of the main local stakeholders, mainly citizens and their political representatives, academic experts and environmental decision-makers. 2. To estimate and forecast the evolution of the MSW generation under different assumptions and scenarios for a future period, using a mathematical model developed on the basis of the historical official data for a given territory such as the Canaries was applied. This help to disentangle how the different factors affect MSW generation and to determine when the current landfill capacity would be overcome in that territory. To achieve those objectives, a decision model based on Delphi, fuzzy TOPSIS, system dynamics (SD) and scenario analysis that supports decision-making for the comparison of different potential alternative MSW treatments was developed. Changes and incentives in public policies and consumption habits may increase the uncertainty and variability of the forecasts regarding the future amount of different waste fractions. Some of the relationships between those factors are dynamic, so the feedback mechanism may provoke non intuitive responses. Many longterm decision-making processes are dynamic, and critical decisions often require multiple and interrelated decision within extremely uncertain and complex scenarios. Therefore, scenarios analysis is a strategic planning method that can help decision makers make more flexible long-term planning (Salmeron et al., 2012). The Canary Islands are an interesting case study because they are among the most important tourist islands in the European Union (EU). The tourist industry contribution to the total gross domestic product (GDP) of Spain reaches 31.4% and the population working in this industry reaches 33.1%, and to improve the MSW management is a key issue for the sustainable tourism development of that territory. In the study area, the topic of MSW management planning has been investigated very little, e.g. Garcia-Falcon and Medina-Munoz ~ (1999) studied the sustainable tourism development in the main island of the Canary archipelago and Santamarta et al. (2014) identified some weaknesses of the MSW Canary management system for the period 2005e2011 and proposed recommendations to improve it but without neither calculations nor modelling. This paper helps by responding to this gap in the Canaries and the tourist islands, because, this study area which can be considered as a closed system and it was nominated as biosphere reserve by UNESCO, no research has been conducted to determine the key factors that directly affect the generation of waste and the impact they may have in the long term. As well no mathematical models or fuzzy or hybrid approaches have been applied as prospective aid to improve solid waste planning. At the same time, this case study is very useful to study the influence of the variations of the tourism on the MSW generation in touristic island, where the contribution of the tourist industry to the GDP is very high (e.g. 31.9% in Canary island in 2015). The main contribution of this paper is the development of a prognostic tool to improve the consistency of decision-making and the forecasts of the future MSW generation in any territory. It is needed because this European territory has to comply with what was established by the European Directive 2008/98/EC, where the countries have to develop waste prevention plans and programmes (articles 28 and 29).

As global sustainable development, energy saving and emission reduction have become necessary and urgent issues. Electric vehicles (EVs), which play a key role in strategic development plans as a promising technology to promote environmental quality, livability, and sustainability without significantly reducing convenience or mobility (Stark Juliane et al., 2018; White and Sintov, 2017), has been given more attention for its outstanding performance in carbon emission reduction (Zhang and Han, 2017) and environment protection. Since transport sector has been one of the top contributors in greenhouse gas emissions (He and Zhan, 2018; S. Wang et al., 2017), significant efforts and series of measurements have been taken to satisfy China’s sustainable development requirements. As a result, China has become the world’s largest electric vehicle market and continues to maintain a high-speed growth (Lin and Wu, 2018) and its sales totaled 777,000 in 2017. Despite the EV development scale, it is noteworthy that demand-supply imbalance in electric vehicle charging infrastructure (EVCI) has become the major obstacle of EV development in China. To satisfy increasingly urgent charging demand, public-private partnership (PPP) mode has been introduced and supported to attract private sectors and make use of their advantages in financing, design, construction and operation for providing high quality, efficient and diverse charging services and creating social benefit (Ou, 2016). PPP projects are always characterized by long construction period, large scale investment, complicated contract structure and various uncertain risk factors in the whole life-cycle of project construction, which arouse a fundamental concern in view of challenges from perspective of private sector, risk evaluation of a PPP project becomes increasingly important and necessary. However, research investigating the risk evaluation of EVCI PPP project is limited and China lacks certain experience in this field. It is therefore important to understand what risk factors might influence the projects and propose a method to evaluate the risk level of a potential project to ensure the continuous success.

A participatory budget (PB) is a group decision-making process where citizens distribute public resources among a set of proposed projects. PB is highly beneficial for multiple parties because: it enables people to shape the local budget, municipalities obtain clear information about social priorities, it helps to integrate local communities and motivates them to cooperate, it educates citizens about costs, and it constrains local investments. All of these benefits have helped PB to grow in terms of the number of processes and budget limits. The present study investigated Polish PBs. Based on this study, we can describe a typical PB in Poland according to four steps: (1) a city announces the PB; (2) citizens propose projects; (3) the city verifies the proposals and formulates the final ballots; and, finally, (4) the citizens vote for projects. We found that major Polish cities included more than 100 projects in their ballots and people only had to choose 3-7, so the winners were usually selected by majority rule. However, this method causes high dispersion of the votes among multiple alternatives, where large numbers of people may vote for less popular projects and the process is completed without any project winning. Despite those issues majority rule has great advantage – it is easy to understand and scale. Complicated decision support systems could solve money distribution problem but people would lost trust to the system. We see our solution as a recommendation system that helps people with information overload during the voting. According to Malhotra [1], negative effects start with 10 or more options while in PB we have around 100 options. Recommendation system helps people to get familiar with potentially interesting projects instead of scanning all titles. Final solution should rank projects by different criteria: category, potential beneficiaries, location and cost. Final decision belongs to the participant. In order to build such a system for PB, an algorithm is essential for ranking projects, which was the focus of the present study. Thus, we propose automated comparisons of PB projects using the “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) method. The ranking of PB projects is a specific problem because multi-criteria comparisons are based on non-quantitative criteria, i.e., nominal and fuzzy criteria such as topic, location, and beneficiaries. The TOPSIS method minimizes the distance to the ideal alternative while maximizing the distance to the worst. In a fuzzy extension of TOPSIS, the ratings of alternatives and the weights of the criteria are fuzzy numbers or linguistic variables. The major modification of the TOPSIS method required for PB is that the objective perfect solution does not exist among the maximum and minimum values for the criteria. Thus, the subjective choice is the ideal solution for the decision maker and the negative ideal solution is the most dissimilar solution. The remainder of this paper is organized as follows. First, we briefly describe the PBs. Next, we present an overview of DSS systems and fuzzy TOPSIS with preliminary definitions. In Section 4, we describe the application of the modified TOPSIS method to PB projects. We then present examples based on the Poznan PB project set. In the final section, we discuss the results obtained.

Over the last decade environmental issues have become an important issue in various industries including the electronics industry due to an increase in environmental awareness, enforced legislation, industrial ecology and corporate citizenship (Prakash & Barua, 2015). The policy and decision makers have to consider environmental issues in each activity of their organization along their supply chain (Kannan, Jabbour, & Jabbour, 2014). Many companies have applied reverse logistics (RL) concept to their policies and strategies for sustainability development which focused on the reduction of waste and created value from return of used products (Sirisawat, Kiatcharoenpol, Choomrit, & Wangphanich, 2016). Rogers and Tibben-Lembke (1998), explained that RL is the process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal. RL focuses on maximizing value from the returned item or minimizing the total RL cost from the backward flow of materials (Kannan, Pokharel, & Kumar, 2009). According to law and legislation, it forced producers to take care of their End of Life (EOL) products and the Waste Electrical and Electronic Equipment (WEEE) directive (directive 2002/96/EC) enforced electronics manufacturers to efficiently manage the return and proper disposal of packaging or used products (Govindan, Soleimani, & Kannan, 2015; Nikolaou, Evangelinos, & Allan, 2013). Even though the RL concept is widely used in many companies, it still has a lots of barriers that make RL practices difficult and unsuccessful. Each barrier cannot be solved at the same time and might require different solutions or treatment (Prakash & Barua, 2015; Sharma, Panda, Mahapatra, & Sahu, 2011). Hence, priority and ranking of barriers and solutions is needed to solve such barriers. Previous research has studied and introduced some barriers, drivers and also solutions for RL practices in many countries (Abdulrahman, Gunasekaran, & Subramanian, 2014; Govindan, Kaliyan, Kannan, & Haq, 2014; Prakash & Barua, 2015; Rahman & Subramanian, 2012; Ravi & Shankar, 2005; Sharma et al., 2011; Zaabi, Dhaheri, & Diabat, 2013). However, the study of barriers and solutions in Thailand’s electronics industry remains unstudied. This research focuses on the identification of barriers in Thailand’s electronics industry and ranks solutions to solve its barriers. Electronics companies or other related Thai industries could use the results from the ranking of solutions to solve RL practices barriers and also develop efficient and appropriate policies and strategies for their companies to improve competitiveness. A hybrid of decision making methods was used for prioritizing and ranking of solutions. And fuzzy approach was used to manage the vagueness and uncertainty of the human options in which human judgment in decision making has often been unclear and difficult to estimate with exact numerical values (Patil & Kant, 2014).

Multi-Criteria Decision Making (MCDM) methods are widely used to assist in decision making when there are different criteria and the best alternative is to be selected. Often one needs to make a best compromise choice from the available options, since finding the best alternative may not be practically feasible. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is one of the most widely used MCDM methods (Zyoud & Fuchs-Hanusch, 2017) in such situations. TOPSIS works on the principle of finding the best compromise solution when compared to an Ideal Solution. However, in contemporary business situations such as technology selection, decisions are often taken in uncertain environments, and evaluators may feel more confident in expressing the ratings of alternatives for given criteria in fuzzy sets or interval-values (Durbach & Stewart, 2012). To address this challenge, researchers have presented different versions of Fuzzy-TOPSIS method for specific decision-making environments (Behzadian, Khanmohammadi Otaghsara, Yazdani & Ignatius, 2012; Joshi & Kumar, 2016; Mardani, Jusoh & Zavadskas, 2015; Walczak & Rutkowska, 2017). Further, it is also possible that there could be different types of fuzzy or interval-valued numbers, with or without subjective weights of criteria by evaluators, and the current approaches do not incorporate these flexibilities and uncertainties in a single method. We therefore seek to enhance the decision making approach to incorporate all the above problem variants. Developing a generalised and flexible selection model is important, since an organisation or decision-maker may not be willing to invest unduly high time or money in the development of different types of selection models. Accordingly, in this paper we propose a Generalised-FuzzyTOPSIS (GFTOPSIS) method, a versatile evaluation model capable of incorporating different kinds of flexibilities and uncertainties in the decision making process. The proposed approach is a generalised, flexible and intelligent fuzzy MCDM method, using IntervalValued Intuitionistic Fuzzy Set (IVIFS) for preference rating, suitable for use in uncertain environments. TOPSIS method is modified to incorporate IVIFS preference rating along with Degree of Optimism (DOpt). The proposed GFTOPSIS method uses DOpt to derive the expected IFS matrix, and subsequent calculations are performed based on the expected IFS matrix and distance between two IFS. This method is different from the work of F. Ye (2010), who recommended TOPSIS based on distances between two IVIFS.

Today, customers are more conscious about their environment than ever before (Mumtaz et al., 2018). Also, the government is making stricter regulations to control the environmental pollution caused by these organizations than ever before (Mathiyazhagan et al., 2014). Organizations irrespective of their size or structure are essential for growth of a country and also contribute substantially towards the degradation of the environment. Similarly, SMEs are the driving force behind the dynamic growth of any economy. But, being smaller in size their impact on environment goes unnoticed both at regional and national levels. It is often quoted that they accord to around 70% of the total industrial waste and pollution (Hillary, 1995; 2004). Consequently, due to surmounting customer awareness, calls by various stakeholders and pressure from the government, eventually has increased the responsibility of these organizations especially SMEs; towards minimizing the impact of industrial activities on the environment (Walker et al., 2008). Various conventions at international level have highlighted the need to protect environmental resources and also eliminate the challenges of climate change through reductions in environment pollution by industries. Most of the countries of the world; at the 2015 Paris convention took a collective pledge in order to reduce environmental pollution and save the mother earth. As mentioned above SMEs are one of the largest producers of industrial pollution, so the government and stakeholders focus is shifting towards this cluster of SMEs in order to help them reduce pollution and maintain ecological balance. But SMEs being resource constrained are not able to act responsively as per growing market needs. Thus, the need of innovation arises; so as to survive this cut-throat competition and sustain competitiveness (Cordeiro and Vieira, 2012). Green innovation involves the usage of new products, methods, materials etc. that reduce the use of natural resources and also limit the discharge of toxic substances in the environment (Ghisetti et al., 2017); it can act as a probable solution to address the growing problem of SMEs. SMEs are trying tirelessly to implement green practices since effective implementation will lead to gaining a competitive advantage over other and sustain in long run (Zhu and Sarkis, 2004; Mathiyazhagan et al., 2014). But, SMEs face a lot of barriers in implementing and adopting green innovation practices at their end. Thus, there is growing need for SMEs to address and overcome these barriers.

Energy is an indispensable resource for all human activities—used in different facets of life such as cooking, heating, cooling, lighting, transportation, and production/manufacturing. Energy projections have shown that the need for energy has grown significantly for the last couple of decades, and is expected to maintain such an increasing trend. According to Energy Industry Agency, world energy consumption will increase 56% by 2040 [13]. Increasing demand and the ever so expanding gap between energy production and consumption could lead to global energy crisis, if not dealt properly in a timely manner. Increasing population, industrialization and urbanization force governments to re-make strategic decisions to meet the increasing energy demands. Many countries, including Turkey, have developed energy action plans to address the issue and to mitigate the potential impact of energy shortages. Energy consumption of most every country on the face of the earth has been dependent on the fossil fuels that include petroleum, coal and natural gas, but the reserves of fossil fuels are limited and nonrenewable (this is true even if we consider the latest developments in the oil and gas production industry—i.e., fracking). It is a common belief that in order to sustain daily lives in a reliable way, we need to minimize the usage rate of fossil fuels, since fossil fuels damage the ecosystem and can cause and accelerate the global warming phenomenon. Because of this, renewable energy and related sources/procedures have been gaining increasingly more attention. Awareness of environmental issues, depleting fossil fuels resources, the precarious nature of dependency on fossil fuel/oil imports laid the foundation for the recent interest in the exploitation of renewable energy sources [19]. Renewable energy is deemed to be clean, sustainable, cost-effective, reliable and environmental friendly, and hence can be relied on for the long-term.