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Industrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.

In this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.

This research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.

The proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.

The proposed model is generic and can be applied for large-scale GSC environments with little modifications.

No prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The main motive behind framing this paper is to provide a compromised solution for trapezoidal fuzzy number–multi-objective fully quadratic fractional optimisation model (TrFN-MOFQFOM) by avoiding ambiguities and confusion of decision-makers (DMs). Many researchers have used Taylor's series and parametric approach to transform fractional objective function into non-fractional ones, but Taylor's series expansion is valid only up to a neighbourhood. To avoid these extra efforts, this article suggests a methodology in which numerator of objective function is optimised under the condition of optimising denominator.

This paper suggests an efficient procedure to search for compromised solution of MOFQFOM with fuzzy coefficients using α-level set and FGP approach. Incomplete data in model is dealt with α-level set. Then after defuzzification, non-fractional models are built from fractional model to get optimal solution of every objective. Finally, the linear weighted sum of negative deviational variables is minimised to satisfy all objective functions up to maximum possible extent.

On applying suggested approach to the example given in end, the authors arrived at compromised solution having μO1(O1(x))=1 and μO2(O2(x))=0.71. The applied procedure requires less computational efforts and provides the preferred compromised solution.

This work has not been done previously by anyone. The idea being developed here of constructing non-fractional model by dealing numerators and denominators separately is completely new. 10; In the end, an algorithm, flowchart and numerical are also given to clarify the applicability of the suggested approach.

In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers.

ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2.

The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures.

The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment.

This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

The purpose of this paper is to use personal fuzzy demand, assisted by system computing to find a job, using job search systems to achieve this goal.

The search system uses the fuzzy goal programming (FGP) method by setting personal preferences as property values and screening the data for comparison and calculation. By presenting information sorted by the inputted property values, the methodology suggests the best job options.

FGP algorithms make job-searching systems meet the needs of users better, which can really affect jobseekers’ approaches to pursuing work.

As it has only considered the local cultural environment, this paper’s findings are limited by being confined to Taiwanese samples.

The experimental results of the proposed method have been compared with other websites to show their effectiveness.

This paper has assisted personal decision making using FGP applied to the internet which has seldom been studied previously.

Energy and environmental concerns have gained a significant role in public policy agenda as well as in energy economics literature. As policies often rely on imprecise information on data and goals, fuzzy goal programming (FGP) modeling is a relevant choice to evaluate multi-criteria sustainability. This technique is suitable for the analysis of the Europe 2020 strategy plan dealing with several possibly conflicting objectives in economy, environment, energy and employment. The paper aims to discuss these issues.

The paper presents a FGP model for sustainable implementations for all European Union (EU) countries with respect to Europe 2020 policy goals and provides insights for decision makers to better satisfy conflicting criteria by suggesting optimal allocations of workers in several economic sectors.

The analysis shows an overall great performance of European Union countries in the environmental and social criteria and outlines the needs for significant additional policy measures to reduce energy consumption while increasing the economic output. Comparing the performance of countries within the European Union between those who adopted the euro and those who maintained national currencies shows that Euro countries tend to perform worse in terms of Europe 2020 sustainability, opening opportunities for further research to better investigate on the causes and determinants of these differences.

The paper presents a conceptual model of sustainable development that improves understanding of the concept and reconciles highly competing policy objectives in a common framework. Applying this model to all European Union countries offers both comparison and policy recommendations at a large new scale.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

The purpose of this paper is to propose an approach to determine the optimal parameters and tolerances in concurrent product and process design in the early design stages utilizing fuzzy goal programming. A wheelchair design is provided for illustration.

The product design is developed on the basis of both customer and functionality requirements. The critical product components are then determined. The design and analysis of experiments are performed by using simulation, and then the probability distributions are adopted to determine the values of desired responses under each combination of critical product parameters and tolerances. Regression nonlinear models are then developed and inserted as constraints in the complete optimization model. Preferences on product specifications and process settings, as well as process capability index ranges, are also set as model constraints. The combined objective functions are finally formulated to minimize the sum of positive and negative deviations from desired targets and maximize process capability. The optimization model is applied to determine the optimal wheelchair design.

The results showed that the proposed approach is effective in determining the optimal values of the design parameters and tolerances of the critical components of the wheelchair with their related process means and standard deviations that enhance desired multiple quality responses under uncertainty.

This work provides a general methodology that can be applied for concurrent optimization of product design and process design in a wide range of business applications. Moreover, the methodology is beneficial when uncertainty exists in quality responses and the parameters and tolerances of product design and its critical processes.

The fuzziness is rarely considered in research and development stage. This research considers membership functions for parameters and tolerances of a product and its related processes rather than crisp values. Moreover, presented optimization model considers multiple objective functions, sum of deviations and process capability. Finally, the indirect quality responses are calculated from the best-fit probability distributions rather than assuming a normal distribution.

The paper aims to introduce a novel concept to solve the bi-level multi-criteria nonlinear fractional programming (BL-MCNFP) problems. Bi-level programming problem (BLPP) is rigorously flourished and studied by several researchers, which deals with decentralized decisions by comprising a sequence of two optimization problems, namely upper and lower-level problems. However, on the other hand, many real-world decision-making problems involve multiple objectives with fraction aspects, called fractional programming problems that reflect technical and economic performance.

This paper introduces a VIKOR (“VlseKriterijumska Optimizacija I Kompromisno Resenje”) approach to solve the BL-MCNFP problem. In this approach, an aggregating function based on L_P metrics is formulated on the basis of the “closeness” scheme from the “ideal” solution. The three steps perform the solution process: First, a new concept is attempted to minimize and maximize of the numerators and denominators from their respective ideal solutions and anti-ideal values simultaneously. Second, for each level, the K-dimensional objective space of each level is converted to a one-dimensional space by an aggregating function. Third, to obtain the final solution, all levels are combined into single-level model where the decision variables of upper levels are interrelated with other levels through fuzzy strategy-based linear and nonlinear membership functions.

The effectiveness of the proposed VIKOR is demonstrated by numerical examples, where the reported results affirm that the extended VIKOR method provides superior results in comparison with the same methods in the literature, and it is a good alternative to BL-MCNFP problems.

In terms of the assistance-based right decision, a parametric analysis for the weight of the majority is provided to exhibit a wide range of compromise solutions for the decision-maker.

The purpose of this paper is to propose a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to design their supply chain networks using three key objectives: the lowest cost and environmental impact and the shortest lead time by incorporating the decision maker’s inputs.

GOOG aims to suggest the best-fitted parameters for supply chain partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes for cradle-to-gate. It integrates Fuzzy Goal Programming and weighted max-min operator for trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved using exact algorithm and validated using an industrial case study.

The comparative analysis between actual, three single-objective, and multi-objective decisions showed that GOOG is capable to optimising three objectives namely cost, lead time, and environmental impact.

Further, GOOG requires validation for different supply chain scenarios and manufacturing strategic decisions. It can improve by including multi-echelon supply chain networks, entire life cycle and relevant environmental legislations.

GOOG helps the decision makers to configuring those supply chain parameters whilst minimising those three objectives.

Companies can use GOOG as a tool to strategically select their supply chain that reduces their footprint and stop rebound effect which imposes significant impact to the society.

GOOG includes overlooked in the previous study in order to achieve the objectives set. It is flexible for the decision makers to change the relative weightings of the inputs for those contradicting objectives.