Multi-Objective Vendor Selection Problem of Supply Chain Management Under Fuzzy Environment

Gupta²,

Kamal

2022

Preprint

Self Cite

In today’s competitive environment, markets have made the purchasing function an ever more necessary action in supply chain management while in the same manner the survival of a business, for the most part, depends largely on its supplier’s. An effective and efficient supplier can give a competitive edge to a business. In general, the supplier selection is a multi-objective decision-making problem that involves several quantitative and qualitative factors that helps in minimizing the net ordering cost, minimizing the transportation cost, and minimizing the late deliveries. If we look at the different circumstances of life, we cannot predict anything in advanced. Therefore, we have taken the case of ambiguity in input parameters. Interval type-2 trapezoidal fuzzy numbers have been used to present the ambiguity in the input parameters, and crisp equivalent form has been obtained by taking the expected value. We also have used exponential smoothing forecasting analysis for getting the specific demand for the product to avoid the losses of shortages. Finally, we have used an efficient fuzzy goal programming approach for obtaining the compromise solution. A numerical example has been given to illustrate the concepts of the proposed methodology.

Section: Model Formulationmentioning

confidence: 99%

“…During the decision process in a supplier selection problem, the required information is generally uncertain, and different types of fuzziness exist. Gupta et al (2018a) recently developed a model for supplier selection issues in a specific and unpredictable environment.…”

Section: Introductionmentioning

confidence: 99%

Application of Time Series Analysis to Supplier Selection Problem: A Problem of Multi-Objective Decision Making

Gupta²,

Kamal

2022

Preprint

Self Cite

“…Fahimnia et al [17] formulated the decision models for sustainable SCM. Kaur and Awasthi [33] presented the situation of barriers in GSCM, and Gupta et al [23] considered the vendor selection issue in an SCM. Saberi et al [56] discussed blockchain technology and its effectiveness in SCM partnership, Gupta et al [24] developed the SC's transportation and delivery time model.…”

Section: Multi-objective Programming In Scnmentioning

confidence: 99%

Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

Haq

Ali

et al. 2021

Complex Intell. Syst.

Self Cite

Determining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

“…Liu et al (2018) proposed a fuzzy compromise programming model based on modified S-curve membership functions for supplier selection and compared it with weighted max-min and weighted additive methods. Gupta et al (2018) assessed a multi-objective supplier selection problem with three different objectives, i.e. minimizing net ordering cost, minimizing percentage of rejected units along with minimizing percentage of late supplied units.…”

Section: Model For Green Supplier Selectionmentioning

confidence: 99%

“…The investigation team consists of representatives from various divisions, such as procurement, marketing, quality assurance and manufacturing, as well as R&D. After the screening process, we step the uncertain situation in green supplier selection problem which exists due to some avoidable factors. As an example of supplier selection problem, we have considered numerical illustration provided by (Gupta et al, 2018;Kumar et al, 2017) with some extensions. Additional data are assumed to illustrate the uncertainty situation in the parameters of multi-level green supplier selection problem.…”

Section: Numerical Illustrationmentioning

confidence: 99%

A multi-level programming model for green supplier selection

Chatterjee

Yazdani

et al. 2021

Self Cite

PurposeIndustrial 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.Design/methodology/approachIn 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.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo 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.