The multi-objective supplier selection problem with fuzzy parameters and solving the order allocation problem with coverage

Abstract

Purpose This study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many real-world supply chain problems are assumed as multiple objectives combinatorial optimization problems. Design/methodology/approach In this paper, the authors propose a multi-objective model with fuzzy parameters to select suppliers and allocate orders considering multiple periods, multiple resources, multiple products and two-echelon supply chain. The objective functions consist of total purchase costs, transportation, order and on-time delivery, coverage and the weights of suppliers. Distance-based partial and general coverage of suppliers makes the number of orders of products more realistic. In this model, the weights of suppliers are determined by fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, as a multi-criteria decision analysis method, in the objective function. Also, the authors consider the parameters related to delays as triangular fuzzy numbers. Findings A small-sized numerical example is provided to clearly show the proposed model. The exact epsilon constraint method is used to solve this given multi-objective combinatorial optimization problem. Subsequently, the sensitivity analysis is conducted to testify the proposed model. The obtained results demonstrate the validity of the proposed multiple objectives mixed integer mathematical programming model and the efficiency of the solution approach. Originality/value In real-life situations, supplier selection parameters are uncertain and incomplete. Hence, the fuzzy set theory is used to tackle uncertainty. In this paper, a multi-objective supplier selection problem is formulated taking into consideration the coverage of suppliers and suppliers’ weights. Integrating coverage of suppliers to select and allocate the order to them can be mentioned as the main contribution of this study. The proposed model considers the delay from suppliers as fuzzy parameters.

An Overview of Fuzzy Techniques in Supply Chain Management: Bibliometrics, Methodologies, Applications and Future Directions

“…Hybrid methods of MCDM and OR: Khalilzadeh et al (2020) proposed a multiple objectives mixed integer mathematical programming problem with fuzzy parameters where the weights of suppliers were determined by the fuzzy TOPSIS to select suppliers and allocate orders considering multiple periods. Haldar et al (2017) proposed a DEA-TOPSIS-LP model to evaluate and select third-party logistics vendors.…”

Sustainable and resilient supplier selection, order allocation, and production scheduling problem under disruption utilizing conditional value at risk

“…The SS&OA problem is a complicated decision problem involving a trade-off between conflicting tangible and intangible criteria. It aims to select the best suppliers and optimally allocate the demand of manufacturers to the selected suppliers, taking into account the real-world constraints (Torabi et al , 2015; Ahmad and Mondal, 2019; Khalilzadeh et al , 2020). Supplier selection models can be classified into three categories: Only supplier selection (SS) models are proposed, and no integrated model is provided to determine the optimal order allocation; supplier selection and order allocation (SS&OA) model; and supplier selection and order scheduling (SS&OS) model (Bodaghi et al , 2018).…”

Challenges in the implementation of circular economy in manufacturing industry

“…Multi-criteria decision-making (MCDM) is a technique which consists of setting criteria, evaluation of criteria weights and execution of a ranking method (Opricovic and Tzeng, 2003). MCDM techniques such as AHP, analytic network process (ANP), Vlse Kriterijumska OptimizacijaI Kompromisno Resenje, technique for order of preference by similarity to ideal solution (TOPSIS), elimination and choice expressing reality and preference ranking organization method for enrichment evaluation have been used in the past to rank the factors but none of the techniques can classify the factors into cause and effect groups (Chandra and Kumar, 2020;Khalilzadeh et al, 2020;Rashidi, 2020). DEMATEL is used as a MCDM technique as this approach can transform the cause and effect relationship of the factors into an intelligent structural model of the system (Tian et al, 2019).…”