Multi-objective Supplier Selection Problem Research Articles

The study of multi-objective supplier selection problem by a novel hybrid method: COA/ε-constraint

Today, paying attention to the interests of suppliers in supply chain management strategies is one of the important points in the success of long-term and strategic relationships with suppliers. Not paying enough attention to these points sometimes causes irreparable damage to the overall structure of the organization. In response to this need, researchers have developed and proposed different models according to different approaches. This research has presented a special model with the approach of answering these problems. This approach, which is based on the cuckoo optimization algorithm (COA), can solve the problems in multi-objective methods in addition to single-objective problems. This method based on the COA and the e-constraint method named COA/e-Constraint. The general approach of this method is to turn a multi-objective problem into a single-purpose problem, which is associated with increased efficiency. The model studied in this paper, with the aim of creating coordination between buyers and suppliers in the problem of supplier selection, is a three-objective model of cost, quality and delivery time, which is implemented to evaluate the performance of the proposed method. The results show the superiority of the proposed method over similar approaches in terms of creating a Pareto frontier.

Supplier selection problem for multiple projects with uncertain demand and project life cycles

This study investigates a multi-project, multi-period, multi-factory and multi-objective supplier selection problem with project life cycles and demand uncertainty. For this problem, we establish a multi-stage mixed integer stochastic programming model with three objectives (i.e., the procurement cost, number of quality defects and delayed delivery quantity). An adapted chaos optimization algorithm based on a maximum similarity method is developed to solve the model. The effectiveness of our solutions is validated by the real data from the auto industry. The sensitivity analysis indicates that all of the values of the three objectives increase and then decrease with the project life cycle

Supplier Selection Problem with Type-2 Fuzzy Parameters: A Neutrosophic Optimization Approach

This paper investigates the multiobjective supplier selection problem (SSP) with type-2 fuzzy parameters. All the involved parameters such as aggregate demand, budget allocation, quota flexibility, rating values, are depicted as type-2 triangular fuzzy (T-2TF) parameters. To tackle the T-2TF, first, critical values (CV)-based reduction method is introduced, and then chance-constrained programming is modeled to obtain the crisp version of the multiobjective SSP. Secondly, an interval-based approximation method is also developed for defuzzifying the T-2TF parameters. Further, a novel interactive neutrosophic programming approach is also suggested to solve the deterministic multiobjective SSP, which allows the decision-makers to incorporate the neutral thoughts or indeterminacy degrees efficiently. The computational study is presented to verify and validate the defuzzfied techniques and the proposed solution approach. An ample opportunity to select the most desired compromise solution with maximum overall satisfaction level is also addressed. Finally, the conclusions and future research direction are revealed based on the discussed work.

Carbon footprint based multi-objective supplier selection problem with uncertain parameters and fuzzy linguistic preferences

In this paper, we have studied supplier selection Problem (SSP) with reference to carbon footprint associated with the activities of each supplier. Carbon footprint in the proposed model is considered as one of the crucial dimension for the evaluation and selection of the suppliers.. In the problem, primary objectives are minimization of the total cost, minimization of rejection, minimization of the late deliveries along with minimization of carbon footprint. These objectives are subjected to some realistic constraints concerning customers’ demand, supplier's capacity, flexibility, allocated budget and accepted amount of carbon footprint. Some parameters in the proposed model are considered to be uncertain values. The proposed multi-objective supplier selection problem with uncertain parameter is solved using fuzzy concept based goal programming approach. The main focus of the proposed model is to deal with human subjectivity by applying the linguistic preference-based method and analyzed the operational effects of supplier selection in terms of environmental efficiency. We have adopted the Expected Constraint programming technique given by Liu (2007) to convert the uncertain parameters into a deterministic one. For the applicability and effectiveness of the model, an illustration has been given in the end.

Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon.

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

PurposeThis 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/approachIn 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.FindingsA 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/valueIn 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.

A New Method for Solving Fully Fuzzy Multi Objective Supplier Selection Problem

Supplier selection is one of the most critical activities of purchasing management in a supply chain. Because selecting right suppliers helps reduce purchasing costs, improve quality of final products and services, etc. In a real situation, for a supplier selection problem, most of the input information is not known precisely, since decision making deal with human judgment and comprehension and its nature includes ambiguity. In fact, on the one hand, deterministic models cannot easily take this vagueness into account. In these cases, the theory of fuzzy sets is one of the best tools to handle uncertainty. On the other hand, Kumar et al. proposed a new approach to find the fuzzy optimal solution of fully fuzzy linear programming problem. So, using this approach in this paper, we present a new mixed integer multi objective linear programming model for supplier selection problem. Due to uncertainty of the data, in continuation, we present a new method to solve multi objective fully fuzzy mixed integer linear programming and implement the method to supplier selection problem. Computational results present the application of the method and the proposal solving method.

Chance constraint-based multi-objective stochastic model for supplier selection

Nowadays, every business organization has to execute all decisions in a highly competitive and uncertain scenario for improving its performance. One of such decision that every organization has to undergo is to select best suppliers for timely delivery of good quality parts at minimum cost. Therefore, effective and cost-efficient supplier selection in a stochastic or uncertain scenario helps an organization in achieving its goal. Hence, this paper deals with the supplier selection problem under stochastic environment. In the present work, an attempt is made to model Stochastic Multi-objective Supplier Selection Problem (SMoSSP) applying chance constraint approach. The proposed model considers operational risks involving uncertainties-related supplier’s capacity, product demand, transportation and variable costs and lead time probability distributions. The SMoSSP is solved using non-preemptive goal programming and weighted aggregate function technique. To validate the proposed model data is generated randomly and solved in LINGO 10. Illustrative examples are presented to demonstrate the SMoSSP.

An Enhanced Two-Phase Fuzzy Programming Model for Multi-Objective Supplier Selection Problem

Supplier selection is an essential task within the purchasing function of supply chain management because it provides companies with opportunities to reduce various costs and realize stable and reliable production. However, many companies find it difficult to determine which suppliers should be targeted as each of them has varying strengths and weaknesses in performance which require careful screening by the purchaser. Moreover, information required to assess suppliers is not known precisely and typically fuzzy in nature. In this paper, therefore, fuzzy multi-objective linear programming (fuzzy MOLP) is presented under fuzzy goals: cost minimization, service level maximization and purchasing risk. To solve the problem, we introduce an enhanced two-phase approach of fuzzy linear programming for the supplier selection. In formulated problem, Analytical Hierarchy Process (AHP) is used to determine the weights of criteria, and Taguchi Loss Function is employed to quantify purchasing risk. Finally, we provide a set of alternative solution which enables decision maker (DM) to select the best compromise solution based on his/her preference. Numerical experiment is provided to demonstrate our approach.

TOPSIS extension for multi-objective supplier selection problem under price breaks

For supplier selection problem (SSP), if suppliers offer quantity discounts as well as buyer wants to buy multi-product, SSP becomes more complicated. In order to solve the complicated problem, an integration of TOPSIS approach and multi-objective mixed integer linear programming (MOMILP) is used to define the optimum quantities among the selected suppliers. In this article, we also apply TOPSIS approach to solve the MOMILP problem. In this solution, TOPSIS minimizes the measure of distance, providing that the closest solution should have the shortest distance from the positive ideal solution (PIS) and the longest distance from the negative ideal solution (NIS) as well. Therefore, a q-dimensional objective space is reduced to a twodimensional space (PIS and NIS). Finally, a single objective function is then proposed as a suitable one to resolve the conflict between the new criteria (the shortest distance from the PIS and the longest distance from the NIS).

A hybrid computational intelligent system for multiobjective supplier selection problem

Solving multiobjective management and engineering problems is generally a very difficult goal. In these kinds of problems, the objectives often conflict across a high-dimensional problem space and may also require existence of computational resources. The solution methods developed for this problem are generally evolutionary algorithms, as the subset of computational intelligence. In this study, combination of the kind of above-mentioned methods with other intelligent systems is introduced as a hybrid system. The system is constructed on fuzzy rule base along with neural networks and genetic algorithms and used for one of the most important multiobjective problems in market planning, which is the supplier selection problem. In addition, a numerical example is provided to clarify performance of developed hybrid systems. Finally some discussions and conclusions are arrived at and recommendations for future studies are made.