scholarly journals An efficient controlled elitism non-dominated sorting genetic algorithm for multi-objective supplier selection under fuzziness

2020 ◽  
Vol 7 (4) ◽  
pp. 469-488 ◽  
Author(s):  
Vahid Hajipour ◽  
Madjid Tavana ◽  
Francisco J Santos-Arteaga ◽  
Alireza Alinezhad ◽  
Debora Di Caprio
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supplier Selection ◽  
Electronic Waste ◽  
Fuzzy Model ◽  
Ideal Solution ◽  
Strategic Decisions ◽  
Order Allocation ◽  
Nsga Ii ◽  
Multi Objective

Abstract Supplier selection and order allocation constitute vital strategic decisions that must be made by managers within supply chain management environments. In this paper, we propose a multi-objective fuzzy model for supplier selection and order allocation in a two-level supply chain with multi-period, multi-source, and multi-product characteristics. The supplier evaluation objectives considered in this model include cost, delay, and electronic-waste (e-waste) minimization, as well as coverage and weight maximization. A signal function is used to model the price discount offered by the suppliers. Triangular fuzzy numbers are used to deal with the uncertainty of delay and e-waste parameters while the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain the weights of the suppliers. The resulting NP-hard problem, a Pareto-based meta-heuristic algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA), is developed. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to validate the applicability of the CENSGA algorithm and the Taguchi technique to tune the parameters of the algorithms. The results are analysed using graphical and statistical comparisons illustrating how the proposed CENSGA dominates NSGA-II and MOPSO in terms of mean ideal solution distance (MID) and spacing metrics.

scholarly journals Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

2017 ◽  
Vol 10 (2) ◽  
pp. 188 ◽  
Author(s):  
Muhammad Hashim ◽  
Muhammad Nazam ◽  
Liming Yao ◽  
Sajjad Ahmad Baig ◽  
Muhammad Abrar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Supplier Selection ◽  
Decision Makers ◽  
Quality Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fuzzy Environment ◽  
Proposed Model ◽  
Textile Sector

Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.

Designing a Bi-Objective Closed-Loop Supply Chain Problem with Shortage and All Unit Discount: “Nondominated Sorting Genetic Algorithm II” and “Multi-Objective Particle Swarm Optimization”

2020 ◽  
Vol 19 (04) ◽  
pp. 701-736
Author(s):  
Masoomeh Vazifeh Pirnagh ◽  
Hamed Davari-Ardakani ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Closed Loop Supply Chain ◽  
The Real ◽  
Multi Objective

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

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