A multi-objective optimization model for sustainable supply chain network with using genetic algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Ehtesham Rasi ◽  
Mehdi Sohanian
Keyword(s):  
Supply Chain ◽  
Performance Indicators ◽  
Supplier Selection ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Sustainability Performance ◽  
Multi Objective ◽  
Sustainable Supplier Selection

Purpose The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network. Design/methodology/approach The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system. Findings The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach. Practical implications The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling. Originality/value There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

A hub location model in the sustainable supply chain considering customer segmentation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Heidari ◽  
Din Mohammad Imani ◽  
Mohammad Khalilzadeh
Keyword(s):  
Supply Chain ◽  
Customer Segmentation ◽  
The Other ◽  
Supply Chain Network ◽  
Meaningful Difference ◽  
Sustainable Supply Chain ◽  
Hub Location ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective

Purpose This paper aims to study the hub transportation system in supply chain networks which would contribute to reducing costs and environmental pollution, as well as to economic development and social responsibility. As not all customers tend to buy green products, several customer groups should be considered in terms of need type. Design/methodology/approach In this paper, a multi-objective hub location problem is developed for designing a sustainable supply chain network based on customer segmentation. It deals with the aspects of economic (cost reduction), environment (minimizing greenhouse gas emissions by the transport sector) and social responsibility (creating employment and community development). The epsilon-constraint method and augmented epsilon-constraint (AEC) method are used to solve the small-sized instances of this multi-objective problem. Due to the non-deterministic polynomial-time hardness of this problem, two non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective grey wolf optimizer (MOGWO) metaheuristic algorithms are also applied to tackle the large-sized instances of this problem. Findings As expected, the AEC method is able to provide better Pareto solutions according to the goals of the decision-makers. The Taguchi method was used for setting the parameters of the two metaheuristic algorithms. Considering the meaningful difference, the MOGWO algorithm outperforms the NSGA-II algorithm according to the rate of achievement to two objectives simultaneously and the spread of non-dominance solutions indexes. Regarding the other indexes, there was no meaningful difference between the performance of the two algorithms. Practical implications The model of this research provides a comprehensive solution for supply chain companies that want to achieve a rational balance between the three aspects of sustainability. Originality/value The importance of considering customer diversity on the one hand and saving on hub transportation costs, on the other hand, triggered us to propose a hub location model for designing a sustainable supply chain network based on customer segmentation.

Determining and applying sustainable supplier key performance indicators

2014 ◽  
Vol 19 (3) ◽  
pp. 275-291 ◽  
Author(s):  
Chunguang Bai ◽  
Joseph Sarkis
Keyword(s):  
Supply Chain ◽  
Performance Measurement ◽  
Rough Set ◽  
Performance Indicators ◽  
Supply Chain Performance ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
Sustainability Performance ◽  
Neighborhood Rough Set ◽  
Performance Results

Purpose – The purpose of this paper is to introduce a methodology to identify sustainable supply chain key performance indicators (KPI) that can then be used for sustainability performance evaluation for suppliers. Design/methodology/approach – Initially the complexity of sustainable supply chain performance measurement is discussed. Then, a two-stage method utilizing neighborhood rough set theory to identify KPI and data envelopment analysis (DEA) to benchmark and evaluate relative performance using the KPI is completed. Additional analysis is performed to determine the sensitivity of the KPI set formation and performance results. Findings – The results show that KPI can be determined using neighborhood rough set, and DEA performance results provide insight into relative performance of suppliers. The supply chain sustainability performance results from both the neighborhood rough set and DEA can be quite sensitive parameters selected and sustainability KPI sets that were determined. Research limitations/implications – The data utilized in this study are illustrative and simulated. Only one model for the neighborhood rough set and DEA was utilized. Additional investigations using a variation of rough set and DEA models can be completed. Practical implications – This tool set is valuable for managers to help identify sustainable supply chain KPI (from among hundreds of potential measures) and evaluate sustainability performance of various units within supply chains, including supply chain partners, departments, projects and programs. Social implications – Sustainability incorporates many business, economic and social implications. The methods introduced in this paper can help organizations and their supply chains become more strategically and operationally sustainable. Originality/value – Few tools and techniques exist in the sustainable supply chain literature to help develop KPIs and evaluate sustainability performance of suppliers and the supply chain. This paper is one of the first that integrates neighborhood rough set and DEA to address this important sustainable supply chain performance measurement issue.

A multi-objective supplier selection model for green supply chain network under uncertainty

2018 ◽  
Vol 13 (3) ◽  
pp. 605-625 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Hadis Derikvand
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Objective Function Value ◽  
Selection Problem ◽  
Green Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model ◽  
Supplier Selection Problem

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

scholarly journals Mathematical modeling for optimizing the blood supply chain network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Rahimzadeh Dehaghani ◽  
Muhammad Nawaz ◽  
Rohullah Sultanie ◽  
Tawiah Kwatekwei Quartey-Papafio
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective ◽  
Blood Supply Chain ◽  
The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

The missing links of sustainable supply chain management and green radical product innovation between sustainable entrepreneurship orientation and sustainability performance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ebenezer Afum ◽  
Kassimu Issau ◽  
Yaw Agyabeng-Mensah ◽  
Charles Baah ◽  
Essel Dacosta ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Product Innovation ◽  
Social Performance ◽  
Sustainable Supply Chain ◽  
Radical Product ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Sustainability Performance

Purpose Anchored on the natural resource-based view and stakeholder theories, this study aims to investigate the mediating roles of sustainable supply chain management and green radical product innovation (GRPI) in the link between sustainable entrepreneurial orientation (SEO) and sustainability performance. Design/methodology/approach The comprehensive research model developed in this study is empirically tested by using data garnered from 248 managers of Ghanaian small and medium-sized enterprises. Partial least square structural equation modeling is applied as the methodological technique to test all the hypothesized relationships. Findings Results of the study indicate that SEO has a direct significant positive impact on environmental performance and social performance but not financial performance. However, through sustainable supply chain management and GRPI (both mediating variables), SEO tends to have significant impact on all sustainability performance dimensions (environmental, financial and social performance). Originality/value This study offers fresh empirical evidence by developing a unified research model that validates the specific mediation role of sustainable supply chain management between SEO and green radical product innovation, as well as the mediating roles of both sustainable supply chain management and GRPI between SEO and sustainability performance dimensions.

Fuzzy Goal Programming With Interval Type-2 for Solving Multi-Objective Sustainable Supplier Selection Problems

2021 ◽  
pp. 164-197
Author(s):  
Nurullah UMARUSMAN
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Supplier Selection ◽  
De Novo ◽  
Fuzzy Goal Programming ◽  
Multi Objective ◽  
Sustainable Supplier Selection ◽  
Fuzzy Goal ◽  
Interval Type

Supply chain management is going on changing and developing in line with the needs of the growing global supply chain. Performance of supply chain, considered as a whole so that businesses can accommodate these evolvements and change, needs to be improved in the long run. Actually, businesses work with suppliers complying with their policies from past to present. However, other dimensions of sustainability should be considered, as well as economic criteria when selecting suppliers. With the right supplier selection made in this respect, by contributing to the efficient functioning of the supply chain, it will increase customer satisfaction, and therefore, the enterprises will reach the goals they set. The solution of the multi-objective sustainable supplier selection problem has been realized by using the “satisfied optimal supplier design” algorithm, also called fuzzy goal programming, with de novo-based interval type-2 proposed in this study.

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