A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation

The growing interest in sustainability increases the challenges for decision makers in selecting the sustainable suppliers in which consider economic, environmental and social aspects. Particularly, decision makers are being increasingly motivated to improve their supply chain activities in coping efficiently with the objectives of sustainable development. Where the era of sustainability threatens the current supply chain partners to either cope with the new regulations of sustainability or leave the field for new players. Notwithstanding, most of the recent studies considered economic and green criteria in handling sustainable supplier selection and order allocation (SSS/OA) problems overlooking the social criteria which represents the third pillar of sustainability. This work aims at putting forward a hybrid Multi Criteria Decision-Making (MCDM)-Fuzzy Multi-Objective Optimization (FMOO) approach for a sustainable supplier selection and order allocation problem by considering economic, environmental and social criteria. Thus, an integrated Fuzzy AHP-Fuzzy TOPSIS is proposed to assess and rank suppliers according to three sets of criteria (i.e. conventional, green and social). A Multi-Objective Optimization Model (MOOM) is developed for choosing suppliers and allocating the optimal order quantities. To cope with the multiple uncertainties in the input data, the MOOM is reformulated into a Fuzzy Multi-Objective Optimization Model. The ε-constraint and LP-metrics approaches are used to reveal two sets of Pareto solutions based on the developed FMOO model. Finally, TOPSIS is applied to select the final Pareto solution that is closest to the ideal solution and furthest from the nadir solution. The effectiveness and the applicability of the developed hybrid MCDM-FMOO approach is demonstrated through a case study

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

End-of-life vehicle (ELV) recycling management: improving performance using an ISM approach

With booming of the automobile industry, China has become the country with increasing car ownership all over the world. However, the end-of-life vehicle (ELV) recycling industry is at infancy, and there is little systematic review on ELV recycling management, as well as low adoption amongst domestic automobile industry. This study presents a literature review and an interpretive structural modeling (ISM) approach is employed to identify the drivers towards Chinese ELV recycling business from government, recycling organizations and consumer’s perspectives, so as to improve the sustainability of automobile supply chain by providing some strategic insights. The results derived from the ISM analysis manifest that regulations on auto-factory, disassembly technique, and value mining of recycling business are the essential ingredients. It is most effective and efficient to promote ELV recycling business by improving these attributes, also the driving and dependence power analysis are deemed to provide guidance on performance improvement of ELV recycling in the Chinese market

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations