With rapid economic and social development, increasing water demands have made water scarcity a worldwide issue. Equitable, stable, and efficient water allocation, which includes water resources management and conflicting water demand management, needs a valid resolution. This paper develops a multiobjective bilevel model with the upper level reflecting the river basin authority’s allocation principle of equity and stability and the lower level aimed at ensuring maximal economic benefit effiency for each subarea. The Gini coefficient is introduced to seek equity in the water allocation strategies, a power index is applied to ensure stability in the water allocation solutions, and an economic efficiency function is designed to maximize the economic benefit efficiency in each subarea. The proposed multiobjective bilevel water allocation model is then applied to a case study in Qujiang River Basin in China, and demonstrates that the proposed model provides reasonable and practical water allocation st...

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.

The adoption of blockchain technology (BCT) in a supply chain holds great potential for textile industries by executing transactions among stakeholders in a most reliable and verifiable way. Textile industries in emerging economies, like Pakistan, confront severe economic pressures and uncertain environment and strive to achieve sustainable supply chain excellence through blockchain implementation. This study is an initiative to analyze the key barriers in adopting BCT-related practices within the textile industry. This study conducts an extensive review of the literature using fuzzy Delphi approach for finalizing the barriers and applied fuzzy analytical hierarchy process (AHP) for prioritizing the barriers under uncertain environment. Based on the extensive review of the literature and panel discussions with experts, a total of five main barriers and 21 sub-barriers were categorized and ranked. The results and findings prioritize technological and system-related barriers (TSB) first, and human resources and R&D (HRB) barriers second among the other barrier dimensions. This paper highlights the need for an inclusive understanding of the various technological, environmental, and socio-economic perspectives to create blockchain applications that work for the textile sector. This study’s key findings and policy guidelines can assist concerned stakeholders in making strategic decisions for adopting BCT within the textile supply chain. The managerial implications are provided for the industrial decision-makers and policymakers aiming to integrate BCT into the supply chain processes. Presently, there exists no research in the context of Pakistan that highlights the challenges faced during the adoption of BCT in the supply chain. For this purpose, an approach in the form of an integrated model based on fuzzy set theory is developed. Finally, the robustness of the proposed model is checked through sensitivity analysis.

While the rapid development of Chinese manufacturing SMEs has contributed significantly to economic growth, it has also resulted in environmental pollution and resource abuse problems. To resolve these problems, Chinese manufacturing SMEs need to accelerate their implementation of comprehensive green manufacturing (GM); However, this is a complex and arduous task as it involves government, enterprise and societal considerations. Therefore, it is has become imperative to identify Chinese manufacturing SMEs green drivers. Focusing on the current situation in China and using a combination of previous research and expert views, this paper comprehensively examines the development of SMEs in China to deconstruct the green driver dimensions. The identified drivers are then evaluated using a novel hybrid multi-criterion decision-making methods (MCDM), the evaluation criteria weights calculated using fuzzy DEMATEL and fuzzy TOPSIS employed to rank the green drivers. It was found that technology innovation, customer demand, incentives, regulations, supply chain pressure and public pressure were the most significant drivers for the implementation of GM in Chinese manufacturing SMEs. Finally, some managerial implications are given to assist the government, enterprises and the public focus on a few crucial drivers to ensure that the green transformation of Chinese manufacturing SMEs is scientific and efficient.