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Sustainable Supply Chain Optimization and Multiple Criteria Decision Making

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13654

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Prof. Dr. Shu-Chu Liu

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Guest Editor

Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 912301, Taiwan
Interests: supply chain management; decision analysis; artificial intelligence applications; e-commerce

Special Issue Information

Dear Colleagues,

Due to customer demands, government regulations and incentives, and benefits to industries, sustainability is gradually being adopted in supply chain decisions. Operations in supply chains include material procurement, production, inventory, routing, location allocation (retailer, warehouse, factory, and supplier), customer relationship management, and supply and/or demand forecasting. The design of sustainable methods (operations or systems) in supply chain optimization to reach a balance between economy, environment, and equity has become a major issue. The aim for this Special Issue is to provide a new insight to design and develop sustainable methods (operations or systems) in supply chains in different industries (including manufacturing, service, agriculture, food, fashion, and health) using multiple criteria decision making (MCDM). All new methodological developments such as artificial intelligence (AI) techniques, big data analytics (BDA), and enterprise resource planning systems (ERP systems) are welcome.

Recommended topics include, but are not limited to, the following:

Designing sustainable supply chains using multiple criteria decision making (multiple objective decision making or multiple attribute decision making).
Designing sustainable methods (operations or systems) such as location allocation, inventory policy and delivery routing using multiple criteria decision making.
Designing supply or demand forecasting methods from a sustainability viewpoint.
Designing pricing decision making methods in supply chains from a sustainability viewpoint.
Designing material procurement methods (operations or systems) in supply chains from a sustainability viewpoint.
Sustainable customer relationship management design.
Green supply chain management design.
Designing circular economy in supply chains.
Up-to-date reviews of sustainable supply chain decisions for different industries.

Prof. Dr. Shu-Chu Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

sustainable supply chain optimization
multiple-criteria decision making (MCDM)
artificial intelligence (AI) technique
big data analytics (BDA)
enterprise resource planning systems (ERP Systems)
green supply chain management
circular economy

Published Papers (5 papers)

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Research

17 pages, 1658 KiB

Open AccessArticle

Environmental and Social Factors in Supplier Assessment: Fuzzy-Based Green Supplier Selection

by Torky Althaqafi

Sustainability 2023, 15(21), 15643; https://doi.org/10.3390/su152115643 - 6 Nov 2023

Viewed by 1718

Abstract

Supplier selection is a key process that entails selecting suppliers who provide high-quality, cost-effective products or services with predetermined schedules and quantities. Organisations are currently reconsidering their supply chain strategies in order to incorporate environmental and ecological issues into their operations. This involves a shift towards environmentally conscientious providers as well as the incorporation of environmental requirements into daily practises. This research paper investigates supplier evaluation strategies and selection criteria in depth. This study presents a novel methodology for assessing supply chain risk management in the setting of supplier management. This study’s focuses are cost, quality, delivery time, environmental performance, and social responsibility. The incorporation of administrative observation into supplier selection is illustrated, with the results compared to those of traditional methods. Our findings highlight the synergies between administrative observation and quantitative metrics, providing crucial insights into supplier sustainability performance and improving decision making. Finally, this study emphasises the importance of managerial observation in sustainable supplier selection, emphasising the relevance of subjective ratings to improve awareness of suppliers’ sustainability practises and minimise risks associated with weak quantitative assessments. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Multiple Criteria Decision Making)

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25 pages, 2360 KiB

Open AccessArticle

Sustainable Last-Mile Delivery Solution Evaluation in the Context of a Developing Country: A Novel OPA–Fuzzy MARCOS Approach

by Chia-Nan Wang, Yu-Chi Chung, Fajar Dwi Wibowo, Thanh-Tuan Dang and Ngoc-Ai-Thy Nguyen

Sustainability 2023, 15(17), 12866; https://doi.org/10.3390/su151712866 - 25 Aug 2023

Cited by 4 | Viewed by 2521

Abstract

With the surge in e-commerce volumes during COVID-19, improving last-mile logistics is extremely challenging, specifically for developing economies, due to poor infrastructures, lack of stakeholders’ cooperation, and untapped resources. In the context of Vietnam, there are certain solutions that can bring more efficient and sustainable last-mile logistics. In this paper, to evaluate and rank these potentially sustainable last-mile solutions (LMSs), we propose a novel hybrid multiple attribute decision-making (MADM) model that combines the Ordinal Priority Approach (OPA) and fuzzy Measurement of Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS). Twelve sustainability factors of technical, economic, social, and environmental aspects were determined through a literature review and experts’ opinions to employ the MADM approach. A case study evaluating five LMSs in Vietnam concerning their sustainable implementation is solved to exhibit the proposed framework’s applicability. From the OPA findings, “efficiency”, “costs of implementation and control”, “voice of customer”, “reliability”, and “flexibility” are the topmost criteria when considering a new LMS implementation in the context of Vietnam. Moreover, sensitivity analysis and comparative analysis were performed to test the robustness of the approach. The results illustrate that the applied methods reach consistent solution rankings, where LMS-03 (convenience store pickup), LMS-02 (parcel lockers), and LMS-01 (green vehicles) are the best solutions in Vietnam. The study holds novelty in evaluating last-mile initiatives for Vietnam by utilizing a unique approach in the form of two novel MADM techniques, thus providing significant insights for research and applications. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Multiple Criteria Decision Making)

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32 pages, 1497 KiB

Open AccessArticle

The Impact of Proactive Resilience Strategies on Organizational Performance: Role of Ambidextrous and Dynamic Capabilities of SMEs in Manufacturing Sector

by Thillai Raja Pertheban, Ramayah Thurasamy, Anbalagan Marimuthu, Kumara Rajah Venkatachalam, Sanmugam Annamalah, Pradeep Paraman and Wong Chee Hoo

Sustainability 2023, 15(16), 12665; https://doi.org/10.3390/su151612665 - 21 Aug 2023

Cited by 6 | Viewed by 5209

Abstract

The challenges of the global business environment foster small medium-sized enterprises (SMEs) to continuously improve their performance in the level of vulnerability to possible impacts and interruptions in their operations that may affect their sustainability. Resilience strategies and ambidextrous capabilities have become important determinants of organizational performance, which has developed as an emerging area of interest in supply chain management in recent years. SMEs are one of the major contributing sectors to the Malaysian economy. Therefore, SMEs have been forced to survive in the current market situation to ensure higher economic growth and competitiveness. The resilience strategies and ambidexterity capabilities are important determinants of SMEs’ performance. As such, this study aims to examine the relationship between proactive resilience strategies, ambidextrous capabilities, and the performance of SMEs in the manufacturing sector, drawing on the dynamic capabilities perspective. A quantitative research design is adopted, a structured survey questionnaire is used, and data are collected from 351 SMEs in the manufacturing sector. Partial least squares structural equation modeling (PLS-SEM), Smart PLS 3.0 is used to test both direct and mediating results. The findings of this study suggested that proactive resilience strategies may have a significant influence on organizational performance of SMEs. Ambidextrous capabilities also act as a strong mediator between proactive resilience strategies and organizational performance. These findings contribute to the dynamic capabilities literature by highlighting the importance of proactive resilience strategies and ambidextrous capabilities in enhancing the positive impact on organizational performance in SMEs. This study provides a plausible explanation of two important management mechanisms for enhancing organizational performance sustainability. The relationships between proactive resilience strategies, ambidextrous capabilities, and organizational performance are malleable. This study also suggests that fostering formal and informal relationships might hold the key to the sustainable performance of SMEs in the long term. This study’s practical contributions are improving the knowledge and performance of supply chain systems for SMEs in the manufacturing sector and enhancing their competitive power in domestic and international markets. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Multiple Criteria Decision Making)

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19 pages, 1232 KiB

Open AccessArticle

A Textual Data-Oriented Method for Doctor Selection in Online Health Communities

by Yinfeng Du, Zhen-Song Chen, Jie Yang, Juan Antonio Morente-Molinera, Lu Zhang and Enrique Herrera-Viedma

Sustainability 2023, 15(2), 1241; https://doi.org/10.3390/su15021241 - 9 Jan 2023

Cited by 1 | Viewed by 1498

Abstract

As doctor–patient interactive platforms, online health communities (OHCs) offer patients massive information including doctor basic information and online patient reviews. However, how to develop a systematic framework for doctor selection in OHCs according to doctor basic information and online patient reviews is a challenged issue, which will be explored in this study. For doctor basic information, we define the quantification method and aggregate them to characterize relative influence of doctors. For online patient reviews, data analysis techniques (i.e., topics extraction and sentiment analysis) are used to mine the core attributes and evaluations. Subsequently, frequency weights and position weights are respectively determined by a frequency-oriented formula and a position score-based formula, which are integrated to obtain the final importance of attributes. Probabilistic linguistic-prospect theory-multiplicative multiobjective optimization by ratio analysis (PL-PT-MULTIMOORA) is proposed to analyze patient satisfactions on doctors. Finally, selection rules are made according to doctor influence and patient satisfactions so as to choose optimal and suboptimal doctors for rational or emotional patients. The designed textual data-driven method is successfully applied to analyze doctors from Haodf.com and some suggestions are given to help patients pick out optimal and suboptimal doctors. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Multiple Criteria Decision Making)

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13 pages, 673 KiB

Open AccessArticle

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

by Shu-Chu Liu, Quan-Ying Jian, Hsien-Yin Wen and Chih-Hung Chung

Sustainability 2022, 14(21), 14101; https://doi.org/10.3390/su142114101 - 28 Oct 2022

Cited by 3 | Viewed by 2029

Abstract

Making an accurate crop harvest time prediction is a challenge for agricultural management. Previous studies of crop harvest time prediction were mainly based on statistical methods, and the features (variables) affecting it were determined by experience, resulting in its inaccuracy. To overcome these drawbacks, the objective of this paper is to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence (long short-term memory) methods based on real production and climate-related data in order to accurately predict harvest time and reduce resource waste for better sustainability. The model integrates a hybrid search for feature selection to identify features (variables) that can effectively represent input features (variables) first. Then, a long short-term memory model taking the selected features (variables) as input is used for harvest time prediction. A practical case (a large fruit and vegetable cooperative) is used to validate the proposed method. The results show that the proposed method (root mean square error (RMSE) = 0.199, mean absolute percentage error (MAPE) = 4.84%) is better than long short-term memory (RMSE = 0.565; MAPE = 15.92%) and recurrent neural networks (RMSE = 1.327; MAPE = 28.89%). Moreover, the nearer the harvest time, the better the prediction accuracy. The RMSE values for the prediction times of one week to harvesting period, two weeks to harvesting period, three weeks to harvesting period, and four weeks to harvesting period are 0.165, 0.185, 0.205, and 0.222, respectively. Compared with other existing studies, the proposed crop harvest time prediction model, LSTMFS, proves to be an effective method. Full article

(This article belongs to the Special Issue Sustainable Supply Chain Optimization and Multiple Criteria Decision Making)

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