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    An augmented Tabu search algorithm for the green inventory-routing problem with time windows
    (Elsevier B.V., 2021) Alinaghian, Mahdi; Tirkolaee, Erfan Babaee; Dezaki, Zahra Kaviani; Hejazi, Seyed Reza; Ding, Weiping
    Transportation allocates a significant proportion of Gross Domestic Product (GDP) to each country, and it is one of the largest consumers of petroleum products. On the other hand, many efforts have been made recently to reduce Greenhouse Gas (GHG) emissions by vehicles through redesigning and planning transportation processes. This paper proposes a novel Mixed-Integer Linear Programming (MILP) mathematical model for Green Inventory-Routing Problem with Time Windows (GIRP-TW) using a piecewise linearization method. The objective is to minimize the total cost including fuel consumption cost, driver cost, inventory cost and usage cost of vehicles taking into account factors such as the volume of vehicle load, vehicle speed and road slope. To solve the problem, three meta-heuristic algorithms are designed including the original and augmented Tabu Search (TS) algorithms and Differential Evolution (DE) algorithm. In these algorithms, three heuristic methods of improved Clarke-Wright algorithm, improved Push-Forward Insertion Heuristic (PFIH) algorithm and heuristic speed optimization algorithm are also applied to deal with the routing structure of the problem. The performance of the proposed solution techniques is analyzed using some well-known test problems and algorithms in the literature. Furthermore, a statistical test is conducted to efficiently provide the required comparisons for large-sized problems. The obtained results demonstrate that the augmented TS algorithm is the best method to yield high-quality solutions. Finally, a sensitivity analysis is performed to investigate the variability of the objective function.
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    An integrated decision support framework for resilient vaccine supply chain network design
    (Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Torkayesh, Ali Ebadi; Tavana, Madjid; Goli, Alireza; Simic, Vladimir; Ding, Weiping
    Designing resilient supply chain networks for vaccine development and distribution requires reliable and robust infrastructure. This stud develops a novel two-stage decision support framework for configuring multi-echelon Supply Chain Networks (SCNs), resilient supplier selection, and order allocation under uncertainty. Resilient supplier selection is done using a hybrid Multi-Criteria Decision-Making (MCDM) approach based on Best-Worst Method (BWM), Weighted Aggregated Sum Product Assessment (WASPAS), and Type-2 Neutrosophic Fuzzy Numbers (T2NN). A robust multi-objective optimization model is then built for order allocation considering resiliency scores, reliability of facilities, and uncertain supply and demand. The objectives are to minimize the total cost of SCN design, maximize the resiliency score, and maximize the reliability of SC, respectively. A Nondominated Sorting Genetic Algorithm II (NSGA-II) is developed to tackle the problem on large scales, tuned by the Taguchi design technique. The NSGA-II solution is compared to the & epsilon;-constraint and Multi-objective Particle Swarm Optimization (MOPSO) solutions using test problems. We demonstrate the superiority of the suggested NSGA-II method over the two competing methods according to five performance metrics. A case study is then investigated to illustrate the applicability and effectiveness of the offered methodology for COVID-19 vaccine distribution in a developing country. It is revealed that the models and algorithms can treat the problem optimally, such that Germany is the main source (approximately 25.61%) while India does not contribute to the supply of vaccines.
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    A linear programming-based QFD methodology under fuzzy environment to develop sustainable policies in apparel retailing industry
    (Elsevier, 2023) Aydın, Nezir; Şeker, Şükran; Deveci, Muhammet; Ding, Weiping; Delen, Dursun
    As the retailing industry becomes more customer oriented, it struggles with integrating the voice-of-customers into quality development policies, determining accurate customer expectations, and understanding how to incorporate the required store attributes in retailing activities. The aim of this study is to provide managers with a more decisive and sustainable framework to fulfill customer satisfaction by determining the most essential customer needs and gain a competitive advantage by applying a benchmarking process for the whole retailing activities. To essentially support managers in determining and implementing required store attributes, this study develops a sustainable linear programming (LP) based Quality Function Deployment (QFD) methodology under IVIF-environment. The proposed method determines more accurate customer expectations (CEs) and related service requirements (SRs). Accordingly, while Clothing Quality, Price Policy, and Staff Behavior are determined as the most important CEs, Design of Customer Persona, Production Cost, and Marketing Ap-plications are obtained as the most affecting SRs. Since no specific study in the literature addresses uncertainty in CEs and SRs in the apparel retailing industry, we developed an LP-based QFD under the IVIF-environment framework, which reflects the ambiguity and vagueness of the evaluations better. Thus, this study contributes to the literature by proposing a sustainable framework for managers to make decisions that are more effective and take sustainable actions. The companies who want to get the advantage in the apparel retailing industry should follow the methodology provided within this study by adding their business specific dimensions. Lastly, to represent the validity and feasibility of the proposed approach sensitivity and comparison analysis are con-ducted. The results of comparison show that the LP based QFD method is as consistent as other method but more effective in terms of handling ambiguity and fuzziness of expert evaluations, comprehensively.
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    MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection
    (Springer, 2024) Wu, Xunjin; Zhan, Jianming; Li, Tianrui; Ding, Weiping; Pedrycz, Witold
    In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality.
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    Optimization-based probabilistic decision support for assessing building information modelling (BIM) maturity considering multiple objectives
    (Elsevier, 2024) Chen, Zhen-Song; Wang, Zhuo-Ran; Deveci, Muhammet; Ding, Weiping; Pedrycz, Witold; Skibniewski, Miroslaw J.
    The phase of operation and maintenance (O&M) is the most time-consuming and cost-intensive stage in the project life cycle. However, the potential benefits of Building Information Modeling (BIM) in this phase have not been fully explored, unlike in the design and construction phases. This is particularly evident in the absence of a comprehensive assessment of its application capabilities. In light of this setting, we develop a BIM maturity assessment model (BIM MAM) for the project's O&M phase. The proposed model comprises of an assessment indicator system that facilitates experts in providing individual assessment results, and a collective opinion aggregation method in a probabilistic context based on a multi-objective optimization model that is employed to generate the ultimate collective assessment results. The established multi-objective optimization model for BIM MAM incorporates the influence of human behavior factors on the final results by introducing the fairness concern utility level as an objective. Finally, we take Wuhan Jiangxia Sewage Treatment Plant as a practical case to illustrate the effectiveness and feasibility of the proposed BIM MAM.