A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce

Designing the supply chain network is one of the significant areas in e-commerce business management. This concept plays a crucial role in e-commerce systems. For example, location-inventory-pricing-routing of an e-commerce supply chain is considered a crucial issue in this field. This field established many severe challenges in the modern world, like maintaining the supply chain for returned items, preserving customers’ trust and satisfaction, and developing an applicable supply chain with cost considerations. The research proposes a multi-objective mixed integer nonlinear programming model to design a closed-loop supply chain network based on the e-commerce context. The proposed model incorporates two objectives that optimize the business’s total profits and the customers’ satisfaction. Then, numerous numerical examples are generated and solved using the epsilon constraint method in GAMS optimization software. The validation of the given model has been tested for the large problems via a hybrid two-level non-dominated sort genetic algorithm. Finally, some sensitivity analysis has been performed to provide some managerial insights

A heterogeneous electric taxi fleet routing problem with recharging stations to maximize the company’s profit

During the past years, many kinds of research have been done in order to reduce the cost of transportation by using different models of the vehicle routing problem. The increase in the amount of pollution caused by vehicles and environmental concerns about the emission of greenhouse gases has led to the use of green vehicles such as electric vehicles in the urban transport fleet. The main challenge in using electric vehicles with limited battery capacity is their long recharging time. For this purpose, several recharging stations are considered in the transportation network so that if the battery needs to be recharged, the electric vehicle can recharge and complete its journey. On the other hand, due to the limited amount of the electric vehicle’s energy, the fuel consumption of this fleet is highly dependent on their load, and it is necessary to consider their load in the planning. In this article, the problem of routing electric taxis is presented considering the economic and environmental aspects of implementing electric taxis for city services. Despite other studies that have only focused on reducing energy consumption or minimizing distance traveled by electric vehicles, for the first time, the problem of urban electric taxi routing has been modeled by considering different types of electric taxis with the aim of achieving the maximum profit of this business. The use of a heterogeneous fleet in this study leads to wider coverage of different types of demand. Therefore, a mathematical programming model is presented to formulate the problem. Then, several problem examples are designed and solved for validation purposes, and the simulated annealing algorithm (SA) will be introduced and used to solve large-scale problems

Optimizing a bi-objective location-allocation-inventory problem in a dual-channel supply chain network with stochastic demands

Integrating strategic and tactical decisions to location-allocation and green inventory planning by considering e-commerce features will pave the way for supply chain managers. Therefore, this study provides an effective framework for making decisions related to different levels of the dual-channel supply chain. We provide a bi-objective location-allocation-inventory optimization model to design a dual-channel, multi-level supply chain network. The main objectives of this study are to minimize total cost and environmental impacts while tactical and strategic decisions are integrated. Demand uncertainty is also addressed using stochastic modeling, and inventory procedure is the periodic review (S, R). We consider many features in inventory modeling that play a very important role, such as lead time, shortage, inflation, and quality of raw materials, to adapt the model to the real conditions. Since a dual-channel supply chain is becoming more important for sustainable economic development and resource recovery, we combine online and traditional sales channels to design a network. We generate five test problems and solve them by using the augmented ε-constraint method. Also, the Grasshopper optimization algorithm was applied to solve the model in a reasonable time for a large size problem. In order to provide managerial insights and investigate the sensitivity of variables and problem objectives with respect to parameters, sensitivity analysis was performed

Augmented data strategies for enhanced computer vision performance in breast cancer diagnosis

Breast cancer remains a formidable global health challenge, exacting a heavy toll on women’s lives and necessitating advanced diagnostic methodologies. This study delves into the domain with an innovative perspective, addressing pertinent limitations in current approaches. Despite significant progress, the prevalence of misclassifications and inadequate diagnostic accuracy persists as a critical concern. Current methods often rely on isolated classification algorithms, leading to suboptimal outcomes and insufficient reliability. To overcome these shortcomings, this research introduces an ensemble learning (voting) framework that reimagines the diagnostic process. This approach leverages a consortium of distinguished convolutional neural network architectures, including DenseNet169, EfficientNetB4, and Xception, collectively enhancing diagnostic precision. By embracing this holistic methodology, the study strives to bridge the existing gap between diagnostic efficiency and clinical reliability. Through meticulous optimization, the proposed model presents a promising trajectory toward significantly elevating the accuracy of breast cancer diagnosis. This study is conducted using the Breast Cancer Histopathological Database (BreakHis) dataset, encompassing diverse magnification factors (40X, 100X, 200X, and 400X), ultimately showcasing a remarkable 98% accuracy in classifying breast cancer images. The findings herald a paradigm shift in diagnostic accuracy, underscoring the potential to revolutionize breast cancer management and bolster the confidence of medical practitioners in their decision-making processes.</p

A diagnostic analytics model for managing post-disaster symptoms of depression and anxiety among students using a novel data-driven optimization approach

Prevalent mental disorders, such as depression and anxiety, commonly manifest in students throughout the transition to early adulthood. Mental illnesses can significantly impact students’ academic and social activities. An automatic or semiautomatic health monitoring approach is very effective for diagnosing depression and anxiety. This study aims to implement and scrutinize a data-driven optimization method for identifying and providing therapy to students with symptoms of depression and anxiety. The proposed method starts with data preprocessing and operating sentiment analysis to identify mentally disordered students. An ensemble learning classifier later divides students with symptoms into three categories based on their health condition: severe, moderate, and mild. A hyperparameter optimization approach is further adopted to improve the model’s performance. Finally, a rule-based dispatching system is implemented for scheduling therapy sessions. The proposed novel data-driven method is a post-disaster intelligent and reliable method that integrates three well-adopted techniques to address students’ depression and anxiety. The findings indicate that the conventional approach to monitoring depression among students previously detected only 7 to 15% of cases. However, the performance of the offered strategy revealed a confirmed rate of 44% of depressed and anxious students

Selecting an Appropriate Configuration in a Construction Project Using a Hybrid Multiple Attribute Decision Making and Failure Analysis Methods

To successfully complete a project, selecting the most appropriate construction method and configuration is critical. There are, however, plenty of challenges associated with these complex decision-making processes. Clients require projects with the desired cost, time, and quality, so contractors should trade-off project goals through project configuration. To address this problem, in this study, an integrated FTA-DFMEA approach is proposed that implements the integrated AHP-TOPSIS method to improve construction project configuration. The proposed approach applies quality management techniques and MADM methods concurrently for the first time to improve construction project configuration considering project risks, costs and quality. At first, the Client’s requirements and market feedback are considered to identify potential failures in fulfilling project goals, and an integrated AHP-TOPSIS is used to select the most critical potential failure. Then fault tree analysis is used to indicate minimal paths. An inverse search in the operational model is performed to determine relevant tasks and identify defective project tasks based on WBS. Afterward, failure modes and effect analysis are applied to identify failure modes, and an integrated AHP-TOPSIS is used to rank failure modes and select the most critical one. Then Corrective actions are carried out for failure modes based on their priority, and project configuration is improved. This study considers construction resource suppliers with different policies, delivery lead times, warranty costs, and purchasing costs. Moreover, redundancy allocation and different configuration systems such as series and parallel are taken into account based on the arrangement and precedence of tasks. Finally, a case study of a building construction project is presented to test the viability of the proposed approach. The results indicate that the proposed approach is applicable as a time-efficient and powerful tool in the improvement of construction project configuration, which provides the optimal output by considering various criteria with respect to the client’s requirements and contractor’s obligations. Moreover, the algorithm provides various options for the contractor to improve the implementation of construction projects and better respond to challenges when fulfilling project goals

Augmented data strategies for enhanced computer vision performance in breast cancer diagnosis

Breast cancer remains a formidable global health challenge, exacting a heavy toll on women’s lives and necessitating advanced diagnostic methodologies. This study delves into the domain with an innovative perspective, addressing pertinent limitations in current approaches. Despite significant progress, the prevalence of misclassifications and inadequate diagnostic accuracy persists as a critical concern. Current methods often rely on isolated classification algorithms, leading to suboptimal outcomes and insufficient reliability. To overcome these shortcomings, this research introduces an ensemble learning (voting) framework that reimagines the diagnostic process. This approach leverages a consortium of distinguished convolutional neural network architectures, including DenseNet169, EfficientNetB4, and Xception, collectively enhancing diagnostic precision. By embracing this holistic methodology, the study strives to bridge the existing gap between diagnostic efficiency and clinical reliability. Through meticulous optimization, the proposed model presents a promising trajectory toward significantly elevating the accuracy of breast cancer diagnosis. This study is conducted using the Breast Cancer Histopathological Database (BreakHis) dataset, encompassing diverse magnification factors (40X, 100X, 200X, and 400X), ultimately showcasing a remarkable 98% accuracy in classifying breast cancer images. The findings herald a paradigm shift in diagnostic accuracy, underscoring the potential to revolutionize breast cancer management and bolster the confidence of medical practitioners in their decision-making processes.</p