According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN’s accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.

This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer Nonlinear Multi-Objective Optimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.

Disassembly 5.0 is crucial for the remanufacturing system, as it has an important role in facilitating adoption of sustainable manufacturing system. In this context, a heuristic algorithm is utilized to determine the tasks allocations for the returned product disassembly with human-robot collaboration taking into consideration the concept of a cyber-physical production system. The heuristic algorithm, modified COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) heuristic, is realized on the random sequence generation. To maintain the worker safety, the direct contact between the worker and robot is prohibited, where sensors are utilized in both human and robot sides. The obtained results explain the efficiency of the heuristic algorithm in reducing the completion of the disassembly workstation, which plays a vital role in increasing the production rate; thus, the profit will increase.

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized overall total tardiness). Computational experiments showed that the heuristic part (DAS-h) of the proposed algorithm significantly enhanced the performance of the GA for large-size problems. The results indicated that the proposed algorithm should only be used for large-scale PMS problems because DAS-h trapped GA in a region of local optima, limiting its capabilities in small- and mainly medium-sized problems.

Purpose: This paper studies a 3-dimensional pallet loading problem considering interlock stacking pattern, box dimensions, humidity, and storage time, where overlapping and overhanging are not allowed. Despite the importance of this problem in the literature, our work provides the first method that considers the environmental conditions such as 1) storage time and 2) humidity, and their tremendous impacts on the strength of the boxes, as has been observed widely in the DHL supply chain.Design/methodology/approach: This paper proposes a two-phase heuristic algorithm to solve a 3-dimensional pallet loading problem under real conditions (relative humidity, and storage time) considering interlock stacking patterns, where overlapping and overhanging are not allowed. In phase 1, the horizontal layer configuration is determined by block techniques. Three types of horizontal layers are created based on box dimensions perpendicular to the base. In phase 2, a novel mathematical model is propounded to improve the pallet volume utilization, and stability considering the pallet's maximum allowable height and weight, and the dynamic compression strength of boxes. The dynamic compression strength of boxes is calculated by the modified McKee formula. Two performance measures, pallet volume utilization and stability (load height), are utilized to evaluate the performance of the proposed heuristic algorithm in real-world instances (DHL Supply Chain). Findings: The results illustrated that the dynamic compression strength of boxes decreases as the relative humidity and storage time increase. The load height changes dynamically along with box dimensions, box alignment, direction, relative humidity, and storage time. Increasing relative humidity and storage time and applying an interlock stacking pattern reduce the pallet utilization, however, enhance the pallet stability. Finally, the proposed heuristic algorithm's efficacy increases as the identical boxes dimensions' heterogeneity increases.Originality/value: It is believed in the supply chain where these characteristics are observed, the implementation of the heuristic algorithm will help them improve the pallet volume utilization and stability.