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The value of integrated planning for production, inventory, and routing decisions : a systematic review and meta-analysis
Get PDFThis paper presents a comparison of sequential and integrated planning for the production routing problem, in which production, inventory, and routing decisions must be made. The aim is to estimate the expected value of treating the problems as a whole, rather than making decisions sequentially. In particular, the following research questions are posed: What is the expected cost reduction when combining production, inventory, and routing in a single modeling framework, compared to solving the problems individually in a sequence? Under which circumstances is it most beneficial to tackle an integrated problem? In other words, the goal is to establish whether the solutions obtained by the integration are clearly better than approximate solutions obtained by a more simplified process, and if so, under which circumstances this difference is the most pronounced. To answer these research questions, a systematic review was performed, resulting in a set of 20 relevant articles that were analyzed in depth. For the first research question, computational results from 15 articles were obtained and analyzed through a meta-analysis. The analysis estimated an expected cost savings provided by integration of 11.08%, with a 95% confidence interval of [6.58%, 15.58%]. For the second research question, individual results obtained via sensitivity analyses in 20 relevant articles were summarized qualitatively, enabling insights into how the potential savings by integration is influenced by parameters such as the degrees of freedom, the cost, and the capacity. Keywords: sequential planning, value of integration, distribution, production routing problempublishedVersio
Variable neighborhood search for binary integer programming problems
Get PDFGeneral solvers exist for several types of optimisation problems, with the commercially available solvers for mixed integer programming (MIP) being a prime example. Although binary integer programming (BIP) can be used to model a wide variety of important combinatorial optimisation problems, relatively few contributions have been made to develop heuristic algorithms for BIP. This paper examines whether variable neighbourhood search can be successfully used to tackle BIP instances, when avoiding very large neighbourhoods explored by the means of external MIP solvers. The results indicate that methods based on variable neighbourhood search are more successful than exact and heuristic commercial solvers on certain types of instances, while the opposite holds true on others. A general variable neighbourhood search proves very effective on instances with up to 200 variables, in particular some instances that are tightly constrained. Keywords: black-box solver, 0-1 integer programming, variable neighbourhood descent, VND, mathematical programmingacceptedVersio
Machine learning-based software defect prediction for mobile applications : a systematic literature review
Get PDFSoftware defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field. Keywords: software defect prediction; software fault prediction; mobile application; review; systematic literature review; deep learning; machine learningpublishedVersio
A literature review of the perishable inventory routing problem
Get PDFThe inventory routing problem arises when inventory management and vehicle routing decisions are integrated instead of treated as separate problems. The technique of combining such decisions could lead to solutions that are better than merging the optimal solutions of the smaller subproblems. Hence, the problem is prominent and has been the focus of extensive research in recent years. When the products considered in such a problem are perishable, the importance is intensified; the current paper presents a literature review of the inventory routing problem for perishable products. This review classifies papers according to five attributes, namely, the number of products, the type of product (including the types of product perishability and types of perishable products), the type of demand, the number of objective functions, and the solution approach. A comprehensive analysis is performed based on these five attributes. Finally, based on 89 relevant reviewed papers, directions for future research on the perishable inventory routing problem are presented. Keywords: inventory routing, perishable products, literature review, vehicle routing, vendor-managed inventorypublishedVersio
Deep learning-based defect prediction for mobile applications
Get PDFSmartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred. Keywords: software defect prediction; software fault prediction; mobile application; Android applications; deep learning; machine learning.publishedVersio
Nurse leaders' experiences of professional responsibility towards developing nursing competence in general wards : a qualitative study
Get PDFAim: To explore nurse leaders' experiences of professional responsibility to facilitate nursing competence in general wards. Background: Nurse leaders are responsible for maintaining high levels of competence among nurses to improve patient safety. Methods: Qualitative analysis was conducted between February and April 2019 using semi-structured interview data from 12 nurse leaders in surgical and medical wards at three Norwegian hospitals. Results: Four main themes were identified: struggle to achieve nursing staff competence; focus on operational and budgetary requirements rather than professional development; demands to organize sick leaves and holiday periods; and challenges in facilitating professional development. Conclusion: Nurse leaders felt that their responsibilities were overwhelming and challenging. They witnessed more support for current administrative tasks than for the implementation of professional development. Additionally, unclear work instructions from the employer provided few opportunities to facilitate professional development. Hospital management failed to ensure quality of care and patient safety in general wards by not supporting the strengthening of nurses' professional competence and preventing turnover. Implications for Nursing Management: Management may integrate formal work instructions that clarify nurse leaders' responsibilities as professional developers, allowing nurse leaders to meet their obligation of maintaining adequate professional competence among nursing staff in general wards.publishedVersio
