Annals of Operations Research

Kaur, Loveleen; Kaur, Rajbir

doi: 10.1007/s10479-024-06125-ypmid: N/A

This paper proposes an energy-efficient smart architecture for a Fog-based Wireless Sensor Network to facilitate safe evacuation in smart buildings during emergencies. The architecture comprises two layers: the sensing layer and the fog layer. Throughout the building, strategically distributed sensors continuously monitor potential emergencies and communicate with a central fog computing server, facilitating efficient emergency response management. To optimize evacuation routes, sensor nodes independently determine quick and safe paths using local intelligence, effectively addressing potential fog computing delays. The implementation of dynamic routing algorithms helps prevent congestion and evenly distribute evacuees across multiple routes. To achieve these objectives, the paper proposes an Improved Layer-wise Clustering Protocol (ILC) to establish an equal number of cluster heads at each floor of the smart building. Furthermore, Non-dominated Sorting Genetic Algorithm III (NSGA-III) is utilized to further enhance the system’s performance. The combination of fog computing and smart sensing in this architecture presents a promising solution for ensuring the safety and effectiveness of building evacuations during critical situations. The extensive experimental analysis demonstrates that the ILC with NSGA-III (ILC-NSGA-III) surpasses the performance of competitive protocols in various key metrics such as stable period, network lifetime, energy conservation, and end-to-end delay.

Saen, Reza Farzipoor; Yousefi, Farzaneh; Azadi, Majid

doi: 10.1007/s10479-024-06088-0pmid: N/A

Emerging advanced digital technologies, such as Blockchain and artificial intelligence (AI), have had a substantial impact on performance improvement and operations optimization in industrial organizations. This study presents a network model designed for sustainable supply chains based on a real case study in the oil industry that deals with recursive outputs using data envelopment analysis (DEA) approach. When designing this network, recurrent loops are considered as factors that exit the stages and re-enter the previous stages as inputs. These factors should be designed in a way to minimize their generation while maximizing their utilization. The designed network model is then extended to a dynamic DEA model. Finally, the performance of supply chains is predicted and evaluated with the least error for future time periods using an explainable artificial neural network before they become inefficient. The findings indicate that a rise in undesirable outputs notably impacts the efficiency of decision-making units (DMUs) across different time periods. This paper's approach not only identifies these factors for forecasting trends in supply chain efficiency but also allows for the observation of the effects of research and development budget allocations as a dual-role factor influencing supply chain efficiency in future time frames. The model presented, which takes into account the interaction between time periods, provides managers with a framework to analyze the nature of each of these factors in the fluctuations seen in supply chain efficiency. This paper emphasizes the role of explainable AI in forecasting supply chain efficiency, enabling decision-makers to anticipate future trends beyond past performance. By integrating growth trends, progress rates, and current efficiency levels, this approach refines unit rankings. Analyzing projected efficiency trends, particularly in relation to investments in green research and development, highlights their significant impact on long-term supply chain performance. Managers can use these insights to allocate resources effectively and optimize strategies for sustained success.

Hernández-Hernández, Ma. Elena; Jacka, Saul D.

doi: 10.1007/s10479-024-06067-5pmid: N/A

Motivated in part by a problem in simulated tempering (a form of Markov chain Monte Carlo) we seek to minimise, in a suitable sense, the time it takes a (regular) diffusion with instantaneous reflection at 0 and 1 to travel to 1 and then return to the origin (the so-called commute time from 0 to 1). Substantially extending results in a previous paper, we consider a dynamic version of this problem where the control mechanism is related to the diffusion’s drift via the corresponding scale function. We are only able to choose the drift at each point at the time of first visiting that point and the drift is constrained on a set of the form [0,ℓ)∪(i,1]\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$[0,\ell )\cup (i,1]$$\end{document}. This leads to a type of stochastic control problem with infinite dimensional state.

Harandi, Ata; Khamseh, Payvand Mirzaeian; Sana, Shib Sankar

doi: 10.1007/s10479-024-06103-4pmid: N/A

Innovation is widely being recognized as a crucial determinant of organizations’ competitive advantage. This study delves into ambidextrous leadership, encompassing two seemingly contrasting yet potentially complementary behaviors—opening and closing leadership. The aim is to elucidate how a leader can pave the way for achieving innovation among employees, and throughout the entire organization by leveraging the dual strategies of knowledge sharing and knowledge search. This research is descriptive in nature, grounded in a positivist research philosophy with an applied research orientation. The proposed research strategy involves a survey employing quantitative methods. Ambidextrous leadership characterized by both opening and closing approaches has the potential to enhance employees’ innovation through knowledge sharing. Furthermore, the proposed study reveals that ambidextrous leadership encompassing Transactional and Transformational leadership styles fosters organizational innovation through knowledge search. As social information processing technology is being updated continuously, leaders’ demonstration on both the opening and closing behaviors can drive innovation at both employee and organizational levels. Moreover, the mediating roles of knowledge sharing and knowledge seeking are vital to achieve these outcomes. However, the eighth hypothesis which explores the moderating influence of strategic flexibility does not yield significant results. A balanced strategy between these dual roles is more innovative and adaptive organizational culture.

Omrani, Hashem; Yang, Zijiang; Imanirad, Raha

doi: 10.1007/s10479-024-06099-xpmid: N/A

One of the most important composite indicators (CIs) to assess the development of countries or regions is the human development index (HDI) which is used by the United Nations (UN) to rank countries. HDI has three dimensions including healthy life, population education, and standard of living. A total of four different sub-indicators are defined for these three dimensions. The UN evaluates and ranks all countries using a simple arithmetic or geometric average of the sub-indicators and then categorizes the countries into four different groups based on their HDI scores. To measure the HDI, the benefit-of-the-doubt (BOD) model is used by researchers instead of the geometric mean. The conventional BOD model has some main drawbacks. The first is not accounting for data uncertainty, and the second is evaluating countries using different weights for the same sub-indicators. Furthermore, BOD model is not capable of predicting countries' future HDI scores. To overcome these deficiencies, this paper proposes a common weight fuzzy BOD (CWFBOD) model to measure the HDI scores. First, to take into account the uncertainty, data are considered fuzzy numbers, and a fuzzy BOD model (FBOD) is introduced. Then, to find a set of common weights for the three dimensions of HDI, the proposed FBOD model is transformed into a multiple-objective CWFBOD model. To convert and solve the multiple objective CWFBOD model to a single objective model, a fuzzy theory approach is used. In addition, of predicting the future HDI scores of countries, an artificial neural network (ANN) is designed and trained, where the original data on sub-indicators health, education, and income are considered as the features, and the HDI scores generated by CWBOD are assumed as the target of ANN. Finally, this study applies the fuzzy C-Means clustering technique to cluster all countries into four different clusters based on the HDI scores generated by FBOD and CWFBOD models. To illustrate the ability of the proposed methodology, the HDI scores of 190 countries during the period of 2015–2021 have been estimated and predicted. The results show that the proposed integrated methodology can be effectively used to estimate and predict the HDI scores as well as to cluster countries.

Arjomandi, Mohammad Ali ; Mousavi Asl, Seyed Sajad; Mosallanezhad, Behzad; Hajiaghaei-Keshteli, Mostafa

doi: 10.1007/s10479-024-06052-ypmid: N/A

Composite materials have become widespread in various industries due to their exceptional properties of strength and flexibility, which creates an entirely new area of design opportunities. However, optimizing structures containing elements made of composite material is a complicated challenge in mechanical engineering due to the natural characteristics of the material. Especially, the way that two different laminates connect together needs meticulous attention. Bolt-nut joints are one such fasteners, characterized by the high concentration of stresses and skewed stress distribution along the bolt plane. To avoid mentioned problems in bolt-nuts, adhesive-bonded joints are commonly used in composite structures. But these joints are potentially vulnerable to other defects like delamination on free ends that reduction of its risk is the core of this paper. Most traditional optimization methods, such as finite element analysis or experimental approaches are characterized by numerous variables and restrictions, and complex relations described by controlling equations. So, it is crucial to seek more powerful and sound alternatives such as metaheuristic optimization techniques which can yield a reliable solution to challenging problems in a reasonable amount of time. In this study, the performance of eight well-known metaheuristic algorithms in the optimization of two distinct multilayer adhesively-bond joints is analyzed for the first time to tackle the strength against delamination which is one of the major concerns in the design of composite material structures. The performance of metaheuristic algorithms is also evaluated using two non-parametric tests of Friedman and Wilcoxon signed rank as well as interval plots. According to the findings, the three algorithms namely the Simulated Annealing, Harmony Search, and Particle Swarm Optimization offer the most reliable performance for finding the solution. Harris Hawks Optimization, Genetic Algorithm, and Bees Algorithm, on the other hand, have the worst performance in solving such problems.

Perazzini, Selene; Gnecco, Giorgio; Pammolli, Fabio

doi: 10.1007/s10479-024-06060-ypmid: N/A

Italy is severely affected by floods, yet the government has still to develop a flood risk management strategy that is able to adequately protect the population from the huge financial, human and welfare losses they cause. In this respect, a major obstacle is the limited understanding of risk at the national level. To date, there are no analyses in the literature that estimate the flood losses for the whole Italian territory at the small area level. This is particularly due to the lack of uniformity in the collection of data by the river basin authorities, which are primarily responsible for collecting information on floods in the country. In this work, we combine different sources of flood data and propose a model for flood loss estimation that allows us to predict expected losses per square meter, per municipality, and per structural typology. We identify the areas that are critical to risk management either because of high inhabited density or because of the structural fragility of the assets. Flood expected losses are then compared with those generated by earthquakes, which constitute the natural hazard of main concern in Italy. We find that, in contrast with earthquakes, floods affect only some municipalities. Nevertheless, floods might generate losses per square meter even higher than earthquakes.

Apicella, Giovanna; Navarro, Eliseo; Requena, Pilar; Sibillo, Marilena

doi: 10.1007/s10479-024-06068-4pmid: N/A

One of the most known gaps between genders relate to survival prospects. The longer life expectancy of women implies greater longevity and morbidity risks and thus involves different needs between genders in silver ages, e.g., health care costs. In this paper, we uncover stylized evidence about the age pattern of the gender gap in mortality, by showing “facts” that are consistently verified in both COVID-19 and non-COVID-19 situations. We thus capture the general shape of the relationship between male and female mortality rates as it evolves with age. We target the ratio of male to female mortality rates, namely the Gender Gap Ratio (GGR). By means of a graduation technique, we show that the GGR evolution over age follows, for all the nations under study, the same pattern, consisting in a systematic interchange between increasing and decreasing trends within specific age intervals. In other terms, the GGR has an almost stylized shape, with distinct age-specific components, in terms of its slope and curvature.

Yu, Shaohua; Puchinger, Jakob

doi: 10.1007/s10479-024-06127-wpmid: N/A

Robot-based urban last-mile deliveries have recently attracted increasing attention from scientific research and industry. In particular, truck–robot delivery models are emerging as viable and competitive operational approaches. Consequently, this study reviews and analyzes the challenges, models, and methods associated with collaborative truck–robot deliveries. This article begins with a brief discussion of technical aspects, application areas, and challenges for introducing this novel technology. It then reviews the research on two primary models for robot delivery, namely hub-based and truck-based, along with their typical variants. It presents fundamental mathematical models that underpin the coordination and synchronization of trucks and robots in last-mile delivery scenarios, which can serve as the backbone for solving truck–robot delivery problems. Finally, this article summarizes both exact and heuristic methods employed to optimize delivery routes, discusses the critical problem in truck–robot collaborative delivery, and provides an outlook for future developments in robot-based logistics. This article will give researchers insights into the latest advancements in problem modeling and solution methods for truck–robot collaborative delivery systems. By using the basic mathematical models summarized in the article, researchers can easily construct and solve related problems. Logistics enterprise managers will obtain an in-depth understanding of the opportunities and challenges faced when deploying the system, as well as evaluations of the system’s performance in terms of operational costs, service time, and applicability. These insights provide critical references for developing effective operational strategies.

Clemente, Gian Paolo; Della Corte, Francesco; Zappa, Diego

doi: 10.1007/s10479-024-06049-7pmid: N/A

This paper addresses the critical issue of road safety and accident prevention by integrating road features, network theory, and advanced statistical models. It emphasises the importance of understanding the relationship between road infrastructure and accident risk, which impacts on various administrative stakeholders and on citizens’ safety. While existing literature focuses on road features and engineering solutions, this paper highlights the need to consider implicit spatial constraints as well. Our study builds on prior research by proposing a novel approach that merges conditional autoregressive modelling with a two-stage mixed Geographically weighted Poisson regression. This integrated methodology allows us to consider both the effect of risk factors at a global level and at a local road level. By leveraging the strengths of these two methods, we aim to capture both overarching trends and local variations of risk factors, thereby offering a comprehensive understanding of accident risk factors. Using data from the Open Street Map database, which covers the wide province of Milan in Italy, our models identify influential street characteristics, providing valuable insights for informed decision-making regarding road safety measures. Our method can be applied to any region in the world. The paper describes the models used, the dataset employed, and presents a detailed numerical analysis demonstrating the effectiveness of the approach in identifying and understanding accident risk factors within road networks. This information can help guide investments for the benefit of society.