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Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues.

This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized.

This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious.

This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE’s scale-free and intuitive interpretation characteristics.

The study proposes and tests a new forecasting accuracy measure for hospitality revenue management (RM). A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures’ (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER’s effectiveness is empirically validated by using a large set of hotel forecasts.

The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.

It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.

Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the “asymmetry of MAPE.”

The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE’s asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to “quantify” the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.

Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.

Using the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.

The study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.

The study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.

To the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

In demand-driven markets, customer value, sometimes called perceived use value or consumer surplus, is defined by the customer rather than the firm. The value a firm can appropriate, its profits, is driven by the customer’s willingness to pay for the value they receive, adjusted by costs. This paper introduces a conceptual framework that helps understand value creation and appropriation in demand-driven markets and shows how to influence them through strategic decision-making.

This paper uses an axiomatic approach combined with an extended analytical formulation of the jobs-to-be-done framework to contextualise demand-driven markets. It mathematically derives implications for managerial decision-making concerning selecting customer segments, optimising customer value creation and maximising firm value appropriation in a competitive environment.

Rooting strategic decision-making in the jobs-to-be-done framework allows distinguishing between what customers want to achieve (goal), what product attributes need to be satisfied (opportunity space/constraints) and what value creation criteria related to features are important (utility function). This paper shows that starting from a job-to-be-done, the problem of identifying which customer segments to serve, what product to offer and what price to charge, can be formulated as an optimisation problem that simultaneously (rather than sequentially) solves for the three decision variables, customer segments, product features and price, by maximising the value that a firm can appropriate, subject to maximising customer value creation and constrained by the competitive environment.

Applying the derived results to simultaneously deciding which customer segments to target, what product features to offer and what price to charge, given a set of competing products, allows managers to increase their chances of winning the competitive game.

This paper shows that starting from a job-to-be-done and simultaneously focusing on customers, product features, price and competitors enhances firm profitability. Strategic decision-making is formulated as an optimisation problem based on an axiomatic approach contextualising demand-driven markets.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.