This study proposes a novel method to solve nonlinear fractional programming (NFP) problems occurring frequently in engineering design and management. Fractional terms composed of signomial functions are first decomposed into convex and concave terms by convexification strategies. Then the piecewise linearization technique is used to approximate the concave terms. The NFP program is then converted into a convex program. A global optimum of the fractional program can finally be found within a tolerable error. When compared with most of the current fractional programming methods, which can only treat a problem with linear functions or a single quotient term, the proposed method can solve a more general fractional programming program with nonlinear functions and multiple quotient terms. Numerical examples are presented to demonstrate the usefulness of the proposed method.

With the increasing reliance on modeling optimization problems in practical applications, a number of theoretical and algorithmic contributions of optimization have been proposed. The approaches developed for treating optimization problems can be classified into deterministic and heuristic. This paper aims to introduce recent advances in deterministic methods for solving signomial programming problems and mixed‐integer nonlinear programming problems. A number of important applications in engineering and management are also reviewed to reveal the usefulness of the optimization methods.

Various optimization problems in engineering and management are formulated as nonlinear programming problems. Because of the nonconvexity nature of this kind of problems, no efficient approach is available to derive the global optimum of the problems. How to locate a global optimal solution of a nonlinear programming problem is an important issue in optimization theory. In the last few decades, piecewise linearization methods have been widely applied to convert a nonlinear programming problem into a linear programming problem or a mixed-integer convex programming problem for obtaining an approximated global optimal solution. In the transformation process, extra binary variables, continuous variables, and constraints are introduced to reformulate the original problem. These extra variables and constraints mainly determine the solution efficiency of the converted problem. This study therefore provides a review of piecewise linearization methods and analyzes the computational efficiency of various piecewise linearization methods.

Delivering high-quality service to passengers can be critical for an airport’s survival, competitiveness, profitability, and long-term growth in a highly competitive environment. The present study aims to examine the relationship between airport service attributes and passenger satisfaction. To this end, we conducted multi-method research consisting of symmetric (multiple regression analysis—MRA) and asymmetric (necessary condition analysis—NCA) approaches. The research data consists of 1463 valid online reviews (n = 1463) of the top 50 busiest airports in Europe retrieved from Skytrax. The MRA was employed to examine the net effect of the eight airport service attributes on passenger satisfaction, while the NCA was used to explore the necessary conditions and level of necessity to achieve passenger satisfaction. Using MRA, the findings reveal that airport staff is the most influential predictor of passenger satisfaction, whereas airport shopping and airport Wi-Fi connectivity do not have a significant effect on passenger satisfaction. Moreover, the NCA results found that six of the eight conditions are necessary to achieve passenger satisfaction at the airport. To complement and comprehend the findings, this study also sheds light on the antecedents underlying airport passenger satisfaction in the post-COVID-19 era using NCA.

The unprecedented coronavirus pandemic (COVID-19) is fluctuating worldwide. Since the COVID-19 epidemic has a negative impact on all countries and has become a significant threat, it is necessary to determine the most effective strategy for governments by considering a variety of criteria; however, few studies in the literature can assist governments in this topic. Selective governmental intervention during the COVID-19 outbreak is considered a Multi-Criteria Decision-Making (MCDM) problem under a vague and uncertain environment when governments and medical communities adjust their priorities in response to rising issues and the efficacy of interventions applied in various nations. In this study, a novel hybrid Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (WASPAS-F) model is proposed to help stakeholders such as governors and policymakers to prioritize governmental interventions for dealing with the COVID-19 outbreak. The SF-AHP is implemented to measure the significance of the criteria, while the WASPAS-F approach is deployed to rank intervention alternatives. An empirical case study is conducted in Vietnam. From the SF-AHP findings, the criteria of “effectiveness in preventing the spread of COVID-19”, “ease of implementation”, and “high acceptability to citizens” were recognized as the most important criteria. As for the ranking of strategies, “vaccinations”, “enhanced control of the country’s health resources”, “common health testing”, “formation of an emergency response team”, and “quarantining patients and those suspected of infection” are the top five strategies. Aside from that, the robustness of the approach was tested by performing a comparative analysis. The results illustrate that the applied methods reach the general best strategy rankings. The applied methodology and its analysis will provide insight to authorities for fighting against the severe pandemic in the long run. It may aid in solving many complicated challenges in government strategy selection and assessment. It is also a flexible design model for considering the evaluation criteria. Finally, this research provides valuable guidance for policymakers in other nations.