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The purpose of this paper is to develop a framework to evaluate and assess the performance of the COVID-19 vaccine distribution process that is sensitive to the unique supply-side and demand-side constraints exhibited in the US vaccine rollout.

A queuing framework that operates under two distinct regimes is formulated to analyze service rates that represent system capacity to vaccinate (under the first regime) and hesitancy-induced throughput (under the second regime). These supply- and hesitancy-constrained regimes form the focus of the present paper, as the former reflects the inherent ability of the nation in its various jurisdictions to mobilize, whereas the latter reflects a critical area for public policy to protect the population’s overall health and safety.

The two-regime framework analysis provides insights into the capacity to vaccinate and hesitancy-constrained demand, which is found to vary across the country primarily by politics and region. The framework also allows analysis of the end-to-end supply chain, where it is found that the ability to vaccinate was likely constrained by last-mile administration issues, rather than the capacity of the manufacturing and transportation steps of the supply chain.

This study presents a new framework to consider end-to-end supply chains as dynamic systems that exhibit different regimes because of unique supply- and demand-side characteristics and estimate rollout capacity and underlying determinants at the national, state and county levels.

The purpose of this paper is to show the feasibility of constrained model predictive control (MPC) for sophisticated helicopter models which are derived by physical considerations.

Physics‐based modeling is used to create control‐oriented helicopter models. Advanced constrained controllers are designed and tested for these sophisticated models.

The helicopter models are valid and constrained MPC shows considerable promise for robust tracking.

MPCs can be implemented for highly constrained helicopter flights.

A complete process of control‐oriented, physics‐based model development for helicopters followed by MPC design is developed. It is also proved that constrained MPC can be used and implemented online to robustly track discontinuous helicopter trajectories with heterogeneous constraints, even when the models are sophisticated and physics based.

The ascendancy of vertical marketing structures as total systems has brought about considerable interest in designing distribution systems. Various administered and co‐operative alignments have been successful in the marketplace by enabling lower total costs through central buying practices and by producing a more predictable demand pattern. As these planned systems begin to create de facto competition among individual firms, the need for methods of discovering optimal vertical arrangements will increase. One method of finding optimal arrangements of interacting phenomena is by modelling—and linear programming techniques have been found to be particularly useful. The transportation problem or distribution model was one of the first applied special cases of linear programming. Mathematical solutions to this “special case” of linear programming began appearing in the literature during World War II. Since that time, the management science literature has been replete with significant contributions in the transportation area such as those of Hitchcock, Dantzig, Chames and Cooper, and Orden.

Multidimensional scaling (MDS) represents a family of various geometric models for the multidimensional representation of the structure in data as well as the corresponding set of methods for fitting such spatial models. Its major uses in business include positioning, market segmentation, new product design, consumer preference analysis, etc. The purpose of this paper is to apply a new stochastic constrained MDS vector model to examine the importance of some 45 different leadership attributes as they impact perceptions of effective leadership practice.

The authors present a new stochastic constrained MDS vector model for the analysis of two‐way dominance data.

This constrained vector or scalar products model represents the column objects of the input data matrix by points and row objects by vectors in a T‐dimensional derived joint space. Reparameterization options are available for row and/or column representations so as to constrain or reparameterize such objects as functions of designated features or attributes. An iterative maximum likelihood‐based algorithm is devised for efficient parameter estimation.

The authors present an application to a study conducted to examine the importance of leadership attributes as they impact perceptions of effective leadership practice. Implications for future research and limitations are discussed.

Who applies for credit, who is credit constrained and who receives credit refusal in France? To address these questions and explore the determinants of certain household credit aspects in France, we exploit a unique dataset from the Household Finance and Consumption Survey (HFCS) led by European Central Bank (ECB).

The anonymized dataset we utilize is based on the third survey wave (2017) and includes 13,555 French households. More specifically, considering a large number of household variables, associated with dimensions such as demographics, employment, income, wealth, assets and expenditures, we estimate three logit regression models, attempting to capture the factors that determine the underlying behavior of households.

We find that variables such as age, education, housing status, employment situation, wealth and evolution of expenses, play a key role and enter with high statistical significance in the estimated models. Our results are consistent with the existing body of literature, also offering further implications about the research questions we pose. Finally, we provide an elaborate discussion which meticulously clarifies the qualitative dimension of our findings.

To the best of our knowledge, no studies appear in the international literature, focusing on household credit in France, utilizing original data from the ECB.

Due to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.

A generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.

Theory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.

For the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

This chapter will address (only) one issue from the 1960s substantivist/formalist debate, the treatment of choice. The substantivists rejected the economic universality of the neoclassical axioms of choice under scarcity and the isolated and selfish nature of the choice process. A common formalist response was that their model based on these axioms could be modified to include whatever specific conditions economic choice was being made under. This chapter rejects that claim, based on a consideration not included in the debate. It is argued that the mathematical structure of the standard formal neoclassical model prevents it from incorporating the substantivist criticisms, and that to modify it in accord with these criticisms would necessarily result in a model that is outside the neoclassical approach to economic decision-making.

Meta-model method has been widely used in structural reliability optimization design. The main limitation of this method is that it is difficult to quantify the error caused by the meta-model approximation, which leads to the inaccuracy of the optimization results of the reliability evaluation. Taking the local high efficiency of the proxy model, this paper aims to propose a local effective constrained response surface method (LEC-RSM) based on a meta-model.

The operating mechanisms of LEC-RSM is to calculate the index of the local relative importance based on numerical theory and capture the most effective area in the entire design space, as well as selecting important analysis domains for sample changes. To improve the efficiency of the algorithm, the constrained efficient set algorithm (ESA) is introduced, in which the sample point validity is identified based on the reliability information obtained in the previous cycle and then the boundary sampling points that violate the constraint conditions are ignored or eliminated.

The computational power of the proposed method is demonstrated by solving two mathematical problems and the actual engineering optimization problem of a car collision. LEC-RSM makes it easier to achieve the optimal performance, less feature evaluation and fewer algorithm iterations.

This paper proposes a new RSM technology based on proxy model to complete the reliability design. The originality of this paper is to increase the sampling points by identifying the local importance of the analysis domain and introduce the constrained ESA to improve the efficiency of the algorithm.

Procurement planning has always been a huge and challenging activity for business firms, especially in manufacturing. With government legislations about global concern over carbon emissions, the manufacturing firms are enforced to regulate and reduce the emissions caused throughout the supply chain. It is observed that procurement and logistics activities in manufacturing firms contribute heavily toward carbon emissions. Moreover, highly dynamic and uncertain business environment with uncertainty in parameters such as demand, supplier and carrier capacity adds to the complexity in procurement planning. The paper aims to discuss these issues.

This paper is a novel attempt to model environmentally sustainable stochastic procurement (ESSP) problem as a mixed-integer non-linear program. The ESSP optimizes the procurement plan of the firm including lot-sizing, supplier and carrier selection by addressing uncertainty and environmental sustainability. The model applies chance-constrained-based approach to address the uncertain parameters.

The proposed ESSP model is solved optimally for 30 data sets to validate the proposed ESSP and is further demonstrated using three illustrations solved optimally in LINGO 10.

The ESSP model simultaneously minimizes total procurement cost and carbon emissions over the entire planning horizon considering uncertain demand, supplier and carrier capacity.