Applications of Management Science: Volume 20

The newsvendor problem is fundamental to many operations management models. The problem focuses on the trade-off between the gains from satisfying demand and losses from unsold products. The newsvendor model and its extensions have been applied to various areas, such as production plan and supply chain management. This chapter examines the study about newsvendor problem. In this research, there is a review of the contributions for the multiproduct newsvendor problem. It focuses on the current literature concerning the mathematical models and the solution methods for the multiitem newsvendor problems with single or multiple constraints, as well as with the risks. The objective of this research is to go over the newsvendor problem and bring into comparison different newsvendor models applied to the flower industry. A few case studies are described addressing topics related to the newsvendor problem such as discounting and replenishment policies, inventory inaccuracies, or demand estimation. Three newsvendor models are put into practice in the field of flower selling. A full database of the flowers sold by an anonymous retailer is available for the study. Computational experiments for practical example have been conducted with use of the CPLEX solver with AMPL programming language. Models are solved, and an analysis of different circumstances and cases is accomplished.

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.

Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.

This chapter proposes a multiobjective model to design a Closed Loop Supply Chain (CLSC) network. The first objective is to minimize the total cost of the network, while the second objective is to minimize the carbon emission resulting from production, transportation, and disposal processes using carbon cap and carbon tax regularity policies. In the third objective, we maximize the service level of retailers by using maximum covering location as a measure of service level. To model the proposed problem, a physical programming approach is developed. This work contributes to the literature in designing an optimum CLSC network considering the service level objective and product substitution.

The conceptual foundations, principles, and mechanisms of territorial branding concerning the prospects of rural development in the Third World countries are the subject of the study. The systematization and study of the problems and experiences of territorial branding as a technology of development and overcoming of poverty in the agrarian society of Ukraine is the purpose of the paper. The socioeconomic condition of the modern agrarian society of Ukraine is analyzed with explaining the nature and extent of poverty in rural areas. The basis of the research was the thesis on the expediency of social stratification, including explanation of the causes of poverty by the criterion of economic behavior of individual groups of agents. The data obtained are available in adjusting further agrarian reforms, especially regarding its social orientation, where it should be involved: sociological stratification of groups of agents of each community to identify and stimulate an economically active society, analysis of the causes of the spread and nature of poverty in this rural area, determination of domestic sources of economic growth for local economy, and the implementation of these factors in the process of modernizing of economic relations.

The main method of research was the study of the unique experience of individual rural communities. The methodology of the study foresaw the study of the prospects of rural development of the post-industrial type through the determining role of the factor of territorial branding. Monitoring the potential of territorial branding for rural areas of Ukraine using SWOT analysis has shown the uniqueness of risks, limitations, and prospects. It has been established that the conditions of neutralization of weaknesses and risks are in the combination of economic (primarily investment) and cultural and political initiatives, where a significant role belongs to the effects of community self-organization. At the same time, the prospects are due to the presence of unique institutional assets, natural, climatic and economic conditions, and possible perception of the idea of the rural population as such, which does not contradict the basic cultural values. The emphasis is placed on the fact that the realization of rural development in Ukraine as a national policy should take into account that Ukrainian rural communities remain “difficult,” mostly depressed economies, where the level of economic activity is traditionally low and unemployment is high. At the same time, studying the experience of the effectiveness of territorial branding allowed to generalize and classify the factors of brand-forming content for the rural areas of Ukraine, which became (1) a unique institutional history; (2) landscape and recreational potential; (3) special economic behavior of local inhabitants; (4) investment attractiveness of the territory; (5) unique economic specialization of the territory; (6) tourism activity; and (7) the role of local government. Significant socioeconomic effect of these examples is fixed. The area of application of these results is, first of all, the activity of local authorities of rural communities, nongovernmental organizations, and universities, as well as regulatory policy in terms of decentralization.

A major consequence of global environmental change is projected to be the alteration in flood periodicity, magnitude, and geographic patterns. There are a number of extant methods designed to help identify areas vulnerable to these consequences, the construction of composite vulnerability indices prominent among them. In this paper we have implemented the Order Rated Effectiveness (ORE) model (Klimberg & Ratick, 2020) to produce composite flood vulnerability indicators through the aggregation of six constituent vulnerability indicators future projected for 204 hydrologic subbasins that cover the contiguous US. The ORE aggregation results, when compared with those obtained using the Weighted Linear Combination and Data Envelopment Analysis, provided a more robust and actionable distribution of composite vulnerability results for decision-makers when prioritizing Hydrologic Unit Codes for further analysis and for effectively and efficiently implementing adaptation and mitigation strategies to address the flooding consequences due to global climate change.

The economic crisis has its roots in the financial services industry, but it certainly impacted the higher education in a way that has far-reaching implications for the colleges and universities in the United States. With unemployment rates of 8% and above, it made it difficult for families to send their kids to colleges and as a result colleges faced decline in enrollments and pressure to cut costs. Discount rates at almost all universities with an average size of 8,000 or less went up significantly. Academic departments at various universities came under pressure to get leaner and perform better with fewer resources. In this study, we benchmark the financial performance of public universities and private universities against each other as well as against themselves over the years by using data envelopment analysis model. The study also compares universities, public and private, with less than 3,000 students and more than 3,000 students against each other as well as over a period of time. The study is important as it will help university policy makers identify their strengths and weaknesses so that they can capitalize on their strong academic programs and make changes to fix weaker academic programs.

This chapter develops a productivity analysis of the US consumer drug store business using data envelopment analysis. This study concerns itself with five major US consumer drug chains. The output variables used are profit, total revenue, and prescription revenues. The input variables are number of pharmacists, number of drug store assets, and capital equity.

In this chapter, the concepts of technical efficiency, efficiency, effectiveness, and productivity are illustrated. It is discussed that when firms are not homogeneous, the situation is the same as when each factor has a different unit of measurement from one firm to another, and therefore, no meaningful discrimination can be expressed, unless a set of known weights are introduced to standardize data. A linear programming data envelopment analysis model is used when a set of known weights are given to calculate the technical efficiency and efficiency of a set of homogeneous DMUs with multiple input factors and output factors. A numerical example is also provided.

Estimating the production function is one of the most interest topics in economics, managements, and operations research. Often the number of decision-making units (DMUs) is not sufficiently large in comparison with the numbers of inputs and outputs. In this case, the available methodologies suffer to distinguish between DMUs and to provide a fair estimation of the production function. In the literature, studies usually suggest that researchers should either decrease the number of input-output variables or increase the number of DMUs. We demonstrate the reasons for such suggestions and provide a geometric visualization to address this issue. A simple but powerful model is introduced which is able to estimate a production function when the number of DMUs are small. A real-life numerical example of 32 DMUs with 45 variables is also used to demonstrate the advantages of the introduced model. From such an approach, researchers can benchmark organizations even if the number of DMUs is less than the number of input-output variables.

When comparing and evaluating performance, decision-makers are concerned with providing a range of effective, efficient, and fair measures that can yield representative relative rankings for the units being evaluated. In this chapter, we apply three multicriteria benchmarking modeling techniques – weighted linear combination, data envelopment analysis (DEA), and ordered weighted average (OWA) – to an example dataset to provide a quantitative assessment of performance. Evaluation of the results demonstrates that each of these techniques has relative strengths and shortcomings. To take advantage of the relative strengths, and avoid some of the shortcomings that we observed, we develop and assess a promising new methodological approach, the order rated effectiveness (ORE) model. ORE uses the OWA unit ratings within a DEA optimization framework to provide an overall relative performance assessment.