Applications of Management Science: Volume 16

A common problem that many universities face, especially with their specialized programs, is coordinating faculty availability and class offerings. The schedule is usually developed using paper and pencil after numerous iterations. As a result, the objectives of the program, such as course integration, length of course, and student workload, are most likely compromised in lieu of faculty availability. This chapter describes a multiple objective approach to this class assignment problem that considers the program’s objectives and faculty preferences. The results of applying this class assignment model to an Executive MBA (EMBA) program are presented.

This chapter examines why U.S. offshore wind farms do not exist and identifies sites most suitable for development based on European offshore wind farms. A survey of current literature indicates that U.S. development is stalled due to a lack of government and financial support. The literature identifies common attributes associated with the successful deployment of European offshore farms and provides a basis for a multi-criteria decision analysis of potential U.S offshore wind farm sites. A review of European wind farms indicates that a small, 10–50 MW farm located in shallow waters of less than 20 m might be more successful than previous U.S. development efforts. The review also identifies common European attributes deemed critical for success. These attributes are modified, taking into account unique U.S. factors, and a set of nine critical attributes are derived for use in a multi-criteria decision analysis model of suitable U.S. locations. The nine critical attributes (wind quality, water depth, shore distance, state support, public support, industrial support, population density, weather, and energy costs), along with associated utility function values, are applied to 23 past and current proposed U.S. sites. The model identified three sites, in Galveston Island, TX, Port Isabel, TX, and Block Island, RI, as being most favorable for a small wind farm.

This chapter presents application of multi-criteria mathematical programming models by integer and mixed-integer programming for optimal allocation of workers among supporting services in a hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criteria of the problem are minimization of operational costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problems are formulated as assignment models, where the decision variables represent the assignment of people to various jobs. Numerical examples are presented. Some computational results modeled on a real data from a hospital in Poland are reported.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

Disturbing increase in the use of virgin resources to produce new products has threatened the environment. Many countries have reacted to this situation through regulations which aim to eliminate negative impact of products on the environment shaping the concept of environmentally conscious manufacturing and product recovery (ECMPRO). The first crucial and the most time-consuming step of product recovery is disassembly. The best productivity rate is achieved via a disassembly line in an automated disassembly process. In this chapter, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem. Therefore, an efficient methodology based on the simulated annealing (SA) is proposed to solve the SDDLBP. Case scenarios are considered and comparisons with ant colony optimization (ACO), particle swarm optimization (PSO), river formation dynamics (RFD), and tabu search (TS) approaches are provided to demonstrate the superior functionality of the proposed algorithm.

This chapter assesses the operating units within electronic shopping stores with regard to their productivity. The methodology used to measure the productivity is data envelopment analysis (DEA). Two different linear programming model formulations of the DEA model are used. In the first linear programming model, the weights are applied to the inputs, with the outputs remaining the same. In the second model, weights are applied to the inputs, but the outputs are different.

Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

The current chapter is a tentative step toward investigating the allocation of advertising budget between the internet platform and the entity platform according to the long-term and short-term achievement of advertising investment. We provide a decision-making framework on how to allocate the advertising budget to the two platforms for the best results. The integrated effect of advertising investment consists of two parts. Goodwill and customer scale reflect the long-term achievement, and sale profit represents the short-term achievement. We selected some representative feasible investment plans as decision-making units (DMUs), and calculated the values of sale profit, goodwill, and customer scale as three outputs. To determine the best advertising investment plan, we use data envelopment analysis (DEA) model to seek efficient plans, and then determine the best one from those efficient plans through preference investigation and super-efficiency technique.

There is always a significant amount of speculation as to how the American diet changes over time. Some of speculation is based on what’s good for people, and others base their speculation on various supply and demand issues and the impact of world social changes. However one approach to forecasting the demand for various food products in the American diet is to extrapolate how America’s eating habits would change based on two different scenarios. The first scenario is an cohort model. In this scenario individuals continue their eating habits as they get older. The second scenario is the aging model, with which it is assumed that as people age they adopt the eating habits of the group that they’re moving into.

In this chapter we will evaluate these two scenario-based extrapolation models for projecting food consumption. Data comes from the National Health and Nutrition Examination survey (NHANES), which is conducted regularly by the National Center for Health Statistics (NCHS). It measures levels of consumption with a level accuracy usually not associated with traditional business data services. The specific food items consumed in a 24-hour period are collected for over 30,000 people along with an extensive list of biometric, anthropometric, social, and clinical variables.

The models we evaluate assume that new consumers will enter the market based on projected population growth rates and that consumers “exit” the market based on projected death rates. This chapter applies the models to a subset of the total food variables in the database. Food groups that are pertinent to current issues were selected, such as beef, carbonated soft drinks, and snack foods.

The models forecast food consumption of the by 5 year increments from age 1 to age 85+ an aging cohort model extrapolate how eating habits could be expected to changeover this time interval. The implications of this exercise are essential to the forecasting and management of food processors. The extrapolations may provide guidance for potential changes in capital investment or entry into other markets.

Various types of warranty programs are offered for consumer products. The two most common are a linear pro-rata warranty or a lump-sum warranty, if product failure occurs prior to the specified warranty time. In this chapter we consider additional types of warranty programs that allow the consumer to purchase a one-time extended warranty in the event of no failure within the initial warranty period. For the extended period, warranty may be linearly pro-rated, starting at an amount that is lower than the initial purchase price. Alternatively, for the extended period, warranty may be a lump-sum amount, that is less than the initial warranty amount. Expressions for the expected costs under each of the programs are derived. Guidelines are provided for determining the parameters of each warranty program under relevant constraints. Sensitivity analysis is also conducted to determine the effect of the problem parameters on the expected warranty costs.

In recent years, many new and interesting business models for Internet-based selling have emerged with the advent of electronic commerce, one of which is the Internet-based group-buying. Since group-buying can be quickly built and removed, and the consumers can pay a lower price for the product through it, the group-buying can be a new online promotion form. In this chapter we build up a two-period pricing model for a supply chain when a supply chain member utilizes group-buying program to promote its products. In detail, we consider a supply chain consisting of a supplier and a retailer, where the supplier or retailer may launch a group-buying program to promote the products via a group-buying web site in the promotion period (i.e., the first period), as well as the supplier may sell its products through the retailer traditionally in both periods. Utilizing game theory, we derive the equilibrium decisions of the two supply chain members in three different scenarios, that is, (i) there is no group-buying program, (ii) the supplier launches a group-buying program, and (iii) the retailer launches a group-buying program. Analysis of the equilibrium decisions illustrates the following results: (i) both, the supplier and the retailer, will set low prices in the promotion period and high prices in the regular period; (ii) this trend will be enhanced when a group-buying program occurs, especially when such program is launched by the supplier; (iii) while the retailer can always benefit from a group-buying program, the supplier’s profit may be reduced under certain conditions; and (iv) in spite of the fact that the supplier’s profit may be damaged by the group-buying program, when the two supply chain members decide to launch a group-buying program, the unique equilibrium is that the supplier will launch such a program.

This work investigates a regional hospital, which has an affiliated low-acuity emergency department (ED) facility that currently struggles to meet its service level goal (85% of its patients should be in the room in 60 minutes or less). A capability analysis using data from existing processes at this facility revealed that with the current processes and current level of resources, the facility is not capable of meeting existing service level goal. A simulation was developed to examine multiple alternatives that could improve patient flow at the facility. A set of scenarios were created that modified one or more of the resources such as doctors, nurses, and rooms by changing their schedules or their quantities. The impact of the response variables related to the facility’s service level goal was recorded for each scenario. Based on the results of the simulation, recommendations to the facility for alternative ways to schedule and allocate its resources in order to meet its current service level goal were given.