Applications of Management Science: Volume 17

Economic incentives, government regulations, and customer perspective on environmental consciousness (EC) are driving more and more companies into product recovery business, which forms the basis for a reverse supply chain. A reverse supply chain consists a series of activities that involves retrieving used products from consumers and remanufacturing (closed-loop) or recycling (open-loop) them to recover their leftover market value. Much work has been done in the areas of designing forward and reverse supply chains; however, not many models deal with the transshipment of products in multiperiods. Linear physical programming (LPP) is a newly developed method whose most significant advantage is that it allows a decision-maker to express his/her preferences for values of criteria for decision-making in terms of ranges of different degrees of desirability but not in traditional form of weights as in techniques such as analytic hierarchy process, which is criticized for its unbalanced scale of judgment and failure to precisely handle the inherent uncertainty and vagueness in carrying out pair-wise comparisons. In this chapter, two multiperiod models are proposed for a remanufacturing system, which is an element of a Reverse Supply Chain (RSC), and illustrated with numerical examples. The first model is solved using mixed integer linear programming (MILP), while the second model is solved using linear physical programming. The proposed models deliver the optimal transportation quantities of remanufactured products for N-periods within the reverse supply chain.

With the use of meta-goal programming, a portfolio model, based on Morningstar Stock Sector and Morningstar Bond Sectors, is developed. These sectors are part of an indexed mutual fund for stock and for bonds. The asset allocation is based upon a set of four meta-goals: (1) forecasted earnings growth, (2) forecasted revenue growth, (3) unwanted deviation of absolute deviation for the risk for stock investments, and (4) unwanted deviation of absolute deviation for the risk for bond investments.

New Technology Business Firms are known to be volatile dynamic organizations whose innovations are subject to short life cycles and product imitability. Venture capitalist firms who allocate funds to these start-ups need to evaluate multiple facets associated with the individual firm’s internal and external characteristics, as well as, its own unique objectives and goals. This study applies a multicriteria decision making model to the identification for venture capital firms of potential New Technology Business Firms who are requesting capital infusions.

This chapter sets out to survey the traditional plane boarding strategies employed by major airlines in the commercial aviation industry. It then reviews the simulation-based and analytical approaches to getting passengers on an aircraft in the existing literature before zeroing in on the latter. A new integer program is proposed to minimize the time required to eliminate the interferences among all passengers when boarding a flight so that the plane can take off as soon as possible. The mathematical model is subsequently put to test in an example involving a hypothetical small aircraft, and the results show that it is more efficient as well as more flexible than those proposed in other studies. Other ways to shorten the enplaning time are also discussed. The contributions of the present work and several directions for future research are described in the conclusions.

A discussion of call center agent work selection preferences is presented in the context of what is referred to herein as the accept–avoid decision. In call centers where agents are given autonomy to select calls from a shared queue for work without a standard routing mechanism, it is likely that each agent uses a different set of criteria and has different preferences which influence their decision to accept a call or avoid it. In order to understand such preferences, simple heuristics are developed and implemented into an additive linear model as an example to estimate the derived utility an agent may receive from the decision to accept a call. The game theoretic implications of such decision making by a group or team of agents are also discussed to illustrate the dilemma of the universally avoided call, and how agents may compete for acceptance or avoidance of particular calls.

Almost all Supply Chain and Operations Management textbooks cover the topic of quantity discount analysis in an inventory management chapter. However, it is a challenging topic for many undergraduate students since the calculations are somewhat complicated and many students are uncomfortable with any math-related topic. In order to determine whether it is appropriate to take the advantage of the quantity discount, the classical one-step EOQ model is expanded to a multistep procedure, as all relevant textbooks illustrate when explaining the use of quantity discounts. This chapter demonstrates an alternative way to teach students in order to facilitate their understanding the topic more intuitively, and to allow the student to reach the optimal solution faster in most cases. Therefore, it is suggested that the method presented in this chapter may reduce students’ anxiety toward this topic. The simulation results show that with only two discount thresholds, this alternative method can save over 70% of the calculations. It can allow instructors to save time in the classroom and help students solve the problems faster when doing quantity discount problems on homework or exams.

As cable and satellite industry undergoes transformation in the 21st century with the onslaught of innovation-driven changes, it is important to know which company is doing better and which company is falling behind. This study compares the relative performance of eight cable companies using three factors: operating expense for every dollar of operating revenue, earnings before interest, taxes, depreciation, and amortization, and return on assets. We also evaluate the performance of each firm against itself for the period 2010–2013 to see if they show improvement or deterioration in operating efficiency.

The Analytical Hierarchy Process (AHP), a multicriteria decision support system that has been successfully applied to numerous decision-making situations, has been applied to patient assessment. The AHP was used with Timeslips™, a group storytelling program that encourages creative expression among dementia patients, to determine the optimal scale for pre and post assessment among the nine most common agitation and anxiety scales. The AHP used the six criteria identified by qualitative assessment of the nine scales: (1) validity/reliability, (2) observation period, (3) training required, (4) time to administer, (5) most appropriate administrator, and (6) accessibility/cost. The AHP indicated that the Overt Agitation & Anxiety Scale was optimal for use with Timeslips; the process and results are discussed.

A nurse home care scheduling system is described. The objective is to provide medical care at patients’ homes using the fewest number of nurses possible to deliver the required care. The heuristic scheduling system is easy to implement as a computerized adaptive system. As such, it is easy to use on a daily basis and easy to update as new data related to completed treatment and new requests are obtained. A case study illustrates the advantages of implementing such a system.

This chapter presents two multicriteria optimization models with bi and triple objectives solved with weighted-sum approach. Solved problems are allocation of personnel in a health care institution. To deal with these problems, mixed integer programming formulation has been applied. Results have shown the impact of problem parameter change for importance of the different objectives. Presented problems have been solved using AMPL programming language with solver CPLEX v9.1, with the use of branch and bound method.

This chapter presents two optimization multicriteria models (bi and triple objective) using a lexicographic approach. Solved models are formulated as assignment of workers to different jobs or services of a real hospital, taking into account the available budget and requirements of each job. Presented problems have been solved using AMPL programming language with solver CPLEX v9.1, with the use of branch and bound method for mixed integer mathematical programming.

Healthcare industry, the largest sector of the US economy, is going through a dramatic transformation as the US economy recovers out of the current recession. In this chapter, we use data envelopment analysis, an operations research technique, to benchmark the performance of 12 publicly managed care organizations against one another for the period 2009–2011. We find that only 6 companies out of 12 are 100% efficient. We also identify the areas in which inefficient companies are lagging behind their efficient peers.