Using revenue management to improve pricing and capacity management in programme management

This paper presents revenue management models for pricing, capacity planning, and capacity reallocation and demonstrates their applicability for programme (project) management. In programme management, the allocation of capacity (resource time) to schedule activities requires the resolution of time versus revenue trade-offs. Thus, capacity planning and scheduling present a hierarchical problem for programme managers. Furthermore, current programme management methods do not consider the issue of price sensitivity exhibited in many programme management situations. Because of this omission, critical linkages between capacity management and scheduling of activities among programmes have not been addressed. Specifically, the issue of the reservation of capacity specifically for higher revenue generating activities has been omitted from programme management research. This paper asserts that, through capacity planning and scheduling, specific capacity should be reserved for customers willing to pay higher prices to have critical activities, for example, change orders, expedited. This capacity has scheduling effects that impact the programme NPV. This paper proposes potential solutions to capacity and programme scheduling problems using revenue management techniques.     

Cash management in a randomly varying environment

We study a cash management system, in which the distribution of the cash flow X_n in period n=1,2,… depends on the state I_n of a randomly varying environment. Sufficient conditions are found for the optimality of a simple transfer rule, generalizing and partially improving the well-known results for the classical case with i.i.d. cash flows. These and further structural results obtained for the cash balance are shown to reduce the computational effort drastically in determining an optimal transfer rule. In addition, structural and computational results w.r.t. the environment are derived. Finally, some examples are given for economic and statistical environments and their interactions with the cash flow process.     

Preemptive expediting to improve project due date performance

Project expediting is often viewed as a corrective action taken in response to prior scheduling errors and is usually applied in the later stages of projects when it appears that predefined due dates will not be met. However, large-scale projects with uncertain activity durations tend to have numerous probabilistic network paths with complex interactions that require some level of expediting to ensure successful scheduling outcomes. Because potential expediting options are consumed over time, delaying expediting efforts until the later stages of projects is likely to result in higher expediting costs or poorer due date performance. This research introduces a preemptive expediting approach that evaluates the probability of completion before the due date throughout the life of a project and selects expediting options per a prespecified probability tolerance or expediting budget. An experiment demonstrates the benefits of this preemptive approach.     

How to improve the performance of ATM multiplexers

We consider an ATM multiplexer with a finite or infinite buffer and stochastic output rate. There are independent, identical sources connected to this multiplexer which transmit fluid directly into the buffer. We show that the throughput of the multiplexer is increasing in the number of sources connected, where we scale the cell stream process in such a way that the mean and peak input rate stays unchanged. In the finite buffer case, the cell loss is decreasing in the number of sources connected. Hence more links improve the performance of ATM multiplexers. In the last part, we prove that correlations within cell stream processes have a negative effect on the performance of ATM multiplexers. These investigations provide easily computable lower bounds to performance measures.     

Prior-Ratio-Analysis procedure to improve data envelopment analysis for performance measurement

This paper proposes the use of a Prior-Ratio-Analysis procedure, analysing output/input ratio indicators, allowing the improvement in efficiency measurement by means of data envelopment analysis (DEA) methodology. This prior analysis is based on the existence of a relationship of individual ratio in the firms to DEA efficiency scores. Use of the proposed procedure allows (i) detection of efficient units whose efficiency could be overestimated and (ii) identification of certain inputs/outputs enhancing particular behaviours. Accordingly, the DEA efficiency analysis could be improved with a major understanding about the factors determining the unit efficiency, and with a measure as a true indicator for discriminating between units, and for ranking them.     

Scheduling healthcare services in a home healthcare system

We consider a scheduling problem in a home healthcare system in which nurses visit patients regularly for relatively minor healthcare services. Intervals between the visits may differ for different patients. On each day in the planning horizon, a nurse must visit the patients assigned to her/him on that day, and then return to the hospital. For the problem of determining the visiting schedule with the objective of minimizing total travel time of the nurse over the planning horizon, we develop a two-phase heuristic algorithm. To evaluate performance of the proposed algorithm, a series of computational tests is performed on a number of randomly generated problem instances and a real instance. Results of the tests show that the heuristic algorithm gives near optimal solutions for problems of practical sizes in a reasonable time.     

Action research to improve methods of delivery and feedback in an Access Grid Room environment

This article describes a qualitative study which was undertaken to improve the delivery methods and feedback opportunity in honours mathematics lectures which are delivered through Access Grid Rooms. Access Grid Rooms are facilities that provide two-way video and audio interactivity across multiple sites, with the inclusion of smart boards. The principal aim was to improve the student learning experience, given the new environment. The specific aspects of the course delivery that the study focused on included presentation of materials and provision of opportunities for interaction between the students and between students and lecturers. The practical considerations in the delivery of distance learning are well documented in the literature, and similar problems arise in the Access Grid Room environment; in particular, those of limited access to face-to-face interaction and the reduction in peer support. The nature of the Access Grid Room classes implies that students studying the same course can be physically situated in different cities, and possibly in different countries. When studying, it is important that students have opportunity to discuss new concepts with others; particularly their peers and their lecturer. The Access Grid Room environment also presents new challenges for the lecturer, who must learn new skills in the delivery of materials. The unique nature of Access Grid Room technology offers unprecedented opportunity for effective course delivery and positive outcomes for students, and was developed in response to a need to be able to interact with complex data, other students and the instructor, in real-time, at a distance and from multiple sites. This is a relatively new technology and as yet there has been little or no studies specifically addressing the use and misuse of the technology. The study found that the correct placement of cameras and the use of printed material and smart boards were all crucial to the student experience. In addition, the inclusion of special tutorial type sessions were necessary to provide opportunities to students for one-on-one discussion with both lecturer and other students. This study contributes to the broader understanding of distance education in general and future Access Grid Room course delivery in particular.     

The effective communication of system dynamics to improve insight and learning in management education

Experienced exponents of system dynamics in management education appreciate that feedback, non-linearities and delays are ubiquitous, and create difficulties for making intuitive judgements about the dynamic behaviour of business systems. They have applied much effort to solving this problem, offering simple, high-level causal mapping techniques to conceptualise dynamic issues, formal simulation modelling, and simulation-based learning environments. However, the use of these methods is still not extensive, and it is often disconnected from other management development pedagogies.System dynamics is ideally suited to operationalising certain concepts central to the management field, notably the resource-based and competence-based view of strategic management. However, three developments must be made if this potential is to be exploited. Firstly, system dynamics must connect with those established concepts and frameworks in the management field. Secondly, the barriers for managers to appreciate the power of system dynamics must be lowered, to make the method an integral part of the managerial mind-set. Thirdly, managers need help to climb the learning curve of understanding regarding the dynamic behaviour of the business systems they endeavour to manage, through a comprehensive set of resource-mapping and gaming simulation tools.This paper describes how these developments can be implemented, reports on experience of using the resulting learning devices and comments on possible future directions. There is an exciting opportunity for system dynamics to make a major contribution to a new strategy paradigm, based on a dynamic resource-system view of the firm, a perspective that can be extended to other fields in management and to non-business contexts.     

Supporting decision making to improve the performance of an Italian Emergency Medical Service

An Emergency Medical Service (EMS) plays a fundamental role in providing good quality health care services to citizens, as it provides the first answer in distressing situations. Early response, one of the key factors in a successful treatment of an injury, is strongly influenced by the performance of ambulances, which are sent to rescue the patient. Here we report the research carried on by the authors on the ambulance location and management in the Milano area (Italy), as a part of a wider research project in collaboration with the EMS of Milano and funded by Regione Lombardia. The question posed by the EMS managers was clear and, at the same time, tricky: could decision making tools be applied, based on the currently available data, to provide suggestions for decision makers? To answer such a question, three different studies have been carried on: first the evaluation of the current EMS system performance through statistical analysis; then the study of operational policies which can improve the system performance through a simulation model; and finally the definition of an alternative set of posts through an optimization model. This paper describes the methodologies underlying such studies and reports on how their main findings were crucial to help the EMS in changing its organization model.     

Stochastic multiobjective problems with complementarity constraints and applications in healthcare management

We consider a class of stochastic multiobjective problems with complementarity constraints (SMOPCCs) in this paper. We derive the first-order optimality conditions including the Clarke/Mordukhovich/strong-type stationarity in the Pareto sense for the SMOPCC. Since these first-order optimality systems involve some unknown index sets, we reformulate them as nonlinear equations with simple constraints. Then, we introduce an asymptotic method to solve these constrained equations. Furthermore, we apply this methodology results to a patient allocation problem in healthcare management.     

Prepositioning can improve the performance of a dynamic stochastic on-demand public bus system

This paper presents the first application of prepositioning in the context of the dynamic stochastic on-demand bus routing problem (DODBRP). The DODBRP is a large-scale dial-a-ride problem that involves bus station assignment and aims to minimize the total user ride time (URT) by simultaneously assigning passengers to alternative stations and determining optimal bus routes.In the DODBRP, transportation requests are introduced dynamically, and buses are dispatched to stations with known requests. This paper investigates the concept of prepositioning, which involves sending buses not only to currently known requests but also to requests that are likely to appear in the future, based on a given probability.To solve this dynamic and stochastic ODBRP, the paper proposes a heuristic algorithm based on variable neighborhood search (VNS). The algorithm considers multiple scenarios to represent different realizations of the stochastic requests.Experimental results demonstrate the superiority of the prepositioning approach over the DODBRP across various levels of forecast accuracy, lengths of time bucket, and probabilities of realization. Furthermore, the paper shows that removing empty stations as a recourse action can further enhance solution quality. Additionally, in situations with low prediction accuracy, increasing the number of scenarios can lead to improved solutions. Finally, a combination of prepositioning, empty station removal, and the insertion of dynamic requests proves to be effective.Overall, the findings of this paper provide valuable insights into the application of prepositioning in the dynamic stochastic on-demand bus routing problem, highlighting its potential for addressing real-world transportation challenges.     

Bounded delay packet scheduling in a bounded buffer

We study buffer management in QoS-enabled network switches in the bounded delay model where each packet has a weight and a deadline. We consider the more realistic situation where the switch has a finite buffer size. A 9.82-competitive algorithm is known for the case of multiple buffers. Recently, for the single buffer case, a 3-competitive deterministic algorithm and a 2.618-competitive randomized algorithm were found. We give a simple deterministic 2-competitive algorithm for the single buffer case.     

Scheduling arrivals to a production system in a fuzzy environment

A frequently encountered scheduling problem is to determine a material and job ready time while simultaneously finding a production sequence given customer-specified due dates. Often the production times and due dates are vague. This paper presents an investigation of scheduling ready times for a set of jobs with fuzzy service times and due dates. The ready time is constrained in that the possibility that a job is late must not exceed a predefined value. The objective in such an instance is to maximize the ready time without violating these constraints. The steps necessary to determine the maximum ready time and cases in which this effort may be significantly reduced are presented for single machine and flow shop production systems. Finally, a branch and bound technique is developed for cases in which the optimal job sequence cannot be determined a priori.     

Using store level scanner data to improve category management decisions: Developing positioning maps

This paper provides evidence of the usefulness of aggregated point-of-sale scanner data to infer the positioning of competing brands, providing valuable information for category management and hence facilitating decision making. Specifically, the authors propose a methodology to study the internal market structure based on market share models with latent heterogeneity when only macro-level time series data (not individual choices) are available. The proposed approach assumes a multidimensional decomposition, latent in the preference structure that is implicit to these types of models. By empirically applying this approach, the authors (1) simultaneously identify both latent dimensions of competing brands and latent segments with different brand preferences, (2) explain the competitive positioning of brands without using disaggregated consumer panel data, and (3) achieve greater predictive performance. The findings offer insights to academics and practitioners interested in improving the practice of category management.