Evaluation of tender solutions for aviation training using discrete event simulation and best performance criteria

This paper describes a novel discrete event simulation (DES) methodology for the evaluation of aviation training tenders where performance is measured against “best performance” criteria. The objective was to assess and compare multiple aviation training schedules and their resource allocation plans against predetermined training objectives. This research originated from the need to evaluate tender proposals for the Australian Defence Aviation Training School that is currently undergoing aviation training consolidation and helicopter rationalization. We show how DES is an ideal platform for evaluating resource plans and schedules, and discuss metric selection to objectively encapsulate performance and permit an unbiased comparison. DES allows feasibility studies for each tender proposal to assure they satisfy system and policy constraints. Consequently, to create an objective and fair environment to compare tendered solutions, what-if scenarios have been strategically examined to consider improved implementations of the proposed solutions.

Framing the socialisation process of the DBA candidate : what can universities offer and what should candidates bring?

In Australia, the Doctorate of Business Administration (DBA) remainsa popular program but considerable anguish persists within theuniversity sector over just what it is offering students. In thisarticle, we use the process of postgraduate socialisation tounderstand how candidates, supervisors and administratorsnavigate pathways to successful completion and offering of a DBAprogram. We identify four modes of knowledge applicable to theDBA and suggest that universities and candidates may draw onone another’s cultural capital to determine which mode(s) can beoffered. We also illustrate how candidates exercise agencythrough their cultural and social capital as they move through theprogram. We present a conceptual framework to help guidefuture research, and resource allocation on the DBA.

Adaptive energy-efficient qos-aware scheduling algorithm for TCP/IP mobile cloud

The expected pervasive use of mobile cloud computing and the growing number of Internet data centers have brought forth many concerns, such as, energy costs and energy saving management of both data centers and mobile connections. Therefore, the need for adaptive and distributed resource allocation schedulers for minimizing the communication-plus-computing energy consumption has become increasingly important. In this paper, we propose and test an efficient dynamic resource provisioning scheduler that jointly minimizes computation and communication energy consumption, while guaranteeing user Quality of Service (QoS) constraints. We evaluate the performance of the proposed dynamic resource provisioning algorithm with respect to the execution time, goodput and bandwidth usage and compare the performance of the proposed scheduler against the exiting approaches. The attained experimental results show that the proposed dynamic resource provisioning algorithm achieves much higher energy-saving than the traditional schemes.

Estimating intergenerational utility distribution preferences

Resource management decisions influence not only the output of the economy but also the distribution of utility between groups within the community. The theory of Benefit Cost Analysis provides a means of incorporating this distributional change through the application of distributional or welfare weights. This paper reports the results of research designed to estimate distributional weights suitable for inclusion in a Benefit Cost Analysis framework. The findings of a choice modelling experiment estimating community preferences with respect to intergenerational utility distribution are presented to illustrate this innovative application of a stated preference technique.

Estimating intergenerational distribution preferences

Resource management decisions influence not only the output of the economy but also the distribution of utility between groups within the community. The theory of cost benefit analysis provides a means of incorporating distributional changes into the decision making calculus through the application of distributional or welfare weights. However, this practice has not been widely adopted in part due to difficulties in the estimation of distributional weights. This paper addresses this problem by using the stated preference method of choice modelling to estimate distributional weights suitable for inclusion in a cost benefit analysis framework. The findings of a choice modelling experiment designed to estimate community preferences with respect to intergenerational utility distribution illustrate the potential of this method in addressing distributional issues.

Interpreting risk allocation mechanism in public-private partnership projects : an empirical study in a transaction cost economics perspective

Risk allocation in public-private partnership (PPP) projects is currently claimed as capability driven. While lacking theoretical support, the claim is often 'violated' by current industrial practice. There is thus a need for formal mechanisms to interpret why a particular risk is retained by government in one project while transferred to private partners in another. From the viewpoint of transaction cost economics (TCE), integrated with the resource-based view (RBV) of organizational capabilities, this paper proposed a theoretical framework for understanding risk allocation practice in PPP projects. The theories underlying the major constructs and their links were articulated. Data gathered from an industry-wide survey were used to test the framework. The results of multiple linear regression (MLR) generally support the proposed framework. It has been found that partners' risk management routine, mechanism, commitment, cooperation history, and uncertainties associated with project risk management could serve to determine the risk allocation strategies adopted in a PPP project. This theoretical framework thus provides both government and private agencies with a logical and complete understanding of the process of selecting the allocation strategy for a particular risk in PPP projects. Moreover, it could be utilized to steer the risk allocation strategy by controlling certain critical determinants identified in the study. Study limitations and future research directions have also been set out.

Recognition of idle resources in time-driven activity-based costing and resource consumption accounting models

Despite its theoretical superiority over traditional volume-based costing models, the Activity-Based Costing (ABC) model has failed to replace traditional volume-based costing models in most organisations. In response to the problems of the model, Time-Driven Activity-Based Costing (TDABC) and Resource Consumption Accounting (RCA) models have been developed as costing models for next generation cost management systems. A key feature that distinguishes TDABC and RCA models from traditional volume-based costing models and the ABC model is the recognition of idle resources in resource pools.

This paper presents a discussion on implications of recognising idle resources in TDABC and RCA models on developments, maintenance and uses of cost management systems. A hypothetical case is presented to illustrate conversions of an ABC-based costing model to ones that are based on the TDABC and RCA models, and the resulting new allocation of resource costs.

Determinants of efficient risk allocation in privately financed public infrastructure projects in Australia

Risk allocation in privately financed public infrastructure projects, which are mainly referred to as public-private partnership (PPP) projects, is a challenging job due to the nature of incomplete contracting. An investigation into the mechanism that guides the formation of efficient risk allocation strategies is thus desirable. Drawing on the transaction cost economics and resource-based view of organizational capability, this paper has identified five main features of the transactions associated with risk allocation in PPP projects. They include partners’ risk management routine, partners’ risk management mechanism, partners’ cooperation history, risk management environmental uncertainty, and partners’ risk management commitment. For achieving cost efficiency, different risk allocation strategies may suit different conditions of the features. Accordingly, a theoretical framework and five hypotheses were proposed for testing. Data collected in an industrywide survey were analyzed using multiple linear regression technique. It was found that generally, the identified features are determinants in the decision-making process of efficient risk allocation. Therefore, the proposed theoretical framework provides both government and private agencies with not only a logical and holistic understanding of but also a support tool for decision making on risk allocation strategy in PPP projects. Study limitations and future research directions are also set out.

Neurofuzzy decision support system for efficient risk allocation in public-private partnership infrastructure projects

The performance of public-private partnership (PPP) infrastructure projects is largely contingent on whether the adopted risk allocation (RA) strategy is efficient. Theoretical frameworks drawing on the transaction cost economics and the resource-based view of organizational capability are able to explain the underlying mechanism but unable to accurately forecast efficient RA strategies. In this paper, a neurofuzzy decision support system (NFDSS) was developed to assist in the RA decision-making process in PPP projects. By combining fuzzy and neural network techniques, a synthesized fuzzy inference system was established and taken as the core component of the NFDSS. Evaluation results show that the NFDSS can forecast efficient RA strategies for PPP infrastructure projects at a highly accurate and effective level. A real PPP infrastructure project is used to demonstrate the NFDSS and its practical significance.

A multi-objective sensitivity approach to training providers' evaluation and quota allocation planning

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.