Background paper : "Aid effectiveness and selectivity : integrating multiple objectives into aid allocation"

Research on development aid has largely focused on the effectiveness of these transfers in promoting growth or on their allocation among developing countries. Rarely if ever did these research areas intersect, in that studies seeking to explain observed or prescribe optimal inter-country aid allocations did not take into account effectiveness issues and vice versa. Collier and Dollar (C-D, 2002), in a move broadly consistent with the IDA’s long-standing approach to its country allocation system, changed this state of affairs with their “aid selectivity” approach to inter-country aid allocation. C-D, building on the empirical work of Burnside and Dollar (B-D, 1997, 2000), which concluded that the effectiveness of aid in promoting growth depended on the policy regimes of recipient countries, derived “poverty efficient” inter-recipient aid allocations. According to the prescriptive C-D selectivity approach, optimal aid allocation favours countries with high levels of poverty, low per capita incomes and sound policy regimes. Such allocations are considered poverty efficient by maximising the number of people pulled out of poverty. Countries with unsound policies regimes receive less aid in the C-D selectivity approach as these regimes lessen aid’s impact on growth and thus poverty reduction.

Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives

In this paper, the single machine job shop scheduling problem is studied with the objectives of minimizing the tardiness and the material cost of jobs. The simultaneous consideration of these objectives is the multi-criteria optimization problem under study. A metaheuristic procedure based on simulated annealing is proposed to find the approximate Pareto optimal (non-dominated) solutions. The two objectives are combined in one composite utility function based on the decision maker’s interest in having a schedule with weighted combination. In view of the unknown nature of the weights for the defined objectives, a priori approach is applied to search for the non-dominated set of solutions based on the Pareto dominance. The obtained solutions set is presented to the decision maker to choose the best solution according to his preferences. The performance of the algorithm is evaluated in terms of the number of non-dominated schedules generated and the proximity of the obtained non-dominated front to the true Pareto front. Results show that the produced solutions do not differ significantly from the optimal solutions.

Incorporating community objectives in improved wetland management: the use of the analytic hierarchy process

Wetlands in Australia provide considerable ecological, economic, environmental and social benefits. However, the use of wetlands has been indiscriminate and significant damage to many Australian wetlands has occurred. During the last 150 years one third of the wetlands in Victoria have been lost. A conspicuous problem in wetland management is the paucity of involvement by stakeholders. This paper uses the Analytic Hierarchy Process (AHP) to incorporate stakeholder objectives in the ‘Wonga Wetlands’ on the Murray River. The study shows that the AHP can explicitly incorporate stakeholder preferences and multiple objectives to evaluate management options. The AHP also provides several approaches for policy makers to arrive at policy decisions.

Research methodologies for planning ecotourism and nature conservation

Ecotourism is an important niche market in the world tourism industry. It is becoming increasingly popular as an alternative to mass tourism. The emergence of ecotourism was facilitated by the environmental damage associated with mass tourism. Ecotourism is defined in many ways and there is no consensus as to its exact meaning. However, a number of salient elements, such as environmental conservation, maintenance of biodiversity, a satisfying experience for the visitors, study and appreciation of nature and sustainable community development, are included in many definitions. Tourism creates negative environmental externalities in the form of environmental damage. Such adverse effects can have serious implications for the tourism industry because they damage the very natural resource that forms the raw material for ecotourism. Ecotourism ventures should thus be properly planned and implemented and carefully monitored. Proper planning of ecotourism is hampered by the paucity of relevant qualitative and quantitative information. The use of analytical tools such as the Contingent Valuation Method, carrying capacity, decision analysis techniques with which multiobjective and uncertain consequences can be analysed, and other management strategies, such as the Safe Minimum Standard, can be useful in enabling better planning of ecotourism. Ecotourism can thus enhance the opportunities for better management of natural resources while providing a satisfying experience for the visitor.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Incorporating stakeholder values into regional forest planning : a value function approach

Forest management involves multiple objectives, multiple stakeholders, complex socio-ecological and political interactions. Public involvement in forest decision making is a challenging task that involves controversies. Various participatory tools such as public consultation forums, public comment processes, opinion polls are used to consult and to obtain inputs from communities. All these methods can provide useful information but they fail to quantify the trade-offs systematically and offer little help in minimizing conflicts. The Australian Regional Forest Agreement (RFA) program was implemented in response to the decades of conflicts and debate between various stakeholder groups and government over the use and management of forest resources. So far, it has not been able to minimize conflicts in the forestry sector, partly due to its poor incorporation and integration of stakeholder values. This paper uses the value functions approach in modelling stakeholder values in regional forest planning. The results of the study indicate that this method can help to incorporate value preferences effectively into the decision making process. It can also increase the transparency and credibility of the forest planning exercises such as RFA process.

Where and when to revegetate : a quantitative method for scheduling landscape reconstruction

Restoration of native vegetation is required in many regions of the world, but determining priority locations for revegetation is a complex problem. We consider the problem of determining spatial and temporal priorities for revegetation to maximize habitat for 62 bird species within a heavily cleared agricultural region, 11 000 km2 in area. We show how a reserve-selection framework can be applied to a complex, large-scale restoration-planning problem to account for multi-species objectives and connectivity requirements at a spatial extent and resolution relevant to management. Our approach explicitly accounts for time lags in planting and development of habitat resources, which is intended to avoid future population bottlenecks caused by delayed provision of critical resources, such as tree hollows. We coupled species-specific models of expected habitat quality and fragmentation effects with the dynamics of habitat suitability following replanting to produce species-specific maps for future times. Spatial priorities for restoration were determined by ranking locations (150-m grid cells) by their expected contribution to species habitat through time using the conservation planning tool, ‘‘Zonation.’’ We evaluated solutions by calculating expected trajectories of habitat availability for each species. We produced a spatially explicit revegetation schedule for the region that resulted in a balanced increase in habitat for all species. Priority areas for revegetation generally were clustered around existing vegetation, although not always. Areas on richer soils and with high rainfall were more highly ranked, reflecting their potential to support high-quality habitats that have been disproportionately cleared for agriculture. Accounting for delayed development of habitat resources altered the rank-order of locations in the derived revegetation plan and led to improved expected outcomes for fragmentation-sensitive species. This work demonstrates the potential for systematic restoration planning at large scales that accounts for multiple objectives, which is urgently needed by land and natural resource managers.

ACE-Obesity : priority setting for obesity prevention

The ACE-Obesity study uses an evidence-based approach to evaluate interventions aimed at reducing the prevalence of obesity in Australian youth. It informs decision-makers about the benefits of individual interventions and the packaging of a coherent strategy for obesity prevention and management. To avoid methodological confounding, the approach employs standardised methods including a two stage concept of benefit; a common comparator, setting and decision context; Australian data; and extensive probabilistic uncertainty testing. The technical cost-effectiveness results (cost per DALY) for each of the selected interventions will be reported. Modelling is undertaken to convert changes in behaviour to BMI outcomes and then to DALYs, and issues of the attribution of costs across multiple objectives arise. Due process is achieved by involving stakeholders on a Working Group, and by consideration of second stage filters (such as equity, acceptability and feasibility). The results are brought together in a 'league table' in which all the interventions are ranked in order of economic merit without the usual methodological concerns about results drawn from studies lacking in comparability. In packaging interventions to meet particular budget allocations, the divisibility, mutual exclusivity and returns to scale of individual interventions are considered, as well as issues of program logic, target group coverage and a range of settings.

A particle swarm based hybrid system for imbalances medical data sampling

Background
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.

Results
One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.

Conclusion
The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.

Resolving conflicts in fire management using decision theory: asset-protection versus biodiversity conservation

Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives

Solving fuzzy programming with a consistent fuzzy number ranking

 Some illustrative examples are provided to identify the ineffective and unrealistic characteristics of existing approaches to solving fuzzy linear programming (FLP) problems (with single or multiple objectives). We point out the error in existing methods concerning the ranking of fuzzy numbers and thence suggest an effective method to solve the FLP. Based on the consistent centroid-based ranking of fuzzy numbers, the FLP problems are transformed into non-fuzzy single (or multiple) objective linear programming. Solutions of FLP are then crisp single or multiple objective programming problems, which can respectively be obtained by conventional methods.

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.

Heuristic approaches for multi-criteria optimisation in kidney exchange programs

The Kidney Exchange Problem (KEP) is an optimisation problem that was first discussed in Rapaport (1986) but has only more recently been the subject of much work by combinatorial optimisation re-searchers. This has been in parallel with its increased prevalence in the medical community. In the basic formulation of a KEP, each instance of the problem features a directed graph D = (V,A) . Each node i ∈ V represents an incompatible pair wherein the patient needs to trade kidneys with the patient of another incompatible pair. The goal is to find an optimal set of cycles such that as many patients as possible receive a transplant. The problem is further complicated by the imposition of a cycle-size constraint, usually considered to be 3 or 4. Kidney exchange programs around the world implement different algorithms to solve the allocation problem by matching up kidneys from potential donors to patients. In some systems all transplants are considered equally desirable, whereas in others, ranking criteria such as the age of the patient or distance they will need to travel are applied, hence the multi-criteria nature of the KEP. To address the multi-criteria aspect of the KEP, in this paper we propose a two-stage approach for the kidney exchange optimisation problem. In the first stage the goal is to find the optimal number of exchanges, and in the second stage the goal is to maximise the weighted sum of the kidney matches, subject to the added constraint that the number of exchanges must remain optimal. The idea can potentially be extended to multiple-objectives, by repeating the process in multiple runs. In our preliminary numerical experiments, we first find the maximum number of kidney matches by using an existing open source exact algorithm of Anderson et al. (2015). The solution will then be used as an initial solution for the stage two optimisation problem, wherein two heuristic methods, steepest ascent and random ascent, are implemented in obtaining good quality solutions to the objective of maximizing total weight of exchanges. The neighbourhood is obtained by two-swaps. It is our intention in the future to implement a varying neighbourhood scheme within the same two heuristic framework, or within other meta-heuristic framework.

Multiple mini-interview : how long is long enough?

Objectives The multiple mini-interview (MMI) overcomes the limitations of the traditional panel interview by multiple sampling to provide improved objectivity and reliability. Reliability of the MMI is affected by number of stations; however, there are few data reporting the influence of interview duration on MMI outcome and reliability. We aimed to determine whether MMI stations can be shortened without affecting applicant rankings or compromising test reliability.
Methods A total of 175 applicants were interviewed and assessed at 10 8-minute stations. Applicants were scored once after 8 minutes at five control stations and twice after 5 minutes and 8 minutes at five experimental stations. Scores at 5 and 8 minutes were compared using t-tests and correlation coefficients. Rankings of applicants based on 5- and 8-minute scores were compared using Spearman's rank order coefficient. The reliability of the MMI was examined for 5- and 8-minute scores using generalisability theory.
Results Mean scores at 5 minutes were lower than mean scores at 8 minutes. Cumulative scores at 5 minutes were also lower. There were highly significant correlations between 5- and 8-minute scores at all experimental stations (0.82–0.91; P < 0.01) and between the cumulative scores at 5 and 8 minutes (0.92; P < 0.01). There was a strong correlation between applicant rankings based on cumulative 5- and 8-minute scores (Spearman's rank order coefficient 0.92). Reliability was not affected.
Conclusions Reducing the duration of MMI stations from 8 to 5 minutes conserves resources with minimal effect on applicant ranking and test reliability.