A flexible multicriteria decision-making methodology to support the strategic management of Science,Technology and Innovation research funding programs

Research funding programs are a policy instrument utilized by governments to influence the innovation process. They are usually elaborated, launched and managed by research funding agencies. In order to select the most adequate research projects, agencies often rely on the peer review process.This paper introduces a methodology to support funding decisions based on the peer review process. The methodology involves the use of a multicriteria decision model to support the assessment, evaluation, prioritization and selection of applications, under a multi-step decision-making process, which fits into a strategic management cycle within the agency. The Multiattribute Value Theory, being considered under a Value Focused Thinking approach, provides a basis for the construction of the multicriteria decision model. The good practices in peer review and also a logical framework for program management are considered by the methodology.A pilot study, presented in the paper, involved a retrospective implementation of a peer review process in the context of a program launched by the Ministry for Science, Technology, Innovations and Communications and the National Council of Technological and Scientific Development, in Brazil. The methodology allowed a clear distinction of roles. The agency staff in the role of decision-makers was responsible for making value judgments on behalf of the agency. The experts, in the role of committee members and ad hoc reviewers, contributed with their expertise by providing objective assessments. Such assessments served as a basis for evaluating the applications, characterizing the possible portfolios, and can be considered as data in future program evaluation studies.     

A multicriteria approach to sustainable energy supply for the rural poor

Despite significant progress in energy technology, about two billion people worldwide, particularly the poor in rural areas of developing countries, have no access to electricity. Decision-making concerning the most appropriate energy technology for supplying these areas has been difficult; existing energy decision-support tools have been useful but are mostly incomplete. Trade-offs, as well as impacts that can be positive or negative, may emerge as a result of implementing modern forms of energy. These can affect both community’s livelihoods as well as the confidence of decision-makers in relation to alternative technologies. The paper discusses a newly designed multicriteria approach and its novel robustness analysis for selecting energy generation systems for the improvement of livelihoods in rural areas. The proposed methodology builds upon a sustainable rural livelihoods framework to address multiple interactions and calculate trade-offs aimed at boosting decision-makers’ confidence in the selected technologies. The methodology is tested via a case study in Colombia.     

A stochastic multiscale model for electricity generation capacity expansion

Long-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton–Jacobi–Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models.     

On the optimal level of a small country's strategic petroleum reserve

Decisions relating to a country's strategic petroleum reserve must take into account the level of risk inherent in its petroleum imports, the cost resulting from any shortfall in the import level, the cost of storage, and finally the effects of stockpiling transactions on the sensitive spot oil markets. Of course, small countries need not take into account their effect on the global market, a fact that drastically simplifies their decision problem. We present such a simple decision model for a small country's petroleum reserve which in addition to the above factors take into account the uncertainty of the country's refining capacity. A complete analytical treatment is feasible for this model, and a specific numerical example is presented for the case of Greece.     

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type models (DC-Ph) consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.     

A linear programming approach to highly precise clock synchronization over a packet network

In this paper, we propose a linear programming-based method suitable for precise and reliable estimation of the skew of a slave clock respective to a master clock using timing information carried over an asynchronous packet network. Solving this problem is key to the viability of deploying low-cost IP-based transport technology in existing GSM networks. The paper is concluded by empirical evidence suggesting that the proposed method indeed has the potential to meet the stringent GSM precision requirements.      

A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management

This paper presents a multi-level Taguchi-factorial two-stage stochastic programming (MTTSP) approach for supporting water resources management under parameter uncertainties and their interactions. MTTSP is capable of performing uncertainty analysis, policy analysis, factor screening, and interaction detection in a comprehensive and systematic way. A water resources management problem is used to demonstrate the applicability of the proposed approach. The results indicate that interval solutions can be generated for the objective function and decision variables, and a variety of decision alternatives can be obtained under different policy scenarios. The experimental data obtained from the Taguchi’s orthogonal array design are helpful in identifying the significant factors affecting the total net benefit. Then the findings from the multi-level factorial experiment reveal the latent interactions among those important factors and their curvature effects on the model response. Such a sequential strategy of experimental designs is useful in analyzing the interactions for a large number of factors in a computationally efficient manner.     

A conflict analysis approach for illuminating distributional issues in sustainability policy

In the area of environmental and resource management and in policies aiming at sustainable development, conflicting issues and interests are the normal state of affairs. Mathematical approaches cannot of course be a panacea able to resolve all real-world conflicts; but they can help to provide more insight into the nature of these conflicts by providing systematic information. Moreover mathematical models are very useful in helping at finding potential social compromises by making a complex situation more transparent to policy-makers and lay people. This is the main objective of the conflict analysis procedure developed here. Distributional issues are taken into consideration by means of an eclectic approach using concepts from land-use planning, fuzzy cluster analysis and social choice. All the various properties presented by the proposed approach are made explicit thus allowing its evaluation on theoretical and empirical grounds. Possible relationships of complementarity or conflictuality with other existing approaches are also discussed briefly. A real-world illustrative example is presented too.     

Using phase-type models to cost stroke patient care across health,social and community services

Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.     

Joint-optimization of inventory policies on a multi-product multi-echelon pharmaceutical system with batching and ordering constraints

This paper presents a methodology to find near-optimal joint inventory control policies for the real case of a one-warehouse, n-retailer distribution system of infusion solutions at a University Medical Center in France. We consider stochastic demand, batching and order-up-to level policies as well as aspects particular to the healthcare setting such as emergency deliveries, required service level rates and a new constraint on the ordering policy that fits best the hospital’s interests instead of abstract ordering costs. The system is modeled as a Markov chain with an objective to minimize the stock-on-hand value for the overall system. We provide the analytical structure of the model to show that the optimal reorder point of the policy at both echelons is easily derived from a simple probability calculation. We also show that the optimal policy at the care units is to set the order-up-to level one unit higher than the reorder point. We further demonstrate that optimizing the care units in isolation is optimal for the joint multi-echelon, n-retailer problem. A heuristic algorithm is presented to find the near-optimal order-up-to level of the policy of each product at the central pharmacy; all other policy parameters are guaranteed optimal via the structure provided by the model. Comparison of our methodology versus that currently in place at the hospital showed a reduction of approximately 45% in the stock-on-hand value while still respecting the service level requirements.     

A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts

Elevated fuel loads are contributing to an increase in the occurrence of, and area burned by, severe wildfires in many regions across the globe. In an attempt to reverse this trend, fire and land management agencies are investing in extensive fuel management programs. However, the planning of fuel treatment activities poses complicated decision-making problems with spatial and temporal dimensions. Here, we present a mixed integer programming model for spatially explicit multi-period scheduling of fuel treatments. The model provides a flexible framework that allows for landscape heterogeneity and a range of ecological and operational considerations and constraints. The model’s functionality is demonstrated on a series of hypothetical test landscapes and a number of implementation issues are discussed.     

Models and algorithms to improve earthwork operations in road design using mixed integer linear programming

In road construction, earthwork operations account for about 25% of the construction costs. Existing linear programming models for earthwork optimization are designed to minimize the hauling costs and to balance the earth across the construction site. However, these models do not consider the removal of physical blocks that may influence the earthwork process. As such, current models may result in inaccurate estimates of optimal earthwork costs, leading to poor choices in road design. In this research, we extend the classical linear program model of earthwork operations to a mixed integer linear program model that accounts for blocks. We examine the economic impact of incorporating blocks via mixed integer linear programming, and find significant savings for most road designs in our test-set. However, the resulting model is considerably harder to solve than the original linear program. Based on structural observations, we introduce a set of algorithms that theoretically reduce the solving time of the model. We confirm this reduction in solve time with numerical experiments.     

New approach for the assignment of new European agricultural subsidies using scores from data envelopment analysis: Application to olive-growing farms in Andalusia (Spain)

More than 60% of olive-growing farms in Andalusia (Spain) would have negative returns without European agricultural subsidies. Agenda 2000 criteria imply that agricultural subsidies currently play the role of enhancing the production quality and the environmental and social values of agriculture. Although the necessity of the modulation of subsidies is stated, the EU regulations do not state which objective criteria should be used or how they should be measured, despite the fact that regulations demand objectivity in this measurement.     

An integrated approach to the one-dimensional cutting stock problem in coronary stent manufacturing

This paper presents a two-stage approach for pattern generation and cutting plan determination of the one-dimensional cutting stock problem. Calculation of the total number of patterns that will be cut and generation of the cutting patterns are performed in the first stage. On the other hand, the second stage determines the cutting plan. The proposed approach makes use of two separate integer linear programming models. One of these models is employed by the first stage to generate the cutting patterns through a heuristic procedure with the objective of minimizing trim loss. The cutting patterns obtained from Stage 1 are then fed into the second stage. In this stage, another integer linear programming model is solved to form a cutting plan. The objective of this model is to minimize a generalized total cost function consisting of material inputs, number of setups, labor hours and overdue time; subject to demand requirements, material availability, regular and overtime availability, and due date constraints. The study also demonstrates an implementation of the proposed approach in a coronary stent manufacturer. The case study focuses on the cutting phase of the manufacturing process followed by manual cleaning and quality control activities. The experiments show that the proposed approach is suitable to the conditions and requirements of the company.     

Applying simulated annealing using different methods for the neighborhood search in forest planning problems

Adjacency constraints along with even flow harvest constraints are important in long term forest planning. Simulated annealing (SA) is previously successfully applied when addressing such constraints. The objective of this paper was to assess the performance of SA under three new methods of introducing biased probabilities in the management unit (MU) selection and compare them to the conventional method that assumes uniform probabilities. The new methods were implemented as a search vector approach based on the number of treatment schedules describing sequences of silvicultural treatments over time and standard deviation of net present value within MUs (Methods 2 and 3, respectively), and by combining the two approaches (Method 4). We constructed three hundred hypothetical forests (datasets) for three different landscapes characterized by different initial age class distributions (young, normal and old). Each dataset encompassed 1600 management units. The evaluation of the methods was done by means of objective function values, first feasible iteration and time consumption. Introducing a bias in the MU selection improves solutions compared to the conventional method (Method 1). However, an increase of computational time is in general needed for the new methods. Method 4 is the best alternative because, for large parts of the datasets, produced the best average and maximum objective function values and had lower time consumption than Methods 2 and 3. Although Method 4 performed very well, Methods 2 and 3 should not be neglected because for a considerable number of datasets the maximum objective function values were obtained by these methods.     

Dynamic programming analysis of the TV game “Who wants to be a millionaire?”

This paper uses dynamic programming to investigate when contestants should use lifelines or when they should just stop answering in the TV quiz show ‘Who wants to be a millionaire?’. It obtains the optimal strategies to maximize the expected reward and to maximize the probability of winning a given amount of money.     

Inexact joint-probabilistic chance-constrained programming with left-hand-side randomness: An application to solid waste management

In practical waste management systems, amounts of waste transported and treated are not always equal on a daily basis. To distinguish between these two kinds of amounts and reflect their random relationships effectively, an inexact joint-probabilistic left-hand-side chance-constrained programming (IJLCP) method was developed and applied to a municipal solid waste management problem under dual uncertainties. Dual uncertainties are defined as two kinds of uncertainties existing in the same programming model. Improving upon conventional right-hand-side chance-constrained programming, the IJLCP can not only reflect uncertainties presented in terms of interval parameters (unit transportation/treatment costs, capacities of waste treatment facilities, waste generation rates, waste transportation/treatment amounts and so on) and left-hand-side random variables (the relationship between waste transportation and treatment amounts), but also examine the reliability of satisfying (or risk of violating) the entire system constraints. A non-equivalent but sufficient linearization form of IJLCP for solving this type of problem was proposed and proved in a straightforward manner. The performance of IJLCP was analyzed under scenarios at joint and individual probabilities and compared with the corresponding internal-parameter programming model. The results indicated that the net system costs would both decrease with increasing joint probability levels and decrease slightly at different individual probabilities with the same joint probabilities. The two types of dual uncertainties were discussed as well.     

A branch-and-price approach to the feeder network design problem

In this paper we consider the problem of designing a container liner shipping feeder network. The designer has to choose which port to serve during many rotations that start and end at a central hub. Many operational characteristics are considered, such as variable leg-by-leg speeds and cargo transit times. Realistic instances are generated from the LinerLib benchmark suite. The problem is solved with a branch-and-price algorithm, which can solve most instances to optimality within one hour. The results also provide insights on the cost structure and desirable features of optimal routes. These insights were obtained by means of an analysis where scenarios are generated varying internal and external conditions, such as fuel costs and port demands.     

Functional approach to the random mean of a compound Cox process

The parametric process and counting statistics of a marked point process whose marks belong to a given subset of the mark space of a compound Cox process are derived in this paper by means of functional data analysis. They are illustrated by means of an example and simulation study with different intensity processes for the CCP. This work was partially supported by projects MTM2004-05992 of Dirección General de Investigación, and MTM2004-04230 of Plan Nacional de I+D+I, Ministerio de Ciencia y Tecnología jointly by the FEDER.     

Measuring productivity growth under factor non-substitution: An application to US steam-electric power generation utilities

A theoretical framework is developed for decomposing partial factor productivity and measuring technical inefficiency when the underlying technology is characterized by factor non-substitution. With Farrell’s (1957) radial index of technical inefficiency being inappropriate in this case, Russell non-radial indices are adapted to measure technical inefficiency in a Leontief-type model. A system of factor demand equations with a regime specific technical inefficiency term is proposed and estimated allowing for dependence across inputs using a copula approach. Then the paper presents a complete decomposition of partial factor productivity changes using a dataset of US steam-power electric generation utilities.