Introducing capacitaties in the location of unreliable facilities

The goal of this paper is to introduce facility capacities into the Reliability Fixed-Charge Location Problem in a sensible way. To this end, we develop and compare different models, which represent a tradeoff between the extreme models currently available in the literature, where a priori assignments are either fixed, or can be fully modified after failures occur. In a series of computational experiments we analyze the obtained solutions and study the price of introducing capacity constraints according to the alternative models both, in terms of computational burden and of solution cost.Peer ReviewedPostprint (author's final draft

Handbook on Climate Change and Disaster Resilient Water, Sanitation and Hygiene Practices

This handbook was developed to help governmental and non-governmental agencies involved in safe water delivery, sanitation hygiene at local level, union and ward disaster management committees, to enhance their respective capacities to cope with climate change and disaster risks. It considers the rural context of Bangladesh and provides field-level workers and practitioners practical ideas about water supply, sanitation and hygiene practices in the context of climate change and disaster risk

An exact approach for the reliable fixed-charge location problem with capacity constraints

Introducing capacities in the reliable fixed charge location problem is a complex task since successive failures might yield in high facility overloads. Ideally, the goal consists in minimizing the total cost while keeping the expected facility overloads under a given threshold. Several heuristic approaches have been proposed in the literature for dealing with this goal. In this paper, we present the first exact approach for this problem, which is based on a cutting planes algorithm. Computational results illustrate its good performancePostprint (published version

The Effects of Experience on Selecting Innovation Projects: Better the Devil You Know

Innovation success depends heavily on firm's ability to set priorities and select the most promising options from its project portfolio before the odds of success or failure become visible and reliable. We ask: What does previous innovation experience tell firms about what not to do in the future? With this in mind, we focus on projects that did not materialise or were abandoned - an important building block for choosing and implementing the right projects. We suggest two major learning mechanisms. On the one hand, real options theory suggests a process based on financial data. On the other hand, research on absorptive capacities finds that previous innovation experience translates into superior ability to value, extract and exploit external knowledge. We test both hypotheses on an empirical basis for more than 600 German firms, covering innovation activities in the period 1997 to 2005. Our results indicate congruence between firms' innovation experience and their project selection patterns. Extensive R&D experience materialises as a stock of knowledge that enables firms to judge projects based on knowledge criteria. Non-R&D innovation experience, stemming from producing and introducing products to markets, resonates as decision-making based on economic factors in the future. Both types of innovation experience appear to generate distinct decision-making capabilities inside the firm which are subsequently exploited in selecting projects for the future. --Project selection,real options,absorptive capacity

The Potential for Wind Energy Meeting Electricity Needs on Vancouver Island

In this paper, an in-depth analysis of power supply and demand on Vancouver Island is used to provide information about the optimal allocation of power across ‘generating’ sources and to investigate the economics of wind generation and penetrability into the Island grid. The methodology developed can be extended to a region much larger than Vancouver Island. Results from the model indicate that Vancouver Island could experience blackouts in the near future unless greater name-plate capacity is developed. While wind-generated energy has the ability to contribute to the Island’s power needs, the problem with wind power is its intermittency. The results indicate that wind power may not be able to prevent shortfalls, regardless of the overall name-plate capacity of the wind turbines. Further, costs of reducing CO2 emissions using wind power are unacceptably large, perhaps more than $100 per t CO2, although this might be attributable to the mix of power sources making up the Island’s grid.Economics of wind power, grid system modeling, operations research

Effect of Continuity Rate on Multistage Logistic Network Optimization under Disruption Risk

Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disruption is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and operational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan

Models, Theoretical Properties, and Solution Approaches for Stochastic Programming with Endogenous Uncertainty

In a typical optimization problem, uncertainty does not depend on the decisions being made in the optimization routine. But, in many application areas, decisions affect underlying uncertainty (endogenous uncertainty), either altering the probability distributions or the timing at which the uncertainty is resolved. Stochastic programming is a widely used method in optimization under uncertainty. Though plenty of research exists on stochastic programming where decisions affect the timing at which uncertainty is resolved, much less work has been done on stochastic programming where decisions alter probability distributions of uncertain parameters. Therefore, we propose methodologies for the latter category of optimization under endogenous uncertainty and demonstrate their benefits in some application areas. First, we develop a data-driven stochastic program (integrates a supervised machine learning algorithm to estimate probability distributions of uncertain parameters) for a wildfire risk reduction problem, where resource allocation decisions probabilistically affect uncertain human behavior. The nonconvex model is linearized using a reformulation approach. To solve a realistic-sized problem, we introduce a simulation program to efficiently compute the recourse objective value for a large number of scenarios. We present managerial insights derived from the results obtained based on Santa Fe National Forest data. Second, we develop a data-driven stochastic program with both endogenous and exogenous uncertainties with an application to combined infrastructure protection and network design problem. In the proposed model, some first-stage decision variables affect probability distributions, whereas others do not. We propose an exact reformulation for linearizing the nonconvex model and provide a theoretical justification of it. We designed an accelerated L-shaped decomposition algorithm to solve the linearized model. Results obtained using transportation networks created based on the southeastern U.S. provide several key insights for practitioners in using this proposed methodology. Finally, we study submodular optimization under endogenous uncertainty with an application to complex system reliability. Specifically, we prove that our stochastic program\u27s reliability maximization objective function is submodular under some probability distributions commonly used in reliability literature. Utilizing the submodularity, we implement a continuous approximation algorithm capable of solving large-scale problems. We conduct a case study demonstrating the computational efficiency of the algorithm and providing insights

Modelling the feedback effects of reconfiguring health services

The shift in the balance of health care, bringing services ‘closer to home’, is a well-established trend, which has been motivated by the desire to improve the provision of services. However, these efforts may be undermined by the improvements in access stimulating demand. Existing analyses of this trend have been limited to isolated parts of the system with calls to control demand with stricter clinical guidelines or to meet demand with capacity increases. By failing to appreciate the underlying feedback mechanisms, these interventions may only have a limited effect. We demonstrate the contribution offered by system dynamics modelling by presenting a study of two cases of the shift in cardiac catheterization services in the UK. We hypothesize the effects of the shifts in services and produce model output that is not inconsistent with real world data. Our model encompasses several mechanisms by which demand is stimulated. We use the model to clarify the roles for stricter clinical guidelines and capacity increases, and to demonstrate the potential benefits of changing the goals that drive activity

A cost effectiveness and capacity analysis for the introduction of universal rotavirus vaccination in Kenya : comparison between Rotarix and RotaTeq vaccines

Background Diarrhoea is an important cause of death in the developing world, and rotavirus is the single most important cause of diarrhoea associated mortality. Two vaccines (Rotarix and RotaTeq) are available to prevent rotavirus disease. This analysis was undertaken to aid the decision in Kenya as to which vaccine to choose when introducing rotavirus vaccination. Methods Cost-effectiveness modelling, using national and sentinel surveillance data, and an impact assessment on the cold chain. Results The median estimated incidence of rotavirus disease in Kenya was 3015 outpatient visits, 279 hospitalisations and 65 deaths per 100,000 children under five years of age per year. Cumulated over the first five years of life vaccination was predicted to prevent 34% of the outpatient visits, 31% of the hospitalizations and 42% of the deaths. The estimated prevented costs accumulated over five years totalled US$1,782,761 (direct and indirect costs) with an associated 48,585 DALYs. From a societal perspective Rotarix had a cost-effectiveness ratio of US$142 per DALY (US$5 for the full course of two doses) and RotaTeq US$288 per DALY ($10.5 for the full course of three doses). RotaTeq will have a bigger impact on the cold chain compared to Rotarix. Conclusion Vaccination against rotavirus disease is cost-effective for Kenya irrespective of the vaccine. Of the two vaccines Rotarix was the preferred choice due to a better cost-effectiveness ratio, the presence of a vaccine vial monitor, the requirement of fewer doses and less storage space, and proven thermo-stability