Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design

This paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well as with two popular genetic algorithms, NSGA-II and SPEA2. We extend an existing case study of green supply chain design in the South Eastern Europe region by optimizing simultaneously costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. The results show that in the considered case the scalarization methods outperform genetic algorithms in finding efficient solutions and that the CO2 and PM emissions can be lowered by accepting a marginal increase of costs over their global minimum

Deficiencies in the transfer and availability of clinical trials evidence: A review of existing systems and standards

Background: Decisions concerning drug safety and efficacy are generally based on pivotal evidence provided by clinical trials. Unfortunately, finding the relevant clinical trials is difficult and their results are only available in text-based reports. Systematic reviews aim to provide a comprehensive overview of the evidence in a specific area, but may not provide the data required for decision making. Methods: We review and analyze the existing information systems and standards for aggregate level clinical trials information from the perspective of systematic review and evidence-based decision making. Results: The technology currently used has major shortcomings, which cause deficiencies in the transfer, traceability and availability of clinical trials information. Specifically, data available to decision makers is insufficiently structured, and consequently the decisions cannot be properly traced back to the underlying evidence. Regulatory submission, trial publication, trial registration, and systematic review produce unstructured datasets that are insufficient for supporting evidence-based decision making. Conclusions: The current situation is a hindrance to policy decision makers as it prevents fully transparent decision making and the development of more advanced decision support systems. Addressing the identified deficiencies would enable more efficient, informed, and transparent evidence-based medical decision making

ADDIS:A decision support system for evidence-based medicine

<p>Clinical trials are the main source of information for the efficacy and safety evaluation of medical treatments. Although they are of pivotal importance in evidence-based medicine, there is a lack of usable information systems providing data-analysis and decision support capabilities for aggregate clinical trial results. This is partly caused by unavailability (i) of trial data in a structured format suitable for re-analysis, and (ii) of a complete data model for aggregate level results. In this paper, we develop a unifying data model that enables the development of evidence-based decision support in the absence of a complete data model. We describe the supported decision processes and show how these are implemented in the open source ADDIS software. ADDIS enables semi-automated construction of meta-analyses, network meta-analyses and benefit-risk decision models, and provides visualization of all results. (C) 2012 Elsevier B.V. All rights reserved.</p>