Developments in linear and integer programming

In this review we describe recent developments in linear and integer (linear) programming. For over 50 years Operational Research practitioners have made use of linear optimisation models to aid decision making and over this period the size of problems that can be solved has increased dramatically, the time required to solve problems has decreased substantially and the flexibility of modelling and solving systems has increased steadily. Large models are no longer confined to large computers, and the flexibility of optimisation systems embedded in other decision support tools has made on-line decision making using linear programming a reality (and using integer programming a possibility). The review focuses on recent developments in algorithms, software and applications and investigates some connections between linear optimisation and other technologies

The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems

In this paper we conceptualize the impact of information technology on marketing decision-making. We argue that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision-making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes. Our analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management. Implications of our analysis are discussed.decision making;decision biases;information technology;marketing management support systems

Current Practices in the Development of Decision Support Systems

Decision support systems are one of the latest developments incomputer-based information systems. There are a variety of indications that their development differs in important ways from othertypes of information systems. This article reports the findings of an investigation of how 18 decision support systems were developed. Six major areas were explored: (1) the nature of the developmental approach; (2) user involvement in system development; (3) the time required for system development; (4) the incorporation of the decision maker\u27s style in the system; (5) the role of information systems and operations research/management science personnel in the developmental effort; and (6) specific procedures and techniques used in system developmen

Implikasi Sistem Pendukung Manajemen Terhadap Lingkungan Organisasi.

ABSTRAK Computer applications for management support are on the rise. The microcomputer revolution made computer available on many managers\u27 desks. Managers may now access thousands of databases and computerized analysis in their decision making. Corporate are developing distributed systems that enable easy accessibility to data stored in multiple locations. Various information systems are being integrated with each other and/or with other automated systems. Managers can make better decisions because they have more accurate information at their fingertips. Decision Support Systems, Group Decisions Support Systems, Executive Information Systems, Expert Systems, and Artificial Neural Networks, collectively called Management Support Systems (MSS), are the major technologies behind this developments. Introduction of MSS may create a significant change in organization and this article discusses the implications of implementing MSS in organizations. Key word: Manajement Support Systems, Network, Rekayasa ulan

Prevention: wrestling with new economic realities

Purpose – The purpose of this paper is to discuss the economic pressures on long-term care systems, and describe how an economic case might be made for better care, support and preventive strategies. Design/methodology/approach – Discussion of recent developments and research responses, with illustrations from previous studies. Findings – Economics evidence is highly relevant to decision makers in health, social care, and related systems. When resources are especially tight, economics evidence can sometimes persuade uncertain commissioners and others to adopt courses of action that improve the wellbeing of individuals, families, and communities. Originality/value – The paper uses long-established approaches in economic evaluation to discuss preventive and other strategies in today's challenging context

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA maturesincluding the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missionsproject focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided

Agronomic and economic performance of organic, conventional and GM-cotton in Central India - First results of a long-term farming systems comparison

Over the past five years the organic cotton production in India has grown many folds. In the conventional cotton arena, the genetically modified cotton is growing at an unprecedented rate. In view of these developments, it was considered necessary to carry out a systematic comparison between the various cotton production systems common in the area. Further, this research attempts to address the larger issues: • Put the discussion regarding the benefits and drawbacks of organic agriculture on a rational footing; • Help to identify challenges for organic agriculture that can then be addressed systematically; • Provide physical reference and meeting points for stakeholders in agricultural research and development and thus support decision-making and agricultural policy dialogue at different levels

Proposal of an Approach to Automate the Generation of a Transitic System's Observer and Decision Support using MDE

International audienceShort term decision support for manufacturing systems is generally difficult because of the initial data needed by the calculations. Previous works suggest the use of a discrete event observer in order to retrieve these data from a virtual copy of the workshop, as up to date as possible at any time. This proposal offered many perspectives, but suffers from the difficulties to generate a decision support tool combining decision calculations and observation. Meanwhile, interesting developments were made in literature about automatic generation of logic control programs for those same manufacturing systems, especially using the Model Driven Engineering. This paper suggests the use of MDE to generate logic control programs, the observer and the decision support tool at the same time, based on the same data collected by the designer of the system. Thus, the last section presents the evolution needed in the initial data structure, as well as the conception flow suggested to automatize the generatio

Fuzzy Bi-level Decision-Making Techniques: A Survey

© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques