669,259 research outputs found

    Supply chain management: An opportunity for metaheuristics

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    In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

    A STOCHASTIC SIMULATION-BASED HYBRID INTERVAL FUZZY PROGRAMMING APPROACH FOR OPTIMIZING THE TREATMENT OF RECOVERED OILY WATER

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    In this paper, a stochastic simulation-based hybrid interval fuzzy programming (SHIFP) approach is developed to aid the decision-making process by solving fuzzy linear optimization problems. Fuzzy set theory, probability theory, and interval analysis are integrated to take into account the effect of imprecise information, subjective judgment, and variable environmental conditions. A case study related to oily water treatment during offshore oil spill clean-up operations is conducted to demonstrate the applicability of the proposed approach. The results suggest that producing a random sequence of triangular fuzzy numbers in a given interval is equivalent to a normal distribution when using the centroid defuzzification method. It also shows that the defuzzified optimal solutions follow the normal distribution and range from 3,000-3,700 tons, given the budget constraint (CAD 110,000-150,000). The normality seems to be able to propagate throughout the optimization process, yet this interesting finding deserves more in-depth study and needs more rigorous mathematical proof to validate its applicability and feasibility. In addition, the optimal decision variables can be categorized into several groups with different probability such that decision makers can wisely allocate limited resources with higher confidence in a short period of time. This study is expected to advise the industries and authorities on how to distribute resources and maximize the treatment efficiency of oily water in a short period of time, particularly in the context of harsh environments

    Solving Limited Memory Influence Diagrams

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    We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 106410^{64} solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.Comment: 43 pages, 8 figure

    Modeling the Environment with Remote Sensing and GIS: Applied Case Studies from Diverse Locations of the United Arab Emirates (UAE)

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    Maintaining a healthy and sustainable environment is of paramount importance for human well-being and economic activity. Hence, environment protection, requiring continuous and reliable monitoring, has become a major mandate at various levels of government. There is a direct link between the availability of information about environmentally endangered areas and sound decision-making for effective and sustainable management. While remote sensing allows acquiring relevant information on a regular and repetitive basis even in areas where accessibility is limited, geographic information systems (GIS) provide unique tools for storing, processing, and integrating this information with other sources enabling the development of spatial models that help identify and characterize environmentally endangered areas. In this chapter, we will discuss how GIS-based modeling is applied for solving diverse environmental problems using case studies from United Arab Emirates

    Random Neural Networks and Optimisation

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    In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain

    An Algorithm for Biobjective Mixed Integer Quadratic Programs

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    Multiobjective quadratic programs (MOQPs) are appealing since convex quadratic programs have elegant mathematical properties and model important applications. Adding mixed-integer variables extends their applicability while the resulting programs become global optimization problems. Thus, in this work, we develop a branch and bound (BB) algorithm for solving biobjective mixed-integer quadratic programs (BOMIQPs). An algorithm of this type does not exist in the literature. The algorithm relies on five fundamental components of the BB scheme: calculating an initial set of efficient solutions with associated Pareto points, solving node problems, fathoming, branching, and set dominance. Considering the properties of the Pareto set of BOMIQPs, two new fathoming rules are proposed. An extended branching module is suggested to cooperate with the node problem solver. A procedure to make the dominance decision between two Pareto sets with limited information is proposed. This set dominance procedure can eliminate the dominated points and eventually produce the Pareto set of the BOMIQP. Numerical examples are provided. Solving multiobjective quadratic programs (MOQPs) is fundamental to our research. Therefore, we examine the algorithms for this class of problems with different perspectives. The scalarization techniques for (strictly) convex MOPs are reviewed and the available algorithms for computing efficient solutions for MOQPs are discussed. These algorithms are compared with respect to four properties of MOQPs. In addition, methods for solving parametric multiobjective quadratic programs are studied. Computational studies are provided with synthetic instances, and examples in statistics and portfolio optimization. The real-life context reveals the interplay between the scalarizations and provides an additional insight into the obtained parametric solution sets

    A decision-theoretic approach to the planning of agricultural extension : a thesis presented in partial fulfilment of the requirements for the degree of Master of Agricultural Science in Farm Management at Massey University

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    The extension agency, faced with the need to make more effective use of its resources, requires information about the value of the alternative extension messages which it expects will assist farmers to increase net income. It is hypothesised that Bernoullian decision theory is applicable to the extension agency's problem by helping it to assess the expected value of the increases in aggregate farm incomes following extension. An extension message is seen as assisting farmers to make decisions and thereby increasing expected income. Where the extension information is aimed at helping the farmer estimate the occurrence of the uncertain events in a decision problem, Bayes' theorem provides the basis for a method of obtaining the value of the information. An extension message can also assist by helping to analyse the decision problem or by providing information about some new or innovative course of action for solving the problem. The difficulty encountered by most published methods for evaluating agricultural extension is that of determining the proportion of the change in farm income due to extension and that due to other factors which are also affecting farm income. The method outlined in this thesis relies on a preposterior estimate of the value of an extension message which largely overcomes the problem of estimating the without-advice situation. A start was made on testing the proposed method by obtaining information from several dairy farmers about specific decision problems, the alternative courses of action and the other details that would enable a model of the decision problems to be synthesised. Because of the difficulty of obtaining that information, and of developing an adequate model of a problem, the attempted application was reduced to one farmer and the particular problem of summer-feeding of the herd. Summer rainfall, pasture growth, milkfat output and milkfat price were the sources of uncertainty which were incorporated into the decision model. The analysis indicated only limited potential for additional information to assist the farmer with the decision problem. The research provided some support for the hypothesis since it was found to be possible to simulate a farmer's decision problem under uncertainty and to obtain a pre-posterior estimate of the farmer's expected income without advice
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