Solving single-product economic lot-sizing problem with non-increasing setup cost,constant capacity and convex inventory cost in O(N log N) time

This paper considers an economic lot sizing model with constant capacity, non-increasing setup cost, and convex inventory cost function. Algorithms with computational time of O(N×TD_N)have been developed for solving the model, where N is the number of planning periods and TD_N is the total demand. This study partially characterizes the optimal planning structure of the model. A new efficient algorithm with computational time of O(N log N) has also been developed based on the partial optimal structure. Moreover, computational study demonstrates that the new algorithm is efficient.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

Least squares twin support vector machines for pattern classification

In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.     

Recommendation engine based on derived wisdom for more similar item neighbors

Information Systems and e-Business Management - Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication...     

Wavefront diffusion and LMSR: algorithms for dynamic repartitioningof adaptive meshes

Current multilevel repartitioning schemes tend to perform well on certain types of problems while obtaining worse results for other types of problems. We present two new multilevel algorithms for repartitioning adaptive meshes that improve the performance of multilevel schemes for the types of problems that current schemes perform poorly while maintaining similar or better results for those problems that current schemes perform well. Specifically, we present a new scratch-remap scheme called Locally-matched Multilevel Scratch-remap (or simply LMSR) for repartitioning of adaptive meshes. LMSR tries to compute a high-quality partitioning that has a large amount of overlap with the original partitioning. We show that LMSR generally decreases the data redistribution costs required to balance the load compared to current scratch-remap schemes. We present a new diffusion-based scheme that we refer to as Wavefront Diffusion. In Wavefront Diffusion, the flow of vertices moves in a wavefront from overweight to underweight subdomains. We show that Wavefront Diffusion obtains significantly lower data redistribution costs while maintaining similar or better edge-cut results compared to existing diffusion algorithms. We also compare Wavefront Diffusion with LMSR and show that these provide a trade-off between edge-cut and data redistribution costs for a wide range of problems. Our experimental results on a Gray T3E, an IBM SP2, and a cluster of Pentium Pro workstations show that both schemes are fast and scalable. For example, both are capable of repartitioning a seven million vertex graph in under three seconds on 128 processors of a Gray T3E. Our schemes obtained relative speedups of between nine and 12 when the number of processors was increased by a factor of 16 on a Gray T3E     

Modeling Instrumental Rationality, Land Tenure and Conflict Resolution

Full instrumental rationality and perfect institutions are two cornerstoneassumptions underlying neoclassical models. However, in the real world, thesetwo assumptions never hold, especially not in developing countries. In thispaper, we develop a game theoretical model to investigate if relaxations inthe full instrumental rationality and perfect institutions premise can explainthe conflicts that have been occurring between the various principals in theNarok district in Kenya with regard to land tenure and use.     

Scalable parallel data mining for association rules

The authors propose two new parallel formulations of the Apriori algorithm (R. Agrawal and R. Srikant, 1994) that is used for computing association rules. These new formulations, IDD and HD, address the shortcomings of two previously proposed parallel formulations CD and DD. Unlike the CD algorithm, the IDD algorithm partitions the candidate set intelligently among processors to efficiently parallelize the step of building the hash tree. The IDD algorithm also eliminates the redundant work inherent in DD, and requires substantially smaller communication overhead than DD. But IDD suffers from the added cost due to communication of transactions among processors. HD is a hybrid algorithm that combines the advantages of CD and DD. Experimental results on a 128-processor Cray T3E show that HD scales just as well as the CD algorithm with respect to the number of transactions, and scales as well as IDD with respect to increasing candidate set size