Learning market prices in real-time supply chain management

This paper proposes a model for dynamic pricing that combines knowledge of production capacity and existing commitments, reasoning about uncertainty and learning of market conditions in an attempt to optimise expected profits. In particular, the market conditions are represented as a set of probabilities over the success rate of product prices, and those prices are learned online as the market develops. The dynamic pricing model is integrated into a real-time supply chain management agent using the Trading Agent Competition Supply Chain Management game as a test framework. We evaluate the agent experimentally in competition with other supply chain agents, and demonstrate the benefits of incorporating more market data into the dynamic pricing mechanism.     

Cost-Based Filtering Techniques for Stochastic Inventory Control Under Service Level Constraints

This paper¹ considers a single product and a single stocking location production/inventory control problem given a non-stationary stochastic demand. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using three cost-based filtering methods. Our approach can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all. This work was supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications Value-Chain-Driven Research (CTVR) and Grant No. 00/PI.1/C075. ¹This paper is an extended version of [19].     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

A C5 parallel robot identification and control

The paper proposes a controller scheme based on a priori identification for a C5 parallel robot. First we realize the identification of dynamic parameters of the robot using the Least Squares technique. Different data are used for this step of identification. The cross validation permitted to select and confirm the identified parameters. After, a control scheme (computed torque) is applied to control the C5 parallel robot. The functions of this control scheme are based on precedent identified parameters. In order to reduce the effect of the identification error, we have added a robustness term based on sliding mode technique. The stability of the system in closed loop is presented using the Lyapunov principle. Experimental results of identification and control are presented and show the effectiveness of our methodology.     

Throughput analysis and bandwidth allocation for IEEE 802.11 WLAN with hidden terminals

Motivated by observations from real world wireless local area network (WLAN) deployments, we develop in this paper a novel analytical model to characterize the saturation throughput of an IEEE 802.11-based access point (AP) and stations under the influence of hidden terminals. Unlike existing models, our model can accommodate different numbers of hidden nodes without increasing the model complexity. Given any number of hidden nodes, only four constraints are needed to describe the interaction between stations and the AP with the consideration of both uplink and downlink traffic. Simulation evaluation shows that our model predicts network performance accurately over a wide range of network sizes and indicates the existence of a throughput starvation problem. To address this problem, based on our model, we formulate a bandwidth allocation problem to optimize the network throughput and fairness under some predefined requirements by systematically tuning the AP and stations contention windows. Simulation results show that the starvation problem is resolved with our approach, and the target throughput is met.     

A two-phase framework for quality-aware Web service selection

Service-oriented computing is gaining momentum as the next technological tool to leverage the huge investments in Web application development. The expected large number of Web services poses a set of new challenges for efficiently accessing these services. We propose an integrated service query framework that facilitates users in accessing their desired services. The framework incorporates a service query model and a two-phase optimization strategy. The query model defines service communities that are used to organize the large and heterogeneous service space. The service communities allow users to use declarative queries to retrieve their desired services without worrying about the underlying technical details. The two-phase optimization strategy automatically generates feasible service execution plans and selects the plan with the best user-desired quality. In particular, we present an evolutionary algorithm that is able to “co-evolve” multiple feasible execution plans simultaneously and allows them to compete with each other to generate the best plan. We conduct a set of experiments to assess the performance of the proposed algorithms.     

Existence of optimal controls for systems driven by FBSDEs

We prove the existence of optimal relaxed controls as well as strict optimal controls for systems governed by non linear forward-backward stochastic differential equations (FBSDEs). Our approach is based on weak convergence techniques for the associated FBSDEs in the Jakubowski S-topology and a suitable Skorokhod representation theorem.     

Study of the impact of a new hurdle technology composed of electro‐activated solution and low heat treatment on the canned pea and corn quality and microbial safety

The objective of this work was to evaluate the quality of sterilised canned pea and corn in electro‐activated brine solutions at moderate temperatures. The lowest change in vitamin C was associated with the lowest heat treatment, while the short treatment time resulted in significant changes in texture and colour of vegetables. Best texture profile was obtained with the acid electro‐activated brine solution for pea and corn. The neutral electro‐activated brine solution resulted in a less firm texture for pea and corn. The green colour and brightness of canned pea were higher when neutral solution was used (a* = ?8.4 ± 0.3) than for the acidic one (a* = ?3.7 ± 0.6). The yellowness of corn was better with the neutral electro‐activated brine solution (b* = 36.32 ± 1.24) than with the acidic one (b* = 28.44 ± 2.39). Thirty‐three percent decrease of energy consumption was obtained using the electro‐activation technology.     

Structural Characteristics and Electrical Conductivity of Vanadium-doped lithium Ultraphosphate Glasses

Glasses of the 33.5Li₂O-66.5P₂O₅-xV₂O₅ (mol. %) system with (0 ≤ x ≤ 5) were prepared by the normal melting and annealing technique. Their physical and spectroscopic properties such as X-ray powder diffraction (XRD), density, infrared spectroscopy and electrical conductivity were investigated. The composition dependent density shows an initial decrease at 0.1 mol.% of V₂O₅ followed by an increase in density with further addition of the V₂O₅ content. The structure of these glasses has been studied by recording the Fourier-transform infrared spectroscopy (FTIR) over the range 400–1800 cm^?1. Vibrational modes are assigned to different types of phosphate groups in the glass network. The temperature (333–452 K) and composition dependence of direct current (DC) electrical conductivity were measured. The electric conductivity is observed to increase with the first addition of vanadium oxide reflecting the presence of V⁴⁺ions that take part within modifying positions in the glass network .Then, the electric conductivity is found to decrease with the further increase in the content of V₂O₅ up to 5 mol.% reflecting the formation of V⁵⁺ions that take part as network forming positions in the glass network. The variations in density and electrical conductivity with V₂O₅ content are discussed in terms of changes in the glass structure.