An intelligent computational approach to the optimization of inventory policies for single company

This study develops and tests a computational approach for determining optimal inventory policies for single company. The computational approach generally comprises of two major components: a meta-heuristic optimizer and an event-driven inventory evaluation module. Meta-heuristic is a powerful search technique, under the intelligent computational paradigm. The approach is capable of determining optimal inventory policy under various demand patterns regardless their distribution for a variety of inventory items. Two prototypes of perishability are considered: (1) sudden deaths due to disasters and (2) outdating due to expirations. Since every theoretical model is specially designed for a certain type of inventory problem while the real world inventory problems are numerous, it is desirable for the newly proposed computational approach to cover as many inventory problems/models as possible. In a way, the proposed meta-heuristic based approach unifies many theoretical models into one and beyond. Experimental results showed that the proposed approach provides comparable results to the theoretical model when demand follows their assumption. For demands not well conformed to the assumption, the proposed approaches are able to handle it but the theoretical approaches do not. This makes the proposed computational approach advantageous in that it can handle various types of real world demand data without the need to derive new models. The main motivation for this work is to bridge the gap between theory and practice so as to deliver a user-friendly and flexible computational approach for rationalizing the inventory control system for single company

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

On Tutte polynomial uniqueness of twisted wheels

AbstractA graph G is called T-unique if any other graph having the same Tutte polynomial as G is isomorphic to G. Recently, there has been much interest in determining T-unique graphs and matroids. For example, de Mier and Noy [A. de Mier, M. Noy, On graphs determined by their Tutte polynomials, Graphs Combin. 20 (2004) 105–119; A. de Mier, M. Noy, Tutte uniqueness of line graphs, Discrete Math. 301 (2005) 57–65] showed that wheels, ladders, Möbius ladders, square of cycles, hypercubes, and certain class of line graphs are all T-unique. In this paper, we prove that the twisted wheels are also T-unique

Computational multiscale methods for granular materials

AbstractThe fine-scale heterogeneity of granular material is characterized by its polydisperse microstructure with randomness and no periodicity. To predict the mechanical response of the material as the microstructure evolves, it is demonstrated to develop computational multiscale methods using discrete particle assembly-Cosserat continuum modeling in micro- and macro- scales, respectively. The computational homogenization method and the bridge scale method along the concurrent scale linking approach are briefly introduced. Based on the weak form of the Hu-Washizu variational principle, the mixed finite element procedure of gradient Cosserat continuum in the frame of the second-order homogenization scheme is developed. The meso-mechanically informed anisotropic damage of effective Cosserat continuum is characterized and identified and the microscopic mechanisms of macroscopic damage phenomenon are revealed

Probing high-momentum component in nucleon momentum distribution by neutron-proton bremsstrahlung {\gamma}-rays in heavy ion reactions

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung $\gamma$ rays produced in $^{86}$Kr + $^{124}$Sn at 25 MeV/u. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum above 30 MeV can be reproduced by the IBUU model calculations incorporating the photon production channel from $np$ process in which the HMTs of nucleons is considered. A non-zero HMT ratio of about $15\%$ is favored by the data. The effect of the capture channel $np \to d\gamma$ is demonstrated

Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; This paper proposes a new stochastic simulation optimization methodology that integrates meta-heuristic with sample average approximation for supply chain inventory optimization under supply uncertainty. The supply uncertainty specifically considered is quality imperfection/deviation modelled as a discrete or continuous distribution function. In order to approximate the expected total inventory-related cost, sufficient samples of quality deviations are generated and then the corresponding sample average function is optimized by a newly developed hybrid meta-heuristic algorithm. The proposed methodology and its individual components are presented. Numerical results of a single-distributor-multiple-retailer supply chain system with each adopting the (s, S) replenishment policy indicate that the proposed methodology is capable of obtaining high quality supply chain inventory policies with percentage optimality gap within 0.01%

Chelating efficiency and thermal, mechanical and decay resistance performances of chitosan copper complex in wood-polymer composites

Wood-polymer composites (WPC) have been extensively used for building products, outdoor decking, automotive, packaging materials, and other applications. WPC is subject to fungal and termite attacks due to wood components enveloped in the thermoplastic matrix. Much effort has been made to improve decay resistance of WPC using zinc borate and other chemicals. In this study, chitosan copper complex (CCC) compounds were used as a potential preservative for wood-HDPE composites. CCC was formulated by reacting chitosan with copper salts under controlled conditions. Inductively coupled plasma (ICP) analytical results indicated that chitosan had high chelating efficiency with copper cations. CCC-treated wood-HDPE composites had a thermal behavior similar to untreated and zinc borate-treated wood-HDPE composites. Incorporation of CCC in wood-HDPE composites did not significantly influence board density of the resultant composites, but had a negative effect on tensile strength at high CCC concentration. In comparison with solid wood and the untreated wood-HDPE composites, 3% CCC-treated wood-HDPE composites significantly improved the decay resistance against white rot fungus Trametes versicolor and brown rot fungus Gloeophyllum trabeum. Especially, CCC-treated wood-HDPE composites were more effectively against the brown rot than the untreated and chitosan-treated wood-HDPE composites. Moreover, CCC-treated wood-HDPE composites performed well as zinc borate-treated wood-HDPE composites on fungal decay resistance. Accordingly, CCC can be effectively used as a preservative for WPC