Supply Hub in Industrial Park (SHIP): The value of freight consolidation

Industrial parks, characterized as a cluster of enterprises situated in one location to share common resources, have played an indispensible role in boosting regional economic and industrial development. However, further development has been impeded by the shortage of land space, especially for the construction of warehouses. This paper proposes the concept of Supply Hub in Industrial Park (SHIP) as a promising approach addressing this challenge. SHIP is defined as a public provider of warehousing and logistics services for manufacturing enterprises located within an industrial park. The research reported in this paper focuses on evaluating the value of freight consolidation, one of the typical benefits of applying the SHIP approach. Two mathematical models of the supply chain in a typical industrial park are formulated: with and without SHIP. Genetic Algorithm (GA) is applied for solving the two models, and sensitivity experiments are conducted for comparative analysis between the two scenarios. The computational results show that through consolidating shipments, SHIP brings benefits to the whole industrial park. Total cost savings resulted from the application of SHIP would increase with the size of the supply chain, the vehicle capacity, and the rates of fixed transportation costs and holding costs of finished products at manufacturers.     

A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem

In most real manufacturing environments, schedules are usually inevitable with the presence of various unexpected disruptions. In this paper, a rescheduling method based on the hybrid genetic algorithm and tabu search is introduced to address the dynamic job shop scheduling problem with random job arrivals and machine breakdowns. Because the real-time events are difficult to express and take into account in the mathematical model, a simulator is proposed to tackle the complexity of the problem. A hybrid policy is selected to deal with the dynamic feature of the problem. Two objectives, which are the schedule efficiency and the schedule stability, are considered simultaneously to improve the robustness and the performance of the schedule system. Numerical experiments have been designed to test and evaluate the performance of the proposed method. This proposed method has been compared with some common dispatching rules and meta-heuristic algorithms that have been widely used in the literature. The experimental results illustrate that the proposed method is very effective in various shop-floor conditions.     

A novel intelligent verification platform based on a structured analysis model

An intelligent verification platform based on a structured analysis model is presented.Using an abstract model mechanism with specific signal interfaces for user callback,the unified structured analysis data,shared by the electronic system level design,functional verification,and performance evaluation,enables efficient management review,auto-generation of code,and modeling in the transaction level.We introduce the class tree,flow parameter diagram,structured flow chart,and event-driven finite state machine as structured analysis models.As a sand table to carry maps from different perspectives and levels via an engine,this highly reusable platform provides the mapping topology to search for unintended consequences and the graph theory for comprehensive coverage and smart test cases.Experimental results show that the engine generates efficient test sequences,with a sharp increase in coverage for the same vector count compared with a random test.     

Theory of Sample Surveys

Orthogonal arrays are used widely in manufacturing and high-technology industries for quality and productivity improvement experiments. For reasons of run size economy or flexibility, nearly-orthogonal arrays are also used. The construction of orthogonal or nearly-orthogonal arrays can be quite challenging. Most existing methods are complex and produce limited types of arrays. This article describes a simple and effective algorithm for constructing mixed-level orthogonal and nearly-orthogonal arrays that can construct a variety of small-run designs with good statistical properties efficiently.     

A heuristic relief transportation planning algorithm for emergency supply chain management

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.     

Computing the positive solutions of the discrete third-order three-point right focal boundary-value problems

The existence of positive solutions to the discrete third-order three-point boundary-value problems (BVPs) was recently established in Ji and Yang [Positive solutions of discrete third-order three-point right focal boundary value problems, J. Differ. Equat. Appl. 15 (2009), pp. 185–195]. In this paper, we propose an algorithm for the computation of such positive solutions. The method is based on the power method for the dominant eigenvalue and the Crout-like factorization algorithm for the sparse system of linear equations. At each iteration of the method, it calls for a linear solver with linear computational complexity. The proposed method is extremely effective for large-scale problems. A numerical example is also included to demonstrate the effectiveness of the algorithm when applied to the third-order three-point BVPs of differential equation.     

求多个数的最大公约数的算法设计

在求两个数的最大公约数算法的基础上,研究了求多个数的最大公约数的算法,并利用C语言实现了枚举法、辗转相除法和更相减损术三种算法的程序设计。     

A 3D multi-grid algorithm for the Chan–Vese model of variational image segmentation

Variational segmentation models provide effective tools for image processing applications. Although existing models are continually refined to increase their capabilities, solution of such models is often a slow process, since fast methods are not immediately applicable to nonlinear problems. This paper presents an efficient multi-grid algorithm for solving the Chan–Vese model in three dimensions, generalizing our previous work on the topic in two dimensions, but this direct generalized method is low performance or unfeasible. So here, we first present two general smoothers for a nonlinear multi-grid method and then give our three new adaptive smoothers which can choose optimal a parameter of the smoothers automatically, also we analyse them using a local Fourier analysis and our theorem to inform how to obtain an optimal parameter and the best smoother selection. Finally, various advantages of our recommended algorithm are illustrated, using both synthetic and real images.     

Optimization of equipment capacity and an operational method based on cost analysis of a fuel cell microgrid

A microgrid requires a stable supply of electric power and heat, which is achieved by the cooperative operation of two or more pieces of equipment. The equipment capacity and the operational method of the equipment were optimized using a newly developed orthogonal array-GA (genetic algorithm) hybrid method for an independent microgrid accompanied by a fuel cell cascade system, solar water electrolysis, battery, and heat storage. This type of system had not been hardly developed until now. The objective function of the proposed system was the minimization of the total amount of equipment and fuel cost over ten years. For the first step in the proposed analysis method, the capacity of each piece of equipment and the operational method, which are considered to be close to the optimal solution of the system, are combined using the orthogonal array and factorial-effect chart, which are an experimental design technique. In the next step, the combination described above provides the initial values to the GA, and the GA searches for the optimal capacity and operational method for each piece of equipment in question. Compared with a simple GA, the convergence characteristic improves greatly using the proposed analysis method developed in this study.     

Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.