Time-Frequency Analysis.

Ingredients of Locational Analysis (J. Krarup & P. Pruzan). The p-Median Problem and Generalizations (P. Mirchandani). The Uncapacitated Facility Location Problem (G. Cornuejols, et al.). Multiperiod Capacitated Location Models (S. Jacobsen). Decomposition Methods for Facility Location Problems (T. Magnanti & R. Wong). Covering Problems (A. Kolen & A. Tamir). p-Center Problems (G. Handler). Duality: Covering and Constraining p-Center Problems on Trees (B. Tansel, et al.). Locations with Spatial Interactions: The Quadratic Assignment Problem (R. Burkard). Locations with Spatial Interactions: Competitive Locations and Games (S. Hakimi). Equilibrium Analysis for Voting and Competitive Location Problems (P. Hansen, et al.). Location of Mobile Units in a Stochastic Environment (O. Berman, et al.). Index.

https://link.springer.com/content/pdf/10.1057%2Fjors.1991.208.pdf

Discrete location theory

A follow-up survey of berth allocation and quay crane scheduling problems in container terminals

https://ir.nctu.edu.tw:443/bitstream/11536/100881/1/982410H009004.PDF

國際期刊 Transportation Research-Part E---Logistics and Transportation Review 特刊編輯補助

Cambridge University Press. Paperback. Book Condition: New. Paperback. 480 pages. Numerical analysis presents different faces to the world. For mathematicians it is a bona fide mathematical theory with an applicable flavour. For scientists and engineers it is a practical, applied subject, part of the standard repertoire of modelling techniques. For computer scientists it is a theory on the interplay of computer architecture and algorithms for real-number calculations. The tension between these standpoints is the driving force of this book, which presents a rigorous account of the fundamentals of numerical analysis of both ordinary and partial differential equations. The exposition maintains a balance between theoretical, algorithmic and applied aspects. This new edition has been extensively updated, and includes new chapters on emerging subject areas: geometric numerical integration, spectral methods and conjugate gradients. Other topics covered include multistep and Runge-Kutta methods; finite difference and finite elements techniques for the Poisson equation; and a variety of algorithms to solve large, sparse algebraic systems. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback.

A First Course in the Numerical Analysis of Differential Equations: Stiff equations

To better reflect the uncertainty existing in the actual disassembly environment, the multi-objective disassembly line balancing problem with fuzzy disassembly times is investigated in this paper First, a mathematical model of the multi-objective fuzzy disassembly line balancing problem (MFDLBP) is presented, in which task disassembly times are assumed as triangular fuzzy numbers (TFNs) Then a Pareto improved artificial fish swarm algorithm (IAFSA) is proposed to solve the problem The proposed algorithm is inspired from the food searching behaviors of fish including prey, swarm and follow behaviors An order crossover operator of the traditional genetic algorithm is employed in the prey stage The Pareto optimal solutions filter mechanism is adopted to filter non-inferior solutions The proposed model after the defuzzification is validated by the LINGO solver And the validity and the superiority of the proposed algorithm are proved by comparing with a kind of hybrid discrete artificial bee colony (HDABC) algorithm using two test problems Finally, the proposed algorithm is applied to a printer disassembly instance including 55 disassembly tasks, for which the computational results containing 12 non-inferior solutions further confirm the practicality of the proposed Pareto IAFSA in solving the MFDLBP

A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

The safety hazards existing in the process of disassembling waste products pose potential harms to the physical and mental health of the workers. In this article, these hazards involved in the disa...

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

We present a numerical implementation of the fast Galerkin method for Fredholm integral equations of the second kind using the piecewise polynomial wavelets. We focus on addressing critical issues for the numerical implementation of such a method. They include a choice of practical truncation strategy, numerical integration of weakly singular integrals and the error control of the numerical quadrature. We also implement a multiscale iteration method for solving the resulting compressed linear system. Numerical examples are given to demonstrate the proposed ideas and methods.

An Implementation of Fast Wavelet Galerkin Methods for Integral Equations of the Second Kind

A theorem characterizing the property of the optimal solution is presented.A hybrid evolutionary algorithm (HEA) is proposed.Extreme machine learning is used to approximate the fitness of most individuals.Two heuristics are incorporated to generate a good initial population.The proposed algorithm is computationally effective. This paper considers the two-stage capacitated facility location problem (TSCFLP) in which products manufactured in plants are delivered to customers via storage depots. Customer demands are satisfied subject to limited plant production and limited depot storage capacity. The objective is to determine the locations of plants and depots in order to minimize the total cost including the fixed cost and transportation cost. However, the problem is known to be NP-hard. A practicable exact algorithm is impossible to be developed. In order to solve large-sized problems encountered in the practical decision process, an efficient alternative approximate method becomes more valuable. This paper aims to propose a hybrid evolutionary algorithm framework with machine learning fitness approximation for delivering better solutions in a reasonable amount of computational time. In our study, genetic operators are adopted to perform the search process and a local search strategy is used to refine the best solution found in the population. To avoid the expensive consumption of computational time during the fitness evaluating process, the framework uses extreme machine learning to approximate the fitness of most individuals. Moreover, two heuristics based on the characteristics of the problem is incorporated to generate a good initial population. Computational experiments are performed on two sets of test instances from the recent literature. The performance of the proposed algorithm is evaluated and analyzed. Compared with other algorithms in the literature, the proposed algorithm can find the optimal or near-optimal solutions in a reasonable amount of computational time. By employing the proposed algorithm, facilities can be positioned more efficiently, which means the fixed cost and the transportation cost can be decreased significantly, and organizations can enhance competitiveness by using the optimized facility location scheme.

Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems

This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Levy flights and the cr...

Yi Wang

Papers

A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

A Pareto firefly algorithm for multi-objective disassembly line balancing problems with hazard evaluation

An Implementation of Fast Wavelet Galerkin Methods for Integral Equations of the Second Kind

Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems

Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm