Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as ma...

In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental ...

The large neighborhood search (LNS) framework introduced by Shaw [11] provides a powerful hybridization of Local Search (LS) and Constraint Programming (CP). The idea of LNS is to combine the expressiveness of CP and the efciency of Local Search (LS) without requiring any meta-heuristic. LNS consists in improving a best-so-far solution by iteratively relaxing it and optimizing this solution usi...

the first integral method is an efficient method for obtaining exact solutions of some nonlinear partial differential equations. this method can be applied to non integrable equations as well as to integrable ones. in this paper, the first integral method is used to construct exact solutions of the 2d ginzburg-landau equation.

in this study, a two-objective mixed-integer linear programming model (milp) for multi-product re-entrant flow shop scheduling problem has been designed. as a result, two objectives are considered. one of them is maximization of the production rate and the other is the minimization of processing time. the system has m stations and can process several products in a moment. the re-entrant flow sho...

This paper presents the integration between a co-operative co-evolutionary genetic algorithm (CCGA) and four evolutionary multiobjective optimisation algorithms (EMOAs): a multi-objective genetic algorithm (MOGA), a niched Pareto genetic algorithm (NPGA), a nondominated sorting genetic algorithm (NSGA) and a controlled elitist nondominated sorting genetic algorithm (CNSGA). The resulting algori...

The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the sea...

The steady state of the quasilinear convection-diffusion-reaction equation ut −∇(D(u)∇u) + b(u)∇u+ c(u) = 0 (1) is studied. Depending on the ratio between convection and diffusion coefficients, equation (1) ranges from parabolic to almost hyperbolic. From a numerical point of view two main difficulties can arise related with the existence of layers and/or the non smoothness of the coefficients....

This paper investigates fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme (FPGA) for searching Pareto-optimal set, which is based on a new approach of fast fitness identification algorithm for individual and a clustering on the basis of external population updating scheme to maintain population diversity and even distribution of Pareto so...

A different approach to the capacitated single allocation hub location problem is presented. Instead of using capacity constraints to limit the amount of flow that can be received by the hubs, we introduce a second objective function to the model (besides the traditional cost minimizing function), that tries to minimize the time to process the flow entering the hubs. Two bi-criteria single allo...