The Vehicle Routing Problem (VRP) is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitated Vehicle Routing Problem (CVRP) constrained by the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm ...

Method for noise suppressing edge enhancement based on genetic algorithm taking into account complexity of target images measured with fractal dimension is proposed. Through experiments with satellite remote sensing imagery data with additive noise, it is found that the proposed method shows appropriate edge enhancing performance with suppressing the additive noise in accordance with complexity...

determination of shipment quantity and distribution problem is an important subject in today’s business. this paper describes the inventory/distribution network design. the system addresses a class of distribution network design problem, which is characterized by multiple products family, multiple warehouses and retail-ers. the maximum capacity of vehicles and warehouses are also known. the res...

This study is devoted to the formulation of the network design problem of one-way streets and the application of simulated annealing (SA) algorithm to solve this problem for a large real network. It discusses some points of views on one-way street networks, the objective function used for design, the way in which design constraints may be considered, and the traffic problems concerning one-way ...

This paper proposes a new algorithm using Simulated Annealing (SA) for stochastic scheduling. Stochastic effects are added with nondeterministic activity durations. Simple simulated annealing can not properly handle such a complex problem so an improved version is produced and utilized within the algorithm. Generally, the availability of resources is not enough to complete all the current activ...

In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete time–cost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The H...

In this paper a Simulated Annealing algorithm (SA) for solving the Protein Folding Problem (PFP) is presented. This algorithm has two phases: quenching and annealing. The first phase is applied at very high temperatures and the annealing phase is applied at high and low temperatures. The temperature during the quenching phase is decreased by an exponential function. We run through an efficient ...

A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, terme...

This paper addresses the dynamic vehicle routing problem. The proposed work includes the optimization in path selection using simulated annealing and hybrid memetic-genetic algorithm. In this paper, first we apply simulated annealing approach to the input of VRP.We use standard SA method that includes various types of move including insertion move, swap move,2-opt move,3-opt move to solve VRP.T...

In this paper we propose a new hybrid algorithm for solving the Unit Commitment Problem (UCP). The algorithm integrates the use of Simulated Annealing (SA) and Tabu Search (TS) for solving the UCP. The algorithm includes a step to find an initial control parameter, at which virtually all trial solutions are accepted. It uses a polynomial-time cooling schedule that is advocated in the SA literat...