Javascript must be enabled to continue!
An Algorithm for the Penalized Multiple Choice Knapsack Problem
View through CrossRef
We present an algorithm for the penalized multiple choice knapsack problem (PMCKP), a combination of the more common penalized knapsack problem (PKP) and multiple choice knapsack problem (MCKP). Our approach is to converts a PMCKP into a PKP using a previously known transformation between MCKP and KP, and then solve the PKP greedily. For PMCKPs with well-behaved penalty functions, our algorithm is optimal for the linear relaxation of the problem.
Title: An Algorithm for the Penalized Multiple Choice Knapsack Problem
Description:
We present an algorithm for the penalized multiple choice knapsack problem (PMCKP), a combination of the more common penalized knapsack problem (PKP) and multiple choice knapsack problem (MCKP).
Our approach is to converts a PMCKP into a PKP using a previously known transformation between MCKP and KP, and then solve the PKP greedily.
For PMCKPs with well-behaved penalty functions, our algorithm is optimal for the linear relaxation of the problem.
Related Results
Droplet Distribution and Weed Control Efficacy of Unmanned Aerial Vehicle Sprayer in Wheat Crop
Droplet Distribution and Weed Control Efficacy of Unmanned Aerial Vehicle Sprayer in Wheat Crop
Herbicide application with Unmanned Aerial Vehicle (UAV) is among few breakthroughs due to drift risk and loading capacity limitations. This study explored a perspective of using U...
An Improved Generalized Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem
An Improved Generalized Quantum-Inspired Evolutionary Algorithm for Multiple Knapsack Problem
This article describes how the 0/1 Multiple Knapsack Problem (MKP), a generalization of popular 0/1 Knapsack Problem, is NP-hard and harder than simple Knapsack Problem. Solution o...
SOLVING 0 - 1 KNAPSACK PROBLEM BASED ON HYBRID GREEDY FIREWORKS ALGORITHM
SOLVING 0 - 1 KNAPSACK PROBLEM BASED ON HYBRID GREEDY FIREWORKS ALGORITHM
Aiming at the classical knapsack problem in combinatorial optimization,
in order to improve the local search ability and global search ability
of the basic fireworks algorithm, an ...
Approximating Multiobjective Knapsack Problems
Approximating Multiobjective Knapsack Problems
For multiobjective optimization problems, it is meaningful to compute a set of solutions covering all possible trade-offs between the different objectives. The multiobjective knaps...
On the Efficiency of the newly Proposed Convex Olanrewaju-Olanrewaju Lo-oλγ(|θ|) Penalized Regression-Type Estimator via GLMs Technique.
On the Efficiency of the newly Proposed Convex Olanrewaju-Olanrewaju Lo-oλγ(|θ|) Penalized Regression-Type Estimator via GLMs Technique.
In this article, we proposed a novel convex penalized regression-type estimator, termed Olanrewaju-Olanrewaju penalized regression-type estimator, denoted by Lo-oλγ(|θ|) for ultra...
Approximating the product knapsack problem
Approximating the product knapsack problem
AbstractWe consider the product knapsack problem, which is the variant of the classical 0-1 knapsack problem where the objective consists of maximizing the product of the profits o...
Parametric Solution for Linear Bicriteria Knapsack Models
Parametric Solution for Linear Bicriteria Knapsack Models
Linear weighing is a common approach to handle multiple criteria and the “knapsack” is a well-known combinatorial optimization problem. A knapsack problem with two linearly weighte...
Computing Two Heuristic Shrinkage Penalized Deep Neural Network Approach
Computing Two Heuristic Shrinkage Penalized Deep Neural Network Approach
Linear models are not always able to sufficiently capture the structure of a dataset. Sometimes, combining predictors in a non-parametric method, such as deep neural networks (DNNs...

