This paper describes a new technique for solving the duration minimization in a resourceconstrained network. It is based on a transformation of the resource-constrained project scheduling problem (RCPSP) to a sequence of (multi)knapsack problem (MKP) solutions. In the first part, three deterministic approaches are summarized and their time complexity is discussed. Due to the combinatorial natur...

The performance of a genetic algorithm is dependent on the genetic operators, in general, and on the type of crossover operator, in particular. The population diversity is usually used as the performance measure for the premature convergence. In this paper, a fuzzy genetic algorithm is proposed for solving binary encoded combinatorial optimization problems. A new crossover operator and probabil...

we present a new model and a new approach for solving fuzzylinear programming (flp) problems with various utilities for the satisfactionof the fuzzy constraints. the model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (dm), andallows for the assignment of distinct weights to the constraints and the objectivefunction. the desired solutio...

We examine a situation in which a decision-maker executes a sequence of resource allocation decisions over time, but the availability of the indivisible resources at future epochs is uncertain due to actions of competitors. We cast this problem as a specialized type of stochastic knapsack problem in which the uncertainty of item (resource) availability is induced by competitors concurrently fil...

In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic tosolve combinatorial and multi-objective optimization problems. First, we propose a taxonomy ofACO algorithms proposed in the literature to solve multi-objective problems. Then, we studydifferent pheromonal strategies for the case of mono-objective multidimensional knapsackproblem. We...

We propose a new hybrid approach to solve the unbounded integer knapsack problem (UKP), where valid inequalities are generated based on intermediate solutions of an equivalent forward dynamic programming formulation. These inequalities help tighten the initial LP relaxation of the UKP, and therefore improve the overall computational efficiency. We also extended this approach to solve the multi-...

This paper deals with the bi-objective multi-dimensional knapsack problem. We propose the adaptation of the core concept that is effectively used in single objective multidimensional knapsack problems. The main idea of the core concept is based on the “divide and conquer” principle. Namely, instead of solving one problem with n variables we solve several sub-problems with a fraction of n variab...

We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the e$cient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assig...

Abstract Knapsack problem (KP) is a representative combinatorial optimization that aims to maximize the total profit by selecting subset of items under given constraints on weights. In this study, we analyze generalized version KP, which termed multidimensional knapsack (GMDKP). As opposed basic GMDKP allows multiple choices per item type weight constraints. Although several efficient algorithm...