The Vehicle Routing Problem (VRP) is a NP-hard and Combinatorial optimization problem. Combinatorial optimization problem can be viewed as searching for best element in a set of discrete items, which can be solved using search algorithm or meta heuristic. In this work, VRP is solved using population based search algorithm, Particle Swarm Optimization (PSO) with crossover and mutation operators....

the network design problem (ndp) is one of the important problems in combinatorial optimization. among the network design problems, the multicommodity capacitated network design (mcnd) problem has numerous applications in transportation, logistics, telecommunication, and production systems. the mcnd problems with splittable flow variables are np-hard, which means they require exponential time t...

The molecule problem is that of determining the relative locations of a set of objects in Euclidean space relying only upon a sparse set of pairwise distance measurements. This NP{hard problem has applications in the determination of molecular conformation. The molecule problem can be naturally expressed as a continuous, global optimization problem, but it also has a rich combinatorial structur...

We show that the optimization problem is NP-hard for a wide class of motion planning puzzles, including classical SOKOBAN. We investigate a new problem, the Box Mover Problem (BMP), in which the agent is allowed to lift and carry boxes on a rectilinear grid in order to rearrange them. Some classical motion planning puzzles are special cases of BMP. We also identify a natural class of BMP instan...

this paper addresses the problem of minimizing the sum of maximum earliness and tardiness on identical parallel machines scheduling problem. each job has a processing time and a due date. since this problem is trying to minimize and diminish the values of earliness and tardiness, the results can be useful for just–in-time production systems. it is shown that the problem is np-hard. using effici...

The following optimization problem is studied. There are several sets of integer positive numbers whose values are uncertain. The problem is to select one representative of each set such that the sum of the selected numbers is minimum. The uncertainty is modeled by discrete and interval scenarios, and the min–max and min–max (relative) regret approaches are used for making a selection decision....

due to project evaluation complexity and resource constraints, the project portfolio optimization is numerous decision making challenges. hence, many researches have been done to introduce model and methods for portfolio optimization. but most of them have not considered the interaction between projects. considering the interactions between projects increase complexity of portfolio optimization...

This work deals with a class of problems under interval data uncertainty, namely interval robusthard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible so...

Product quality is the most important issue in the Computer Numerical Control (CNC) process. Hence, with exercise equipment parts Surface Roughness as the target, this study selected Spindle speed, Cutting depth, Feeding, and Tool runoff, as control parameters in CNC parametric optimization of target quality. By using the Theory of Inventive Problem Solving (TRIZ) to define the Cause and Effect...

This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique ...