Ashkan Safari- PhD at Maastricht University
Ashkan Safari
- PhD at Maastricht University
About
6
Publications
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Introduction
Ashkan is a PhD candidate at the School of Business and Economics, Maastricht University, currently working on the research project "Rigorous Analysis of Local Search." His academic journey is driven by a passion for solving complex problems and uncovering innovative solutions in Optimization, Operations Research, Scheduling, and Computational Geometry.
Current institution
Publications
Publications (6)
Ant Colony Optimization (ACO) is a well-known method inspired by the foraging behavior of ants and is extensively used to solve combinatorial optimization problems. In this paper, we first consider a general framework based on the concept of a construction graph - a graph associated with an instance of the optimization problem under study, where fe...
Local search is a widely used technique for tackling challenging optimization problems, offering simplicity and strong empirical performance across various problem domains. In this paper, we address the problem of scheduling a set of jobs on identical parallel machines with the objective of makespan minimization, by considering a local search neigh...
![The first algorithm in O(n2)\documentclass[12pt]{minimal}...](publication/377532039/figure/fig1/AS:11431281232851355@1711849955919/The-first-algorithm-in-On2documentclass12ptminimal-usepackageamsmath_Q320.jpg)
![The second algorithm in O(nlog2n)\documentclass[12pt]{minimal}...](publication/377532039/figure/fig2/AS:11431281232941967@1711849956078/The-second-algorithm-in-Onlog2ndocumentclass12ptminimal-usepackageamsmath_Q320.jpg)
In this paper, we consider the problem of path planning in a weighted polygonal planar subdivision. Each polygon has an associated positive weight which shows the cost of path per unit distance of movement in that polygon. The goal is to find a minimum cost path under the Manhattan metric for two given start and destination points. First, we propos...
In this paper, we consider the problem of path planning in a weighted polygonal planar subdivision. Each polygon has an associated positive weight which shows the cost of path per unit distance of movement in that polygon. The goal is finding a minimum cost path under the Manhattan metric for two given start and destination points. We propose an O(...
