مقالههای Ellips Masehian
توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقالههای نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده میشوند.
اطلاعات انتشار: هشتمین کنفرانس بین المللی مهندسی صنایع، سال ۱۳۹۱
تعداد صفحات: ۷
The n–queens problem is a classical combinatorial optimization problem that is proved to be NP–hard. In this paper, a simulated annealing–basedheuristic for solving the n–queens problem is presented.Since the parameters of heuristic and metaheuristic algorithms have a great influence on the performance of the search, parameter tuning is used for handling the problems in an efficient manner. Hence, a TOPSIS–based parameters tuning is proposed, which notonly considers the number of iterations, but also aims to minimize the running time of the presented heuristic. Inorder to investigate the performance of the suggestedapproach, a computational analysis on the n–queens problem is performed. Experimental results showed thatthe average running time and the average number of iterations of the heuristic approach were 10 times faster and 12 times less than the basic simulated annealing search, respectively.<\div>
اطلاعات انتشار: Journal of Optimization in Industrial Engineering، دوم،شماره۲، ۲۰۰۹، سال ۰
تعداد صفحات: ۱۴
Solvable Graphs (also known as Reachable Graphs) are types of graphs that any arrangement of a specified number of agents located on the graph’s vertices can be reached from any initial arrangement through agents’ moves along the graph’s edges, while avoiding deadlocks (interceptions). In this paper, the properties of Solvable Graphs are investigated, and a new concept in multi agent motion planning, called Minimal Solvable Graphs is introduced. Minimal Solvable Graphs are the smallest graphs among Solvable Graphs in terms of the number of vertices. Also, for the first time, the problem of deciding whether a graph is Solvable for m agents is answered, and a new algorithm is presented for making an existing graph solvable and lean for a given number of agents. Finally, through an industrial example, it is demonstrated that how the findings of this paper can be used in designing and reshaping transportation networks (e.g. railways, traffic roads, AGV routs, robotic workspaces, etc.) for multiple moving agents such as trains, vehicles, and robots.
۳New Heuristic Algorithms for Solving Single–Vehicle and Multi–Vehicle Generalized Traveling Salesman Problems (GTSP)
نویسنده(ها): Ellips Masehian
اطلاعات انتشار: Journal of Optimization in Industrial Engineering، دوم،شماره۳، ۲۰۰۹، سال ۰
تعداد صفحات: ۱۰
Among numerous NP–hard problems, the Traveling Salesman Problem (TSP) has been one of the most explored, yet unknown one. Even a minor modification changes the problem’s status, calling for a different solution. The Generalized Traveling Salesman Problem (GTSP)expands the TSP to a much more complicated form, replacing single nodes with a group or cluster of nodes, where the objective is to find a minimum–length tour containing exactly one node from each cluster. In this paper, a new heuristic method is presented for solving singlevehicle single–depot GTSP with the ability of controlling the search strategy from conservative to greedy and vice versa. A variant algorithm is then developed to accommodate the multi–vehicle single–depot condition, which is modified afterwards to accommodate the multi–vehicle multi–depot GTSP.
اطلاعات انتشار: Journal of Optimization in Industrial Engineering، سوم،شماره۵، ۲۰۱۰، سال ۰
تعداد صفحات: ۱۶
In this paper, a new online robot motion planner is developed for systematically exploring unknown environ¬ments by intelligent mobile robots in real–time applications. The algorithm takes advantage of sensory data to find an obstacle–free start–to–goal path. It does so by online calculation of the Generalized Voronoi Graph (GVG) of the free space, and utilizing a combination of depth–first and breadth–first searches on the GVG. The planner is equipped with components such as step generation and correction, backtracking, and loop handling. It is fast, simple, complete, and extendable to higher spaces.
اطلاعات انتشار: International Journal of Industrial Engineering & Production Research، بيست و چهارم،شماره۲، Jun ۲۰۱۳، سال ۰
تعداد صفحات: ۸
The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large–scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP–hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S\N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n–queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un–tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un–tuned GA.
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