جستجوی مقالات مرتبط با کلیدواژه « جستجوی محلی » در نشریات گروه « صنایع »

The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only in a few runs, and in other runs they are caught in the local optimum.

The proposed method uses appropriate genetic operators to increase diversity of the population and performs local search around the best answer to exploit promising areas of the solution space. The use of hub mutation operators along with allocation mutation operators in the proposed algorithm has increased its exploration ability and effectiveness, which has led to discovery of the optimal answer in most runs for large size problems. Also, searching for the local neighborhood of the best answer made convergence faster and reduced the total running time for large instances.

Evaluation of the proposed method and base algorithm on the Australian Post (AP) dataset showed that the improvements increased efficiency of the genetic algorithm in achieving optimal solutions for problems as large as 200 nodes from 2% to more than 85%.

 This study showed that meta-heuristic algorithms and their improved versions are suitable methods for solving hub location problems in a short and limited time.

The timetable is the problem of placing particular resources due to constraints in a limited number of times lots and space, in order to satisfy a set of goals that is used to a variety of problems. Among these problems, one can point out the University Examination Timetabling Problem (UETP), which is the particular importance in educational problems. The university examination timetabling problem defined as the assignment of a certain set of exams to a fixed number of time slots and rooms, so that it meets all the hard constraints, also soft constraints are optimized as much as possible. This research presents a modified approach to optimize the incapacitated UETP. In this approach, a proposed Genetic Algorithm (GA) is modified by local search operators. These operators will make alterations to the timetable. This involves shifting or changing scheduled exams and thus greatly improve the ability of the algorithm to search. The efficiency of the proposed approach is compared with other techniques from literature using the Carter’s benchmark. The computational results show that this approach is quite effective and competitive in improving the solutions and is able to produce better solutions in most of the datasets compared with other algorithms.