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Showing 4 results for Moslehi

Danial Khorasanian, Ghasem Moslehi,
Volume 23, Issue 4 (IJIEPR 2012)
Abstract

In this paper, we propose an iterated greedy algorithm for solving the blocking flow shop scheduling problem with total flow time minimization objective. The steps of this algorithm are designed very efficient. For generating an initial solution, we develop an efficient constructive heuristic by modifying the best known NEH algorithm. Effectiveness of the proposed iterated greedy algorithm is tested on the Taillard's instances. Computational results show the high efficiency of this algorithm with comparison state-of-the-art algorithms. We report new best solutions for 88 instances of 120 Taillard's instances.
Mohammad Reisi, Ghasem Moslehi,
Volume 24, Issue 4 (IJIEPR 2013)
Abstract

Increasing competition in the air transport market has intensified active airlines’ efforts to keep their market share by attaching due importance to cost management aimed at reduced final prices. Crew costs are second only to fuel costs on the cost list of airline companies. So, this paper attempts to investigate the cockpit crew pairing problem. The set partitioning problem has been used for modelling the problem at hand and, because it is classified in large scale problems, the column generation approach has been used to solve LP relaxation of the set partitioning model. Our focus will be on solving the column generation sub-problem. For this purpose, two algorithms, named SPRCF and SPRCD, have been developed based on the shortest path with resource constraint algorithms. Their efficiency in solving some problem instances has been tested and the results have been compared with those of an algorithm for crew pairing problem reported in the literature. Results indicate the high efficiency of the proposed algorithms in solving problem instances with up to 632 flight legs in a reasonable time.
Kamran Kianfar, Ghasem Moslehi,
Volume 28, Issue 3 (IJIEPR 2017)
Abstract

This paper addresses the Tardy/Lost penalty minimization on a single machine. According to this penalty criterion, if the tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. Besides its application in real world problems, Tardy/Lost measure is a general form for popular objective functions like weighted tardiness, late work and tardiness with rejection and hence, the results of this study are applicable for them. Initially, we present two approximation algorithms. Then, two special cases of the main problem are considered. In the first case, all jobs have the same tardiness weights where an FPTAS is developed using the technique of “structuring the execution of an algorithm". The second special case occurs when none of the jobs can be early. For this case, a 2-approximation algorithm is developed as well as a dynamic programming algorithm which is converted to an FPTAS.


Ghasem Moslehi, Omolbanin Mashkani,
Volume 29, Issue 1 (IJIEPR 2018)
Abstract

In single machine scheduling problems with availability constraints, machines are not available for one or more periods of time. In this paper, we consider a single machine scheduling problem with flexible and periodic availability constraints. In this problem, the maximum continuous working time for each machine increases in a stepwise manner with two different values allowed. Also, the duration of unavailability for each period depends on the maximum continuous working time of the machine in that same period, again with two different values allowed. The objective is to minimize the number of tardy jobs. In the first stage, the complexity of the problem is investigated and a binary integer programming model, a heuristic algorithm and a branch-and-bound algorithm are proposed in a second stage. Computational results of solving 1680 sample problems indicate that the branch-and-bound algorithm is capable of not only solving problems of up to 20 jobs but also of optimally solving 94.76% of the total number of problems. Based on numerical results obtained, a mean average error of 2% is obtained for the heuristic algorithm.



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