Operation Sequencing Modeling
Saeed Khalili
Abstract
Considering maintenance strategy in models which schedule and allocate jobs to machines, will make the proposed models compatible with production environments. Furthermore, this will cause higher model efficiency in optimizing the production systems. To this end, a mathematical model for scheduling unrelated ...
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Considering maintenance strategy in models which schedule and allocate jobs to machines, will make the proposed models compatible with production environments. Furthermore, this will cause higher model efficiency in optimizing the production systems. To this end, a mathematical model for scheduling unrelated parallel machines is developed to minimize total weighted completion times. Also in this approach, availability constraints have been considered, and preemption is allowed. Due to executing preventive maintenance and emergency maintenance programs, machine inaccessible times have been added to job completion times. Since the proposed model has high complexity, in order to solve the problem, two meta-heuristic methods including simulated annealing and genetic algorithm are used. In addition, their performances are compared to each other. The results indicate the superiority of simulated annealing over genetic algorithm for this particular problem.
Scheduling Modeling
Mohsen Bagheri; Neda Babaei Meybodi; Amir Hossein Enzebati
Abstract
Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider ...
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Energy consumption considerations in production systems have recently attracted the attention of researchers. In conventional production scheduling models, the importance has more often been given to time-related rather than to energy-related performance measures. In this paper, we simultaneously consider energy consumption, completion time and tardiness in the presented Multi-Objective Mixed Integer Programming flow shop scheduling model. After validating the model by solving small-scale numerical examples with Weighted Sum and Epsilon-constraint method in GAMS, the large and medium-scale examples are solved via NSGA-II and SPEA-II metaheuristic-algorithms. The results prove the efficiency of the proposed algorithms.
supply chain management analyzing/modelling
Masoud Rabbani; Maryam Tohidi Fard; Mohammad Partovi; Hamed Farrokhi-Asl
Abstract
Todays, meeting the healthcare needs of patients at home has many benefits. By providing regular and timely healthcare servicing, in addition to reducing costs, the patient's recovery process also speeds up. In this paper, a multi-depot vehicle routing problem is considered with regard to time windows ...
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Todays, meeting the healthcare needs of patients at home has many benefits. By providing regular and timely healthcare servicing, in addition to reducing costs, the patient's recovery process also speeds up. In this paper, a multi-depot vehicle routing problem is considered with regard to time windows and fuzzy demands. This paper attempts to optimize provided mathematical formulation in such a way that the distance traveled, total travel time, the number of transportation vehicles and transportation cost be minimized; also by taking the hard time window to meet patients , patient satisfaction rate will increase. This is a complex and difficult problem, and it takes a long time to solve it through linear programming and existing software. Therefore, in this paper, two general approaches including genetic algorithm and particle swarm optimization are used to tackle the problem. The response surface methodology (RSM) has been used to set parameters for meta-algorithms. To illustrate the efficiency of proposed algorithms, a number of test problems are solved and computational results are compared with the solutions obtained with the GAMS software.