Mathematical Modeling for a Flexible Manufacturing Scheduling Problem in an Intelligent Transportation System
This paper presents a new mathematical model for a production system through a scheduling problem considering a material handling system as an intelligent transportation system by automated guided vehicles (AGVs). The traditional systems cannot respond to the changes and customer’s demands and for this reason, a flexible production system is used. Therefore, for this purpose, automated transportation systems are used for more flexibility in production. Thus, several AGVs are considered to perform various jobs among different machines and warehouses. In this production system, there are possibilities of failure and breakdown of AGVs and machines simultaneously. A modified rate is considered for determining the maintenance duration time as a percentage of the setup time when the maintenance time is dependent on the total setup time of machines and the total transfer jobs time of AGVs. Hence, we consider the probability of breakdown of AGVs and machines simultaneously and show the effect of these problems. The objective function is to minimize the maximum completion time (i.e., makespan or Cmax), the tardiness penalty, and the total transportation cost bearing in mind that the impact of new constraints with mathematical innovation on how failure and repair time are affected by the entire production scheduling. The proposed model belongs to mixed-integer linear programming (MILP). Finally, several small-sized problems are generated and solved by the CEPLEX solver of GAMS software to show the efficiency of the proposed model.
This paper considers a bi-objective scheduling problem in a flexible manufacturing cell (FMC) which minimizes the maximum completion time (i.e., makespan) and maximum tardiness simultaneously. A new mathematical model is considered to reflect all aspect of the manufacturing cell. This type of scheduling problem is known to be NP-hard. To cope with the complexity of such a hard problem, a genet...متن کامل
A scheme for the scheduling of Flexible Manufacturing Systems (FMS) has been developed which integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms. and machine learning. Modular neural networks are used to generate a small set of atlractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo Simulation predicts rhe impact of each on...متن کامل
A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system
The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to...متن کامل
With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in...متن کامل
Real-time Scheduling of a Flexible Manufacturing System using a Two-phase Machine Learning Algorithm
The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...متن کامل
دوره 14 شماره 1
صفحات 189- 208
تاریخ انتشار 2021-01-01
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