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<p>The Machine scheduling problems are solved to optimize the objectives of material flow time which depends upon processing time, setup time and material handling time. In flexible manufacturing system, automated guided vehicles (AGV) are used for material handling purpose from one machine to another machine. However, in most of the cases, machine scheduling problems and AGV scheduling problems are solved separately. Some authors have solved machine scheduling and AGV scheduling problem simultaneously, but they have considered fixed number of AGVs. However, as the number of AGV increases, it will minimize the material flow time, but this will increase the idle time and cost of the AGVs. This material flow time will be stabilized after reaching to the certain number of AGVs. Hence, this problem leads to identify the optimum number of AGV required for the material handling system. In this paper, an attempt is made to identify the best sequence of operations of the products on different machines. From this sequence of operations of all the products, the processing time, sequence-dependent setup time, material handling time and waiting time will be calculated. Hence, in this paper, the objective function is set to minimize the material flow time which depends upon processing time, setup time, material handling time and waiting time. The Artificial Intelligence (AI) techniques used in this paper are (i) Metaheuristics and their applications in intelligent automation: discrete artificial bee colony algorithm (DABC) and (ii) Industrial experiences in the application of the above techniques, e.g. case studies of a flexible manufacturing system with AGVs for material handling. The discrete artificial bee colony algorithm (DABC) algorithm is applied on the case studies. There is around 10% improvement in the results obtained by discrete artificial bee colony algorithm (DABC). The proposed approach thus can be effectively implemented to reduce the material flow time in industries such as automotive industries, electronics industries, and consumer goods manufacturing industries especially while operating in flexible environment.</p>

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Investigations into effect of waiting time in integrated machine scheduling and automated guided vehicles scheduling

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • K. C. Bhosale,
  • P. J. Pawar

摘要

The Machine scheduling problems are solved to optimize the objectives of material flow time which depends upon processing time, setup time and material handling time. In flexible manufacturing system, automated guided vehicles (AGV) are used for material handling purpose from one machine to another machine. However, in most of the cases, machine scheduling problems and AGV scheduling problems are solved separately. Some authors have solved machine scheduling and AGV scheduling problem simultaneously, but they have considered fixed number of AGVs. However, as the number of AGV increases, it will minimize the material flow time, but this will increase the idle time and cost of the AGVs. This material flow time will be stabilized after reaching to the certain number of AGVs. Hence, this problem leads to identify the optimum number of AGV required for the material handling system. In this paper, an attempt is made to identify the best sequence of operations of the products on different machines. From this sequence of operations of all the products, the processing time, sequence-dependent setup time, material handling time and waiting time will be calculated. Hence, in this paper, the objective function is set to minimize the material flow time which depends upon processing time, setup time, material handling time and waiting time. The Artificial Intelligence (AI) techniques used in this paper are (i) Metaheuristics and their applications in intelligent automation: discrete artificial bee colony algorithm (DABC) and (ii) Industrial experiences in the application of the above techniques, e.g. case studies of a flexible manufacturing system with AGVs for material handling. The discrete artificial bee colony algorithm (DABC) algorithm is applied on the case studies. There is around 10% improvement in the results obtained by discrete artificial bee colony algorithm (DABC). The proposed approach thus can be effectively implemented to reduce the material flow time in industries such as automotive industries, electronics industries, and consumer goods manufacturing industries especially while operating in flexible environment.