فهرست مقالات Unrelated Parallel Machine

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  1 - Minimizing the operational costs in a flexible flow shop scheduling problem with unrelated parallel machines
  Ali Hassani Seyed Mohamad Hasan Hosseini Foroogh Behroozi
  10.22094/joie.2021.1890852.1718
  This paper investigates a flexible flow shop scheduling problem with the aim of minimizing the operational costs as a new objective function. In this production system, there are some unrelated parallel machines with different performances and different technology level چکیده کامل

  This paper investigates a flexible flow shop scheduling problem with the aim of minimizing the operational costs as a new objective function. In this production system, there are some unrelated parallel machines with different performances and different technology levels in the first stage and each other stage consists of a single machine. Setup times are assumed as sequence-dependent and are need when a machine starts to process a new job. Some of the parallel machines in the first stage are multifunctional and can do several processes on jobs. So, the jobs that are assigned to these machines do not need to be processed in some next stages. This problem is described with an example, and its parameters and decision variables are defined. Then a mathematical model based on mixed-integer linear programming (MIP) is developed to solve the problem in small-sized scales. As this problem is discussed in an Np-hard environment, the Genetic Algorithm (GA) is applied to solve the considered problem on practical-sized scales. Due to the result, the operational costs conflict with makespan as a common objective function in scheduling problems. Therefore, the supplementary analysis has been presented considering a restriction on the makespan. پرونده مقاله
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  2 - DOE-based enhanced genetic algorithm for unrelated parallel machine scheduling to minimize earliness/tardiness costs
  Parsa Kianpour Deepak Gupta Krishna Krishnan Bhaskaran Gopalakrishnan
  10.22094/joie.2023.1967925.1992
  This study presents an enhanced genetic algorithm (E-GA) to minimize earliness/tardiness costs in the job shop environment. It considers an unrelated parallel machine scheduling problem with a limit on maximum tardiness levels. This problem is motivated by the experienc چکیده کامل

  This study presents an enhanced genetic algorithm (E-GA) to minimize earliness/tardiness costs in the job shop environment. It considers an unrelated parallel machine scheduling problem with a limit on maximum tardiness levels. This problem is motivated by the experience of one of the authors in a job shop supporting the local aircraft industry that requires strict control on delivery times. Current literature does not consider this critical restriction and unsuccessfully tries to deal with them using higher penalty costs. The proposed method uses the design of experiment (DOE) concept while optimizing the GA operators. Furthermore, it improves the initial solution using a hybrid dispatch rule through a strategic combination of construction and improvement heuristics. The model was applied to a local job shop. The results indicate that E-GA provides a schedule with lower cost and reduced computational time compared to existing dispatch rules in the literature and existing algorithms (OptQuest). پرونده مقاله
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  3 - Solving the Problem of Scheduling Unrelated Parallel Machines with Limited Access to Jobs
  Mohammadreza Naghibi Abolfazl Adressi
  Nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. Unrelated parallel machin چکیده کامل

  Nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. Unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. In some of the applications of unrelated parallel machines scheduling, when machines have different technological levels and are not necessarily able to process each one of the existing jobs in the group of jobs and in many of the industrial environments, a sequence dependent setup time takes place during exchanging jobs on the machines. In this research, the unrelated parallel machines scheduling problem has been studied considering the limitations of sequence dependent setup time of processing of jobs and limited accessibility to machines and jobs with the purpose of minimizing the total weighting lateness and earliness times. An integer scheduling model is proposed for this problem. Also, a meta-heuristically combined method consisting of Genetic algorithm and Particle swarm optimization (PSO) algorithm for its solutions is proposed. The obtained results of the proposed algorithm show that the proposed algorithm is very efficient especially in problems with large dimensions. پرونده مقاله