An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times  
[PERSON]  
1 Institute of Material Handling and Industrial Engineering, Technische Universitat Dresden, 01062 Dresden, Germany; [EMAIL_ADDRESS] (M.V. ); [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Material Handling and Industrial Engineering, Technische Universitat Dresden, 01062 Dresden, Germany; [EMAIL_ADDRESS] (M.V. ); [EMAIL_ADDRESS]  
[PERSON]  
1 Institute of Material Handling and Industrial Engineering, Technische Universitat Dresden, 01062 Dresden, Germany; [EMAIL_ADDRESS] (M.V. ); [EMAIL_ADDRESS]  
15 November 20202011 December  
Manufacturing Execution Systems (MES) with integrated Advanced Planning and Scheduling Systems (APS-Systems) [5] (pp. 63-64) nor decentrally used Simple Priority Rules (SPRs, e.g., FIFO (First In-First Out)) can be used to control these requirements [6] (p. 8). In sequence control performed by MES and APS, production follows a deterministic schedule that defines the next job [7] (p. 392). For the industrial domain under consideration, this approach is only suitable to a limited extent, since permanent rescheduling is necessary due to the fuzzy process parameters. Due to the lack of feedback on the processing status of individual work packages [8] (pp. 80-81), it is not clear whether a schedule is still valid or not. In short, the schedule can be obsolete after release. Furthermore, the necessary input of master and transaction data is often insufficient [9]. Sequencing with Priority Rules (PRs) means that a priority (e.g., shortest processing time) is assigned to each job. The priority determines the next job [10] (p. 111). However, the effect of SPRs on the objective values cannot be predicted in a dynamic production environment [11]. Moreover, project-specific objectives are not explicitly taken into account. Promising approaches consist in the combination of priority rules, the so-called Combined Priority Rules (CPRs). Here, different job attributes are combined, and thus, a priority index is calculated for determining the next job. In project manufacturing, CPRs are rarely used for decentralized control. On the one hand, the time for generation is very high; on the other hand, approaches for project-specific objectives are rare. In this paper, we take up the challenge of computational time for the generation of CPRs and develop a concept for project-specific objective fulfillment under stochastic influences. In the presented paper, we generate project-specific CPRs by combining different individual job attributes with a weighted sum approach. These CPRs are assigned to the project jobs and used for short-term project scheduling. The proposed Genetic Algorithm (GA) used as a hyper-heuristic optimizes weight allocation of these individual attributes. For this purpose, we generate the CPRs by iterative simulation and evaluate the results with the Pareto dominance concept. Since the resulting optimized CPRs in deterministic condition do not necessarily lead to good results under stochastic conditions, we propose a 2-phase algorithm to determine the CPRs. The first phase serves to explore the solution space and to select the best individuals for the second stochastic phase. We validate the concept with computer-aided experiments on a software platform developed especially for this problem. As a simulation method, we use discrete event simulation combined with the theory of simulation-based optimization. Within the scope of the paper, we perform experiments on computation time and performance of the algorithm. In the context of the paper, we address the following research questions:  
Figure 1: Example of job processing time deviations for a type representative in individual furniture assembly. * Is it possible to improve mean value and standard deviation of project individual objectives by applying the generated CPRs? * Is it possible to save computation time with the 2-phase algorithm? The structure of the paper is as follows.