The focus areas of our research are simulation and optimization of complex assembly lines for heavy machinery (airplanes, turbines, industrial machines etc.). These production facilities have several specific characteristics: many isolated project networks with precedence constraints and thousands of multi-mode activities, time-bounds for activities and projects, many priority rules, limited numbers of multi-skilled resources with individual shift regimes, internal and subcontracted personnel, and resource locking rules. Formally, it is defined as a Multi-Mode Resource-Constrained Multi-Project Scheduling Problem with activity splitting. A promising way of dealing with problems in this domain is simulation-based optimization. In this paper, we introduce a specific custom-built simulator designed for this problem domain. The tool supports a variety of real-world extensions and dedicated behavior which usually comes at enormous runtime and development cost when it has to be built into a commercial off-the-shelf simulation tool.
Our paper deals with the scheduling of complex assembly lines with a focus on Job Shop Scheduling Problems that exhibit several assembly specific characteristics: many isolated project networks with precedence constraints and thousands of jobs, time bound requirements for jobs and projects, limited resources with individual scheduling and resource lock rules. Formally it is defined as a Multi-Mode Resource-constrained Multi-Project Scheduling Problem with splitting activities. Problems that display these characteristics are often difficult to solve with classical scheduling approaches within acceptable runtime. Simulation-Based Optimization offers an auspicious manner of dealing with those domain specific problems. Using this approach we present a decentralized heuristic evident in self-organization in nature. Typical algorithms attempt to solve the above problems globally. In our solution, the jobs of the network take over the active role. They communicate with their neighbors and the allocated resources, each having the local goal to optimize their own situation. INTRODUCTIONThis paper explores solution approaches for scheduling problems in complex assembly lines in industrial environments. Presently the global market demands reduced production costs and on time delivery, especially in assembly lines with workforce planning. Our research focuses on small series or even unique items such as turbines, planes or industrial machines. A scenario contains various products with different production plans (also referred to some authors as projects (Pinedo 2007;T'kindt 2006) or networks (Brucker and Knust 2006;Pinedo 2008)), precedence constraints and thousands of activities. Many elements, such as activities and products, have many time bound requirements. The activities do not have a fixed processing time but many possible modes, in which their time is closely linked to the resources assigned to it. In addition, the production has limited resources with individual scheduling and finite buffers. The scheduling of this type of assembly production lines is a very complex task even for experienced foremen.The scheduling of such complex assembly lines is a large combinatorial optimization problem and is often mentioned in the literature as a Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MMRCMPSP) with activity splitting (Angelidis, Naumann, and Rose 2012). Unfortunately this is NP-hard and real-life problems of this size cannot be solved with classic approaches in short runtimes. The research in this area is mainly based on small or medium sized problems, which are often solved by exact approaches or genetic algorithms. These strategies are not suitable for our large real-life problems, especially with respect to short runtimes. Pappert, Angelidis, and Rose (2010) give a detailed explanation of this scheduling domain and its plausible solution strategies. They also present a promising alternative that produces efficient solutions for real-life problems in short runtime (Simulation-Based Optimization app...
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