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Session Overview
D3S1T2: Production Planning and Maintenance I
Friday, 25/Feb/2022:
11:30am - 12:30pm

Session Chair: Yilmaz Uygun

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Managing complexity in variant-oriented manufacturing: A System Dynamics approach

Kießner, Phillip1; Perera, Niles2

1Technische Universität Dortmund, Germany; 2University of Moratuwa, Sri Lanka

This paper proposes a System Dynamics (SD) approach to support decision-making to manage variety induced complexity. Offering product variety leads to increasing internal complexity, which results in higher inventory and increasing setup processes. Managing the trade-off between marketing-, logistics- and product management complicates the decision process in offering sufficient variety to the market. This leads to numerousness of stock keeping units (SKUs), all of which are required to maintain various key performance indicators and inventory levels. Managing this variety induced complexity to optimize the overall business success requires an understanding of its System Dynamics behavior and interrelation. The reviewed literature reveals that existing metrics do not capture the necessary dynamic system behavior sufficiently to measure the impact of long-term strategies. The proposed model combines System Dynamics and the portfolio-fitness index (PFI) metric to capture the required dynamic system behavior. Applying scenarios to a national electronics company through a case study demonstrates the ability to manage complexity using System Dynamics and the PFI metric. The outcome of this research is a System Dynamics model that can manage variety induced complexity by offering scenario analysis to support decision-making. The findings in the case study posit that reducing the complexity does not automatically lead to competitive advantages. Understanding the dynamic behavior of complexity impacts forms a basis for decision-making. Thus, the model's findings help to manage complexity in the most efficient manner.

Dynamic lot size optimization with reinforcement learning

Voß, Thomas; Bode, Christopher; Heger, Jens

Leuphana Universität Lüneburg, Germany

Production planning and control have a great influence on the economic efficiency and logistical performance of a company.

In this context, this article gives an insight into the use of simulation as a virtual model of a filling machine in the process industry.

Furthermore, it shows the possibilities of a reinforcement learning (RL) approach for dynamic lot sizing.

The contribution indicates a possible implementation in an ERP system and shows how a decision support tool can support the planner to save up to 5 % of costs.

Energy aware coordination of heterogeneous production equipment with precedence relations and stochastic job arrivals

Scholz, Sebastian; Meisel, Frank

Christian-Albrechts-Universität Kiel, Germany

Industrial manufacturing is based on a variety of energy sources, e.g. electricity, oil and gas. Electricity appears to be particularly relevant as it is used to operate most types of production equipment. From a company’s perspective, production scheduling of machines as well as the consideration of additional production supply processes is necessary for an integrated energy load management, as industrial companies have to take care of peak loads to prevent increases in energy costs. To this end, we present two optimization models that account for the heterogeneity of different types of production equipment and propose an agent-based production coordination platform (PCP) that coordinates their decision-making. We focus on a job shop environment with stochastic job arrivals, where jobs have to be processed by several machines with job specific machine routings. Machine operations may call for further supply processes that provide resources to the machines. In a simulation study based on real world data, we evaluate related decisions on production machine scheduling and supply processes’ recharging with regard to the company´s internal load management. Additionally, we integrate a forecast for impending renewable energy feed-in reduction into the production decision process. Thus, the PCP is capable to counteract potential renewable energy losses through increased local consumption and contributes to sustainable production decisions. In the sense of volatile and uncertain renewable energy generation, we furthermore investigate the effect of arising forecast changes on the production decisions.

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