@misc{HerrmannSchoenClausetal., author = {Herrmann, Frank and Schoen, Maximilian and Claus, Thorsten and Dai, Chenghao}, title = {Aggregate Production Planning under Risk of Disruption}, series = {OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich}, journal = {OR 2024, International Conference on Operations Research 2024, September 3-6, 2024, Munich}, abstract = {In recent years, large scale disruptions to global supply chains, like the Covid pandemic, a ship blocking the Suez canal or sanctions against Russia, have caused production to slow down or even to stand still, causing shortages and massive losses for affected businesses. Even if only specific companies were originally affected, shortages and delays rippled along the supply network. The established approach to deal with disruptions is to utilize safety stock and capacity to compensate for fluctuations in uncertain quantities like customer demand. This approach is tried and tested for small fluctuations. To address larger disruptions, like the above given, very high safety stock and capacity would be needed, which would lead to unnecessarily high costs. Resilience has often been viewed as an expensive capability that drives costs. Recent studies however advocate for the development of lean resilience concepts, creating new capabilities, which enable resilience and can deal with large fluctuations, reimagining resilience from the perspectives of efficiency and value creation. This contribution identifies gaps in current research and establishes structural deficits of approaches discussed in the literature regarding supply chains. Firstly, the need for rigorous, quantitative definitions of resilience and relevant disruptions is justified. Then, a stochastic model for aggregate production planning that includes capabilities to compensate for such large fluctuations along several dimensions is proposed. Lastly, this model is then applied to a case study pertaining to a realistic supply chain under the risk of large scale disruptions and the results of this approach are evaluated.}, language = {en} } @inproceedings{DaiSchoenClausetal., author = {Dai, Chenghao and Sch{\"o}n, Maximilian and Claus, Thorsten and Herrmann, Frank}, title = {Multi-Site Aggregate Production Planning With Resilience Consideration}, series = {Proceedings of the 38th ECMS International Conference on Modeling and Simulation (ECMS 2024): June 4th - June 7th, 2024, Cracow, Poland}, booktitle = {Proceedings of the 38th ECMS International Conference on Modeling and Simulation (ECMS 2024): June 4th - June 7th, 2024, Cracow, Poland}, publisher = {ECMS}, isbn = {978-3-937436-84-5}, issn = {2522-2422}, doi = {10.7148/2024-0345}, pages = {345 -- 351}, abstract = {The recent years have shown a high frequency of disruptions like natural disasters or manmade disruptions, leading, for example, to transport routes or production facilities being unavailable for extended periods. Many manufacturing companies, which nowadays operate in a global production network, are heavily exposed to these disruptions. This results in both significant costs and substantial exceeding of promised deadlines. If it concerns the first company in a supply chain, it causes correspondingly significant delays in promised deadlines for subsequent companies in the supply chain, including a correspondingly significant increase in costs. Both are exemplified in this paper through a case study. This case study demonstrates that through resilience, both implications can be significantly reduced.}, language = {en} } @misc{HerrmannDilefeld, author = {Herrmann, Frank and Dilefeld, Maximilian}, title = {Improving the scheduling of AGV and AMR using simulation}, series = {OR23, International Conference on Operations Research, August 29 to September 1, 2023, Hamburg}, journal = {OR23, International Conference on Operations Research, August 29 to September 1, 2023, Hamburg}, abstract = {Automated Guided Vehicles (AGV) and intelligent Autonomous Mobile Robots (AMR) are an important tool for designing a flexible Industry 4.0 shopfloor. Both types of Mobile Robots are usually deployed with a Fleet Management software to manage (transport) orders and vehicles together with other superordinate control systems. The planning tasks in the superordinate control system used by both AGVs and AMRs can be divided into Material Flow Management, Job Management, Vehicle Dispatching and Job Processing. Requests for supply and removal of material is issued by the stations and has to be combined to transport jobs. Alternatively, more complex system often user external Material Flow Controllers (e.g. MES). Jobs are collected in a queue and their execution order is determined. Vehicles which can execute the next job have to be identified and the best one is selected. Jobs are split into smaller tasks which are sent to the Mobile Robot and executed. For AGVs navigation functions are mostly integrated into the Fleet Manager as part of the Job Processing, while AMRs perform lower-level navigation functions directly on the vehicle and use the superordinate control system for implementing restrictions and traffic control functions. A need for further research and development was identified especially for decision-making algorithms within the Fleet Management. The goal is to compare existing and develop new strategies to solve the different planning problems. For the individual planning tasks different techniques have been discussed and used in research and practice. However, there is no universal best solution and different options must be compared in the context of the specific use case.}, language = {en} } @inproceedings{TrostTerbrackClausetal., author = {Trost, Marco and Terbrack, Hajo and Claus, Thorsten and Herrmann, Frank}, title = {Established production planning and control and its enhancement with sustainability}, series = {Proceedings of the 37th ECMS International Conference on Modeling and Simulation (ECMS 2023): June 20th - June 23rd, 2023, Florence, Italy}, booktitle = {Proceedings of the 37th ECMS International Conference on Modeling and Simulation (ECMS 2023): June 20th - June 23rd, 2023, Florence, Italy}, publisher = {ECMS}, isbn = {978-3-937436-80-7}, issn = {2522-2422}, doi = {10.7148/2023-0320}, pages = {320 -- 327}, abstract = {Against the background of the energy crisis, shortage of skilled workers, demographic change and other drivers, sustainable development is also becoming increasingly important for industrial companies. In this respect, production planning and control has an enormous influence on relevant objectives. In classical approaches of production planning and control as presented in this paper, economic-oriented objectives are taken into account to a large extent in decision making. This paper demonstrates that besides these classical models, a variety of approaches exist to influence ecological and social targets through production planning. These different models are assigned to sustainability areas and outlined by exemplary literature sources. Further research is needed, for instance, in the joint consideration of sustainability criteria along different planning levels and sustainable dimensions.}, language = {en} } @article{EnglHerrmann, author = {Engl, Fabian and Herrmann, Frank}, title = {A Machine Learning based Approach on Employee Attrition Prediction with an Emphasize on predicting Leaving Reasons}, series = {Anwendungen und Konzepte der Wirtschaftsinformatik}, journal = {Anwendungen und Konzepte der Wirtschaftsinformatik}, number = {18}, publisher = {AKWI}, issn = {2296-4592}, doi = {10.26034/lu.akwi.2023.4488}, pages = {30 -- 40}, abstract = {Using Vitesco Technologies as an example, this article examines whether machine learning models are suitable for detecting employee attrition at an early stage, with the aim of uncovering underlying reasons for leaving. Nine different machine learning algorithms were examined: K-nearest-neighbors, Naive Bayes, logistic regression, a support vector machine, a neural network, a random forest, adaptive boosting, and two gradient boosting models. A three-way-holdout validation method was implemented to assess the quality of the results and measure both the f-score and the degree of model generalization. Initially, it was found that tree-based methods are best suited for classifying employees. A multiclass classification approach showed that under certain conditions it is even possible to predict the underlying leaving reasons.}, language = {en} } @article{TrostClausHerrmann, author = {Trost, Marco and Claus, Thorsten and Herrmann, Frank}, title = {Master Production Scheduling with Consideration of Utilization-Dependent Exhaustion and Capacity Load}, series = {Sustainability}, volume = {15}, journal = {Sustainability}, publisher = {MDPI}, doi = {10.3390/su15086816}, pages = {1}, abstract = {A large number of researchers have addressed social aspects in hierarchical production planning. This article responds to research gaps identified in our previous literature review. Accordingly, consideration of social aspects and the economic implications of social improvements are required in a longer term planning approach. For this, we integrate work intensity as employee utilization in a general mixed-integer programming model for master production scheduling. Following existing fatigue functions, we represent the relationship between work intensity and exhaustion through an employee-utilization-dependent exhaustion function. We account for the economic implications through exhaustion-dependent capacity load factors. We solve our model with a CPLEX standard solver and analyze a case study based on a realistic production system and numerical data. We demonstrate that the consideration of economic implications is necessary to evaluate social improvements. Otherwise, monetary disadvantages are overestimated, and social improvements are, thus, negatively affected. Moreover, from a certain level of work-intensity reduction, demand peaks are smoothed more by pre-production, which requires more core employees, while temporary employment is reduced. Further potential may arise from considering and quantifying other interdependencies, such as employee exhaustion and employee days off. In addition, the relationship between social working conditions and employee turnover can be integrated.}, language = {en} } @misc{TerbrackClausHerrmann, author = {Terbrack, Hajo and Claus, Thorsten and Herrmann, Frank}, title = {An Optimization Model for Production Scheduling with Energy Consumption and Energy-related Emissions}, series = {OR 2022, International Conference on Operations Research 2022, September 6-9, 2022, Karlsruhe}, journal = {OR 2022, International Conference on Operations Research 2022, September 6-9, 2022, Karlsruhe}, abstract = {Due to the scarcity of resources, rising costs as well as the further need for ecological improvement in industrial production, firms are increasingly motivated to take into account energy consumption in production planning. Associated with this, the consideration of energy-related emissions is becoming an additional point of interest for decisionmakers in both, economic and ecological terms. Based on an extensive systematic literature review, the study at hand presents a multicriterial scheduling problem that addresses tardiness, energy consumption and energy-related emissions in production scheduling. A model is introduced that takes into account job release dates, multiple machine states, a power demand threshold as well as time-varying emission conversion factors. Multiple case studies are analysed and the effects of different technological implementations as energy onsite generation and energy storage systems are evaluated. Furthermore, several price mechanisms regarding energy procurement and emission trading are discussed. Moreover, by coupling the optimization model to a discrete event simulation model, we are able to compare the model results to real case scenarios. First computational experiments have demonstrated that the model and its extensions can effectively reduce the environmental impact associated with production while simultaneously considering corresponding economic objectives. Based on the proposed approach, our research aims on answering the question with which economic trade-offs an ecological improvement in terms of energy consumption and emission output can be achieved in production.}, language = {en} } @article{Herrmann, author = {Herrmann, Frank}, title = {Human Experience Versus Rule Decision at Krones AG}, series = {Biomedical Journal of Scientific \& Technical Research}, volume = {42}, journal = {Biomedical Journal of Scientific \& Technical Research}, number = {1}, publisher = {Biomedical Research Network+}, issn = {2574 -1241}, doi = {10.26717/BJSTR.2022.42.006691}, pages = {33255 -- 33260}, abstract = {Final assembly at Krones AG must make the best possible use of its production space and meeting the specified customer due dates is critical. Via a self developed simulation tool, the present scheduling procedure is compared with the one by priority rule shortest slack. As a consequence slack should have a higher importance in the planning}, language = {en} } @article{TrostClausHerrmann, author = {Trost, Marco and Claus, Thorsten and Herrmann, Frank}, title = {Social Sustainability in Production Planning: A Systematic Literature Review}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {13}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/su14138198}, pages = {1 -- 31}, abstract = {Sustainable production includes economic, environmental, and social aspects. However, social sustainability has received less attention, especially compared to the economic aspects. Next to technical and organizational measures, social improvements within supply chains can also be achieved through suitable production planning. Within production planning, production programs are determined, and the assignment of available resources (e.g., employees) is specified. Thus, the utilization and workload of employees are defined. This systematic literature review investigates to what extent such employee-related social aspects are reflected in production planning and discusses whether economic aspects dominate them. For this, a Scopus database search was carried out and 76 identified approaches were analyzed and categorized regarding the occurring employee-related social aspects and their implementation. Thus far, the approaches mainly consider single aspects on single planning levels. A consideration of a broad set of aspects along the entire production planning has rarely been studied. In particular, health and safety aspects are considered on the levels of assembly line balancing and job rotation. However, their impact is primarily determined by the specific settings of the decision-maker. To support decision-makers, only a few studies have investigated the effects based on real application scenarios. Further potential might be an extended modeling of social and economic interdependencies and a consideration of employee-related social aspects in medium- to long-term production planning.}, language = {en} } @article{DavendraHerrmannBialicDavendra, author = {Davendra, Donald and Herrmann, Frank and Bialic-Davendra, Magdalena}, title = {Chaotic Flower Pollination Algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations}, series = {Neural computing \& applications}, journal = {Neural computing \& applications}, publisher = {Springer Nature}, doi = {10.1007/s00521-022-08044-0}, abstract = {This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.}, language = {en} }