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Produktionswirtschaft
(2020)
Dieses Buch gibt einen umfassenden Überblick über wichtige Bereiche der Produktionswirtschaft. Der Schwerpunkt liegt auf dem operativen Produktionsmanagement. Es zeigt den Einsatz der Produktionsfaktoren Mensch, Betriebsmittel und Material, erklärt die Bedeutung von Informationen für die betriebliche Führung und bietet anhand zahlreicher Beispiele einen breiten Einblick in die Planung und Steuerung industrieller Produktion. Ausführlich werden neben produktionswirtschaftlichen Grundlagen der Ablauf und Inhalt von Planungs- und Steuerungsprozessen erläutert. Auch das aktuelle Thema Industrie 4.0 sowie cyber-physische Systeme werden dargestellt.
In der 2. Auflage wurden umfangreiche Überarbeitungen hinsichtlich Sprache und Aufbau vorgenommen sowie anschauliche Beispielaufgaben, neue Unternehmensbeispiele und hilfreiche Hintergrunderklärungen ergänzt.
Independent Requirements Consumption in Master Production Scheduling in an Uncapacitated Environment
(2012)
This research paper has evaluated the potential of reducing energy consumption by scheduling jobshop production systems with machines using standby modes in free times. The initial introduction of the planning issue is followed by a description of the approach to assess the available potential. Subsequently, the research procedure by means of simulation, a detailed discussion of the results and a perspective on future research is given. Moreover, the notion that scheduling influences the energy consumption in jobshop production systems is supported. The presented simulation research also documents that there is no direct correlation between energy consumption and total lead time, although this was presumed usually. Finally, this paper provides a forecast for a possible optimisation model as well as an exemplary model with an energy-optimised schedule plan.
A large number of current research focuses on the sustainable orientation of business processes. However, it is often neglected that, next to the ecological and economic dimension, the social dimension is also an important pillar of sustainability. Therefore, the intended contribution focuses on the integration of social aspects into the Master Production Scheduling and thus achieves a network between production planning and human resources requirements planning. The study shows that previous papers on production planning and control usually ignore the social dimension. Exceptions are works that take advantage of learning and forgetting effects at the lot-sizing and scheduling. In addition, there are various papers in the scheduling area which aim to reduce employee workload by optimizing resource allocation. Thus, however, only an optimal distribution of burden is achieved. Therefore, this paper presents a long-term management of the burden. To this end, the classic Master Production Scheduling, which has been statically limited in terms of capacity, whereby additional capacities have been used, will be expanded. At the same time, human resources requirements planning is based on assumptions regarding the expected capacity requirements. The created linear optimization model, addresses these weak points. A flexible available capacity is integrated on the basis of existing sales orders. It is making possible, to build up and reduce capacities, whereby technical restrictions (number of workplaces) and labor market conditions (number of available employees, qualification of employees and experience of employees) are also mapped. This represents a significant progress compared to previous models, whereby the target function minimizes costs. Further progress has been made in investigating the effects of exhaustion. The ratio of capacity requirements to available capacity was controlled externally as employee utilization and processing times dependent on the employee's utilization were taken into account. Subsequently, different utilization situations were examined under the assumption of different exhaustion and random demand patterns with the aim of determining an optimal employee utilization. A key finding of this study is that - contrary to common assumptions - achieving cost benefits is not necessarily associated with maximizing utilization, but rather with controlling employee utilization. An additional effect results from the reduction of the employee burden. However, the flexible capacity available does not limit the performance of the production system. In addition, we are currently working on the investigation of effects in a long-term planning horizon and of consequences for the downstream planning levels of production planning and control. Overall, the contribution presents an innovative research project that aims to integrate social aspects into production planning in order to make a contribution to truly sustainable models in the future.
In recent years, a considerable amount of interest has arisen for scheduling problems with technological restrictions. For example, no-buffer flow-shop scheduling problem are well investigated. Here a real world flow shop with a transportation restriction is regarded. It has to produce small batches, very often with a lot size of one, with short response times. Thus, scheduling algorithms are needed to ensure that under the constraint of a high average load of the flow shop, the duedates of the production orders are met. This transportation restriction reduces the set of feasible schedules even more than the nobuffer restrictions discussed in the literature in the case of limited storage. Still this problem is NP-hard. Due to the transportation restriction, the duration of a job A on the flow-shop depends on the other jobs processedon the flow-shop in the same time frame which is called a cycle. Realistic processing times are achieved by a simulation of the scheduling of A which includes the next jobs until A has left the flow-shop. The usage of such realistic processing times instead of net processing times improves the priority rules by around 16%. There are pools of jobs where a large variance of the cycle times is beneficial and pools of jobs where a small variance is better. This is partially detected by agenetic algorithm. This improves the performance by another 34%.