Konstantin Biel

Systematic literature review of decision support models for energy-efficient production planning

A framework for classifying energy-efficient production planning models is proposed.The integration of energy efficiency from the modeling perspective is stressed.Existing models rarely incorporate renewable energy sources or energy storages.The relationship between emissions and energy consumption is often oversimplified.Most models consider predetermined energy pricing instead of real-time pricing. Following the scarcity of resources, rising energy prices, and an increasing awareness of the role manufacturing plays in the generation of greenhouse gas emissions, the consumption of energy has more and more been the subject of research on production planning over the last decade. Even though recent years have witnessed a dramatic increase in the number of works published in this area, several related research questions have been opened up without sufficiently linking research approaches and research insights. The aim of this paper is to investigate the links between these questions and to highlight how the modeling approaches developed for different manufacturing systems, energy pricing policies, and energy efficiency criteria can benefit from each other and lead to more advanced energy-efficient production planning approaches. Therefore, this paper provides a review of the state-of-the-art of decision support models that integrate energy aspects into mid-term and short-term production planning of manufacturing companies. The paper first highlights the increasing importance of energy consumption in manufacturing and shows how considering energy consumption in production planning can contribute to more energy-efficient production processes. Subsequently, the paper outlines the review methodology used and descriptively analyzes the sampled papers. Afterwards, the selected papers are categorized according to the production planning tasks considered. From this classification, gaps in the existing literature are derived and potential areas for future research are suggested.

Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP.

Over the last decade, manufacturing companies have identified renewable energy as a promising means to cope with time-varying energy prices and to reduce energy-related greenhouse gas emissions. As a result of this development, global installed capacity of wind power has expanded significantly. To make efficient use of onsite wind power generation facilities in manufacturing, production scheduling tools need to consider the uncertainty attached to wind power generation along with changes in the energy procurement cost and in the products’ environmental footprints. To this end, we propose a solution procedure that first generates a large number of wind power scenarios that characterise the variability in wind power over time. Subsequently, a two-stage stochastic optimisation procedure computes a production schedule and energy supply decisions for a flow shop system. In the first stage, a bi-objective mixed integer linear programme simultaneously minimises the total weighted flow time and the expected energy cost, based on the generated wind power scenarios. In the second stage, energy supply decisions are adjusted based on real-time wind power data. A numerical example is used to illustrate the ability of the developed decision support tool to handle the uncertainty attached to wind power generation and its effectiveness in realising energy-related objectives in manufacturing.

Governing the dynamics of multi-stage production systems subject to learning and forgetting effects: A simulation study

Managing production systems where production rates change over time due to learning and forgetting effects poses a major challenge to researchers and practitioners alike. This task becomes especially difficult if learning and forgetting effects interact across different stages in multi-stage production systems as rigid production management rules are unable to capture the dynamic character of constantly changing production rates. In a comprehensive simulation study, this paper first investigates to which extent typical key performance indicators (KPIs), such as the number of setups, in-process inventory, or cycle time, are affected by learning and forgetting effects in serial multi-stage production systems. The paper then analyses which parameters of such production systems are the main drivers of these KPIs when learning and forgetting occur. Lastly, it evaluates how flexible production control based on Goldratt’s Optimised Production Technology can maximise the benefits learning offers in such systems. The results of the paper indicate that learning and forgetting only have a minor influence on the number of setups in serial multi-stage production systems. The influence of learning and forgetting on in-process inventory and cycle time, in contrast, is significant, but ambiguous in case of in-process inventory. The proposed buffer management rules are shown to effectively counteract this ambiguity.

Losgrößenplanung in einem Presswerk mithilfe gemischt-ganzzahliger Programmierung

Kurzfassung Dieser Beitrag untersucht die Einsatzmöglichkeiten der gemischt-ganzzahligen Programmierung zur dynamischen Planung von Losgrößen anhand eines Presswerks eines großen deutschen Automobilherstellers. Dabei wird deutlich, dass die gemischt-ganzzahlige Programmierung flexibel auf unterschiedliche Anwendungsfälle anpassbar ist und spezielle technische und planerische Anforderungen umsetzen kann. Daneben wird gezeigt, dass die gemischt-ganzzahlige Programmierung der manuellen Planung im Umgang mit der Komplexität heutiger industrieller Planungsprobleme eindeutig überlegen ist.