The assembly line feeding problem: classification and literature review

In recent years some trends in several product assembly systems emerged, namely mass-customization [Boysen et al., 2007], integration of new product functionalities [Göpfert et al., 2016] and increase in the number of models (e.g. BMW, a German car manufacturer, increased the average number of models offered per year to 37.85 in the period between 2010 and 2016, compared to only 22.3 in the period between 2000 and 2010). These trends have a significant impact on assembly systems since all of them lead to an increasing number of parts required for the final assembly, either by increasing the number of parts required at the border of line (BOL) in general (for new functionalities or new models) or by increasing the number of part variants (mass-customization). Within the assembly line feeding problem (ALFP), the optimal way of supplying assembly stations with parts is examined by assigning different feeding policies to parts. This is mostly based on cost minimizing considerations. The most prevalent line feeding policies are line stocking, kanban, sequencing and kitting [Limere et al., 2015, Sali and Sahin, 2016]. Line stocking and kanban both provide parts in homogeneous filled load carriers. Applying line stocking, a full load carrier (as supplied by the supplier) is provided to the BOL, whereas applying kanban means providing smaller quantities by splitting load carriers into bins. In sequencing and kitting, parts are prepared in the order of demand. In case of sequencing, a container holds variants of one particular component, whereas in case of kitting, different components are grouped and load carriers are filled heterogeneously. The focus of this research is on reviewing literature about the actual ALFP, namely on the assignment of line feeding policies to parts, as well as about related subproblems like e.g. the optimization of milkrun transports within assembly systems. This problem is highly complex due to different decision levels, processes, variable parameters and constraints. In order to help structuring previous and future work, we provide a classification with a three tuple notation as firstly introduced by Graham et al. for machine scheduling [Graham et al., 1979]. Significant decisions and subproblems, occuring in different processes within the ALFP, are classified. This problem includes only in-house logistics and can hence be delimited from external logistics or SCM. Research in this field is becoming more and more attractive to researchers, which can be seen by the rising number of publications. Research is mainly initiated in 1992 [Bozer and McGinnis, 1992] and round about 100 papers are analyzed in this review. The main contribution of this work is twofold. First, we do not only summarize previous research topics but also indicate open research fields, which hopefully motivates researchers to fill the research gaps. Secondly, this work provides, through the classification, a comprehensive framework for researchers to easily identify decisions and subproblems of the ALFP, which can be included in future work

REMIND-D: A Hybrid Energy-Economy Model of Germany

This paper presents a detailed documentation of the hybrid energy-economy model REMIND-D. REMIND-D is a Ramsey-type growth model for Germany that integrates a detailed bottom-up energy system module, coupled by a hard link. The model provides a quantitative framework for analyzing long-term domestic CO2 emission reduction scenarios. Due to its hybrid nature, REMIND-D facilitates an integrated analysis of the interplay between technological mitigation options in the different sectors of the energy system as well as overall macroeconomic dynamics. REMIND-D is an intertemporal optimization model, featuring optimal annual mitigation effort and technology deployment as a model output. In order to provide transparency on model assumptions, this paper gives an overview of the model structure, the input data used to calibrate REMIND-D to the Federal Republic of Germany, as well as the techno-economic parameters of the technologies considered in the energy system module.Hybrid Model, Germany, Energy System, Domestic Mitigation

Line feeding with variable space constraints for mixed-model assembly lines

Nowadays, assembly systems are used for the assembly of an increasing amount of models, which are often mass-customized to meet customers’ demands. This results in a rising number of parts used for assembly and, consequently, space scarcity at the line. There-fore, parts must not only be fed to the line cost-efficiently, but also meet space constraints (Limère et al., 2015). The assembly line feeding problem (ALFP) deals with the assignment of parts to line feeding policies in order to reduce costs and obtain a feasible solution. Within this paper, we examine all distinct line feeding policies at the same time, namely line stocking, kanban, sequencing and kitting (stationary and traveling kits). There is, to the best of our knowledge, no research conducted, including more than three line feeding policies in a single model (cf. Sali and Sahin, 2016). Furthermore, we assume space at the border of line (BoL) being variable. For this reason, space is not constrained per individual station, but we assume one overall space constraint for the entire line (Hua and Johnson, 2010). The main focus of this work is on accurately modeling the problem. This includes a rep-resentation of all line feeding processes, being storage, preparation, transportation, line side presentation and usage. By incorporating the variable space constraints at the BoL, we pro-vide a decision model reducing the overall costs for line feeding in assembly systems, since rigid space constraints at the BoL usually lead to more expensive line feeding policies. In contrast, variable space constraints enable balancing unequal space usage of different sta-tions, allowing cheaper line feeding policies to be selected. Some preliminary results on the cost impact of variable versus fixed space constraints will be discussed

The influence of line balancing on line feeding for mixed-model assembly lines

Though, recent research on mixed-model Assembly Line Balancing Problems (MALBP) and Assembly Line Feeding Problems (ALFP) aims to incorporate real-world aspects, research on the integration of both areas is still limited. This paper helps closing this gap by studying the influence of different balancing objectives on line feeding decisions and costs. For line balancing, different objective functions were formulated and the results were used as input when solving the ALFP. Although, no large cost differences were found, we observed that decision making in line feeding does depend on the balance

Runtime Verification of P4 Switches with Reinforcement Learning

© ACM 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2019 Workshop on Network Meets AI & ML - NetAI’19, http://dx.doi.org/10.1145/3341216.3342206.We present the design and early implementation of p4rl, a system that uses reinforcement learning-guided fuzz testing to execute the verification of P4 switches automatically at runtime. p4rl system uses our novel user-friendly query language, p4q to conveniently specify the intended properties in simple conditional statements (if-else) and check the actual runtime behavior of the P4 switch against such properties. In p4rl, user-specified p4q queries with the control plane configuration, Agent, and the Reward System guide the fuzzing process to trigger runtime bugs automatically during Agent training. To illustrate the strength of p4rl, we developed and evaluated an early prototype of p4rl system that executes runtime verification of a P4 network device, e.g., L3 (Layer-3) switch. Our initial results are promising and show that p4rl automatically detects diverse bugs while outperforming the baseline approach.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNetBMBF, 01IS17052, Adaptiver, Virtueller Assistent zur LAwinenwarnung Nach CHarakter Eigenschaften (AVALANCHE