Grand Challenges in Discrete Event Logistics Systems

With the experience of dealing with systems as complex as wafer fabrication plants as well as aerospace spare parts logistics networks, I would consider the following four factors as the greatest contemporary challenges for Discrete Event Logistics Systems: (i) Domain-specific characteristics of Discrete Event Logistic Systems, (ii) Cycle time for model generation and model maintenance, (iii) Involvement of humans in operating such systems, (iv) The difficulty of quantifying the value generated by decision-support software for managing and optimising such systems

10102 Executive Summary -- Grand Challenges for Discrete Event Logistics Systems

In March 2010, the Dagstuhl Seminar 10102 explored the grand challenges confronting research and practice in the domain of discrete event logistics systems. This Executive Summary describes the process of the seminar and discusses the key conclusions regarding grand challenges for research and practice. Abstracts of the presentations given during the seminar are put together in the online proceedings

10102 Abstracts Collection -- Grand Challenges for Discrete Event Logistics Systems

From 03/08/2010 to 03/12/2010, the Dagstuhl Seminar 10102 ``Grand Challenges for Discrete Event Logistics Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Panel on future challenges in modeling methodology

This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction

Taking the pulse of snowmelt: in situ sensors reveal seasonal, event and diurnal patterns of nitrate and dissolved organic matter variability in an upland forest stream

Highly resolved time series data are useful to accurately identify the timing, rate, and magnitude of solute transport in streams during hydrologically dynamic periods such as snowmelt. We used in situ optical sensors for nitrate (NO3 −) and chromophoric dissolved organic matter fluorescence (FDOM) to measure surface water concentrations at 30 min intervals over the snowmelt period (March 21–May 13, 2009) at a 40.5 hectare forested watershed at Sleepers River, Vermont. We also collected discrete samples for laboratory absorbance and fluorescence as well as δ18O–NO3 − isotopes to help interpret the drivers of variable NO3 − and FDOM concentrations measured in situ. In situ data revealed seasonal, event and diurnal patterns associated with hydrological and biogeochemical processes regulating stream NO3 − and FDOM concentrations. An observed decrease in NO3 − concentrations after peak snowmelt runoff and muted response to spring rainfall was consistent with the flushing of a limited supply of NO3 − (mainly from nitrification) from source areas in surficial soils. Stream FDOM concentrations were coupled with flow throughout the study period, suggesting a strong hydrologic control on DOM concentrations in the stream. However, higher FDOM concentrations per unit streamflow after snowmelt likely reflected a greater hydraulic connectivity of the stream to leachable DOM sources in upland soils. We also observed diurnal NO3 − variability of 1–2 μmol l−1 after snowpack ablation, presumably due to in-stream uptake prior to leafout. A comparison of NO3 − and dissolved organic carbon yields (DOC, measured by FDOM proxy) calculated from weekly discrete samples and in situ data sub-sampled daily resulted in small to moderate differences over the entire study period (−4 to 1% for NO3 − and −3 to −14% for DOC), but resulted in much larger differences for daily yields (−66 to +27% for NO3 − and −88 to +47% for DOC, respectively). Despite challenges inherent in in situ sensor deployments in harsh seasonal conditions, these data provide important insights into processes controlling NO3 − and FDOM in streams, and will be critical for evaluating the effects of climate change on snowmelt delivery to downstream ecosystems

Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications

[EN] The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems have been designed with the main goal of reducing their monetary cost while offering a specified quality of service. During the last decade, however, sustainability concepts are also being considered as a critical component of transportation systems, i.e., the environmental and social impact of transportation activities have to be taken into account when managers and policy makers design and operate modern transportation systems, whether these refer to long-distance carriers or to metropolitan areas. This paper reviews the existing work on different scientific methodologies that are being used to promote Sustainable Transportation Systems (STS), including simulation, optimization, machine learning, and fuzzy sets. This paper discusses how each of these methodologies have been employed to design and efficiently operate STS. In addition, the paper also provides a classification of common challenges, best practices, future trends, and open research lines that might be useful for both researchers and practitioners.This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities (PID2019-111100RB-C21-C22/AEI/10.13039/501100011033, RED2018-102642-T) and the SEPIE Erasmus+ Program (2019-I-ES01-KA103-062602), and the IoF2020-H2020 (731884) project.Torre-Martínez, MRDL.; Corlu, CG.; Faulin, J.; Onggo, BS.; Juan-Pérez, ÁA. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability. 13(3):1-21. https://doi.org/10.3390/su1303155112113

Proposing A Cyber-Physical Production Systems Framework Linking Factory Planning And Factory Operation

The challenges for industrial companies in the area of factory planning and operation are characterised on the one hand by permanently shortening product life cycles and increasing product diversity. Furthermore, the demand for ecologically sustainable processes is growing and the complexity of production systems is increasing due to higher product complexity. This results in a complex decision-making space for companies within factory planning and factory operation which is difficult to plan. The advancing digitalisation can bring a great opportunity here. Modelling and simulation can create greater transparency in the context of planning and operation, and processes can be designed to be ecologically sustainable and efficient. Currently, research approaches in the context of factory planning and operation are focussing on the application and use of digital methods and tools of the Digital Factory (DF). However, the application is limited to individual areas in factory planning or factory operation. For this reason, this paper focuses on the design of a framework that addresses both factory planning and factory operation aspects and links them through modelling and simulation. Cyber-physical production systems (CPPS) can help here by mapping the individual modules within planning and operation using individual agents in agent-based simulation (AB). By linking planning and real data, the processes from planning and operation can be taken into account. From this, insights gained from planning can be simulated in an early phase and subjected to optimisation during operation. The cycle-oriented CPPS can be used on an ongoing basis by preparing the generic building blocks on the planning and operational sides through structured data acquisition and implementing them in the real world with the help of decision support from the virtual world

Towards a model for managing uncertainty in logistics operations – A simulation modeling perspective

Uncertainty rules supply chains. Unexpected changes constantly occur on all levels; strategically through globalization, introduction of novel technology, mergers and acquisitions, volatile markets, and on an operational level through demand fluctuations, and events such as late arrival of in-bound material, machine equipment breakdown, and quality problems. The problem with uncertainty is increasing as the focus on cost reductions and efficiency in the industry tends to stretch supply chains to become longer and leaner, thus making them more vulnerable to disturbances. The aim of this thesis is to explore strategies for evaluating and managing uncertainties in a logistics context with the objectives; “to propose a method for modeling and analyzing the dynamics of logistics systems with an emphasize on risk management aspects”, and “to explore the impact of dynamic planning and execution in a logistics system”. Three main strategies for handling uncertainties are being discussed; robustness, reliability, and resilience. All three strategies carry an additional cost that must be weighed against the cost and risk of logistical disruptions. As an aid in making this trade-off, a hybrid simulation approach, based on discrete-event simulation and Monte Carlo simulation, is proposed. A combined analytical, and simulation approach is further used to explore the impact of dynamic planning and execution in a solid waste management case. Finally, a draft framework for how uncertainty can be managed in a logistics context is presented along with the key reasons why the proposed simulation approach has proven itself useful in the context of logistics systems