Optimizing transport logistics under uncertainty with simheuristics: concepts, review and trends

Background: Uncertainty conditions have been increasingly considered in optimization problems arising in real-life transportation and logistics activities. Generally, the analysis of complex systems in these non-deterministic environments is approached with simulation techniques. However, simulation is not an optimization tool. Hence, it must be combined with optimization methods when our goal is to: (i) minimize operating costs while guaranteeing a given quality of service; or (ii) maximize system performance using limited resources. When solving NP-hard optimization problems, the use of metaheuristics allows us to deal with large-scale instances in reasonable computation times. By adding a simulation layer to the metaheuristics, the methodology becomes a simheuristic, which allows the optimization element to solve scenarios under uncertainty. Methods: This paper reviews the indexed documents in Elsevier Scopus database of both initial as well as recent applications of simheuristics in the logistics and transportation field. The paper also discusses open research lines in this knowledge area. Results: The simheuristics approaches to solving NP-hard and large-scale combinatorial optimization problems under uncertainty scenarios are discussed, as they frequently appear in real-life applications in logistics and transportation activities. Conclusions: The way in which the different simheuristic components interact puts a special emphasis in the different stages that can contribute to make the approach more efficient from a computational perspective. There are several lines of research that are still open in the field of simheuristics.Peer ReviewedPostprint (published version

Past Challenges and the Future of Discrete Event Simulation

The American scientist Carl Sagan once said: “You have to know the past to understand the present.” We argue that having a meaningful dialogue on the future of simulation requires a baseline understanding of previous discussions on its future. For this paper, we conduct a review of the discrete event simulation (DES) literature that focuses on its future to understand better the path that DES has been following, both in terms of who is using simulation and what directions they think DES should take. Our review involves a qualitative literature review of DES and a quantitative bibliometric analysis of the Modeling and Simulation (M&S) literature. The results from the bibliometric study imply that demographics of the M&S community are rapidly changing, both in terms of the nations that use M&S and the academic disciplines from which new simulationists hail. This change in demographics has the potential to help aid the community face some of its future challenges. Our qualitative literature review indicates that DES still faces some significant challenges: these include integrating human behavior; using simulation for exploration, not replication; determining return on investment; and communication issues across a splitting community

Assessing the eco-efficiency benefits of empty container repositioning strategies via dry ports

Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry ports—street turns and extended free temporary storage—considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multiparadigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping lines’ collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions

Brownfield Factory Layout Planning using Realistic Virtual Models

To stay competitive in an increasingly digitalised and global context, manufacturing companies need to increase productivity and decrease waste. This means their production systems must improve; something they can achieve in a multitude of ways. For example, increasing the level of automation, improving scheduling and improving product and process flows. Often, these production system improvements entail redesigning the system to incorporate these ensuing changes; a unique and temporary endeavour that is often structured as a project. One part of the production system design process is layout planning, in which the positions of operators, workstations, machines and other parts of the system are decided. This planning process can have a major impact on the overall efficiency of operations.In industrial settings, factory layout planning is often conducted in brownfield settings. In other words, in operational facilities. Since every production system and facility is unique, so is every factory layout planning project. Each such project has different preconditions, existing knowledge, availability and quality of data, lead-times, expectations and driving forces, to name just a few. If factory layout planning were treated as a design problem (more subjective than mathematical in nature), it would be hard to produce a mathematical solution for an optimal layout that would also work in reality. Instead, if a layout is developed and adapted to all real constraints and factors while it is being developed, the result would more likely be installable and work as expected.The long-term vision of this thesis is of a future in which sustainable manufacturing industry continues playing a vital role in society, because its contribution is more than just economic. A future in which the manufacturing industry is appreciated and engaged with by the local community; in which high performance is connected to the successful adoption and efficient use of digital tools in developing and improving existing brownfield production systems. This thesis aims to ensure that manufacturing industry adopts realistic virtual models in its brownfield factory layout planning processes. It does this by identifying and describing common challenges and how they may be reduced by developing and using realistic virtual models. This leads to improvements in the planning, installation and operational phases of production systems.The findings of this thesis show that brownfield factory layout planning represents a significant proportion of industrial layout planning. Its challenges lie mainly in the areas of data accuracy and richness. There are difficulties in grasping scale and perspective, communicating ideas and gathering input in the layout planning phase. By applying 3D laser scanning to provide accurate data and virtual reality to provide immersion and scale, realistic virtual models have been created. These reduce or eliminate the challenges stated above and allow more employees to be involved in the layout planning process. This, in turn, results in the identification of flaws in the layout and improvements in the early stages, rather than during or after installation. There is also an overall improvement to brownfield factory change processes, with costs that pale by comparison to the total cost of layout changes

Towards A Design Of A Software-Defined Manufacturing System Based On A Systematic Literature Review For Enabling A Decentralised High-Rate Electrolyser Production

Hydrogen is critical for the transition to an environmentally sound and reliable energy supply. This transition requires large capacities of performant and cost-effective electrolysers. Although performant electrolysers already exist, they cannot yet be manufactured at a high rate in series production. The project H2Giga-FRHY is researching a reference factory for large-scale production of electrolysers, developing new production and testing modules. As an essential building block of the reference factory, a research group at Fraunhofer IPA is designing and implementing a comprehensive software-defined manufacturing system (SDMS), which supports the decentralized high-rate production of electrolysers and allows for far-reaching insights regarding high-rate capability, quality, and cost of products, processes, and technologies involved. For the SDMS implementation, different enterprise architecture (EA) approaches are considered and evaluated in the scope of a structured literature review with respect to criteria arising from the project context and related research questions. In this paper, an approach to designing a software-defined manufacturing system is described, and its necessity is based on the use case-specific criteria discussed

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

Dynamic allocation of operators in a hybrid human-machine 4.0 context

La transformation numérique et le mouvement « industrie 4.0 » reposent sur des concepts tels que l'intégration et l'interconnexion des systèmes utilisant des données en temps réel. Dans le secteur manufacturier, un nouveau paradigme d'allocation dynamique des ressources humaines devient alors possible. Plutôt qu'une allocation statique des opérateurs aux machines, nous proposons d'affecter directement les opérateurs aux différentes tâches qui nécessitent encore une intervention humaine dans une usine majoritairement automatisée. Nous montrons les avantages de ce nouveau paradigme avec des expériences réalisées à l'aide d'un modèle de simulation à événements discrets. Un modèle d'optimisation qui utilise des données industrielles en temps réel et produit une allocation optimale des tâches est également développé. Nous montrons que l'allocation dynamique des ressources humaines est plus performante qu'une allocation statique. L'allocation dynamique permet une augmentation de 30% de la quantité de pièces produites durant une semaine de production. De plus, le modèle d'optimisation utilisé dans le cadre de l'approche d'allocation dynamique mène à des plans de production horaire qui réduisent les retards de production causés par les opérateurs de 76 % par rapport à l'approche d'allocation statique. Le design d'un système pour l'implantation de ce projet de nature 4.0 utilisant des données en temps réel dans le secteur manufacturier est proposé.The Industry 4.0 movement is based on concepts such as the integration and interconnexion of systems using real-time data. In the manufacturing sector, a new dynamic allocation paradigm of human resources then becomes possible. Instead of a static allocation of operators to machines, we propose to allocate the operators directly to the different tasks that still require human intervention in a mostly automated factory. We show the benefits of this new paradigm with experiments performed on a discrete-event simulation model based on an industrial partner's system. An optimization model that uses real-time industrial data and produces an optimal task allocation plan that can be used in real time is also developed. We show that the dynamic allocation of human resources outperforms a static allocation, even with standard operator training levels. With discrete-event simulation, we show that dynamic allocation leads to a 30% increase in the quantity of parts produced. Additionally, the optimization model used under the dynamic allocation approach produces hourly production plans that decrease production delays caused by human operators by up to 76% compared to the static allocation approach. An implementation system for this 4.0 project using real-time data in the manufacturing sector is furthermore proposed

A traffic simulation tool for assessing smart city policies (CitScale)

Over the last century, cities have developed as a function of increased usage of automobiles as the standard transport mode. The number of cars increased along with the population as highways and parking spots became essential in city planning. Now, there is more focus on how the existing infrastructure could be used as efficiently as possible rather than increasing capacity by merely building new roads. An important part of traffic planning is a sustainable transport system, which thereby reduces congestion and emissions by using the available capacity in a more efficient way. Traffic simulation models are tools for assessing new mobility solutions and analysing changes in the infrastructure, such as rearranging intersections and building new roads. Transportation is undergoing a profound and significant transformation as it seeks to fulfil the promise of connected mobility for people and goods while limiting its carbon footprint. Physical changes to the road network mean large investments that must be comprehensively considered before acting. Modelling different scenarios of infrastructural changes allows making forecasts without any physical changes. Autonomous vehicles are potentially changing the economics of ownership as well as the use of the transportation networks, which will likely accelerate trends towards greater use of app-based ride hailing and/or sharing by private transportation network companies. American and European cities are seeing a rise in several potential business models with varying degrees of ride sharing and public vs. private involvement in delivering mobility services (MaaS). Implications for transit agencies and mobility service providers must be evaluated, and this can be done by traffic simulation models that provide a model-based framework for evaluating the mobility impact of new services.Peer ReviewedPostprint (author's final draft

Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis