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Ambitious goals set by the European Union strategy towards the emission reduction of multimodal logistic chains and new requirements for intermodal terminals set by the evolution of customer needs, contribute to a shift in the driver for the infrastructure development: from economy of scale to economy of density. This paper aims to present an innovative method for designing a process oriented technology chain for intermodal terminals in order to fulfill these new demanding requirements. The results of the case study of the Zero Emission Logistic Terminal Reutlingen are presented, highlighting how this particular context enables the design and development of a modular concept, paving the way for the generalization of the findings towards the transfer to similar contexts of other European cities.
In this paper it is first identified the trade-off among costs, flexibility and performances of autonomous robotic solutions for material handling processes, where adding value with automation is not as trivial as in production processes: hence the requirement for automated solutions to be simple, lean and efficient becomes even stricter. Then a method for modelling and comparing differential performances and costs of manual and autonomous solutions is developed. As a result of the method, a smart man-machine collaborative interface is designed and its impact evaluated on a specific case of study. Results are then generalized and prove the strong conclusions that in unconstrained environments, where full standardization cannot be achieved, the risk of investing in autonomous solutions can only be mitigated by creating a fast and smart man-machine collaborative interface.
Compared to the automotive sector, where automation is the rule, in many other less standardized sectors automation is still the exception. This could soon hurt the productivity of industrialized countries, where the unemployment is low and the population is aging. Phenomena like the recent downfall in productivity, due to lockdowns and social distancing for prevention of health hazards during the COVID19 pandemic, only add to the problem. For these reasons, the relevance, motivation and intention for more automation in less standardized sectors has probably never been higher. However, available statistics say that providers and users of technologies struggle to bring more automation into action in automation-unfriendly sectors. In this paper, we present a decision support method for investment in automation that tackles the problem: the STIC analysis. The method takes a holistic and quantitative approach tying together technological, context-related and economic input parameters and synthetizing them in a final economic indicator. Thanks to the modelling of such parameters, it is possible to gain sensibility on the technological and/or process adjustments that would have the highest impact on the efficiency of the automation, thereby delivering value for both technology users and technology providers.
The success of an autonomous robotic system is influenced by several interdependent factors not easily identifiable. This paper is set to lay the foundation of a new integrated approach in order to deeply examine all the parameters and understand their contribution to success. After introducing the problem, two cutting edge autonomous systems for the process of unloading of containers will be presented. Then the STIC analysis, a recently developed method for modelling and interpreting all the parameters, will be introduced. The preliminary results of applying such a methodology to a first study case, based on one of the two systems available to the authors, will be shortly presented. Future research is in the end recommended in order to prove that this methodology is the only way to efficiently and effectively mitigate the risk that stops potential users from investing in autonomous systems in the logistics sector.
In standardized sectors such as the automotive, the cost-benefit ratio of automation solutions is high as they contribute to increase capacity, decrease costs and improve product quality. In less standardized application fields, the contribution of automation to improvements in capacity, cost and quality blurs. The automation of complex and unstructured tasks requires sophisticated, expensive and low-performing systems, whose impact on product quality is oftentimes not directly perceived by customers. As a result, the full automation of process chains in the general manufacturing or the logistic sectors is often a sub optimal solution. Taking the distance from the false idea that a process should be either fully automated, or fully manual, this paper presents a novel heuristic method for design of lean human-robot interaction, the Quality Interaction Function Deployment, with the objective of the “right level of automation”. Functions are divided among human and automated agents and several automation scenarios are created and evaluated with respect to their compliance to the requirements of all process´ stakeholders. As a result, synergies among operators (manual tasks) and machines (automated tasks) are improved, thus reducing time-losses and increasing productivity.
Milk-run systems are becoming more and more popular when it comes to in-plant material supply. Planning and dimensioning such a system poses challenges, which are difficult to overcome, especially in scenarios characterized by a large number of hard constraints and by well-established processes. This paper is set to ease the task of the planner by presenting an innovative flexible method for the planning and dimensioning of in-plant milk-run systems in high constrained scenarios. After an overview on tugger train systems and existing planning methods, an extensive description of the new method will be given. The new method proposed will be critically analyzed and discussed before suggesting forthcoming research.
Planning of available resources considering ergonomics under deterministic highly variable demand
(2020)
In this paper, a method for hybrid short- to long-term planning of available resources for operations is presented, which is based on a known or deterministically forecasted but highly variable demand. The method considers quantitative measures such as the performance and the availability of resources, ergonomically relevant KPI and ultimately process costs in order to serve as a pragmatic planning tool for operations managers in SMEs. Specifically, the method enables exploiting the ergonomic advantages of available flexible automation technology (e.g. AGVs or picking robots), while assuring that these do not represent a capacity bottleneck. After presenting the method along with the necessary assumptions, mainly concerning the availability of data for the calculations, we report a case study that quantifies the impact of throughput variability on the selection of different process alternatives, where different teams of resources are used.
The appeal of a forklift-free shop floor is pushing enterprises towards lean logistic systems and tugger trains are becoming popular means of supply in intensive material handling production systems. Planning a tugger train system is a complex task influenced by a large set of interrelated parameters. The only standard available to help the planner in designing the tugger train logistic system is the draft norm VDI 5586 (April 2016). However this norm is only applicable under a set of restricting assumptions. In this paper a methodology to complement the approach proposed by the VDI is introduced and then applied to a numerical example. The results are briefly presented and discussed before suggesting forthcoming research.
This article discusses the scientifically and industrially important problem of automating the process of unloading goods from standard shipping containers. We outline some of the challenges barring further adoption of robotic solutions to this problem, ranging from handling a vast variety of shapes, sizes, weights, appearances, and packing arrangements of the goods, through hard demands on unloading speed and reliability, to ensuring that fragile goods are not damaged. We propose a modular and reconfigurable software framework in an attempt to efficiently address some of these challenges. We also outline the general framework design and the basic functionality of the core modules developed. We present two instantiations of the software system on two different fully integrated demonstrators: 1) coping with an industrial scenario, i.e., the automated unloading of coffee sacks with an already economically interesting performance; and 2) a scenario used to demonstrate the capabilities of our scientific and technological developments in the context of medium- to long-term prospects of automation in logistics. We performed evaluations that allowed us to summarize several important lessons learned and to identify future directions of research on autonomous robots for the handling of goods in logistics applications.
The success of an autonomous robotic system is influenced by several not easily identifiable interdependent factors. This paper is set to lay the foundation of an integrated approach in order to examine all the parameters and understand their contribution to success. After introducing the problem, two autonomous systems for the process of unloading of containers are presented. Then a recently developed method for modelling and interpreting all the parameters, the STIC analysis, are introduced. The preliminary results of applying such a methodology to a first study case is shortly presented. Future research is in the end recommended in order to prove that this methodology is the only way to overcome barriers to the investment in autonomous systems in the logistics sector.