Notion of notation >< notation of notion

Choreo-graphic Figures: Deviations from the Line (2014–2017) is an interdisciplinary research project involving artist Nikolaus Gansterer, choreographer Mariella Greil and writer-artist Emma Cocker (working in dialogue with Alex Arteaga, Christine de Smedt and Lilia Mestre). The project unfolds through two interconnected aims: to explore the nature of 'thinking-feeling-knowing' operative within artistic practice, and to develop systems of notation for reflecting on this often hidden or undisclosed aspect of the creative process. We ask: What systems of notation can we develop for articulating the barely perceptible micro-movements and transitions at the cusp of awareness within the process of artistic "sense-making"? How might we communicate the instability and mutability of the flows and forces within practice, without fixing that which is contingent as a literal sign? Drawing on findings from the first year of the research project Choreo-graphic Figures: Deviations from the Line (including field-work undertaken during a month-long research residency within ImPulsTanz [Vienna, 2014] and a one-week residency-workshop working with researchers at a.pass [Centre of Advanced Performance & Scenography Studies, Brussels, 2015]), we consider notation (and its related technologies) through a diagramming of the multiple, at times competing, forces and energies operative as drawing, writing and choreography enter into dialogue through shared live artistic exploration. Conceived as two interweaving artists' pages we explore these concerns through two interrelated concepts: the notion and notation of (I) figuring and (II) the (choreo-graphic) figure

The on-demand warehousing problem

Warehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand–supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform's objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic

Non-Redundant Graph Neural Networks with Improved Expressiveness

Message passing graph neural networks iteratively compute node embeddings by aggregating messages from all neighbors. This procedure can be viewed as a neural variant of the Weisfeiler-Leman method, which limits their expressive power. Moreover, oversmoothing and oversquashing restrict the number of layers these networks can effectively utilize. The repeated exchange and encoding of identical information in message passing amplifies oversquashing. We propose a novel aggregation scheme based on neighborhood trees, which allows for controlling the redundancy by pruning branches of the unfolding trees underlying standard message passing. We prove that reducing redundancy improves expressivity and experimentally show that it alleviates oversquashing. We investigate the interaction between redundancy in message passing and redundancy in computation and propose a compact representation of neighborhood trees, from which we compute node and graph embeddings via a neural tree canonization technique. Our method is provably more expressive than the Weisfeiler-Leman method, less susceptible to oversquashing than message passing neural networks, and provides high classification accuracy on widely-used benchmark datasets

Fault-tolerant aggregation: Flow-Updating meets Mass-Distribution

Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.- A preliminary version of this work appeared in [2]. This work was partially supported by the National Science Foundation (CNS-1408782, IIS-1247750); the National Institutes of Health (CA198952-01); EMC, Inc.; Pace University Seidenberg School of CSIS; and by Project "Coral - Sustainable Ocean Exploitation: Tools and Sensors/NORTE-01-0145-FEDER-000036" financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Instance extensions for the CMLLSP

Cloud Manufacturing is an emerging concept that enables the orchestration, matching, and sharing of services or resources among collaboration partners or intra-plant facilities. In this context, we introduce the capacitated multi-level lot sizing problem with transshipments and set up carry-over. We consider components that can only be produced by one specific agent as well as components, that can be provided by more than one producer. Since capacities are limited, agents might have to share resources and cover required demands jointly. In this case, finished components are trans- shipped between agents. As an agent can be in charge of producing more than one component, we include the concept of set up carry-over into our modeling. We address a centralized planning approach, where the objective is to find a globally optimized lot sizing plan for all participating agents. Thus, we cover both horizontal and vertical collaboration between agents. The new prob- lem class is formulated mathematically. The used dataset extends publicly available instances (http://www.dmlulsp.com/) and provides production capacities for 2 (first block) and 5 (second block) agents

Vehicle scheduling for rental-with-driver services

In this paper, we introduce a new vehicle scheduling problem (VSP) with driver consistency faced by rental-with-driver companies. A weekly time-horizon is considered and a set of potential customers, each one associated with a list of required tasks, is assumed. The company can choose to accept or reject a customer, but if accepted, all required tasks must be performed by the same driver. A profit is associated with each customer. The goal is to maximize the company's total profit, by respecting a list of daily and the weekly drivers’ workload limitations imposed by drivers’ contracts. We propose a mathematical formulation of the problem and design an exact solution method based on the combinatorial Benders cuts approach. A computational study based on several sets of instances reveals that the proposed solution method strongly outperforms the straightforward MIP approach. A deep analysis of the impact of different parameters is presented. Finally, we provide a measure of the cost of consistency, expressed as the loss of profit necessary to guarantee driver consistency. Results indicate that, for instance, if task durations are long, consistency can be achieved for almost no cost. However, if task durations are short, the loss of profit is below 6%. This provides an important managerial insight for companies offering luxury services, where the perceived quality of a service is key to its success

Instances for decentralized collaborative lot sizing

Increasing customer demands for individualized products push manufacturing companies towardsagile and modularized production processes. Digitalization enables them to efficiently connectwith other companies in order to build up collaboration networks and to overcome inefficienciesby sharing resources among each other. Recently, it has been shown that collaborative lot sizingand orchestration of plans can yield considerable cost savings. However, literature mainly focuseson centrally planned frameworks, where collaboration partners are forced to reveal critical infor-mation. This condition can be seen as a barrier since manufacturers might be reluctant to entersuch a collaboration. To overcome this, decentralized mechanisms are to be developed. In case ofmulti-level lot sizing decisions, however, extremely complex coordination problems can arise. Wepropose such a mechanism, which is based on bilateral negotiations between collaboration partners.In a computational study, we show that the gap in solution quality between a centrally planned anda myopic decentrally plannend approach is huge. Potential gains by far exceed losses of all individ-ual participants in the collaboration. The proposed negotiation-based mechanism is than comparedagainst the other approaches. We can show that even with simple negotiation approaches, where nocritical information has to be revealed, considerable gains can be achieved. However, in order tocompensate participation losses and to ensure collaboration stability, no-information negotiationsare not suitable.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Bundle generation for last-mile delivery with occasional drivers

In this paper, we present the vehicle routing problem (VRP) with occasional drivers (OD) and order bundles (OB). The problem VRP-OD-OB is an extension of the VRP-OD, where instead of assigning one customer per driver, drivers are assigned bundles of customers. To deal with the bundle-to-driver assignment, a bidding system is exploited, in which a company offers a set of bundles and the drivers raise their bids. These bids depend on features such as the drivers’ destination, flexibility in deviating from the shortest path, and willingness to offer service. To generate valuable bundles of customers, we propose two strategies: (i) an innovative approach based on the creation of corridors, and (ii) a traditional approach based on clustering. Through an experimental study, carried out on randomly generated instances and on a real road network, we show that the innovative corridor-based approach strongly outperforms the clustering-based approach. Given a set of bundles and a corresponding set of bids, we provide a mathematical formulation and valid inequalities to solve the VRP-OD-OB. To address larger instances, we design an efficient large neighborhood search-based matheuristic. The results of an extensive computational study show that this method provides near-optimal solutions within very short run times. An analysis of the impact of drivers’ flexibility and willingness levels on the percentage of customers assigned to ODs is presented. Moreover, the case in which ODs dynamically appear at regular time intervals is investigated. Also in this dynamic setting, considerable total cost reductions are shown. Moreover, we derive several important managerial insights, which include the observation that it is not necessary to provide a high number of bundles to achieve good quality solutions. Companies should rather focus on generating fewer but more attractive bundles