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

Characteristics and effectiveness of co-designed mental health interventions in primary care for people experiencing homelessness: a systematic review

People experiencing homelessness (PEH) face a disproportionately high prevalence of adverse mental health outcomes compared with the non-homeless population and are known to utilize primary healthcare services less frequently while seeking help in emergency care facilities. Given that primary health services are more efficient and cost-saving, services with a focus on mental health that are co-designed with the participation of users can tackle this problem. Hence, we aimed to synthesize the current evidence of such interventions to assess and summarize the characteristics and effectiveness of co-designed primary mental healthcare services geared towards adult PEH. Out of a total of 10,428 identified records, four articles were found to be eligible to be included in this review. Our findings show that co-designed interventions positively impacted PEH’s mental health and housing situation or reduced hospital and emergency department admissions and increased primary care utilization. Therefore, co-designed mental health interventions appear a promising way of providing PEH with continued access to primary mental healthcare. However, as co-designed mental health interventions for PEH can improve overall mental health, quality of life, housing, and acute service utilization, more research is needed

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

Computing Approximate Eigenpairs of Symmetric Block Tridiagonal Matrices

A divide-and-conquer method for computing approximate eigenvalues and eigenvectors of a block tridiagonal matrix is presented. In contrast to a method described earlier [W. N. G55(yyu[-- R. C. Ward, and R. P. Muller, ACM Trans. Matfi Software, 28 (2002), pp. 45--58], the o#-diagonal blocks can have arbitrary ranks. It is shown that lower rank approximations of the o#-diagonal blocks as well as relaxation of deflation criteria permit the computation of approximate eigenpairs with prescribed accuracy at significantly reduced computational cost compared to standard methods such as, for example, implemented in Lapack

Proactive Replica Placement Using Mobility Prediction

International audienceThis work brings together data replication management and mobility modeling for mobiledistributed computing scenarios. Based on a novel mobility model and on the predictionof future mobile behavior, techniques for proactive data replication and placementmanagement are developed for mobile users. Based on this joint approach the responsiveness of a flexible replica placement algorithm is increased for the end users and the overall resource consumption is minimized. In thisarticle we describe and evaluate our approach using GPS traces stored by taxis in the city of Vienna, Austria

Proactive Replica Placement Using Mobility Prediction

International audienceThis work brings together data replication management and mobility modeling for mobiledistributed computing scenarios. Based on a novel mobility model and on the predictionof future mobile behavior, techniques for proactive data replication and placementmanagement are developed for mobile users. Based on this joint approach the responsiveness of a flexible replica placement algorithm is increased for the end users and the overall resource consumption is minimized. In thisarticle we describe and evaluate our approach using GPS traces stored by taxis in the city of Vienna, Austria