Optimal material resource planning is crucial to run safe and cost-efficient hospital services. In this article, we investigate a real problem in hospitals, motivated by an environmental and economically inefficient u...
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Optimal material resource planning is crucial to run safe and cost-efficient hospital services. In this article, we investigate a real problem in hospitals, motivated by an environmental and economically inefficient use of disposable, single-use, endoscopes. We develop a mathematical model and create a decision support tool to determine when reusable, multi-use, bronchoscopes should be sent for inspection including information to what extent single-use bronchoscopes can cover the remaining demand. The results show that the proposed approach can contain operational costs which consist of costs for buying single-use devices, inspection costs and reprocessing costs, i.e., sterilization of reusable devices. Our tool can assist hospitals to predict when reusable bronchoscopes should undergo inspection and whether the current inventory of reusable devices is sufficient to cover the demand. Finally, we evaluate the impact of variation in demand on total costs.
The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and...
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The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such Big Data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.
We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. T...
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We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. The proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.
In this paper, we address a new variant of the petrol replenishment problem (PRP), which is a rich real -word multi-depot multi-period problem (MDMPPRP). We show that it is possible to solve this complex variant with ...
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In this paper, we address a new variant of the petrol replenishment problem (PRP), which is a rich real -word multi-depot multi-period problem (MDMPPRP). We show that it is possible to solve this complex variant with an exact branch-and-price approach and some derived heuristics. On one side, this prob-lem could be modeled as a set partitioning type problem with low to moderate density (the number of ones per column, i.e., clients to visit, is not large). Such problems have some nice polyhedral proper-ties to consider for favoring integrality. In the other side, some complex handling rules apply due to the problem's context. A natural way is to address them in the column generation subproblem as an elemen-tary shortest path problem with resource constraints, which constitutes the major bottleneck. To succeed in this challenge, we need to design some sophisticated techniques i) for branching to profit from the polyhedral properties and ii) for solving the column generation subproblem. Direct use of on-the-shelf algorithms does not work, unfortunately. Numerical results on a real network (four depots, five types of petroleum products, four main groups of clients, heterogeneous fleet of highly compartmented tank trucks) prove the effectiveness and high potential of the proposed approach.& COPY;2023 Elsevier B.V. All rights reserved.
Robotic cells are serial production systems that consist of a number of machines and a material handling robot that transfers parts between the machines. Energy consumption of a robot during its move between two machi...
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Robotic cells are serial production systems that consist of a number of machines and a material handling robot that transfers parts between the machines. Energy consumption of a robot during its move between two machines can be decreased by carefully selecting its speed. This paper presents models and algorithms that make robot speed and robot activity sequencing decisions together for a dual-gripper robot that can handle two parts simultaneously. We consider both robot energy consumption and cycle time objectives so that a decision maker can observe the trade off between energy cost and throughput rate of the system, and select the best solution among alternatives. Computational experiments demonstrate that proposed approaches can generate efficient solutions for practical size instances, and speed control can significantly conserve energy.
The resource-constrained project scheduling problem describes a situation in which the duration of a project must be minimized by choosing a start time for each project activity subject to given precedence constraints...
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We are interested in the design of robust (or resilient) capacitated rooted Steiner networks in the case of terminals with uniform demands. Formally, we are given a graph, capacity, and cost functions on the edges, a ...
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We are interested in the design of robust (or resilient) capacitated rooted Steiner networks in the case of terminals with uniform demands. Formally, we are given a graph, capacity, and cost functions on the edges, a root, a subset of vertices called terminals, and a bound k$$ k $$ on the number of possible edge failures. We first study the problem where k=1$$ k=1 $$ and the network that we want to design must be a tree covering the root and the terminals: we give complexity results and propose models to optimize both the cost of the tree and the number of terminals disconnected from the root in the worst case of an edge failure, while respecting the capacity constraints on the edges. Secondly, we consider the problem of computing a minimum-cost survivable network, that is, a network that covers the root and terminals even after the removal of any k$$ k $$ edges, while still respecting the capacity constraints on the edges. We also consider the possibility of protecting a given number of edges. We propose three different formulations: a bilevel formulation (with an attacker and a defender), a cutset-based formulation and a flow-based one. We compare the formulations from a theoretical point of view, and we propose algorithms to solve them and compare their efficiency in practice.
Estimating the elements of a matrix, when only the margins (row and column sums) are known, but a supposedly similar 'reference matrix' is available, is a standard problem in many disciplines. After discussing...
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Estimating the elements of a matrix, when only the margins (row and column sums) are known, but a supposedly similar 'reference matrix' is available, is a standard problem in many disciplines. After discussing the main types, issues and applications of these two-directional matrix adjustment problems the paper concentrates on the case of negative matrix elements and models with quadratic objective functions. The solution of the Improved Normalized Squared Differences (INSD) model is proved to be the same as the result of that iteration algorithm which is presented in the paper. It is also argued that if the sign-preservation requirement is dropped then the iteration procedure suggested by Huang et al. (Econ Syst Res 20(1):111-123, 2008) boils down to the same algorithm. Using the numerical example of the earlier literature it is also demonstrated that even in this not sign-preserving case, which even requires sign-flips for some elements, the INSD-model produces good fit in mathematical terms.
Corrosion will cause cross-sectional area loss of steel members, which will lead to redistribution of internal force and affect the performance of structures. In this paper, a generalized formula for the internal forc...
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Corrosion will cause cross-sectional area loss of steel members, which will lead to redistribution of internal force and affect the performance of structures. In this paper, a generalized formula for the internal force variation of pin-jointed structures due to variation of external force, cross-sectional area, and initial length is derived. The area-loss sensitivity coefficient (ASC) and the area-loss evenness coefficient (AEC) based on statistical measures are then defined. By calculating these two coefficients, the influence of single member's area-loss on the behavior of the structure as well as the evenness of internal force variation are evaluated. Besides, a method to determine the area-loss limit of pin-jointed structures is proposed based on the structural reliability theory and mathematical nonlinear programming. According to the above process, the effect of members' area loss on structural properties is analyzed without complicated numerical approaches such as FEM, and then the safety of corroded structures is assessed preliminarily. The proposed method is verified by two numerical examples, and provides a convenient tool for pre-manufacturing, maintenance during service life, and structural health monitoring of pin-jointed structures.
The main challenge addressed in this paper is how to handle and recycle the large amount of C&D waste that is generated from infrastructure projects. The study is motivated by B AE rum Ressursbank in Norway and th...
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The main challenge addressed in this paper is how to handle and recycle the large amount of C&D waste that is generated from infrastructure projects. The study is motivated by B AE rum Ressursbank in Norway and their aim of finding logistical solutions to an expected surplus of 15 million m3 of waste from infrastructure projects in the next decade. We identify the key decisions as the design of the distribution network for both surplus waste materials and new construction materials and the investments in processing machinery at each recycling facility, and we call the problem representing this situation the Infrastructure Waste Management Problem (IWMP). The methodologies used are mathematical programming and operations research. We formulate the IWMP as a mixed integer linear program and identify two objectives;to minimize transportation costs and to minimize the envi-ronmental impact of the operations. The description of the problem, assumptions, and data are based on cases that represent the situation of B AE rum Ressursbank. A special emphasis in the analysis is to quantify the gains from collaboration. Comparing individual planning of each project with an ideal situation of complete collab-oration gives a cost reduction of more than 29% and a reduction in emissions of more than 14%. The study supports the conjecture by B AE rum Ressursbank that large cost savings and considerable reductions in environ-mental impact are possible through collaboration.
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