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Optimized staff allocation for inpatient phlebotomy and electrocardiography services via mathematical modelling in an acute regional and teaching hospital

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HEALTH SYSTEMS 2017年第2期6卷 102-111页

作者： Yip, Kenneth C. M. Huang, Kevin W. H. Ho, Esther W. Y. Chan, W. K. Lee, Irene L. Y. Hosp Author Financial Planning Finance Div 147B Argyle St Kowloon Hong Kong Peoples R China Hosp Author Hong Kong Childrens Hosp Commissioning Team Hong Kong Hong Kong Peoples R China Hosp Author Queen Mary Hosp Cent Nursing Dept Hong Kong Hong Kong Peoples R China Hosp Author Queen Mary Hosp Accid & Emergency Dept Hong Kong Hong Kong Peoples R China

Adhering to pre-defined service routes that cover a fixed set of wards in a shift, the inpatient phlebotomy service provides 24-hour coverage for a 27-storey, 1,400-bed hospital. We present an application of mathematical optimization to improve its service efficiency without injecting additional resources. A mixed integer programming model was implemented to revamp the service route configuration to minimize workload discrepancies among service routes, limit maximum daily workload per route and restrict routes to span a maximum number of floor levels, while taking into consideration the ward-specific demand for each duty (i.e. daytime, evening, and night time) throughout the day. This data-driven and evidence-based approach has facilitated an overhaul of the existing route configuration of the inpatient phlebotomy service, which resulted in a more effective and contented workforce, as well as a more efficient service with an evened-out workload among phlebotomists and increased time spent on direct patient care by phlebotomists. Subsequent scenario analysis revealed that more manpower on a micro-level is not necessarily better and highlighted the importance to strategically design duty hours and allocate manpower across different duties on a system level.

关键词： healthcare service planning resource allocation optimization mixed-integer programming

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A decomposition heuristic for short-term planning of assessment centres

A decomposition heuristic for short-term planning of assessm...

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15th International Conference on Project Management and Scheduling (PMS)

作者： Rihm, Tom Trautmann, Norbert Univ Bern Dept Business Adm Bern Switzerland

In an assessment centre, several candidates for a job vacancy perform a set of predefined tasks while being observed and evaluated by so-called assessors. For the organisers of such assessment centres, a challenging job is to schedule the tasks and assign the prescribed number of assessors to the tasks such that the total waiting time for the assessors is minimised. This planning situation has been reported to us by a human resource management service provider. Application-specific restrictions distinguish this problem from related scheduling problems discussed in the literature, e.g., the resource-constrained project scheduling problem. We present a mixed-integer programming-based decomposition heuristic, which iterates between pre-scheduling, assignment, and re-scheduling subproblems. Our computational results demonstrate that this novel heuristic outperforms the state-of-the-art approaches on a set of 240 benchmark instances. Furthermore, this heuristic provides optimal solutions to a set of four real-life instances.

关键词： OR in the service industries human resource management assessment centre project scheduling decomposition heuristic mixed-integer programming MIP

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Implementation and Use of Coarse-grained Parallel Branch-and-bound in Everest Distributed Environment

Implementation and Use of Coarse-grained Parallel Branch-and...

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International Conference on Computational Science (ICCS)

作者： Voloshinov, Vladimir Smirnov, Sergey Sukhoroslov, Oleg Russian Acad Sci Inst Informat Transmiss Problems Moscow Russia

This paper examines the coarse-grained approach to parallelization of the branch-and-bound (B&B) algorithm in a distributed computing environment. This approach is based on preliminary decomposition of a feasible domain of mixed-integer programming problem into a set of subproblems. The produced subproblems are solved in parallel by a distributed pool of standalone B&B solvers. The incumbent values found by individual solvers are intercepted and propagated to other solvers to speed up the traversal of B&B search tree. Presented implementation of the approach is based on SCIP, a non-commercial MINLP solver, and Everest, a web-based distributed computing platform. The implementation was tested on several mixed integer programming problems and a noticeable speedup has been achieved. In the paper, results of a number of experiments with the Traveling Salesman Problem are presented. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science

关键词： branch-and-bound domain decomposition coarse grained parallelism distributed computing mixed-integer programming Traveling Salesman Problem algebraic modeling languages

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A Decentralized Real-Time Electricity Market Mechanism for Autonomous Microgrids

A Decentralized Real-Time Electricity Market Mechanism for A...

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2017 IEEE Power & Energy Society General Meeting

作者： Sabounchi, Moein Wei, Jin

ISBN: (纸本)9781538622124

The emergence of new technologies has led to the development of modern power grids and the explosion of "smarter" power grids in which the electricity generation is getting more decentralized. In this paper, we propose a real-time electricity marketing mechanism for the autonomous micro-grids. Our model is designed to meet the local energy demands while being self-sufficient and achieving high social satisfaction level of its associated producer/consumers (prosumers). To achieve this goal, in our proposed mechanism, the prosumers are clustered and represented by one high-level representative that is called the aggregator. Each aggregator is responsible to participate in the real-time market and make the decision on energy trading to meet the local power consumption demands, maintain the local financial stability, and maximize the satisfaction level of its associated prosumers. Our proposed marketing mechanism leverages a variation of the El-Farol bar problem to model the strategy of the aggregators. Furthermore, to evaluate the satisfaction of the prosumers toward the policy of the local electricity marketing, our proposed mechanism also defines and implements a social sensing model. In our simulations, we evaluate the performance of our electricity marketing mechanism by using a microgrid consisting of eight clusters of prosumers. The simulation results illustrate the effectiveness of our mechanism in achieving self-sufficiency and relative optimality in terms of load shedding and cumulative financial profit and residential customers' satisfaction.

关键词： Electricity market Social Sensing Real-Time Pricing mixed-integer programming Load Management

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In Search of Balance: The Challenge of Generating Balanced Latin Rectangles 14th

In Search of Balance: The Challenge of Generating Balanced L...

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14th International Conference on the Integration of Artificial Intelligence and Operations Research Techniques in Constraint programming (CPAIOR)

作者： Diaz, Mateo Le Bras, Ronan Gomes, Carla Cornell Univ Ctr Appl Math Ithaca NY 14853 USA Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9783319597768;9783319597751

Spatially Balanced Latin Squares are combinatorial structures of great importance for experimental design. From a computational perspective they present a challenging problem and there is a need for efficient methods to generate them. Motivated by a real-world application, we consider a natural extension to this problem, balanced Latin Rectangles. Balanced Latin Rectangles appear to be even more defiant than balanced Latin Squares, to such an extent that perfect balance may not be feasible for Latin rectangles. Nonetheless, for real applications, it is still valuable to have well balanced Latin rectangles. In this work, we study some of the properties of balanced Latin rectangles, prove the nonexistence of perfect balance for an infinite family of sizes, and present several methods to generate the most balanced solutions.

关键词： Latin Rectangles Experimental design Local search Constraint satisfaction problem mixed-integer programming

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Constructing block schedules for internal medicine residents

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IISE Transactions on Healthcare Systems Engineering 2017年第1期7卷 1-14页

作者： Bard, Jonathan F. Shu, Zhichao Morrice, Douglas J. Leykum, Luci K. The University of Texas Austin TX United States School of Medicine The University of Texas Health Science Center San Antonio TX United States

Before medical school graduates can practice independently, they must spend a minimum of three years in a residency program, where each year is divided into different clinical assignments called blocks. In this study, the goal is to generate annual block schedules that hierarchically (i) minimize the imbalance in the number of clinic sessions each resident must attend over the year (each rotation has a given clinic requirement), (ii) limit the number of times residents are given a night float block immediately before or after an intensive care unit block, and (iii) minimize the maximum deviation between the number of patients that can be seen in clinic during any month over the year from the average. The problem was formulated as a mixed-integer program (MIP) and solved with a heuristic that yielded locally optimal alternative solutions. Using these solutions as input, a second MIP was formulated with the objective of obtaining a diversity of solutions with minimum similarity. These provide the chief resident with a range of options when assigning blocks and trying to accommodate individual preferences at the same time. The models are illustrated with data provided by University of Texas Health Science Center in San Antonio. © 2017 “IISE”.

关键词： block scheduling medical rotations mixed-integer programming Resident scheduling soft constraints

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Electrical Power Distribution System Reconfiguration: Case Study of a Real-life Grid in Croatia

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IFAC-PapersOnLine 2017年第1期50卷 61-66页

作者： Novoselnik B. Bolfek M. Bošković M. Baotić M. Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb HR-10000 Croatia HEP-ODS d.o.o. Elektra Koprivnica Služba za tehničke poslove Ulica Hrvatske državnosti 32 Koprivnica HR-48000 Croatia HEP-ODS d.o.o. Sektor za tehničke poslove Služba za mjerenje i obračun Ulica grada Vukovara 37 Zagreb HR-10000 Croatia

This paper describes application of a nonlinear model predictive control algorithm to the problem of dynamic reconfiguration of an electrical power distribution system with distributed generation and storage. Power distribution systems usually operate in a radial topology despite being physically built as interconnected meshed networks. The meshed structure of the network allows one to modify the network topology by changing status of the line switches (open/closed). The goal of the control algorithm is to find the optimal radial network topology and the optimal power references for the controllable generators and energy storage units that will minimize cumulative active power losses while satisfying all system constraints. Validation of the developed algorithm is conducted on a case study of a real-life distribution grid in Croatia. The realistic simulations show that large loss reductions are feasible (more than 13%), i.e. that the developed control algorithm can contribute to significant savings for the grid operator. © 2017

关键词： mixed-integer programming model predictive control power distribution system reconfiguration real-life case study

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A First Look at Picking Dual Variables for Maximizing Reduced Cost Fixing 14th

A First Look at Picking Dual Variables for Maximizing Reduce...

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14th International Conference on the Integration of Artificial Intelligence and Operations Research Techniques in Constraint programming (CPAIOR)

作者： Bajgiran, Omid Sanei Cire, Andre A. Rousseau, Louis-Martin Univ Toronto Scarborough Dept Management Toronto ON Canada Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada Interuniv Res Ctr Enterprise Networks Logist & Tr Montreal PQ Canada

ISBN: (纸本)9783319597768;9783319597751

Reduced-cost-based filtering in constraint programming and variable fixing in integer programming are techniques which allow to cut out part of the solution space which cannot lead to an optimal solution. These techniques are, however, dependent on the dual values available at the moment of pruning. In this paper, we investigate the value of picking a set of dual values which maximizes the amount of filtering (or fixing) that is possible. We test this new variable-fixing methodology for arbitrary mixed-integer linear programming models. The resulting method can be naturally incorporated into existing solvers. Preliminary results on a large set of benchmark instances suggest that the method can effectively reduce solution times on hard instances with respect to a state-of-the-art commercial solver.

关键词： mixed-integer programming Variable fixing methodology Reduced-cost based filtering

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Controlling Some Statistical Properties of Business Rules Programs 11th

Controlling Some Statistical Properties of Business Rules Pr...

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11th International Conference on Learning and Intelligent Optimization (LION)

作者： Wang, Olivier Liberti, Leo IBM France 9 Rue Verdun F-94250 Gentilly France Ecole Polytech CNRS LIX F-91128 Palaiseau France

ISBN: (纸本)9783319694047;9783319694030

Business Rules programs encode decision-making processes using "if-then" constructs in a way that is easy for non-programmers to manipulate. A common example is the process of automatic validation of a loan request for a bank. The decision process is defined by bank managers relying on the bank strategy and their own experience. Bank-side, such processes are often required to meet goals of a statistical nature, such as having at most some given percentage of rejected loans, or having the distribution of requests that are accepted, rejected, and flagged for examination by a bank manager be as uniform as possible. We propose a mathematical programming-based formulation for the cases where the goals involve constraining or comparing values from the quantized output distribution. We then examine a simulation for the specific goals of (1) a max percentage for a given output interval and (2) an almost uniform distribution of the quantized output. The proposed methodology rests on solving mathematical programs encoding a statistically supervised machine learning process where known labels are an encoding of the required distribution.

关键词： Distribution learning mixed-integer programming Statistical goals Business Rules

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A Multiparametric mixed-integer Bi-level Optimization Strategy for Supply Chain Planning Under Demand Uncertainty

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IFAC-PapersOnLine 2017年第1期50卷 10178-10183页

作者： Avraamidou S. Pistikopoulos E.N. Department of Chemical Engineering Centre of Process Systems Engineering (CPSE) Imperial College London London SW7 2AZ United Kingdom Texas A&M Energy Institute Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX 77843 United States

Supply chain planning problems with seasonal demand variability can be often expressed within a hierarchical structure, where optimal decisions at an aggregate upper level provide constraints for the decision making at a lower level. In this work, we are addressing the case of a hierarchical distribution-production planning problem, where each decision level is controlled by a different company, each trying to optimize its own objective. This type of problems can be posed as bi-level programming problems, and since discrete decisions are involved the resulting formulations typically correspond to bi-level mixed-integer linear programming problems (B-MILP). The solution of these problems is very challenging, and typically requires the use of global optimization techniques, even for the derivation of approximate solutions. To overcome this, we propose the use of a novel algorithm capable of providing the exact, global and parametric solution of bi-level programming problems for the solution of distribution-production planning problems under demand uncertainty. The main idea of our approach is to treat the lower production planning level as a multi-parametric programming problem in which the distribution center demand (optimization variable of the upper level distribution planning problem) is considered as a parameter. The resulting exact parametric solutions are then substituted into the upper level distribution planning problem, which can be solved as a set of single-level deterministic programming problems. Through the use of this algorithm, we are able to derive the exact solution of a distribution-production planning problem with or without demand uncertainty. © 2017

关键词： Bi-level programming Distribution systems mixed-integer programming Multi-parametric optimisation Production systems Supply chain planning

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