To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients sc...
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To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage stochastic Mixed Integer Linear programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients' expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average *** employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.
Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for bio...
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Biomass is an abundant resource for energy production and it has gained attention as a mainstream option to meet increasing energy demands. Pyrolysis has been one of the most prevalent thermochemical processes for biomass conversion. In the pyrolysis process, the biomass decomposes into three byproducts: bio-oil (60-75%), biochar (15-25%), and syngas (10-20%), depending on the feedstock and its composition. The energy required to convert the biomass varies depending on the levels of cellulose, hemicellulose, and lignin. This work proposes a novel two-stage stochastic model that designs an efficient biomass supply chain mindful of the trade-offs between pyrolysis byproducts (bioethanol and biochar). Remarkably, the model integrates biomass quality-related costs associated with moisture and ash content such as the energy consumption of preprocessing equipment and boiler maintenance due to excess ash. Biomass quality directly affects the production yield as well as the total cost of production and distribution. The results from our case study indicate a shortage of biomass from the suppliers to fulfill the demand for biochar from the power plants and bioethanol from the cities. Furthermore, the bioethanol price has the most impact on the total supply chain according to our sensitivity analysis.
This paper is concerned with gradual land conversion problems, placing the main focus on the interaction between time and uncertainty. This aspect is extremely relevant since most decisions made in the field of natura...
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This paper is concerned with gradual land conversion problems, placing the main focus on the interaction between time and uncertainty. This aspect is extremely relevant since most decisions made in the field of natural resources and sustainable development are irreversible decisions. In particular, we discuss and develop a scenario-based multi-stage stochastic programming model in order to determine the optimal land portfolio in time, given uncertainty affecting the market. The approach is then integrated in a decision tree framework in order to account for domain specific (environmental) uncertainty that, diversely from market uncertainty, may depend on the decision taken. Although, the designed methodology has many general applications, in the present work we focus on a particular case study, concerning a semi-degraded natural park located in northern Italy.
This paper introduces a two-stage stochastic integer linear programming model to improve phlebotomist scheduling in laboratory facilities of healthcare delivery systems. The model developed enables laboratory manageme...
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This paper introduces a two-stage stochastic integer linear programming model to improve phlebotomist scheduling in laboratory facilities of healthcare delivery systems. The model developed enables laboratory management to determine optimal scheduling policies that minimize work overload. The stochastic programming model considers the uncertainty associated with the blood collection demand in laboratory environments when optimizing phlebotomist scheduling. The paper presents an application of the model to a hospital laboratory in urban North Carolina as a case study discussing the implications for hospital laboratory management.
In this paper, propose a quasi-linear pattern based on expectation and variance to process the random constraints, after analyzing the essence of stochastic programming and the deficiencies of existing methods. Give a...
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ISBN:
(纸本)9781424464203
In this paper, propose a quasi-linear pattern based on expectation and variance to process the random constraints, after analyzing the essence of stochastic programming and the deficiencies of existing methods. Give a stochastic programming pattern (generalized expected value model) with good operability, and establish its corresponding generalized expected value model in stochastic vehicle routing problem. Its performance is then discussed through an example. All these indicate that this generalized model generalizes the existing methods to some extent, and can effectively solve the stochastic vehicle routing problem with unknown distribution of the random variable under random environment. It is worth to point out that the solution reflects the consciousness of the decision maker, so it enriches the theory and methods of stochastic programming.
Uncertain nature of renewable resources can endanger the balance of generation and consumption. Therefore, the system operator shall commit more schedulable and controllable resources to maintain the security of grid....
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ISBN:
(纸本)9781728176604
Uncertain nature of renewable resources can endanger the balance of generation and consumption. Therefore, the system operator shall commit more schedulable and controllable resources to maintain the security of grid. Among various type of energy resources, battery storages seem to be a reliable alternative to provide the regulation service. In smart grids, consumers can use these household resources to supply their required electricity and sell the surplus energy to grid. Energy storage aggregators as intermediate between prosumers and the local market can merge the capacity of prosumers' resources and submit their power bids in the market. In this work, a linear model is presented for the participation of energy storage aggregators in the ancillary service market. To model the uncertainty of energy price in the ancillary service market, the stochastic programming approach is used. The optimization problem is formulated based on the linear programming method. Finally, the performance and efficiency of the proposed model are evaluated via a case study and different scenarios.
Traffic Matrices, which capture the network traffic volumes among end-points, are widely used in Internet Engineering. A traffic matrix is generally estimated from link loads because it is impossible to measure each f...
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ISBN:
(纸本)9781479928606
Traffic Matrices, which capture the network traffic volumes among end-points, are widely used in Internet Engineering. A traffic matrix is generally estimated from link loads because it is impossible to measure each flow directly in a large scale IP network. The estimation problem is usually considered to be an optimization model with a routing constraint which relates the traffic matrix with link loads. However, the information provided by link loads is far less than that required, and so the estimation problem becomes very ill-posed. Moreover, the random measuring noise of link loads often makes the route constraint violated. To deal with this problem, this paper proposes a stochastic programming (SP) model, which relaxes the routing constraint to a probabilistic constraint and then enlarges the feasible space of the optimization problem. By using the Abilene dataset, we simulated the proposed SP model and the Tomogravity method respectively. The simulation results show that the SP model can improve the estimation performance obviously.
Multi-stage decision making, a fundamental tenet of stochastic programming, resonates well with the practice of the electricity markets. The day-ahead market, used to commit the generators, bears uncertainty in the po...
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ISBN:
(纸本)9781934325216
Multi-stage decision making, a fundamental tenet of stochastic programming, resonates well with the practice of the electricity markets. The day-ahead market, used to commit the generators, bears uncertainty in the power demand and physical conditions of the generators and transmission lines. The situation becomes less uncertain in the real-time market, where the dispatch is decided. Although traditional approaches such as operating reserve requirements have been effectively employed to ensure reliable system operations, the incorporation of stochastic methods offer the potential for superior solutions.
This paper describes and analyses a stochastic programming (SP) model that is used for a specific inventory control problem for a perishable product. The decision maker is confronted with a non-stationary random deman...
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ISBN:
(纸本)9783642311369;9783642311376
This paper describes and analyses a stochastic programming (SP) model that is used for a specific inventory control problem for a perishable product. The decision maker is confronted with a non-stationary random demand for a fixed shelf life product and wants to make an ordering plan for a finite horizon that satisfies a service level constraint. In literature several approaches have been described to generate approximate solutions. The question dealt with here is whether exact approaches can be developed that generate solutions up to a guaranteed accuracy. Specifically, we look into the implications of a stochastic Dynamic programming (SDP) approach.
The evolving military capability requirements (CRs) must be meted continuously by the multi-stage weapon equipment mix production planning (MWEMPP). Meanwhile, the CRs possess complex uncertainties with the variant mi...
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The evolving military capability requirements (CRs) must be meted continuously by the multi-stage weapon equipment mix production planning (MWEMPP). Meanwhile, the CRs possess complex uncertainties with the variant military tasks in the whole planning horizon. The mean-value deterministic programming technique is difficult to deal with the multi-period and multi-level uncertain decision-making problem in MWEMPP. Therefore, a multi-stage stochastic programming approach is proposed to solve this problem. This approach first uses the scenario tree to quantitatively describe the bi-level uncertainty of the time and quantity of the ('Rs, and then build the whole off-line planning alternatives assembles for each possible scenario, at last the optimal planning alternative is selected on-line to flexibly encounter the real scenario in each period. A case is studied to validate the proposed approach. The results confirm that the proposed approach can better hedge against each scenario of the CRs than the traditional mean-value deterministic technique.
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