A lot-sizing and scheduling problem with sequence-dependent setups is addressed in this paper. In the production system, manufactories receive raw materials from upstream sites, and after production, the final product...
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A lot-sizing and scheduling problem with sequence-dependent setups is addressed in this paper. In the production system, manufactories receive raw materials from upstream sites, and after production, the final products are shipped to downstream sites and customers. The key is to find a good production planning so that their cost is minimized. A two-stage stochastic programming model is formulated to minimize the total production, inventory and backorder costs. The first stage decides the baseline production including the production quantity of each product and the sequence of production while the second stage focuses on the possible updates of baseline production such as overtime production. The goal is to find the best sequence of production quantities under random demand with backorders allowed. Uncertainty is explicitly represented with a scenario tree then selecting the most representative scenarios in order to obtain a smaller subset while preserving essential properties. Both setup time and setup cost are product dependent. A case study for a manufacturing company producing braking equipment has been conducted to illustrate and validate the model. The results show that the stochastic model outperforms the deterministic model, especially when there are sufficient production resources. (C) 2016 Elsevier B.V. All rights reserved.
Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Sm...
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Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business. (C) 2016 Elsevier Ltd. All rights reserved.
Product configuration is to make decisions on component selections and combination to constitute a customized product under mass customization production. However, the uncertainties (such as component supplies) in pro...
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Product configuration is to make decisions on component selections and combination to constitute a customized product under mass customization production. However, the uncertainties (such as component supplies) in product configuration setting are not considered in the existing product configurators. To handle the uncertainty in component replenishment lead-time, a new stochastic decision model is proposed in this paper using two-stage stochastic programming approach. Further, a pre-procuring strategy for component supply is employed to reduce total configuration costs and shorten the delivery date of customized products. The stochastic decision model for product configuration is solved by using Lagrangian relaxation algorithm. The effectiveness of the stochastic decision model is demonstrated through case studies from both computer configuration and ranger drilling machine configuration. Computational comparisons with a commercial solver (CPLEX) indicate that the proposed stochastic decision model provides competitive solution results. (C) 2017 Elsevier B.V. All rights reserved.
A challenging problem for Emergency Department (ED) managers is determining the best allocation of the medical staff that is required to promptly attend patients in the face of increasing demand for emergency care, an...
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A challenging problem for Emergency Department (ED) managers is determining the best allocation of the medical staff that is required to promptly attend patients in the face of increasing demand for emergency care, and the ensuing long patient waiting times. We propose a solution framework that supports physician staffing and scheduling in the ED, considering uncertainties related to patient arrivals. For this, we introduce a two-stage stochastic programming model with fixed recourse that solves in an integrated manner the staffing and scheduling problems and aligns physician scheduling with patient arrivals while minimizing the total number of patients waiting and accounting for all scheduling requirements and contractual agreements. We create possible realization scenarios to consider demand uncertainty using Sample Average Approximation (SAA). In addition, we use discrete-event simulation to estimate the benefits derived from the schedule generated by our model. We validate our methodology with two case studies using real data from hospital EDs. The proposed method enhances alignment between service capacity and demand, significantly improving all queue and wait time indicators. In our first case study, the frequency of queue and average time door-to-doctor were reduced by 73% and 92%, respectively, compared to the current manually-defined ED schedule, and in the second case study, the frequency of queue decreased about 22%, and the average time door-to-doctor decreased 48%. Finally, sensitivity analysis showed that our model-generated optimal schedule is robust to variations in both demand and service rates, indicating that, under small perturbations of current operating conditions, hospital managers would not need to rerun the model.
The optimisation of material handling systems (MHSs) can lead to substantial cost reductions in manufacturing systems. Choosing adequate and relevant performance measures is critical in accurately evaluating MHSs. The...
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The optimisation of material handling systems (MHSs) can lead to substantial cost reductions in manufacturing systems. Choosing adequate and relevant performance measures is critical in accurately evaluating MHSs. The majority of performance measures used in MHSs are time-based. However, moving materials within a manufacturing system utilise time and cost. In this study, we consider both time and cost measures in an optimisation model used to evaluate an MHS with automated guided vehicles. We take into account the reliability of the MHSs because of the need for steadiness and stability in the automated manufacturing systems. Reliability is included in the model as a cost function. Furthermore, we consider bi-objective stochastic programming to optimise the time and cost objectives because of the uncertainties inherent in the optimisation parameters in real-world problems. We use perceptron neural networks to transform the bi-objective optimisation model into a single objective model. We use numerical experiments to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.
The treasurer of a bank is responsible for the cash management of several banking activities. In this work, we focus oil two of them: cash management in automatic teller machines (ATMs), and in the compensation of cre...
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The treasurer of a bank is responsible for the cash management of several banking activities. In this work, we focus oil two of them: cash management in automatic teller machines (ATMs), and in the compensation of credit card transactions. In both cases a decision must be taken according to a future customers demand, which is uncertain. From historical data we can obtain it discrete probability distribution of this demand. which allows the application of stochastic programming techniques. We present stochastic programming models for each problem. Two short-term and one mid-term models are presented for ATMs. The short-term model with fixed costs results in an integer problem which is solved by a fast (i.e. linear running time) algorithm. The short-term model with fixed and staircase costs is solved through its MILP equivalent deterministic formulation. The mid-term model with fixed and staircase costs gives rise to a multi-stage stochastic problem, which is also solved by its MILP deterministic equivalent. The model for compensation of credit card transactions results in a closed form solution. The optimal solutions of those models arc the best decisions to be taken by the bank, and provide the basis for a decision support system. (c) 2007 Elsevier B.V. All rights reserved.
With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power p...
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With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk-based two-stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day-ahead (DA) and real-time (RT) markets. In order to include the risk parameter in the proposed decision-making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.
Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resource...
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Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by selecting the correct number of machines of each type. This paper presents an efficient algorithm to find the best set of computing resources to allocate to the workload. This research is applicable to users provisioning cloud computing resources and to data center owners making purchasing decisions about physical hardware. Studies have shown that cloud computing machines have measurable variability in their performance. Some of the causes of performance variability include small changes in architecture, location within the datacenter, and neighboring applications consuming shared network resources. The proposed algorithm models the uncertainty in the computing resources and the variability in the tasks in a many-task computing environment to find a robust number of machines of each type necessary to process the workload. In addition, reward rate, cost, failure rate, and power consumption can be optimized, as desired, to compute Pareto fronts.
We discuss methods for the solution of a multi-stage stochastic programming formulation for the resource-constrained scheduling of clinical trials in the pharmaceutical research and development pipeline. First, we pre...
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We discuss methods for the solution of a multi-stage stochastic programming formulation for the resource-constrained scheduling of clinical trials in the pharmaceutical research and development pipeline. First, we present a number of theoretical properties to reduce the size and improve the tightness of the formulation, focusing primarily on non-anticipativity constraints. Second, we develop a novel branch and cut algorithm where necessary non-anticipativity constraints that are unlikely to be active are removed from the initial formulation and only added if they are violated within the search tree. We improve the performance of our algorithm by combining different node selection strategies and exploring different approaches to constraint violation checking. (C) 2009 Elsevier B.V. All rights reserved.
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