Uncertain factors can significantly reduce the applicability of production plans and the production stability of enterprises. Therefore, considering the uncertain factors caused by external demands and internal capabi...
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Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studi...
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Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand. [Graphical abstract]
Scheduling and coordinating constrained resources in community healthcare settings at a centralized Pathways Community HUB is challenging due to limited resources and the inherent dynamics of the processes and the org...
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Scheduling and coordinating constrained resources in community healthcare settings at a centralized Pathways Community HUB is challenging due to limited resources and the inherent dynamics of the processes and the organizational structures. In this work, we introduce a stochastic programming (SP) approach for connected community health for optimally scheduling community health pathways (CHPs) under uncertainty in resource availability. A CHP is a standardized tool that details multiple steps of a healthcare-related service and the required resources for each step. The SP methodology was implemented and applied to data for a real Pathways Community HUB for a U.S. county involving several CHPs, community health workers, physicians, and other resources. The computational results are promising and they show that client access times depend on the HUB resources uncertain future availability and the level of client demand, with high client demand resulting in relatively longer access time. The study reveals that schedules provided by a deterministic approach where resource availability is assumed to be known can be too optimistic. Several managerial insights are learned from this study, including the observation that the SP model provides client schedules that are equitable across the same type of community health workers.
This article proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to ...
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This article proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement.
To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed distributions of stock returns while reducing the huge cost of parameter estimation, we use the Fama-French three-factor model t...
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To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed distributions of stock returns while reducing the huge cost of parameter estimation, we use the Fama-French three-factor model to describe stock returns and then model the factor dynamics by using the ARMA-GARCH and Student-t copula models. A factor-based scenario tree generation algorithm is thus proposed, and the corresponding multi-stage international portfolio selection model is constructed and its reformulation is derived. Different from the current literature, our proposed models can capture the dynamic dependence among international markets and the dynamics of exchange rates, and what's more important, make it possible for the practical solution of large-scale multi-stage international portfolio selection problems. Considering three different objective functions and international investments in the USA, Japanese and European markets, we carry out a series of empirical studies to demonstrate the practicality and efficiency of the proposed factor-based scenario tree generation algorithm and multi-stage international portfolio selection models.
Reverse water-gas shift (RWGS) microreactor systems offer the flexibility to efficiently utilize CO2 and renewable H2 for syngas production. However, the inherent fluctuation in the green H2 supply presents a substant...
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Reverse water-gas shift (RWGS) microreactor systems offer the flexibility to efficiently utilize CO2 and renewable H2 for syngas production. However, the inherent fluctuation in the green H2 supply presents a substantial obstacle for cost-effective production, particularly at larger scales. In this work, the design and operation of RWGS microreactor systems with fluctuating H2 feed are addressed by multi-objective stochastic optimization. The optimization objectives are the reaction performance, the annual total cost, and the carbon footprint of the microreactor system, while the decision variables include the channel geometry, channel topology, and interstack flow controller configuration. The results demonstrate that a well-designed hierarchical microreactor system achieves excellent reaction performance even without inter-stack flow controllers under both deterministic and uncertain conditions. Small plate-level distribution channels and moderate-sized higher-level distribution channels allow good and stable reaction performance. To balance all the design objectives, it is suggested to first increase the number of stacks at smaller production scales and then increase the number of substrates within each stack at larger production scales. Our study provides new design insights for distributed production using modular devices under high penetration of renewable energy.
For drivers in ride-hailing companies, allocation within the city is paramount to get matched with rides. This decision depends on many factors, where some of them (such as demand and allocation of others) are unknown...
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For drivers in ride-hailing companies, allocation within the city is paramount to get matched with rides. This decision depends on many factors, where some of them (such as demand and allocation of others) are unknown for the drivers, but are available for the company. In this work, we investigate whether it is beneficial or not for the ride-hailing company to share this information with their drivers. To do so, we study the problem through the lens of Stackelberg games, and we propose a new indicator called the Expected Value of Shared Information. We present a simplified model to conduct a proof-of-concept study: we provide explicit single-level reformulations of the bilevel programming problems derived from the model, and perform several simulations with randomly generated data. Our preliminary results suggest that sharing information could be beneficial and deserves to be further studied.
Distribution network design is both strategic and tactical level problem that deals with the alternative locations selection, assignment of the customers to suppliers, and determining the product flow quantities among...
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Distribution network design is both strategic and tactical level problem that deals with the alternative locations selection, assignment of the customers to suppliers, and determining the product flow quantities among the echelons. There are various factors that affect decisions on this problem such as transportation mode availability, lead times, facility capacities, penalties, demand, unit costs, storing costs etc. In real life distribution networks, all the activities are realized in a dynamic environment and most of the above-mentioned factors include uncertainties. Obtaining an applicable solution for real life distribution network design requires a stochastic approach consideration. In this study, a two stage stochastic programming method is applied for modeling and solving the problem. In the first stage, the model tries to decide the location decision of the facilities of the network. In the second stage, the model aims to make a decision about the transported, stored and unmet demand quantities considering the demand and related handling cost. These demand and handling costs are obtained by combining the various scenarios. The proposed model is solved for a distribution network active in FMCG sector. The results are analyzed and compared with the deterministic solutions.
stochastic multistage decision problems appear in many -if not all -application areas of Operations Re-search. While to define such problems is easy, to solve them is quite difficult, since they are of infinite dimens...
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stochastic multistage decision problems appear in many -if not all -application areas of Operations Re-search. While to define such problems is easy, to solve them is quite difficult, since they are of infinite dimension. Numerical solution can only be found by solving an approximate, easier problem. In this pa-per, we show good approximations can be found, where we emphasize the recursive structure of the involved algorithms and data structures. In a second part, the problem of coping with the model error of approximations is discussed. We present algorithms for finding distributionally robust solutions for the model error problem. We also review some application cases of such situations from the literature.(c) 2022 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )
This paper proposes a novel two-stage stochastic program to facilitate two-way energy trading (including up-regulation service) for an electric vehicle aggregator (EVA) in the electricity markets. Different from the e...
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This paper proposes a novel two-stage stochastic program to facilitate two-way energy trading (including up-regulation service) for an electric vehicle aggregator (EVA) in the electricity markets. Different from the existing works, the proposed model considers the randomness in the charging and discharging preferences of the EV owners on the operating day. Such uncertainty arises because the EV owners may randomly decide to participate on the operating day. Further, the EVA faces other uncertainties related to both the market-level data (i.e., electricity prices, real-time dispatch signal) and the fleet-level data (e.g., energy consumption of EVs, arrival and departure times) prior to participating in the electricity markets. The computation complexity of the proposed problem is tackled by constructing a small set of scenarios and applying the Benders decomposition algorithm. The performance of the proposed model is analyzed by comparing with the traditional model which ignores the randomness in the participation of the EV owners. The results show that the proposed model significantly outperforms the traditional model in terms of the total expected net cost incurred by the EVA. Further, we analyze the market-wise and fleet-wise performance under both the models through extensive simulations.
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