Agribusiness supply chains are sensitive to various sources of uncertainty. Therefore, it is essential to consider resiliency for such supply chains to decrease the impacts of various types of risks. This article pres...
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ISBN:
(纸本)9781728117751
Agribusiness supply chains are sensitive to various sources of uncertainty. Therefore, it is essential to consider resiliency for such supply chains to decrease the impacts of various types of risks. This article presents a two-stage stochastic model considering disruption scenarios for both suppliers and distribution centers to design resilient agro-food supply chain. To react quickly despite of any disruptive incidents, three following strategies are intended to develop a resilient model;1) devoting backup facility to suppliers and distribution centers, 2) multiple-sourcing in suppliers and distribution centers, 3) mitigation strategies for suppliers to reduce the disruption probability. The model is solved using simulated data set and the results prove that implementing resilient strategies for mentioned supply chains will gain more profit and economize its costs. An analytical comparison between both classic and resilient models as well as analysis on wrong strategy selecting effect have been considered.
Considered here are extremal convolutions concerned with allocative efficiency, risk sharing, or market equilibrium. Each additive term is upper semicontinuous, proper concave, maybe non-smooth, and possibly extended-...
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Considered here are extremal convolutions concerned with allocative efficiency, risk sharing, or market equilibrium. Each additive term is upper semicontinuous, proper concave, maybe non-smooth, and possibly extended-valued. In a leading interpretation, each term, alongside its block of coordinates, is controlled by an independent economic agent. Vectors are construed as contingent claims or as bundles of commodities. These are diverse, divisible, and perfectly transferable. At every stage two randomly selected agents make bilateral direct exchanges. The amounts transferred between the two parties depend on the difference between their generalized gradients. The resulting process-and the associated convergence analysisfits the frames of stochastic programming. Motivation stems from exchange markets.
In this study, a fixed-mix stochastic fractional programming (FSFP) method is developed for balancing the water allocation conflict between upstream hydropower generation and downstream agricultural irrigation. FSFP h...
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In this study, a fixed-mix stochastic fractional programming (FSFP) method is developed for balancing the water allocation conflict between upstream hydropower generation and downstream agricultural irrigation. FSFP has advantages in dealing with ratio-objective problem under uncertainty, reflecting the dynamic and stochastic characteristics over a long-term planning context, as well as analyzing interrelationships between system efficiency and violation risk of water-allocation target. Then, FSFP is firstly applied to Tuyamuyun reservoir in the lower reach of Amu Darya River basin (Central Asia), where multiple scenarios based on different hydropower generation targets and inflow levels are examined for identifying the complex relationship between hydropower generation and crop irrigation. Major findings and managerial insights can be summarized as: (i) with the reduction of reservoir inflow, water allocation for downstream agricultural irrigation would decrease by 30.4% once the minimum demand is satisfied, and hydropower generation should be higher priority for pursuing higher marginal benefit;(ii) with the shrinking water supply and rising hydropower-generation target, cotton planting should be firstly restrained due to its high water demand and grape planting is encouraged;(iii) under extreme water scarcity (i.e., low and very-low inflow levels), low-level hydropower generation target (i.e., alpha = 0.45) is desired for meeting the food requirement in the study basin;(iv) for alleviating the water shortage during dry seasons, it is recommended that water storage should be conducted in autumn and winter, and water release for crop irrigation should be implemented during spring and summer. These findings can help managers identify sustainable water-allocation schemes for agricultural irrigation and hydropower generation against water shortage, environmental destruction and energy insecurity in arid regions.
This paper constructs bounds on the expected value of a scalar function of a random vector. The bounds are obtained using an optimization method, which can be computed efficiently using state-of-the-art solvers, and d...
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ISBN:
(纸本)9781728113982
This paper constructs bounds on the expected value of a scalar function of a random vector. The bounds are obtained using an optimization method, which can be computed efficiently using state-of-the-art solvers, and do not require integration or sampling the random vector. This optimization based approach is especially useful in stochastic programming, where the criteria to be minimized takes the form of an expected value. In particular, we minimize the bounds to solve problems of discrete time finite horizon open-loop control with stochastic perturbations and also uncertainty in the system's parameters. We illustrate this application with two numerical examples.
This work studies a personnel scheduling problem considering uncertainty demand (i.e., customer traffic) in retailing. stochastic employee scheduling comprises two stages, the here-and-now decision (i.e., first-stage)...
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ISBN:
(纸本)9781728119410
This work studies a personnel scheduling problem considering uncertainty demand (i.e., customer traffic) in retailing. stochastic employee scheduling comprises two stages, the here-and-now decision (i.e., first-stage), before the actual demand is known, is to allocate number of full-time employees to shifts by using some empirical data or distribution information;the wait and-see decision (i.e., second-stage) involving takes some recourse actions, such as recruits part-time employees and extends shift length of full-time employee (i.e, overtime shift), since the actual demand realization. In this work, the information, contrary to previously known exact probability distribution, of uncertainty parameter demand is partial known, namely ambiguous. Given sectional distribution information such as mean value, mean absolute deviation (MAD) and support set, an ambiguous set is established. To solve this problem, we construct heuristically a worst-case discrete joint probability distribution and utilized a sample average approximation (SAA) algorithm to approximately solve the problem.
We investigate a multiperiod, stochastic portfolio optimization model for diversified funds choices associated with traditional 401K or 403B plans. This optimization model minimizes the L-1-norm for negative return ra...
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We investigate a multiperiod, stochastic portfolio optimization model for diversified funds choices associated with traditional 401K or 403B plans. This optimization model minimizes the L-1-norm for negative return rate risk (the downside mean absolute deviation) while examining parameters that maintain model feasibility. Important components of the model are the incorporation of appropriate time-series components and evaluation of scenarios based on investor outlook. A case study experimentation of the model on five potential investment funds using historical data from 2003 to 2013 was conducted, and parameter constraints for diversification and minimum acceptable return rates were manipulated to produce contour plots. The maximum geometric rate of return investment strategy provided by the optimization would have resulted in a 9.7% geometric return rate in 2014 as compared with a 5.0% for a uniform distribution of investment funds across choices.
Human resource policy is one of the key factors in managing a project. The cost associated with the individuals assigned to a project and the quality of the tasks completed by the assigned individuals are two of the m...
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Human resource policy is one of the key factors in managing a project. The cost associated with the individuals assigned to a project and the quality of the tasks completed by the assigned individuals are two of the main concerns in developing a project team. In this study, we present a new multi-objective mathematical model for developing a project team for multi-disciplinary projects under uncertainty. The objective functions optimize cost and competency simultaneously. To cope with the uncertainty and the multiple objectives in the presented model, we applied the two-stage stochastic programming approach and the Augmented Epsilon Constraint Method, respectively. The presented model and its solution methodology can be applied to different types of projects. In this study, a project that involves an overhaul of a set of aircrafts is presented as a case study.
As the massive popularity of e-commerce has a great effect on business logistics, the Vehicle Routing and Loading Problem (VRLP) has been introduced to address package management and package delivery. With sophisticat...
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ISBN:
(纸本)9781728112176
As the massive popularity of e-commerce has a great effect on business logistics, the Vehicle Routing and Loading Problem (VRLP) has been introduced to address package management and package delivery. With sophisticated package delivery to reach many customer destinations with high satisfaction, shipping and packing processes have to ensure that the packages will be delivered properly and safely. Most of the VRLPs have been studied under deterministic scenarios, assuming that all parameters are known. However, in reality, the customer demand is uncertain, and randomness in the VRLP has to be taken into consideration. Therefore, in this paper, we formulate the stochastic integer programming problem for planning package delivery. The goal is to help suppliers to minimize operating costs and to make the best decision for planning package delivery through item placement and truck routing assignment. We conducted an extensive performance evaluation to demonstrate flexibility and effectiveness of the proposed solution by using test data from benchmark datasets and real datasets of Singapore real road network and real services.
This paper presents a novel mathematical approach for modelling microgrids' Energy Management System (EMS), administrating distributed energy resources, such as diesel gensets, wind turbines, photovoltaic units, a...
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ISBN:
(纸本)9781728147215
This paper presents a novel mathematical approach for modelling microgrids' Energy Management System (EMS), administrating distributed energy resources, such as diesel gensets, wind turbines, photovoltaic units, and energy storage systems, optimally and reliably. In the proposed approach, the EMS is designed based on a coordinated Unit Commitment-Optimal Power Flow (UC-OPF) framework, yielding optimal power dispatch decisions for gensets and storage units. In this approach, the UC operational constraints, such as ramp characteristics and minimum up and down times of gensets are considered while the power flow equations are taken into account, simultaneously. Being a severe source of uncertainty, the wind power generation deviation is handled using scenario-based stochastic programming technique. In order to evaluate the proposed system, a complex microgrid based on a CIGRE benchmark is used. The results indicate that the EMS optimal dispatch decisions are highly influenced by the uncertainty of wind generation compared to the deterministic assumptions made for this source of generation.
In this paper we introduce the concepts of the Value of the Right Distribution (VRD), the Performance Bound (PB) and the Worst-Case Performance Bound (WPB), which allow us to quantify how much we lose if we guess the ...
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In this paper we introduce the concepts of the Value of the Right Distribution (VRD), the Performance Bound (PB) and the Worst-Case Performance Bound (WPB), which allow us to quantify how much we lose if we guess the wrong distribution of the uncertain parameters affecting a stochastic optimization problem. In order to show how they apply, we introduce a cost-based variant of the classical Newsvendor problem and model it as a two-stage stochastic programming model. For this problem, we first provide optimal solutions in closed form for different probability distributions and then compute, both analytically and computationally, the VRD measure and the corresponding performance bounds PB and WPB. Finally, systematic numerical results are provided.
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