scholarly journals Leaving safety to visit a feeding site: is it optimal to hesitate while exposed?

2017 ◽  
Vol 4 (1) ◽  
pp. 160910 ◽  
Author(s):  
Sean A. Rands
Keyword(s):  
Short Distance ◽  
Stochastic Dynamic Programming ◽  
Travel Behaviour ◽  
Stochastic Dynamic ◽  
Complex Environments ◽  
Trade Off ◽  
Feeding Site ◽  
State Dependent ◽  
Foraging Site ◽  
Optimal Behaviour

Animals living in complex environments experience differing risks of predation depending upon their location within the landscape. An animal could reduce the risk it experiences by remaining in a refuge site, but it may need to emerge from its refuge and enter more dangerous sites for feeding and other activities. Here, I consider the actions of an animal choosing to travel a short distance between a safe refuge and a dangerous foraging site, such as a bird leaving cover to visit a feeder. Although much work has been conducted examining the choice between a refuge and a foraging site when faced with a trade-off between starvation and predation risk, the work presented here is the first to consider the travel behaviour between these locations. Using state-dependent stochastic dynamic programming, I illustrate that there are several forms of optimal behaviour that can emerge. In some situations, the animal should choose to travel without stopping between sites, but in other cases, it is optimal for the animal to travel hesitantly towards the food, and to stop its travel at a point before it reaches the refuge. I discuss how this hesitant ‘dawdling’ behaviour may be optimal, and suggest further work to test these predictions.

scholarly journals THE MODEL OF ALLOTMENTS BOOKING IN A SHUTTLE TRAIN

Transport ◽  
2005 ◽  
Vol 20 (6) ◽  
pp. 236-239 ◽  
Author(s):  
Andrius Jaržemskis
Keyword(s):  
Capacity Planning ◽  
Stochastic Dynamic Programming ◽  
Programming Model ◽  
Rolling Stock ◽  
Stochastic Dynamic ◽  
Short Term ◽  
Trade Off ◽  
Departure Time ◽  
Dynamic Programming Model ◽  
The Cost

The article presents the model of allotments booking for trucks or containers in shuttle trains. This model could be used in practice if railway companies sale free allotments in rolling stock for forwarders. This paper proposes a stochastic dynamic programming model of a short‐term capacity planning model for necessary allotment. The allotment could be acquired by freight forwarders many months ahead on a contract basis, but usually the forecast demand is unreliable. The preplanning of allotment for transport units is needed when the date draws nearer to the train departure time. The presented model evaluates the optimal cost policy based on economic trade‐off between the cost of backlogged shipment and the cost of acquiring additional allotment.

scholarly journals A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-dependent Maximum Discharge Constraints

2021 ◽  
Author(s):  
Linn Schäffer ◽  
Arild Helseth ◽  
Magnus Korpås
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Stochastic Dynamic ◽  
Environmental Constraint ◽  
Maximum Discharge ◽  
State Dependent ◽  
Hydropower System ◽  
Hydropower Scheduling ◽  
Water Values

<div>We present a medium-term hydropower scheduling model that includes state-dependent environmental constraints on maximum discharge. A stochastic dynamic programming algorithm is used to enable modelling of nonconvex relationships in the problem formulation. The model is applied in a case study of a Norwegian hydropower system with multiple reservoirs. We find that the maximum discharge constraint significantly impacts the water values and simulated operation of the hydropower system. A main finding is that the nonconvex characteristics of the environmental constraint is reflected in the water values, implying a nonconvex objective function. Operation according to the computed water values is simulated for cases with and without the environmental constraint. Even though operation of the system changes considerably when the environmental constraint is included, the total electricity generation over the year is kept constant, and the total loss in expected profit limited to less than 0.8%.</div>

scholarly journals A Stochastic Dynamic Programming Model for Hydropower Scheduling with State-dependent Maximum Discharge Constraints

2021 ◽  
Author(s):  
Linn Schäffer ◽  
Arild Helseth ◽  
Magnus Korpås
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Stochastic Dynamic ◽  
Environmental Constraint ◽  
Maximum Discharge ◽  
State Dependent ◽  
Hydropower System ◽  
Hydropower Scheduling ◽  
Water Values

<div>We present a medium-term hydropower scheduling model that includes state-dependent environmental constraints on maximum discharge. A stochastic dynamic programming algorithm is used to enable modelling of nonconvex relationships in the problem formulation. The model is applied in a case study of a Norwegian hydropower system with multiple reservoirs. We find that the maximum discharge constraint significantly impacts the water values and simulated operation of the hydropower system. A main finding is that the nonconvex characteristics of the environmental constraint is reflected in the water values, implying a nonconvex objective function. Operation according to the computed water values is simulated for cases with and without the environmental constraint. Even though operation of the system changes considerably when the environmental constraint is included, the total electricity generation over the year is kept constant, and the total loss in expected profit limited to less than 0.8%.</div>

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

scholarly journals On Uncertainty Relations and Entanglement Detection with Mutually Unbiased Measurements

2015 ◽  
Vol 22 (01) ◽  
pp. 1550005 ◽  
Author(s):  
Alexey E. Rastegin
Keyword(s):  
The State ◽  
Uncertainty Relations ◽  
Trade Off ◽  
Entropic Uncertainty Relations ◽  
Finite Dimensional ◽  
State Dependent ◽  
Entanglement Detection

We formulate some properties of a set of several mutually unbiased measurements. These properties are used for deriving entropic uncertainty relations. Applications of mutually unbiased measurements in entanglement detection are also revisited. First, we estimate from above the sum of the indices of coincidence for several mutually unbiased measurements. Further, we derive entropic uncertainty relations in terms of the Rényi and Tsallis entropies. Both the state-dependent and state-independent formulations are obtained. Using the two sets of local mutually unbiased measurements, a method of entanglement detection in bipartite finite-dimensional systems may be realized. A certain trade-off between a sensitivity of the scheme and its experimental complexity is discussed.

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