scholarly journals A Bilevel Stochastic Dynamic Programming Model to Assess the Value of Information on Actual Food Quality at Wholesale Markets

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Xiangyu Hou ◽  
Rene Haijema ◽  
Dacheng Liu
Keyword(s):  
Dynamic Programming ◽  
Programming Model ◽  
Market Price ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Wholesale Market ◽  
Dynamic Programming Approach ◽  
Additional Information ◽  
Similar Appearance ◽  
Inventory Decision

In the fresh produce wholesale market, the market price is determined by the total demand and supply. The price is stochastic, and either wholesaler or retailer has few influence on it. In the wholesaler’s inventory decision, the price’s uncertainty plays an important role as well as the uncertainty from the demand side: the wholesaler makes his decision based on the retailer’s ordering, which is influenced by the stochastic market price and the distribution of the consumer’s demand. In addition, when at the wholesale stage, the products show a similar quality of similar appearance. With more efforts being input, the wholesaler could detect and record more additional information than that reflected from the appearance. Based on this, he can classify the quality into different levels. No experience shows how the wholesaler could use the underlying quality information and how much this information could improve his profit. To describe and explore this problem, a bilevel dynamic programming approach is employed. We evaluate different strategies of using the underlying information, show the features of the optimal policy, develop heuristics, and discuss the influence of factors such as quality and market price. We also develop the managerial principles for the practical use.

A Decoupled and Prioritized Stochastic Dynamic Programming Approach for Automated Transport of Multiple Particles Using Optical Tweezers

2009 ◽  
Author(s):  
Ashis Gopal Banerjee ◽  
Wolfgang Losert ◽  
Satyandra K. Gupta
Keyword(s):  
Dynamic Programming ◽  
Motion Planning ◽  
Optical Tweezers ◽  
Stochastic Dynamic Programming ◽  
Graph Matching ◽  
Programming Approach ◽  
Stochastic Dynamic ◽  
Step Method ◽  
Dynamic Programming Approach ◽  
Multiple Particles

Automated transport of multiple particles using optical tweezers requires the use of motion planning to move them simultaneously while avoiding collisions amongst themselves and with randomly moving obstacles. This paper develops a decoupled and prioritized stochastic dynamic programming based motion planning framework by sequentially applying a partially observable Markov decision process algorithm on every particle that needs to be transported. An iterative version of a maximum bipartite graph matching algorithm is used to assign given goal locations to such particles. The algorithm for individual particle transport is validated using silica beads in a holographic tweezer set-up. Once the individual plans are computed, a three-step method consisting of clustering, classification, and branch and bound optimization is employed to determine the final collision-free paths. Simulation results in the form of sample trajectories and performance characterization plots are presented to illustrate the usefulness of the developed approach.

Sign in / Sign up

Export Citation Format

Share Document