A Linear Approximation of the Value Function of an Approximate Dynamic Programming Approach for the Ship Scheduling Problem

2010 ◽  
pp. 184-187
Author(s):  
Kazuhiro Kobayashi
Keyword(s):  
Dynamic Programming ◽  
Linear Approximation ◽  
Value Function ◽  
Approximate Dynamic Programming ◽  
Programming Approach ◽  
Scheduling Problem ◽  
Dynamic Programming Approach ◽  
Ship Scheduling ◽  
The Value Function

ONLINE CAPACITY PLANNING FOR REHABILITATION TREATMENTS: AN APPROXIMATE DYNAMIC PROGRAMMING APPROACH

2018 ◽  
Vol 34 (3) ◽  
pp. 381-405
Author(s):  
Ingeborg A. Bikker ◽  
Martijn R.K. Mes ◽  
Antoine Sauré ◽  
Richard J. Boucherie
Keyword(s):  
Dynamic Programming ◽  
Capacity Planning ◽  
Approximate Dynamic Programming ◽  
Capacity Allocation ◽  
Access Time ◽  
Programming Approach ◽  
Planning Problem ◽  
Dynamic Programming Approach ◽  
Negative Effect ◽  
Long Access

AbstractWe study an online capacity planning problem in which arriving patients require a series of appointments at several departments, within a certain access time target.This research is motivated by a study of rehabilitation planning practices at the Sint Maartenskliniek hospital (the Netherlands). In practice, the prescribed treatments and activities are typically booked starting in the first available week, leaving no space for urgent patients who require a series of appointments at a short notice. This leads to the rescheduling of appointments or long access times for urgent patients, which has a negative effect on the quality of care and on patient satisfaction.We propose an approach for allocating capacity to patients at the moment of their arrival, in such a way that the total number of requests booked within their corresponding access time targets is maximized. The model considers online decision making regarding multi-priority, multi-appointment, and multi-resource capacity allocation. We formulate this problem as a Markov decision process (MDP) that takes into account the current patient schedule, and future arrivals. We develop an approximate dynamic programming (ADP) algorithm to obtain approximate optimal capacity allocation policies. We provide insights into the characteristics of the optimal policies and evaluate the performance of the resulting policies using simulation.

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