scholarly journals Dynamic scheduling with cancellations: an application to chemotherapy appointment booking

2018 ◽  
Vol 8 (2) ◽  
pp. 161-169
Author(s):  
Yasin Göçgün
Keyword(s):  
Decision Process ◽  
Approximate Dynamic Programming ◽  
Dynamic Scheduling ◽  
Time Windows ◽  
Scheduling Problem ◽  
Computational Results ◽  
Due Dates ◽  
Different Types ◽  
Markov Decision ◽  
Myopic Policy

We study a dynamic scheduling problem that has the feature of due dates and time windows. This problem arises in chemotherapy scheduling where patients from different types have specific target dates along with time windows for appointment. We consider cancellation of appointments. The problem is modeled as a Markov Decision Process (MDP) and approximately solved using a direct-search based approximate dynamic programming (ADP) tehnique. We compare the performance of the ADP technique against the myopic policy under diverse scenarios. Our computational results reveal that the ADP technique outperforms the myopic policy on majority of problem sets we generated.

scholarly journals Train Scheduling with Deep Q-Network: A Feasibility Test

2020 ◽  
Vol 10 (23) ◽  
pp. 8367
Author(s):  
Intaek Gong ◽  
Sukmun Oh ◽  
Yunhong Min
Keyword(s):  
Decision Process ◽  
Value Function ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Q Value ◽  
Train Scheduling ◽  
Mip Models ◽  
Express Train ◽  
Markov Decision ◽  
Feasibility Test

We consider a train scheduling problem in which both local and express trains are to be scheduled. In this type of train scheduling problem, the key decision is determining the overtaking stations at which express trains overtake their preceding local trains. This problem has been successfully modeled via mixed integer programming (MIP) models. One of the obvious limitation of MIP-based approaches is the lack of freedom to the choices objective and constraint functions. In this paper, as an alternative, we propose an approach based on reinforcement learning. We first decompose the problem into subproblems in which a single express train and its preceding local trains are considered. We, then, formulate the subproblem as a Markov decision process (MDP). Instead of solving each instance of MDP, we train a deep neural network, called deep Q-network (DQN), which approximates Q-value function of any instances of MDP. The learned DQN can be used to make decision by choosing the action which corresponds to the maximum Q-value. The advantage of the proposed method is the ability to incorporate any complex objective and/or constraint functions. We demonstrate the performance of the proposed method by numerical experiments.

scholarly journals The Novel Heuristic for Data Transmission Dynamic Scheduling Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Hao Xu ◽  
Yue Zhao ◽  
Li-Ning Xing ◽  
You Zhou
Keyword(s):  
Resource Utilization ◽  
Data Transmission ◽  
Dynamic Scheduling ◽  
Time Windows ◽  
Experimental Results ◽  
Scheduling Problem ◽  
The Novel ◽  
Scheduling Problems ◽  
Transmission Dynamic ◽  
Simple Rules

The data transmission dynamic scheduling is a process that allocates the ground stations and available time windows to the data transmission tasks dynamically for improving the resource utilization. A novel heuristic is proposed to solve the data transmission dynamic scheduling problem. The characteristic of this heuristic is the dynamic hybridization of simple rules. Experimental results suggest that the proposed algorithm is correct, feasible, and available. The dynamic hybridization of simple rules can largely improve the efficiency of scheduling.

A Grasp with Path-Relinking for the Workover Rig Scheduling Problem

2010 ◽  
Vol 1 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Alexandre Venturin Faccin Pacheco ◽  
Glaydston Mattos Ribeiro ◽  
Geraldo Regis Mauri
Keyword(s):  
Time Windows ◽  
Oil Wells ◽  
Oil Field ◽  
Path Relinking ◽  
Scheduling Problem ◽  
Search Procedure ◽  
Computational Results ◽  
Production Loss ◽  
Adaptive Search ◽  
Special Services

Onshore oil wells depend on special services like cleaning, reinstatement and stimulation. These services, which are performed by a short number of workover rigs, are important to keep oil production as optimum as possible. Consequently, scheduling must be determined, where several factors interfere, such as production, service to be performed on each well, and time windows for each service. When a well needs service, its production is interrupted. In this regard, the workover rig scheduling problem consists of finding the best sequence of wells, which minimizes the production loss associated with the wells waiting for maintenance. In this paper, the authors present a Greedy Randomized Adaptive Search Procedure (GRASP) with Path-Relinking (PR) to solve this problem. Computational results are obtained from real problems of a Brazilian oil field.

Intelligent Questionnaires Using Approximate Dynamic Programming

i-com ◽  
2020 ◽  
Vol 19 (3) ◽  
pp. 227-237
Author(s):  
Frédéric Logé ◽  
Erwan Le Pennec ◽  
Habiboulaye Amadou-Boubacar
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Supervised Learning ◽  
Markov Decision Process ◽  
User Experience ◽  
Decision Process ◽  
Approximate Dynamic Programming ◽  
Budget Constraint ◽  
Markov Decision ◽  
The Right

Abstract Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy. This setting is quite flexible and can incorporate easily initial available data and grouped questions.

scholarly journals A Heuristic Procedure for a Ship Routing and Scheduling Problem with Variable Speed and Discretized Time Windows

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Krystel K. Castillo-Villar ◽  
Rosa G. González-Ramírez ◽  
Pablo Miranda González ◽  
Neale R. Smith
Keyword(s):  
Time Windows ◽  
Waiting Times ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Computational Results ◽  
Solution Quality ◽  
Ship Routing ◽  
Routing And Scheduling ◽  
Ship Routing And Scheduling ◽  
Tramp Shipping

This paper develops a heuristic algorithm for solving a routing and scheduling problem for tramp shipping with discretized time windows. The problem consists of determining the set of cargoes that should be served by each ship, the arrival, departure, and waiting times at each port, while minimizing total costs. The heuristic proposed is based on a variable neighborhood search, considering a number of neighborhood structures to find a solution to the problem. We present computational results, and, for comparison purposes, we consider instances that can be solved directly by CPLEX to test the performance of the proposed heuristic. The heuristics achieves good solution quality with reasonable computational times. Our computational results are encouraging and establish that our heuristic can be utilized to solve large real-size instances.

A Grasp with Path-Relinking for the Workover Rig Scheduling Problem

2012 ◽  
pp. 90-103
Author(s):  
Alexandre Venturin Faccin Pacheco ◽  
Glaydston Mattos Ribeiro ◽  
Geraldo Regis Mauri
Keyword(s):  
Time Windows ◽  
Oil Wells ◽  
Oil Field ◽  
Path Relinking ◽  
Scheduling Problem ◽  
Search Procedure ◽  
Computational Results ◽  
Production Loss ◽  
Adaptive Search ◽  
Special Services

Onshore oil wells depend on special services like cleaning, reinstatement and stimulation. These services, which are performed by a short number of workover rigs, are important to keep oil production as optimum as possible. Consequently, scheduling must be determined, where several factors interfere, such as production, service to be performed on each well, and time windows for each service. When a well needs service, its production is interrupted. In this regard, the workover rig scheduling problem consists of finding the best sequence of wells, which minimizes the production loss associated with the wells waiting for maintenance. In this paper, the authors present a Greedy Randomized Adaptive Search Procedure (GRASP) with Path-Relinking (PR) to solve this problem. Computational results are obtained from real problems of a Brazilian oil field.

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