Customer Scheduling with Incomplete Information

1995 ◽  
Vol 9 (2) ◽  
pp. 269-284 ◽  
Author(s):  
Ulrich Rieder ◽  
Jürgen Weishaupt
Keyword(s):  
Dynamic Programming ◽  
Incomplete Information ◽  
Bayesian Approach ◽  
Stochastic Scheduling ◽  
Partial Ordering ◽  
Scheduling Problem ◽  
Gittins Index ◽  
Scheduling Model ◽  
Bayesian Dynamic Programming ◽  
Monotonicity Results

A stochastic scheduling model with linear waiting costs and unknown routing probabilities is considered. Using a Bayesian approach and methods of Bayesian dynamic programming, we investigate the finite-horizon stochastic scheduling problem with incomplete information. In particular, we study an equivalent nonstationary bandit model and show the monotonicity of the total expected reward and of the Gittins index. We derive the monotonicity and well-known structural properties of the (greatest) maximizers, the so-called stay-on-a-winnerproperty and the stopping-property. The monotonicity results are based on a special partial ordering on .

Research on Production Scheduling Problems in Process Industry Based on Ant Colony System

2010 ◽  
Vol 108-111 ◽  
pp. 519-524
Author(s):  
Lie Ping Zhang ◽  
Yun Sheng Zhang
Keyword(s):  
Production Scheduling ◽  
Stochastic Scheduling ◽  
Ant Colony ◽  
Process Industry ◽  
Process Time ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Process Step ◽  
Scheduling Model

In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.

On transforming an index for generalised bandit problems

1995 ◽  
Vol 32 (1) ◽  
pp. 168-182 ◽  
Author(s):  
K. D. Glazebrook ◽  
S. Greatrix
Keyword(s):  
Dynamic Programming ◽  
Policy Evaluation ◽  
Gittins Index ◽  
Bandit Problem ◽  
Bandit Problems ◽  
Index Policies

Nash (1980) demonstrated that index policies are optimal for a class of generalised bandit problem. A transform of the index concerned has many of the attributes of the Gittins index. The transformed index is positive-valued, with maximal values yielding optimal actions. It may be characterised as the value of a restart problem and is hence computable via dynamic programming methodologies. The transformed index can also be used in procedures for policy evaluation.

Research on a Main-Branch Hybrid Flow Shop Scheduling Problem in Production Manufacturing System

2012 ◽  
Vol 252 ◽  
pp. 354-359
Author(s):  
Xin Min Zhang ◽  
Meng Yue Zhang
Keyword(s):  
Manufacturing System ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Quantum Genetic Algorithm ◽  
Shop Scheduling ◽  
Scheduling Model ◽  
Hybrid Flow Shop Scheduling ◽  
Two Stages

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.

scholarly journals Metaheuristics for Order Scheduling Problem with Unequal Ready Times

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jan-Yee Kung ◽  
Jiahui Duan ◽  
Jianyou Xu ◽  
I-Hong Chung ◽  
Shuenn-Ren Cheng ◽  
...  
Keyword(s):  
Real Life ◽  
Experimental Tests ◽  
Service Systems ◽  
Scheduling Problem ◽  
Due Dates ◽  
Order Scheduling ◽  
Customer Order ◽  
Scheduling Model ◽  
Unequal Ready Times ◽  
Customer Order Scheduling

In recent years, various customer order scheduling (OS) models can be found in numerous manufacturing and service systems in which several designers, who have developed modules independently for several different products, convene as a product development team, and that team completes a product design only after all the modules have been designed. In real-life situations, a customer order can have some requirements including due dates, weights of jobs, and unequal ready times. Once encountering different ready times, waiting for future order or job arrivals to raise the completeness of a batch is an efficient policy. Meanwhile, the literature releases that few studies have taken unequal ready times into consideration for order scheduling problem. Motivated by this limitation, this study addresses an OS scheduling model with unequal order ready times. The objective function is to find a schedule to optimize the total completion time criterion. To solve this problem for exact solutions, two lower bounds and some properties are first derived to raise the searching power of a branch-and-bound method. For approximate solution, four simulated annealing approaches and four heuristic genetic algorithms are then proposed. At last, several experimental tests and their corresponding statistical outcomes are also reported to examine the performance of all the proposed methods.

Bayesian dynamic programming

1975 ◽  
Vol 7 (2) ◽  
pp. 330-348 ◽  
Author(s):  
Ulrich Rieder
Keyword(s):  
Dynamic Programming ◽  
Weak Convergence ◽  
Decision Model ◽  
Transition Probabilities ◽  
General State ◽  
Parameter Spaces ◽  
State Action ◽  
Total Rewards ◽  
Bayesian Dynamic Programming ◽  
Dynamic Decision Model

We consider a non-stationary Bayesian dynamic decision model with general state, action and parameter spaces. It is shown that this model can be reduced to a non-Markovian (resp. Markovian) decision model with completely known transition probabilities. Under rather weak convergence assumptions on the expected total rewards some general results are presented concerning the restriction on deterministic generalized Markov policies, the criteria of optimality and the existence of Bayes policies. These facts are based on the above transformations and on results of Hindererand Schäl.

scholarly journals Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Smart Grids

2018 ◽  
Author(s):  
Joao Soares ◽  
Fernando Lezama ◽  
Bruno Canizes ◽  
M. Ali Fotouhi Ghazvini ◽  
Zita Vale ◽  
...  
Keyword(s):  
Smart Grids ◽  
Stochastic Scheduling ◽  
Scheduling Model
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