Batch Process Scheduling Using Simulated Annealing

1997 ◽  
pp. 245-259
Author(s):  
Jonas Mockus ◽  
William Eddy ◽  
Audris Mockus ◽  
Linas Mockus ◽  
Gintaras Reklaitis
Keyword(s):  
Simulated Annealing ◽  
Batch Process ◽  
Process Scheduling

An evaluation of simulated annealing for batch process scheduling

1991 ◽  
Vol 30 (1) ◽  
pp. 163-169 ◽  
Author(s):  
Hong Ming Ku ◽  
Iftekhar Karimi
Keyword(s):  
Simulated Annealing ◽  
Batch Process ◽  
Process Scheduling

Incorporating heat integration in batch process scheduling

2003 ◽  
Vol 23 (14) ◽  
pp. 1743-1762 ◽  
Author(s):  
R Adonyi ◽  
J Romero ◽  
L Puigjaner ◽  
F Friedler
Keyword(s):  
Batch Process ◽  
Heat Integration ◽  
Process Scheduling

Multiobjective evolutionary optimization of batch process scheduling under environmental and economic concerns

AIChE Journal ◽  
2012 ◽  
Vol 59 (2) ◽  
pp. 429-444 ◽  
Author(s):  
Elisabet Capón-García ◽  
Aarón D. Bojarski ◽  
Antonio Espuña ◽  
Luis Puigjaner
Keyword(s):  
Batch Process ◽  
Evolutionary Optimization ◽  
Process Scheduling

An efficient and user-friendly optimization framework for batch process scheduling

2010 ◽  
Vol 43 (17) ◽  
pp. 28-33
Author(s):  
Thomas Tometzki ◽  
Tobias Neymann ◽  
Jochen Steimel ◽  
Subanatarajan Subbiah ◽  
Sebastian Engell
Keyword(s):  
Batch Process ◽  
Process Scheduling ◽  
Optimization Framework ◽  
User Friendly

scholarly journals Investigation on Evolutionary Computation Techniques of a Nonlinear System

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Tran Trong Dao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Simulated Annealing ◽  
Nonlinear System ◽  
Dynamic Behaviour ◽  
Batch Process ◽  
Nonlinear Mathematical Model ◽  
Artificial Intelligent ◽  
Simulation And Optimization ◽  
System A

The main aim of this work is to show that such a powerful optimizing tool like evolutionary algorithms (EAs) can be in reality used for the simulation and optimization of a nonlinear system. A nonlinear mathematical model is required to describe the dynamic behaviour of batch process; this justifies the use of evolutionary method of the EAs to deal with this process. Four algorithms from the field of artificial intelligent—differential evolution (DE), self-organizing migrating algorithm (SOMA), genetic algorithm (GA), and simulated annealing (SA)—are used in this investigation. The results show that EAs are used successfully in the process optimization.

scholarly journals Novel Heuristic for Low-Batch Manufacturing Process Scheduling Optimisation with Reference to Process Engineering

2011 ◽  
Vol 6 (2) ◽  
Author(s):  
S. Maqsood ◽  
M. K. Khan ◽  
A. S. Wood
Keyword(s):  
Energy Efficient ◽  
Job Scheduling ◽  
Batch Process ◽  
Benchmark Problems ◽  
Manufacturing Processes ◽  
Scheduling Problem ◽  
Process Scheduling ◽  
Solution Approach ◽  
Job Scheduling Problem ◽  
Manufacturing Problems

Scheduling is an important element that has a major impact on the efficiency of all manufacturing processes. It plays an important role in optimising the manufacturing times and costs resulting in energy efficient processes. It has been estimated that more than 75% of manufacturing processes occur in small batches. In such environments, processes must be able to perform a variety of operations on a mix of different batches. Batch-job scheduling optimisation is the response to such low batch manufacturing problems. The optimisation of batch-job process scheduling problem is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. In this paper, a novel hybrid heuristic (HybH) solution approach for batch-job scheduling problem is presented with the objective of optimising the overall Makespan (Cmax). The proposed HybH is the combination of Index Based Heuristic (IBH) and the Finished Batch-Job (FBJ) process schedule. The heuristic assigns the first operation to a batch-job using IBH and the remaining operations on the basis FBJ process schedule. The FBJ process schedule gives priority to the batch-job with early finished operations, without violating the constraints of process order. The proposed HybH is explained with the help of a detailed example. Several benchmark problems are solved from the literature to check the validity and effectiveness of the proposed heuristic. The presented HybH has achieved batch-job process schedules which have outperformed the traditional heuristics. The results are encouraging and show that the proposed heuristic is a valid methodology for batch process scheduling optimisation.

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