A Decision Support Tool Coupling a Causal Model and a Multi-objective Genetic Algorithm

2005 ◽  
pp. 628-637 ◽  
Author(s):  
Ivan Blecic ◽  
Arnaldo Cecchini ◽  
Giuseppe A. Trunfio
Keyword(s):  
Genetic Algorithm ◽  
Decision Support ◽  
Causal Model ◽  
Decision Support Tool ◽  
Support Tool ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

2019 ◽  
Vol 7 (2) ◽  
pp. 64-75
Author(s):  
Eugene Lesinski ◽  
Steven Corns
Keyword(s):  
Decision Support ◽  
Evolutionary Algorithm ◽  
Lower Cost ◽  
Decision Support Tool ◽  
Decision Makers ◽  
Support Tool ◽  
Track Maintenance ◽  
Multi Objective ◽  
Military Installation ◽  
Prioritization Scheme

Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition.

scholarly journals Online Decision-Support Tool “TECHoice” for the Equipment Technology Choice in Sterile Filling Processes of Biopharmaceuticals

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 448 ◽  
Author(s):  
Haruku Shirahata ◽  
Sara Badr ◽  
Yuki Shinno ◽  
Shuta Hagimori ◽  
Hirokazu Sugiyama
Keyword(s):  
Decision Support ◽  
Drug Product ◽  
Decision Support Tool ◽  
Objective Evaluation ◽  
Easy Access ◽  
Technology Choice ◽  
Support Tool ◽  
Multi Objective ◽  
Evaluation Models ◽  
Biopharmaceutical Manufacturing

In biopharmaceutical manufacturing, a new single-use technology using disposable equipment is available for reducing the work of change-over operations compared to conventional multi-use technology that use stainless steel equipment. The choice of equipment technologies has been researched and evaluation models have been developed, however, software that can extend model exposure to reach industrial users is yet to be developed. In this work, we develop and demonstrate a prototype of an online decision-support tool for the multi-objective evaluation of equipment technologies in sterile filling of biopharmaceutical manufacturing processes. Multi-objective evaluation models of equipment technologies and equipment technology alternative generation algorithms are implemented in the tool to support users in choosing their preferred technology according to their input of specific production scenarios. The use of the tool for analysis and decision-support was demonstrated using four production scenarios in drug product manufacturing. The online feature of the tool allows users easy access within academic and industrial settings to explore different production scenarios especially at early design phases. The tool allows users to investigate the certainty of the decision by providing a sensitivity analysis function. Further enrichment of the functionalities and enhancement of the user interface could be implemented in future developments.

scholarly journals Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

2021 ◽  
Vol 22 (1) ◽  
pp. 385
Author(s):  
Said Tkatek ◽  
Saadia Bahti ◽  
Otman Abdoun ◽  
Jaafar Abouchabaka
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Support ◽  
Intelligent System ◽  
Decision Support Tool ◽  
Good Decision ◽  
Support Tool ◽  
Hr Managers ◽  
The Right ◽  
Recruitment Model

<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right decision to ensure the best possible compatibility with the desired objectives. Operationally, this system can also predict the altered choice of parallel genetic algorithm (PGA) or sequential genetic algorithm (SeqGA) depending on the size of the instance and constraints of the recruiting posts to produce the quality solution in a reduced CPU time for recruiting decision-making. The results obtained in various tests confirm the performance of this intelligent system which can be used as a decision support tool for intelligently optimized recruitment.</p>

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