Multi-stage and multi-objective decision-support tool for biopharmaceutical drug product manufacturing: Equipment technology evaluation

2020 ◽  
Vol 161 ◽  
pp. 240-252 ◽  
Author(s):  
Philipp Zürcher ◽  
Haruku Shirahata ◽  
Sara Badr ◽  
Hirokazu Sugiyama
Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 448 ◽  
Author(s):  
Haruku Shirahata ◽  
Sara Badr ◽  
Yuki Shinno ◽  
Shuta Hagimori ◽  
Hirokazu Sugiyama

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.


2019 ◽  
Vol 7 (2) ◽  
pp. 64-75
Author(s):  
Eugene Lesinski ◽  
Steven Corns

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.


Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2836
Author(s):  
Seyed M. K. Sadr ◽  
Matthew B. Johns ◽  
Fayyaz A. Memon ◽  
Andrew P. Duncan ◽  
James Gordon ◽  
...  

Despite considerable efforts to improve water management, India is becoming increasingly water stressed due to multiple factors, including climate change, increasing population, and urbanization. We address one of the most challenging problems in the design of water treatment plants: how to select a suitable technology for a specific scenario or context. The process of decision making first requires the identification of feasible treatment configurations based on various objectives and criteria. In addition, the multiplicity of water quality parameters and design variables adds further complexity to the process. In this study, we propose a novel Decision Support Tool (DST), designed to address and support the above challenges. In this user-friendly tool, both Multi-Criteria Decision Analysis (MCDA) and Multi-Objective Optimization (MOO) methods are employed. The integration of MCDA with MOO facilitates the generation of feasible drinking water treatment solutions, identifies optimal options, and ultimately, improves the process of decision making. This implemented approach has been tested for different contexts, including for different types of raw water sources and system implementation scales. The results show that this tool can enhance the process of decision making, supporting the user (e.g., stakeholders and decision makers) to implement the most suitable water treatment systems, keeping in view the trade-offs.


Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 316
Author(s):  
Nadide Caglayan ◽  
Sule Itir Satoglu

Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.


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