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In the field development phase, this calculation can be very complex, a consequence of high uncertainty and multiple decision variables.
The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand).
Thus, differences in the degree in which neurons encode multiple decision variables or integrate across variables might depend on the task requirements.
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Many of the decisions that humans and animals face on a daily basis require a consideration of multiple-decision variables, each of which may have separate effects on the neural representation of value in a particular brain region.
Thus, single ACC neurons are capable of encoding the value of multiple-decision variables across trials, a type of multiplexed value representation that may allow the integration of the individual components of a decision into a common value signal (see The Encoding of Action Value section for further discussion on this topic) and underlie ACC's critical contribution to decision making.
Here we present a strategy called the trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service).
For large-scale TSP problems in many real-world cases, the interactions among multiple random parameters and decision variables might lead to serious complexities.
The optimal set can provide more than one best solution to support multiple decisions depending on different decision variables.
Supplier's lot size multiplier (decision variable).
To determine the optimal levels of these key factors, we employ RSM to develop metamodels that best describe the relationship between key decision variables and the multiple system responses.
We infer the best knockout strategies to perform specific tasks in bacteria, which involve concurrent maximization/minimization of multiple functions (codomain) and optimization of several decision variables (domain).
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