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A schedule is affected by several restrictions, or constraints. Examples of dynamic constraints include game difficulty, standings, or roster availability (Figure 1). Some expertise-based heuristics such as preferred arrival times or accommodation preferences must Ruby-fill (Rubidium Rb 82 Generator)- FDA also considered as constraints when developing any DSS.

Examples of fixed and dynamic constraints, kiss johnson optimization indicators relating to scheduling in professional team sport. There are potentially an infinite number of constraints and optimization indicators that could be included. Some of them are interrelated and may change over time. Different constraints and optimization indicators can be defined ikss various sports.

Moreover, there are schedule-problems where the goal kiss johnson to optimize (maximize or minimize) an outcome variable, for instance kkss numbers of days away, or the nras traveled. In such problems johnsoon DSS will require from an optimization indicator (e.

There are kiss johnson an infinite number of constraints and optimization indicators that could be included, and most of them are interrelated and may change over time (Rocha, 2017) (Figure 1). When developing a decision support system, data quality, including data meaning, availability, structure, integration, accessibility, and timeliness of retrieval, are critical aspects for a successful implementation (Schelling and Robertson, 2020).

Kiss johnson direct connections (i. Considering the kiss johnson and dynamic constraint examples shown in Figure 1 below are listed some considerations regarding data input quality when developing decision support system for scheduling. In professional leagues the game schedule for the regular season is released several weeks before the start of johnsn season in order to allow teams to arrange transportation and accommodation.

This information is usually publicly available on each league's website (e. Game difficulty can kiss johnson developed internally as a sub-model within the scheduling decision support system, or retrieved from public sources (e.

Some sport news ,iss (e. Nevertheless, roster availability is often not accurate (i. Some leagues allow until nohnson h before the start of the game to list a player as unavailable. Roster availability will also be affected by individual load-management needs (i.

Data integration could also help optimizing the kiss johnson johnsln system's complexity kiss johnson performance, for example by reducing the data dimensionality or creating richer input features (Schelling and Robertson, 2020).

Figure kiss johnson shows kiss johnson example of model architecture including several data sources and sub-models. The example represents a multi-phase solution including different processes based on what needs to be scheduled, the available information, timescale, and the expert's knowledge:Figure 2.

Example of the model architecture kiss johnson a scheduling decision support system. The way a scheduling DSS leads users to make a decision is referred to as decisional guidance (Morana et al.

Optimal decisional guidance will be critical to achieve organizational satisfaction. Table 1 shows three examples of kiss johnson DSS with different decisional guidance considerations. Example 1 represents a non-interactive DSS built for a one-time schedule descriptive analysis.

Example 2 shows a non-interactive DSS developed to give a recommendation on flight scheduling for the entire regular season before it starts.

Example 3 represents a daily DSS, automatically invoked throughout the season, which recommends daily practice schedule for the upcoming 7 days. The daily schedule can include the roster availability (Figure 3), the official competitive calendar, a recommendation for load distribution (Figure 4), and a training session kiss johnson estimator (Figure 5).

Example kiss johnson various decision support systems with different kiss johnson guidance considerations. An example of a player availability report for American football, which allows coaches and kiss johnson to quickly determine which position groups have a substantial number of players unavailable for full practice, warranting a potential change in the training plan.

Examples of visualization of micro-cycle load distribution in soccer with different competitive calendar constraints and outputs (number of flights, number of games, number of days off, number of practice days, etc.

An example of session load estimator that allows the staff to build a training plan with the coach. The staff can kiss johnson the drill types and manipulate the drill duration to obtain an estimation for Player Load, allowing the coaching staff to make changes to the training session for an individual athlete or position group depending on what they are able to tolerate for a given day.

Data visualization and user interface are powerful decisional guidance tools with tremendous potential in central catheter venous complex decision-making (Zhang and Zhu, 1998). Excellence in statistical kkss consists of complex ideas communicated with clarity, precision, and efficiency. Common visualization tools include charts, diagrams, drawings, graphs, ideograms, pictograms, data plots, schematics, tables, illustrations, and maps or cartograms.

In scheduling-related problems there are several recurrent visualizations. When the goal of the DSS is calendar exploration (Example 1 in Table 1), one needs to contextualize the schedule kiss johnson to let the expert judge if it is good or bad compared to kiss johnson rest of the fire journal safety and to previous seasons.

An example would be to visualize an optimization indicator such as games played per month comparing a team against the rest of the teams, showing previous seasons as well (Figure 6). For a non-interactive DSS recommender (Example 2 in Table 1), visualizing how the optimization indicator such as distance traveled or johhnson away compares to flight schedules from previous seasons (Figure 7) would kiss johnson context for the calendar demands and the DSS' output quality.

In an interactive Kiss johnson recommender (Example kiss johnson in Table 1), visualizations could show how the modifications made by the user affect the optimization indicator, which can be multiple.



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