Basics of Decision Support Systems (GEUS) Flashcards

Initial vocabulary and conceptual definitions from the introduction to Decision Support Systems and Operations Research.

Operational Systems (OS)

Applications for supporting routine decisions in daily business, often integrated into ERP systems and serving as the basis for advanced planning tasks.

Management Information Systems (MIS)

Applications built on top of operational systems that provide periodical reports by aggregating information (e.g., monthly sales reports).

Decision Support Systems (DSS)

Systems that provide support for business affairs beyond routine tasks, focusing on models, ad-hoc queries, and graphical visualization for unstructured problems.

Executive Support Systems (ESS)

Systems that support management by filtering and visualizing data with options for interactive changes in granularity, such as drill down and roll up.

Group Decision Support Systems (GDSS)

Systems designed to support multiple, cooperative decision makers in solving unstructured problems.

Data-Driven Decision Making

A variant of decision support that enables users to analyze data through methods like Online Analytical Processing (OLAP) or data mining.

Model-Driven Decision Making

A variant of decision support that focuses on mathematical models and techniques from disciplines like Operations Research (OR).

Operations Research (OR)

A discipline supporting practical decision making through mathematical modeling, statistical analysis, optimization, simulation, and decision analysis.

Structured Problems

Routine problems with high clarity, examples of which include machine scheduling or tour planning at the operational level.

Unstructured Problems

Complex problems with low clarity, such as mergers and acquisitions or research and development planning at the strategic level.

Tactical Decisions

Decisions with a medium-term temporal scope, such as project scheduling or budget planning.

Strategic Decisions

Decisions with a long-term temporal scope and often low structure, such as facility location planning or investment planning.

Data Mining

The process of discovering patterns in data involving methods of machine learning, statistics, and database systems.

Online Analytical Processing (OLAP)

Also known as business intelligence, this involves queries usually based on multi-dimensional data stored in databases or data warehouses.

Scientific Process of OR

A sequence involving problem formulation, building a mathematical model, hypothesizing model validity, and conducting experiments for validation.

Optimal Solution

The best possible course of action identified by searching for the best solution for the model representing a problem.

Julia/JuMP

A programming language and modeling language used for solving Linear Programming and network optimization problems.

Hungarian Method

A dedicated solution method used specifically for the Assignment Problem.

Knapsack Problem

A problem modeled as a linear program and solved through methods like enumeration, profitability index, or dynamic optimization.

Vogel’s Approximation Method

One of several dedicated solution methods for the Transportation Problem.

Traveling Salesman Problem (TSP)

A specific type of network optimization problem concerning the identification of the shortest possible route visiting a set of locations.

Validation (in OR)

The process of verifying that a model's conclusions are valid for the real-world problem through experiments.

Drill down / Roll up

Interactive options in ESS for changing data granularity to see more specific or more aggregated information.

Transportation Problem

A planning problem that can be modeled as a linear program and solved using methods like the NW corner method or the stepping stone method.

Transshipment Problem

A problem type that can be transformed into a transportation problem for solving, often involving modeling in Julia/JuMP.

Minimum Spanning Tree

A type of network optimization problem aimed at connecting all nodes in a network with the minimum total edge weight.

Maximum Flow Problem

A network optimization task that seeks to find the maximum amount of flow that can pass through a network from source to sink.