Business Analytics for Decision Making /
| Main Authors: | , |
|---|---|
| Corporate Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton, FL :
Chapman and Hall/CRC,
[2018].
|
| Edition: | First edition. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- I: STARTERSIntroductionThe Computational Problem Solving CycleExample: Simple Knapsack ModelsAn Example: The Eilon Simple Knapsack ModelScoping Out Post-Solution AnalysisParameter Sweeping: A Method for Post-Solution AnalysisDecision SweepingSummary of Vocabulary and Main PointsFor ExplorationFor More InformationConstrained Optimization Models: Introduction and Concepts Constrained OptimizationClassification of ModelsSolution ConceptsComputational Complexity and Solution MethodsMetaheuristicsDiscussionFor ExplorationFor More InformationLinear Programming IntroductionWagner Diet ProblemSolving an LPPost-Solution Analysis of LPsMore than One at a Time: The 100% RuleFor ExplorationFor More InformationII: OPTIMIZATION MODELING Simple Knapsack Problems IntroductionSolving a Simple Knapsack in ExcelThe Bang-for-Buck HeuristicPost-Solution Analytics with the Simple KnapsackCreating Simple Knapsack Test ModelsDiscussionFor ExplorationFor More InformationAssignment ProblemsIntroductionThe Generalized Assignment ProblemCase Example: GAP 1-c5-15-1Using Decisions from Evolutionary ComputationDiscussionFor ExplorationFor More InformationThe Traveling Salesman ProblemIntroductionProblem DefinitionSolution ApproachesDiscussionFor ExplorationFor More InformationVehicle Routing ProblemsIntroductionProblem DefinitionSolution ApproachesExtensions of VRPFor ExplorationFor More InformationResource-Constrained Scheduling IntroductionFormal DefinitionSolution ApproachesExtensions of RCPSPFor ExplorationFor More InformationLocation Analysis IntroductionLocating One Service CenterA Nave Greedy Heuristic for Locating n CentersUsing a Greedy Hill Climbing HeuristicDiscussionFor ExplorationFor More InformationTwo-Sided Matching Quick Introduction: Two-Sided Matching ProblemsNarrative Description of Two-Sided Matching ProblemsRepresenting the ProblemStable Matches and the Deferred Acceptance AlgorithmOnce More, in More DepthGeneralization: Matching in Centralized MarketsDiscussion: ComplicationsFor More InformationIII: METAHEURISTIC SOLUTION METHODSLocal Search Metaheuristics IntroductionGreedy Hill ClimbingSimulated AnnealingRunning the Simulated Annealer CodeThreshold Accepting AlgorithmsTabu SearchFor ExplorationFor More InformationEvolutionary AlgorithmsIntroductionEPs: Evolutionary ProgramsThe Basic Genetic Algorithm (GA)For ExplorationFor More InformationIdentifying and Collecting Decisions of Interest Kinds of Decisions of Interest (DoIs)The FI2-Pop GADiscussionFor ExplorationFor More InformationIV: POST-SOLUTION ANALYSIS OF OPTIMIZATION MODELSDecision Sweeping IntroductionDecision Sweeping with the GAP 1-c5-15-1 ModelDeliberating with the Results of a Decision SweepDiscussionFor ExplorationFor More InformationParameter Sweeping Introduction: Reminders on Solution Pluralism and Parameter SweepingParameter Sweeping: Post-Solution Analysis by Model Re-SolutionParameter Sweeping with Decision SweepingDiscussionFor ExplorationFor More InformationMultiattribute Utility Modeling IntroductionSingle Attribute Utility ModelingMultiattribute Utility ModelsDiscussionFor ExplorationFor More InformationData Envelopment Analysis IntroductionImplementationDemonstration of DEA ConceptDiscussionFor ExplorationFor More InformationRedistricting: A Case Study in Zone Design IntroductionThe Basic Redistricting FormulationRepresenting and Formulating the ProblemInitial Forays for Discovering Good Districting PlansSolving a Related Solution Pluralism ProblemDiscussionFor ExplorationFor More InformationV: CONCLUSIONConclusionLooking BackRevisiting Post-Solution AnalysisLooking ForwardResources A.1 Resources on the WebBibliography Index.