Decision Policies for Production Networks 2012
DOI: 10.1007/978-0-85729-644-3_7
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A Control Theoretic Evaluation of Schedule Nervousness Suppression Techniques for Master Production Scheduling

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Cited by 3 publications
(1 citation statement)
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“…[2] Assessing internal nervousness in semiconductors SC planning system Simulation-based analysis [3] Demand supply network nervousness Experiential study [4] Evaluating the relationships between the different determinants of schedule nervousness Statistical analysis [5] Studying the relationship between nervousness and the bullwhip effect Vendor managed inventory (VMI) approach [6] Reducing demand nervousness Mathematical modeling [7] System nervousness cost Mathematical modeling [8] Exploring causes and effects of SCN in the MENA region Delphi-based AHP [9] Identifying combinations of factors linked to GSCN Delphi-FAHP [10] Examining setups added to production scheduling nervousness and costs Wagner-Whitin algorithm [11] Scheduling stability and forecast errors Simulation experiment [12] Reducing MRP system nervousness Modified Wagner-Whitin algorithm [13] MRP nervousness Simulation modeling [14] Investigating alternative strategies dealing with nervousness Simulation experiments [15] Order nervousness Simulation modeling [16] Lot-sizing rules and MRP nervousness Simulation modeling [17] Nervousness in manufacturing firms Simulation modeling [18] MRP nervousness Simulation modeling [19] Cost-performance of the MRP system and forecast errors Simulation modeling [20] Classifying nervousness-dampening procedures and their relative effectiveness Static dampening procedure [21] Studying the schedule nervousness increased by uncertainty in the demand forecast Empirical transfer function estimate (ETFE) [22] Reducing frequent revisions of the schedule, which leads to scheduling nervousness Resource-task network (RTN) [23] Comparing the effectiveness of the planning policy for reducing demand uncertainty Mixed-integer linear programming (MILP) [24] Quantifying nervousness in scheduling nervousness based on field observations Mathematical modeling and analytical hierarchy process (AHP)…”
Section: Referencementioning
confidence: 99%
“…[2] Assessing internal nervousness in semiconductors SC planning system Simulation-based analysis [3] Demand supply network nervousness Experiential study [4] Evaluating the relationships between the different determinants of schedule nervousness Statistical analysis [5] Studying the relationship between nervousness and the bullwhip effect Vendor managed inventory (VMI) approach [6] Reducing demand nervousness Mathematical modeling [7] System nervousness cost Mathematical modeling [8] Exploring causes and effects of SCN in the MENA region Delphi-based AHP [9] Identifying combinations of factors linked to GSCN Delphi-FAHP [10] Examining setups added to production scheduling nervousness and costs Wagner-Whitin algorithm [11] Scheduling stability and forecast errors Simulation experiment [12] Reducing MRP system nervousness Modified Wagner-Whitin algorithm [13] MRP nervousness Simulation modeling [14] Investigating alternative strategies dealing with nervousness Simulation experiments [15] Order nervousness Simulation modeling [16] Lot-sizing rules and MRP nervousness Simulation modeling [17] Nervousness in manufacturing firms Simulation modeling [18] MRP nervousness Simulation modeling [19] Cost-performance of the MRP system and forecast errors Simulation modeling [20] Classifying nervousness-dampening procedures and their relative effectiveness Static dampening procedure [21] Studying the schedule nervousness increased by uncertainty in the demand forecast Empirical transfer function estimate (ETFE) [22] Reducing frequent revisions of the schedule, which leads to scheduling nervousness Resource-task network (RTN) [23] Comparing the effectiveness of the planning policy for reducing demand uncertainty Mixed-integer linear programming (MILP) [24] Quantifying nervousness in scheduling nervousness based on field observations Mathematical modeling and analytical hierarchy process (AHP)…”
Section: Referencementioning
confidence: 99%