scholarly journals A polynomial time repeated cuts algorithm for the time cost tradeoff problem: The linear and convex crashing cost deadline problem

2016 ◽  
Vol 95 ◽  
pp. 64-71 ◽  
Author(s):  
Dorit S. Hochbaum
2015 ◽  
Vol 22 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Zhigang SHEN ◽  
Ashkan HASSANI ◽  
Qian SHI

Existing research on construction time-cost tradeoff issues rarely explore the origin of the crashing cost. Crashing cost function was either assumed without much justification, or came from historical data of some real pro­jects. As a result the conclusions of the papers can hardly be used to guide allocations of labor and equipment resources respectively. The authors believe Cobb-Douglas function provides a much-needed piece to modeling the cost functions in the construction time-cost tradeoff problem during the crashing process. We believe this new perspective fills a gap of existing time-cost tradeoff research by considering project duration, labor and equipment cost as parameters of the Cobb- Douglas production function. A case study was presented to show how the proposed framework works. Our conclusion is that introducing Cobb-Douglas function into time-cost tradeoff problem provides us extra capacity to further identify the optimal allocations of labor and equipment resources during crashing.


2015 ◽  
Vol 32 (05) ◽  
pp. 1550039 ◽  
Author(s):  
Byung-Cheon Choi ◽  
Myoung-Ju Park

We consider a linear time–cost tradeoff problem with multiple milestones and uncertain processing times such that all jobs are completely ordered. The performance measure is expressed as the sum of total weighted number of tardy jobs and total crashing cost. The processing times uncertainty is described through two types of scenarios: discrete and interval scenarios. The objective is to minimize maximum deviation from optimality over all scenarios. For the discrete scenario case, we prove its NP-hardness, develop a pseudo-polynomial time approach, and present a polynomially solvable case. Finally, we show that the interval scenario case is also NP-hard.


1982 ◽  
Vol 14 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Suleyman Tufekci
Keyword(s):  

2018 ◽  
Vol 60 (2) ◽  
pp. 360-375
Author(s):  
A. V. Vasil'ev ◽  
D. V. Churikov

Author(s):  
Ali Hameed Al-Badri

Appendicitis is a common and urgentsurgical illness with protean manifestations,generous overlap with other clinical syndromes,and significant morbidity,whichincreases with diagnostic delay. No single sign,symptom,or diagnostic test accurately confirms the diagnosis ofappendiceal inflammation in all cases. The surgeon's goals are to evaluate a relatively small population of patients referred for suspected appendicitis and to minimize the negative appendectomy rate without increasing the incidence of perforation. The emergency department clinician must evaluate the larger group of patients who present to the ED with abdominal pain of all etiologies with the goal of approaching 100% sensitivity for the diagnosis in a time-,cost-,and consultation-efficient manner.IN 1886Reginald fitz, pathologist 1st described the clinical condition of A.A.Fewyears laterCharles mcBurney describe the clinical finding ofA.A.55% of patients presented with classical symptom of A.A so complication occurbecauseof atypical presentation which due to variation in app. Position, age of patient & degree of inflammation.Migrating pain 80% sensitive and specific Vomiting 50% Nausea60 -90 %Anorexia 75 % Diarrhea18 % 32 % has similar attach 90 % RLQ tenderness Marklesign 74 %Dunphy's sign (sharp pain in the RLQ elicited by a voluntary cough) may be helpful in making the clinical diagnosis of localized peritonitis. Similarly,RLQ pain in response to percussion of a remote quadrant of the abdomen,or to firm percussion of the patient's heel,suggests peritoneal Inflammation


10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.


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