Client-contractor bargaining on net present value in project scheduling with limited resources

The client-contractor bargaining problem addressed here is in the context of a multi-mode resource constrained project scheduling problem with discounted cash flows, which is formulated as a progress payments model. In this model, the contractor receives payments from the client at predetermined regular time intervals. The last payment is paid at the first predetermined payment point right after project completion. The second payment model considered in this paper is the one with payments at activity completions. The project is represented on an Activity-on-Node (AON) project network. Activity durations are assumed to be deterministic. The project duration is bounded from above by a deadline imposed by the client, which constitutes a hard constraint. The bargaining objective is to maximize the bargaining objective function comprised of the objectives of both the client and the contractor. The bargaining objective function is expected to reflect the two-party nature of the problem environment and seeks a compromise between the client and the contractor. The bargaining power concept is introduced into the problem by the bargaining power weights used in the bargaining objective function. Simulated annealing algorithm and genetic algorithm approaches are proposed as solution procedures. The proposed solution methods are tested with respect to solution quality and solution times. Sensitivity analyses are conducted among different parameters used in the model, namely the profit margin, the discount rate, and the bargaining power weights

Climbing depth-bounded adjacent discrepancy search for solving hybrid flow shop scheduling problems with multiprocessor tasks

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The problem even in its simplest form is NP-hard in the strong sense. The great deal of interest for this problem, besides its theoretical complexity, is animated by needs of various manufacturing and computing systems. We propose a new approach based on limited discrepancy search to solve the problem. Our method is tested with reference to a proposed lower bound as well as the best-known solutions in literature. Computational results show that the developed approach is efficient in particular for large-size problems

A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp

On the use of the SOS metaheuristic algorithm in hybrid image fusion methods to achieve optimum spectral fidelity

Image fusion aims to spatially enhance a low-resolution multispectral (MS) image by utilizing a high-resolution panchromatic (Pan) band. Various image fusion methodologies have been proposed with the aim to improve the spatial detail quality without deteriorating the colour content of the input MS image. Previous studies revealed the fact that there is no such thing as 'the best image fusion method' since all fusion methods cause either spectral distortion or spatial detail loss to some extent, which motivates the researchers to develop more advanced methods to keep the colour content while increasing the spatial detail quality. This study proposed to use the Symbiotic Organisms Search (SOS) metaheuristic algorithm in hybrid image fusion methods to achieve the optimum colour quality in the fused images. The SOS algorithm was used in two hybrid fusion approaches, one including the Intensity-Hue-Saturation (IHS) and Discrete Wavelet Transform (DWT) methods and the other one including the IHS and Discrete Wavelet Frame Transform (DWFT) methods. The results of the proposed methods were qualitatively and quantitatively compared in three test sites against those of eighteen widely-used image fusion methods. It was concluded that the proposed methods led to superior colour quality with both singlesensor and multisensor input images, regardless of the spatial resolution difference between the input images. The proposed methods were also found to be very successful in sharpening the images, despite the fact that their main purpose was to keep the colour content as much as possible

A biobjective genetic algorithm approach to project scheduling under risk

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed and tested. The experiments conducted indicate that GAs provide a fast and effective solution approach to the proble m. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Four payment models for the multi-mode resource constrained project scheduling problem with discounted cash flows

In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and outflows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: Lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it

Cerebellar Infarction in Childhood: Delayed-Onset Complication of Mild Head Trauma

How to Cite This Article: Oz II, Bozay Oz E, Şerifoğlu I, Kaya N, Erdem O. Cerebellar Infarction in Childhood: Delayed-Onset Complication of Mild Head Trauma. Iran J Child Neurol. Summer 2016; 10(3):82-85.Objective Cerebellar ischemic infarction is a rare complication of minor head trauma. Vertebral artery dissection, vasospasm or systemic hypo perfusion can cause infarct. However, underlying causes of the ischemic infarct cannot be explained in nearly half of cases. The accurate diagnosis is essential to ensure appropriate treatment. Here we report a five yr old boy patient of cerebellar infraction after minor head trauma, admitted to emergency serves of Bulent Ecevit University, Turkey in 2013. We aimed to remind minor head trauma that causes cerebellar infarction during childhood, and to review the important points of the diagnosis, which should be keep in mind. ReferencesSchutzman SA, Greenes DS. Pediatric minor head trauma. Ann Emerg Med 2001;37(1):65-74.Shaffer L, Rich PM, Pohl KR, Ganesan V. Can mild head injury cause ischaemic stroke? Arch Dis Child 2003;88(3):267-9.Lin JJ, Lin KL, Chou ML, Wong AM, Wang HS. Cerebellar infarction in the territory of the superior cerebellar artery in children. Pediatr Neurol 2007;37(6):435-7.Matsumoto H, Kohno K. Posttraumatic cerebral infarction due to progressive occlusion of the internal carotid artery after minor head injury in childhood: a case report. Childs Nerv Syst 2011;27(7):1169-75.Williams LS, Garg BP, Cohen M, Fleck JD, Biller J. Subtypes of ischemic stroke in children and young adults. Neurology 1997;49(6):1541-5.Kieslich M, Fiedler A, Heller C, Kreuz W, Jacobi G. Minor head injury as cause and co-factor in the aetiology of stroke in childhood: a report of eight cases. J Neurol Neurosurg Psychiatry 2002;73(1):13-6.O’Brien NF, Reuter-Rice KE, Khanna S, Peterson BM, Quinto KB. Vasospasm in children with traumatic brain injury. Intensive Care Med 2010;36(4):680-7.Barkovich A, Schwartz E. Brain and Spine Injuries in Infancy and Childhood. In: Barkovich A, Raybaud C, editors. Pediatric Neuroimaging. 5th ed. Philidelphia, PA: Lippincott Williams & Wilkins; 2012. p. 335-8.Lansberg MG, Albers GW, Beaulieu C, Marks MP. Comparison of diffusion-weighted MRI and CT in acute stroke. Neurol 2000;54(8):1557-61. 

Kaynak kısıtlı proje çizelgelemede indirgenmiş nakit akışı maksimizasyonu için bir genetik algoritma yaklaşımı

Bu çalısmada kaynak kısıtlı proje çizelgelemede indirgenmis nakit akısını ençoklamak için gelistirilen bir genetik algoritma sunulmaktadır. Problem hem yenilenebilir hem de yenilenemez kaynaklar göz önüne alınarak tanımlanmaktadır. Kaynakların uygulanmasında sonlu sayıda mod söz konusudur. Genetik algoritmada, çok-bilesenli, düzgün, sıralama temelli bir çaprazlama operatörü kullanılmıstır. Bu çaprazlama operatörünün öncüllük kısıtlarını ihlal etmeyisi önemli bir avantaj sağlamaktadır. Genetik algoritmanın parametrelerinin saptanması için bir meta-seviye genetik algoritma uygulanmıstır. Önerilen algoritmanın sınanması için teknik yazında mevcut 93 problemlik bir test problem kümesi kullanılmıstır. Ayrıca, salt yenilenebilir kaynaklar problemi için, özel amaçlı bir algoritma ile karsılastırma yapılmıs ve önerilen algoritmanın özellikle büyük boyutlu problemlerde basarılı olduğu gösterilmistir

Diffusion-weighted imaging in the head and neck region: usefulness of apparent diffusion coefficient values for characterization of lesions

PURPOSEWe aimed to evaluate the role of apparent diffusion coefficient (ADC) values calculated from diffusion-weighted imaging for head and neck lesion characterization in daily routine, in comparison with histopathological results.METHODSNinety consecutive patients who underwent magnetic resonance imaging (MRI) at a university hospital for diagnosis of neck lesions were included in this prospective study. Diffusion-weighted echo-planar MRI was performed on a 1.5 T unit with b factor of 0 and 1000 s/mm2 and ADC maps were generated. ADC values were measured for benign and malignant whole lesions seen in daily practice.RESULTSThe median ADC value of the malignant tumors and benign lesions were 0.72×10-3 mm2/s, (range, 0.39–1.51×10-3 mm2/s) and 1.17×10-3 mm2/s, (range, 0.52–2.38×10-3 mm2/s), respectively, with a significant difference between them (P < 0.001). A cutoff ADC value of 0.98×10-3 mm2/s was used to distinguish between benign and malignant lesions, yielding 85.3% sensitivity and 78.6% specificity. The median ADC value of lymphomas (0.44×10-3 mm2/s; range, 0.39–0.58×10-3 mm2/s) was significantly smaller (P < 0.001) than that of squamous cell carcinomas (median ADC value 0.72×10-3 mm2/s; range, 0.65–1.06×10-3 mm2/s). There was no significant difference between median ADC values of inflammatory (1.13×10-3 mm2/s; range, 0.85–2.38×10-3 mm2/s) and noninflammatory benign lesions (1.26×10-3 mm2/s; range, 0.52–2.33×10-3 mm2/s).CONCLUSIONDiffusion-weighted imaging and the ADC values can be used to differentiate and characterize benign and malignant head and neck lesions