On the interaction between roadrunner or railway scheduling and priority lists or resource flow networks

In previous research (Tian and Demeulemeester 2010), we have shown that in realistic situations railway scheduling improves both the stability and the expected project length over roadrunner scheduling. In this paper, we introduce the concept of resource flow networks in this analysis and determine what the impact of combining roadrunner or railway scheduling with priority lists or resource flow networks is on the average project length, the standard deviation of the project length, the timely project completion probability and the stability cost. Extensive computational results will be presented on both small and larger projects and a statistical analysis will be conducted by using SAS PROC GLM, indicating that in smaller projects the optimal combination strongly depends on the performance indicator of interest, while in larger projects a combination of roadrunner scheduling with a resource flow network seems optimal.     

Scheduling of anticancer drugs

Many cancer patients are treated with a combination of anticancer drugs. Here, we discuss the importance of drug scheduling and the need for studies that investigate the optimal timing of the various anticancer drugs. Positron emission tomography (PET) using radiolabeled anticancer drugs could be an important tool for those studies.     

Flower pollination algorithm with runway balance strategy for the aircraft landing scheduling problem

Aircraft landing scheduling is a challenging problem in the field of air traffic, whose objective is to determine the best combination of assigning the sequence and corresponding landing time for a given set of aircraft to a runway, and then minimize the sum of the deviations of the actual and target landing times under the condition of safe landing. In this paper, a flower pollination algorithm embedded with runway balance is proposed to solve it. Context cognitive learning and runway balance strategy are devised here to enhance its searching ability. 36 scheduling instances are selected from OR-Library to validate its performance. The experimental results show that the proposed algorithm can get the optimal solutions for instances up to 100 aircrafts, and is also capable of obtaining better solutions compared with SS, BA and FCFS for instances up to 500 aircrafts in a shorter time.     

An integrated scheduling and control model for multi-mode projects

In today’s highly competitive uncertain project environments, it is of crucial importance to develop analytical models and algorithms to schedule and control project activities so that the deviations from the project objectives are minimized. This paper addresses the integrated scheduling and control in multi-mode project environments. We propose an optimization model that models the dynamic behavior of projects and integrates optimal control into a practically relevant project scheduling problem. From the scheduling perspective, we address the discrete time/cost trade-off problem, whereas an optimal control formulation is used to capture the effect of project control. Moreover, we develop a solution algorithm for two particular instances of the optimal project control. This algorithm combines a tabu search strategy and nonlinear programming. It is applied to a large scale test bed and its efficiency is tested by means of computational experiments. To the best of our knowledge, this research is the first application of optimal control theory to multi-mode project networks. The models and algorithms developed in this research are targeted as a support tool for project managers in both scheduling and deciding on the timing and quantity of control activities.     

Optimal scheduling of radiotherapy and angiogenic inhibitors

We incorporate a previously validated mathematical model of a vascularized tumor into an optimal control problem to determine the temporal scheduling of radiotherapy and angiogenic inhibitors that maximizes the control of a primary tumor. Our results reveal that optimal antiangiogenic monotherapy gives a large initial injection to attain a 20: 1 ratio of tumor cell volume to supporting vasculature volume. It thereafter maintains this 20: 1 ratio via a continuous dose rate that is intensified over time. The optimal radiation monotherapy schedule is characterized by amodest dose intensification over time. The best performance is achieved by our optimal combination regimen, where the antiangiogenic treatment again maintains a constant tumor-to-vasculature ratio, but is administered in a dose-intensified manner only during the latter portion of the radiation fractionation schedule.     

一类种群进食速度与时间有关的排序问题

本文考虑了工件的加工速度随交货期的接近不断加快的排序模型,给出了初始速度不为零情况下最小化加工时间的最优算法.并讨论了不完全信息情形下工件的最大延迟比该模型的研究结果可应用在种群对食物的最优收寻问题中.     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

A network aware approach for the scheduling of virtual machine migration during peak loads

Live virtual machine migration can have a major impact on how a cloud system performs, as it consumes significant amounts of network resources such as bandwidth. Migration contributes to an increase in consumption of network resources which leads to longer migration times and ultimately has a detrimental effect on the performance of a cloud computing system. Most industrial approaches use ad-hoc manual policies to migrate virtual machines. In this paper, we propose an autonomous network aware live migration strategy that observes the current demand level of a network and performs appropriate actions based on what it is experiencing. The Artificial Intelligence technique known as Reinforcement Learning acts as a decision support system, enabling an agent to learn optimal scheduling times for live migration while analysing current network traffic demand. We demonstrate that an autonomous agent can learn to utilise available resources when peak loads saturate the cloud network.     

Multi-objective workflow scheduling in Amazon EC2

Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimised. Multi-objective scheduling of scientific applications in these systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of (nearly) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we analyse MOHEFT, a Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected. We demonstrate the potential of our method for multi-objective workflow scheduling on the commercial Amazon EC2 Cloud. We compare the quality of the MOHEFT tradeoff solutions with two state-of-the-art approaches using different synthetic and real-world workflows: the classical HEFT algorithm for single-objective scheduling and the SPEA2* genetic algorithm used in multi-objective optimisation problems. The results demonstrate that our approach is able to compute solutions of higher quality than SPEA2*. In addition, we show that MOHEFT is more suitable than SPEA2* for workflow scheduling in the context of commercial Clouds, since the genetic-based approach is unable of dealing with some of the constraints imposed by these systems.     

Strategies for improving antitumor activity utilizing IL-2: Preclinical models and analysis of antitumor activity of lymphocytes from patients receiving IL-2

Conclusions Considerable enthusiasm remains for the successful utilization of the immune system for the immunotherapy of human cancers. Immunotherapeutic maneuvers have been able to mediate impressive antitumor responses for some patients with advanced and refractory malignancies. Unfortunately, the number of patients who benefit from current immunotherapies is low, while the toxicity for many of the patients receiving these treatments is high. It is becoming quite clear that the development of successful immunotherapeutic strategies will involve a carefully chosen combination of immunotherapeutic modalities or of immunotherapy combined with either surgery, radiation therapy, or chemotherapy. The use of an IL-2 based regimen which is clinically tolerable and can provide significant immune activation continues to remain central to many of these treatment approaches. Preclinicalin vitro and animal model systems can evaluate promising treatment strategies, including combination approaches. As an effective immunotherapeutic approach will likely require use of a combination of biologically active agents, the scheduling of these therapies may have profound importance both for optimal antitumor responses as well as clinical tolerance.     

Aproximating static list schedules in dynamic multithreaded applications

List scheduling algorithms are known to be efficient when the application to be executed can be described statically as a Directed Acyclic Graph (DAG) of tasks. Regardless of knowing the entire DAG beforehand, obtaining an optimal schedule in a parallel machine is a NP-hard problem. Moreover, many programming tools propose the use of scheduling techniques based on list strategies. This paper presents an analysis of scheduling algorithms for multithread programs in a dynamic scenario where threads are created and destroyed during execution. We introduce an algorithm to convert DAGs, describing applications as tasks, into Directed Cyclic Graphs (DCGs) describing the same application designed in a multithread programming interface. Our algorithm covers case studies described in previous works, successfully mapping from the abstract level of graphs to the application environment. These mappings preserve the guarantees offered by the abstract model, providing efficient scheduling of dynamic programs that follow the intended multithread model. We conclude the paper presenting some performance results we obtained by list schedulers in dynamic multithreaded environments. We also compare these results with the best scheduling we could obtain with similar static task schedulers.     

Agent-based supply-chain planning in the forest products industry

The new economic challenges and recent trends in globalization have made it very difficult for Canadian forest product companies to improve their financial position without the coordinated involvement of the entire company, including their supply chains (distributed facilities, company offices, industrial customers, and distributors). Such a new level of efficiency involves their distributed facilities and offices spread around the world, and their customers. One consequence of this new reality is that forest products companies are now facing the need to re-engineer their organizational processes and business practices with their partners. To do this they must adopt new technologies to support the coordination of their planning and control efforts in a customer-centered environment. This paper first proposes a generic software architecture for development of an experimentation environment to design and test distributed advanced planning and scheduling systems. This architecture enables combination of agent-based technology and operations research-based tools in order to first take advantage of the ability of agent technology to integrate distributed decision problems, and, second, to take advantage of the ability of operations research to develop and exploit specific normative decision models. Next, this paper describes how this architecture has been configured into an advanced planning and scheduling tool for the lumber industry. Finally, we present how an application of this advanced planning tool is currently being validated and tested in a real manufacturing setting.     

Activation of elicitor defensive properties by systemic signal molecules during the interaction between potato and Phytophthora

The elicitor arachidonic acid in combination with jasmonic acid (JA) induced a higher level of defense against the late blight agent in potato (Solanum tuberosum L.) tissues than in combination with salicylic acid (SA). On the contrary, the elicitor chitosan displayed a higher inductive effect in combination with SA as compared with JA. The optimal concentrations of tested compounds were selected for designing the compositions activating wound repair, induction of proteinase inhibitors, and resistance to the biotrophic pathogen Phytophthora infestans (Mont.) de Bary. It was demonstrated that the compositions of elicitor and systemic signal molecules provided a faster spreading of an inducing effect in the potato tissues.     

A stochastic approach to estimating earliest start times of nodes for scheduling DAGs on heterogeneous distributed computing systems

Previously, DAG scheduling schemes used the mean (average) of computation or communication time in dealing with temporal heterogeneity. However, it is not optimal to consider only the means of computation and communication times in DAG scheduling on a temporally (and spatially) heterogeneous distributed computing system. In this paper, it is proposed that the second order moments of computation and communication times, such as the standard deviations, be taken into account in addition to their means, in scheduling “stochastic” DAGs. An effective scheduling approach which accurately estimates the earliest start time of each node and derives a schedule leading to a shorter average parallel execution time has been developed. Through an extensive computer simulation, it has been shown that a significant improvement (reduction) in the average parallel execution times of stochastic DAGs can be achieved by the proposed approach.     

Activation of elicitor defensive properties by systemic signal molecules during the interaction between potato and the late blight agent

The elicitor arachidonic acid in combination with jasmonic acid (JA) induced a higher level of defense against the late blight agent in potato (Solanum tuberosum L.) tissues than in combination with salicylic acid (SA). On the contrary, the elicitor chitosan displayed a higher inductive effect in combination with SA as compared with JA. The optimal concentrations of tested compounds were selected for designing the compositions activating wound repair, induction of proteinase inhibitors, and resistance to the biotrophic pathogen Phytophthora infestans (Mont.) de Bary. It was demonstrated that the compositions of elicitor and systemic signal molecules provided a faster spreading of an inducing effect in the potato tissues.     

Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing

There are typically multiple heterogeneous servers providing various services in cloud computing. High power consumption of these servers increases the cost of running a data center. Thus, there is a problem of reducing the power cost with tolerable performance degradation. In this paper, we optimize the performance and power consumption tradeoff for multiple heterogeneous servers. We consider the following problems: (1) optimal job scheduling with fixed service rates; (2) joint optimal service speed scaling and job scheduling. For problem (1), we present the Karush-Kuhn-Tucker (KKT) conditions and provide a closed-form solution. For problem (2), both continuous speed scaling and discrete speed scaling are considered. In discrete speed scaling, the feasible service rates are discrete and bounded. We formulate the problem as an MINLP problem and propose a distributed algorithm by online value iteration, which has lower complexity than a centralized algorithm. Our approach provides an analytical way to manage the tradeoff between performance and power consumption. The simulation results show the gain of using speed scaling, and also prove the effectiveness and efficiency of the proposed algorithms.     

The Effect of Freezing Rate on the Survival of Cryopreserved African Sharptooth Catfish (Clarias gariepinus) Spermatozoa

Cryoprotective agents were evaluated to find the optimal concentration of the cryoprotectant and most suitable combination of solution and cryoprotectant. A cryoprotective agent composed of 4% glucose and 9% glycerol yielded the best results. It was established that the optimal freezing rate is dependent on the composition of the cryoprotective agent. Maximal survival of catfish spermatozoa (60%) occurs at 5°C min^-1 and faster and slower freezing rates result in poor survival or no survival at all. Incorporation of an isothermal holding period into the freezing rate led to remarkable increase (20-30%) in sperm survival when Me₂SO was present in the cryoprotective agent. Cryoprotective agents containing glucose also showed improved survival when a three phase freezing rate was used. These results lead to the conclusion that the presence of an isothermal holding period in the freezing rate is beneficial for the cryoprotective action of Me₂SO and glucose.     

A branch and bound algorithm for hoist scheduling of a circuit board production line

The article studies the scheduling problem of a material handling hoist in a circuit board production line. The existing models for the problem assume that the times required to perform inter-tank moves are given constants. However, as shown in a simple example, the optimal solutions obtained under this assumption may not be the actual optimal solutions. In this article the times for inter-tank moves are decision variables of a mixed integer program proposed for the problem. An efficient branch and bound algorithm is developed for solving the problem optimally. A numerical example is used to illustrate the algorithm. Computational experience with benchmark problems and randomly generated test problems is discussed.     

Performance analyses of a neutralizing agent combination strategy for the production of succinic acid by <Emphasis Type="Italic">Actinobacillus succinogenes</Emphasis> ATCC 55618

A neutralizing agent combination strategy was developed to enhance the succinic acid production by Actinobacillus succinogenes ATCC 55618. First, a maximal succinic acid production of 48.2 g/L was obtained at a culture pH of 7.5. Second, NaOH and KOH were screened to identify the optimal neutralizing agent for pH control. However, the production of succinic acid did not increase, and severe cell flocculation was observed due to a high concentration of metal ions when only one neutralizing agent was used to control pH. Finally, a neutralizing agent combination strategy was developed with a supply of neutralizing agents with OH⁻ and carbonate. The cell flocculation was eliminated, and a maximum succinic acid production of 59.2 g/L was obtained with 5 M NaOH and 40 g/L of MgCO₃; this production was 27.9% higher than that obtained with NaOH alone. The results obtained in this study may be useful for the large-scale industrial production of succinic acid.