Optimal solution for a cancer radiotherapy problem

We address the problem of finding the optimal radiotherapy fractionation scheme, representing the response to radiation of tumour and normal tissues by the LQ model including exponential repopulation and sublethal damage due to incomplete repair. We formulate the nonlinear programming problem of maximizing the overall tumour damage, while keeping the damages to the late and early responding normal tissues within a given admissible level. The optimum is searched over a single week of treatment and its possible structures are identified. In the two simpler but important cases of absence of the incomplete repair term or of prevalent late constraint, we prove the uniqueness of the optimal solution and we characterize it in terms of model parameters. The optimal solution is found to be not necessarily uniform over the week. The theoretical results are confirmed by numerical tests and comparisons with literature fractionation schemes are presented.     

New meta-heuristics for the resource-constrained project scheduling problem

In this paper, we study the resource-constrained project scheduling problem and introduce an annealing-like search heuristic which simulates the cooling process of a gas into a highly-ordered crystal. To achieve this, we develop diversification procedures that simulate the motion of high energy molecules as well as a local refinement procedure that simulates the motion of low energy molecules. We further improve the heuristic by incorporating a genetic algorithm framework. The meta-heuristic algorithms are applied to Kolisch’s PSPLIB J30, J60 and J120 RCPSP instances. Experimental results show that they are effective and are among the best performing algorithms for the RCPSP.     

A new method for a class of parallel batch machine scheduling problem

Flexible Services and Manufacturing Journal - This paper studies the scheduling problem of jobs with release times, non-identical sizes, and incompatible job families on unrelated parallel batch...     

Steady-state solution of a two-species biofilm problem

Through a thorough investigation of the boundary conditions for a general two-species biofilm model, a simple and fast method for solving the steady-state case is developed and presented. The methods used may be extended to biofilm models in which more than two species are considered. Four different sets of boundary conditions are possible for the two-species biofilm model. Each set is shown to be asymptotically stable. A biofilm model describing the competition between autotrophic and heterotrophic bacteria and a biofilm model considering only Nitrosomonas and Nitrobacter are used for illustration. A parameter L(crit), critical film thickness for bacterial coexistence, is introduced from which criteria on the bulk concentrations for coexistence are derived. From these criteria it is seen that the thinner the biofilm, the more restrictive the conditions are for steady-state coexistence. For thin biofilms there may, in many cases, be no point in considering more than one species in the biofilm model. Furthermore, the gradients of the bacterial concentrations are in many cases negligible in thin biofilms, and the biofilm may then be assumed to be homogeneous. The criteria on the bulk concentrations together with the four sets of boundary conditions provide the necessary information for a direct solution of the steady-state two-species biofilm model by means of an ordinary differential and algebraic equation solver. (c) 1996 John Wiley & Sons, Inc.     

Genetic algorithm solution for double digest problem

The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data.     

A scheduling problem for a novel container transport system: a case of mobile harbor operation schedule

Mobile Harbor (MH) is a movable floating platform with a container handling system on board so that it can load/discharge containers to/from an anchored container ship in the open sea. As with typical quay crane operation, an efficient schedule for its operation is a key to enhancing its operational productivity. A MH operation scheduling problem is to determine a timed sequence of loading/discharging tasks, assignment of MH units to each task, and their docking position, with an objective of minimizing the makespan of a series of incoming container ships. A mixed integer programming model is formulated to formally define the problem. As a practical solution method to the problem, this paper proposes a rule-based algorithm and a random key based genetic algorithm (rkGA). Computational results show that the rkGA method produces a better-quality solution than the rule-based method, while requiring longer computation time.     

A new formulation for the project scheduling problem under limited resources

In this paper we propose a new formulation for the Resource Constrained Project Scheduling Problem (RCPSP) and minimum makespan objective. The new formulation exploits three variables, one associated with the start time of an activity, one associated with the finish time of an activity, and the last one associated with the amount of an activity in progress at a given time. We provide an extensive experimentation, and a comparison with known mathematical formulations for the RCPSP in the literature.     

An ILP solution for the gene duplication problem

Background

The gene duplication (GD) problem seeks a species tree that implies the fewest gene duplication events across a given collection of gene trees. Solving this problem makes it possible to use large gene families with complex histories of duplication and loss to infer phylogenetic trees. However, the GD problem is NP-hard, and therefore, most analyses use heuristics that lack any performance guarantee.

Results

We describe the first integer linear programming (ILP) formulation to solve instances of the gene duplication problem exactly. With simulations, we demonstrate that the ILP solution can solve problem instances with up to 14 taxa. Furthermore, we apply the new ILP solution to solve the gene duplication problem for the seed plant phylogeny using a 12-taxon, 6, 084-gene data set. The unique, optimal solution, which places Gnetales sister to the conifers, represents a new, large-scale genomic perspective on one of the most puzzling questions in plant systematics.

Conclusions

Although the GD problem is NP-hard, our novel ILP solution for it can solve instances with data sets consisting of as many as 14 taxa and 1, 000 genes in a few hours. These are the largest instances that have been solved to optimally to date. Thus, this work can provide large-scale genomic perspectives on phylogenetic questions that previously could only be addressed by heuristic estimates.

     

False cell lines: The problem and a solution

Hundreds of misleading reports are published every year containing data on human cancer cell lines that are derived from some other species, tissue or individual to that claimed. In consequence, millions of dollars provided for cancer research are being spent on the production of misleading data. This review describes how cross-contamination occurs, catalogues the use of false cell lines in leading biomedical journals, and suggests ways to resolve the problem. This revised version was published online in August 2006 with corrections to the Cover Date.     

Pointwise bounds for the solution of a nonlinear problem in cell membrane theory

Pointwise upper and lower bounds for the solution of a class of nonlinear problems arising in the steady-state finite cable model of cell membranes are presented. Simple analytical bounding curves are obtained for an illustrative example in the theory of nerve membranes.     

Gel electrophoresis of micron-sized particles: a problem and a solution

The gel electrophoresis of spherical particles with a radius above 0.2 micron has not been reported yet. In the present study, video phase-contrast light microscopy is used to observe the motion of individual latex spheres, 0.52 micron in radius, during electrophoresis in 0.1% agarose gels. At 2 V/cm, the spheres initially migrate in the direction of the electrical field. However, each sphere eventually undergoes a cessation of all motion. Brownian motion is restored when the electrical potential gradient is reduced to zero. Arrest can be prevented by periodically inverting the direction of the electrical field. These observations are explained by electrical field-induced steric trapping of the spheres by gel fibers. Inversion of the electrical field should assist the application of agarose gel electrophoresis to micron-sized cellular organelles and cells.     

Metabolic control: a new solution to an old problem

The phenomenon whereby the presence of oxygen regulates the rate of glucose metabolism was first described by Louis Pasteur. A novel mechanism has now been discovered, involving the AMP-activated protein kinase cascade, that can account for the Pasteur effect in ischaemic heart muscle.     

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 efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.     

Two hybrid compaction algorithms for the layout optimization problem

In this paper we present two new algorithms for the layout optimization problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present two nature-inspired algorithms for this problem, the first based on simulated annealing, and the second on particle swarm optimization. We compare our algorithms with the existing best-known algorithm, and show that our approaches out-perform it in terms of both solution quality and execution time.     

How to correctly match 175 000 neurites: Two postulates for a quick solution

I propose two postulates that allow precise matching of an indefinite number of neurites when repairing a transected nerve. The first one, based on the principle of de Saint-Venant, indicates how to keep nerve fibers in proper longitudinal alignment, by diverting stress to an appropriate distance from the tip of the stumps. The second postulate, based on the symmetrical mechanical resistance of the stumps, indicates how to restore correct transversal alignment of the fibers, by circularizing the severed nerve extremities.