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Colorectal Cancer (CRC) is one of the most common malignant tumors of digestive tract in the world; the incidence of CRC is increasing year by year. With the development of early screening, the improvement of surgical technique and the application of new treatment methods such as targeted therapy and immunotherapy, the mortality rate of patients with colorectal cancer has decreased obviously, but the long-term therapeutic effect remains suboptimal. Early detection, early diagnosis and early treatment are still the main treatment strategies for CRC. Currently, effective tumor markers are the primary basis for analysis and reference for clinical decision making in CRC treatment. Due to the large variety and quantity of data, the sensitivity and specificity between tumor marker data are difficult to determine, which brings some inconvenience to treatment. We use two-layer neural network’s self-organizing-mapping (SOM) model and unsupervised learning algorithms to map high-dimensional data to a two-dimensional topology, allowing the relationships between the data to be visually represented, which can help doctors better judge the prognosis of CRC and monitor the recurrence and metastasis of CRC from the relationship between the data.

Currently specialists’ outpatient appointments in large hospitals in China are made by patients’ one-side choice of specialists, and most are “first select first served”. A specialist cannot choose a patient according to his specialty. Because of the “worship of famous doctors” and asymmetric information between specialists and patients, the appointments are often made with certain blindness, thus it is difficult for patients to get the best diagnosis and treatment from specialists. In this paper, we apply the two-sided matching theory, from the both views of patients and specialists, we design specialists-outpatients matching appointment system, in the system, we propose the process of the appointment and the one-to-many appointment matching algorithm. In order to provide fairness to both sides, we apply the theory of balance-matching, construct the algorithm of one-to-one and one-to-many two-sided balance matching. At last, through the computational examples we prove the model is effective in hospital specialists outpatient appointment.

At present, electromechanical systems are becoming more and more complex, and the quality of their key components plays a vital role in the reliability of the whole system. Considering the economy, redundant configuration has become an important reliability assurance method for electromechanical systems. However, there is no good method to determine the redundant configuration level of the system reliability, which results in over-design or under-configuration. For this reason, the reliability redundancy optimization method for vital parts of electromechanical systems is proposed, which provides a theoretical basis for reliability design of redundant system. In this paper, the following aspects are studied. The concept of reliability entropy is defined and the optimal value of reliability redundancy allocation is found through the analysis of reliability entropy. The reliability solution function of redundant system is established, the solution function is deduced and analyzed, the variation law of reliability function is found, and the reliability redundancy optimization is carried out on this basis. Finally, the simulation calculation of reliability entropy is studied to verify the effectiveness of the proposed method. The results show that the proposed method can meet the requirements of reliability index of redundant system.

This paper considers two-person cooperative games on scheduling problem which is motivated by the medication dispensing at outpatient pharmacies. We prove that two-person cooperative games on minimizing the number of tardy jobs is NP-hard, and two-person cooperative games on minimizing the total weighted number of tardy jobs and on minimizing the total weighted completion time are NP-hard even if the jobs have the same processing times. We develop dynamic programming algorithms for problems of two-person cooperative games on minimizing the total (weighted) number of tardy jobs and on minimizing the total (weighted) completion time respectively, which run in pseudo-polynomial time and indicate that they are binary NP-hard.

A scheduling theory model is applied to study surgery scheduling in hospitals. If a surgical patient is regarded as a job waiting to be processed, and the related surgeons, anesthesiologists, nurses and surgical equipment as machines that are simultaneously needed for the processing of job, then the surgery scheduling can be described as a parallel machines scheduling problem in which a job is processed by multiple machines simultaneously. We adopt the two-stage approach to solve this scheduling problem and develop a computerized surgery scheduling system to handle such a task. This system was implemented in the Shanghai First People's Hospital and increased the quantity of average monthly finished operations by 10.33 %, the utilization rate of expensive equipment by 9.66 % and the patient satisfaction degree by 1.12 %, and decreased the average length of time that patients wait for surgery by 0.46 day.

Recently, Biskup [2] classifies the learning effect models in scheduling environments into two types: position-based and sum-of-processing-time-based. In this paper, we study scheduling problem with sum-of-logarithm-processing-time-based and position-based learning effects. We show that the single machine scheduling problems to minimize the makespan and the total completion time can both be solved by the smallest (normal) processing time first (SPT) rule. We also show that the problems to minimize the maximum lateness, the total weighted completion times and the total tardiness have polynomial-time solutions under agreeable WSPT rule and agreeable EDD rule. In addition, we show that m-machine permutation flowshop problems are still polynomially solvable under the proposed learning model.

This paper studies a two-person cooperative game in which a set of jobs has to be processed jointly by two people. Each of them has a single machine and his processing cost is defined as the minimum value of the maximum latency of his negotiably assigned jobs. The objective is to maximize the multiplication of their rational positive cooperative profits. In the case where all jobs have the same processing time, if they have a common due date, the problem is polynomial-time solvable; if due dates can be different, there exits an optimal schedule in which the jobs assigned to each person are scheduled in Earlier Due Date first (EDD) order and a polynomial-time dynamic programming is further proposed. In the case where processing times can be different, the NP-completeness of this problem is proved, and a pseudo-polynomial-time dynamic programming algorithm is developed.

If resources and facilities from different partners need to be engaged for a large-scale project with a huge number of tasks, any of which is indivisible, decision on the number of tasks assigned to any collaborating partner often requires a certain amount of coordination and bargaining among these partners so that the ultimate task allocation can be accepted by any partner in a business union for the project. In the current global financial crisis, such cases may appear frequently. In this paper, we first investigate the behavior of such a discrete bargaining model often faced by service-based organizations. In particular, we address the general situation of two partners, where the finite Pareto efficient (profit allocation) set does not possess any convenient assumption for deriving a bargaining solution, namely a final profit allocation (corresponding to a task assignment) acceptable to both partners. We show that it is not appropriate for our discrete bargaining model to offer the union only one profit allocation. Modifying the original optimization problem used to derive the Nash Bargaining Solution (NBS), we develop a bargaining mechanism and define a related bargaining solution set to fulfil one type of needs on balance between profit-earning efficiency and profit-earning fairness. We then show that our mechanism can also suit both Nash's original concave bargaining model and its continuous extension without the concavity of Pareto efficient frontier on profit allocation.

In this paper, we study re-entrant flow shop scheduling problems with the objective of minimizing total completion time. In a re-entrant scheduling problem, jobs may visit some machines more than once for processing. The problem is NP-hard even for machine number m=2. A heuristic algorithm is presented to solve the problem, in which an effective k-insertion technique is introduced as the improvement strategy in iterations. Computational experiments and analyses are performed to give guidelines of choosing parameters in the algorithm. We also provide a lower bound for the total completion time of the optimal solution when there are only two machines. Objective function values of the heuristic solutions are compared with the lower bounds to evaluate the efficiency of the algorithm. For randomly generated instances, the results show that the given heuristic algorithm generates solutions with total completion times within 1.2 times of the lower bounds in most of the cases.

For the problem of scheduling n-jobs on one-machine with agreeable job release dates and due dates to minimize the number of late jobs, Kise, Ibaraki and Mine (Oper. Res. 26:121–126, 1978) gave an O(n2)-time algorithm and proved its optimality by four lemmas. Li et al. (Oper. Res. 58:508–509, 2010) gave a counter-example to show that one lemma is invalid in some case and therefore the optimality is also invalid. In this paper, we revise the lemma and prove that the algorithm is still valid.

This paper considers the single-machine scheduling problems with learning effect and deteriorating jobs. Release time of jobs is a positive and strictly decreasing function about resource consumption. The optimal algorithm is presented for the problem to minimize the makespan with the total resource consumption constraints. Then, for a given sequence, we propose an optimal solution for the problem to minimize the total resource consumption with the makespan constraints.

The traditional software architectures and design model cannot effectively solve some problems on SAAS ((Software as a Service) pattern, and guarantees the software product the quality. Therefore this article has carried on the discussion to the development process, the maturity model, the evolutionary process, and the pattern value chain's transformation of the SAAS, and proposed the SAAS architectures and design method based on five layers driving model. It can solve some difficulties in the SAAS software's complexity and the development process, and raise the SAAS software's development efficiency and quality well.

Kise, Ibaraki and Mine (Operations Research 26:121---126, 1978) give an O(n 2) time algorithm to find an optimal schedule for the single-machine number of late jobs problem with agreeable job release dates and due dates. Li, Chen and Tang (Operations Research 58:508---509, 2010) point out that their proof of optimality for their algorithm is incorrect by giving a counter-example. In this paper we give a correct proof of optimality for their algorithm.

Container shipping of sea-carriage optimization is an important way to effectively enhance the competitiveness of shipping companies' efficiency. The optimization model of container shipping of sea carriage based on chance-constrained programming is established to maximize the profit of a shipping company, which is the objective function. The variables include the number of heavy and empty containers shipped by lines, the shortage number of heavy containers, and the renting number of empty containers from requiring ports to meet the demand. The constraints include meeting the requirements of heavy and empty containers, weight and volume limit to lines, and supplying ability for empty containers. The number of empty containers supplied is stochastic for being affected by several factors. The chance-constrained programming is translated into an integer programming, and the Lingo 9.0 is used to solve the model. Simulation is conducted under varied parameters to show the effectiveness of the model in optimizing the shipping plan. The model can be used to optimize container shipping plan of sea carriage to a shipping company and get profit raise.

Dense schedules are easy to construct and can be used as heuristic solutions for open-shop problems. It is conjectured that in the worst-case a dense schedule will result in a makespan no more than $(2-\frac{1}{m})$ times of the makespan of the optimal schedule, where m is the number of machines. Different approaches have been used in proofs for different number of machines. The conjecture has been proved for the number of machines m?6, and for some special types of dense schedules. In this paper, we first summarize some useful properties of dense schedules, and then provide some special types of dense schedules that make the conjecture hold. Finally, we give complete proofs of the conjecture for m=7 and 8.

In this paper, we consider the single machine scheduling problem with inventory operations. The objective is to minimize makespan subject to the constraint that the total number of tardy jobs is minimum. We show the problem is strongly NP-hard. A polynomial [Formula: see text]-approximation scheme for the problem is presented, where m is defined as the total job's processing times ∑ pj divided by the capacity c of the storage, and an optimal algorithm for a special case of the problem, in which each job is one unit in size.

We study open-shop scheduling problems with job release times. The objective is to minimize the makespan. Dense schedules, easy to construct, are often used as approximate solutions. Performance ratios of the makespans from dense schedules and that of the optimal schedule of the problem are used to evaluate the quality of approximate schedules. It is conjectured (proved for a limited number of machines) that this performance ratio is bounded above by (2−1/m) for m-machine open-shop problems without job release times. In this paper, we extend the conjecture to open-shop problems with job release times. The results proved in this paper are: 1. Dense schedule performance ratio is bounded above by 2 for three-machine open-shop problems with job release times; 2. The conjectured performance ratio upper bound of 5/3 is proved for two special cases of three-machine open-shop problems with job release times; 3. A performance ratio upper bound of 7/4 is proved for three-machine problems.

Two new types of membranes were synthesized by UV curing in our laboratory. The first type of membrane was made of three monomers: 2-hydroxy-3-phenoxypropylacrylate(A), 4-hydroxybutyl acrylate(B), and 2-methyl-2-nitropropyl methacrylate(C1). The second type of membrane was made of the same monomers A and B, and 2-butoxyethyl methacrylate(C2). Permeation properties of clonidine releasing through two new types of copolymer membranes were studied. The effects of the ratios of monomers, the thicknesses of membranes, and the concentration of clonidine on the permeation rates were studied. It was found that both copolymer membranes could control clonidine zero-order release. The permeation rates of the first optimized membrane were linearly dependent on the square root of the drug concentration. The permeation rates of the second optimized membrane had no significant difference when the concentration of clonidine varied in the range of 3.0–5.0 mg mL−1. Furthermore, both optimized membranes were characterized by FTIR, DSC, and SEM. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007

Poly(2-hydroxy-3-phenoxypropylacrylate, 4-hydroxybutyl acrylate, dibutyl maleate) membrane was synthesized by UV curing method in our laboratory for the first time. When above-mentioned monomers were in the weight ratio of 4:4:2, the membrane not only had perfect permeation property but also had excellent plasticity, so the membrane made from monomers in the ratio of 4:4:2 was chosen as an optimized membrane. The optimized membrane provided perfect linear permeation properties in clonidine transdermal drug delivery system. The permeation rate decreased in proportion to the thickness of membrane. When the concentrations of clonidine were in the range of 0.5-7.0mg/ml, the permeation rate was proportional to the square root of clonidine concentrations. The optimized membrane was characterized by FTIR, DSC and SEM.