A bicriterion Steiner tree problem on graph

This paper presents a formulation of bicriterion Steiner tree problem which is stated as a task of finding a Steiner tree with maximal capacity and minimal length. It is considered as a lexicographic multicriteria problem. This means that the bottleneck Steiner tree problem is solved first. After that, the next optimization problem is stated as a classical minimums Steiner tree problem under the constraint on capacity of the tree. The paper also presents some computational experiments with the multicriteria problem

Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems

A Comparative Evaluation Of Fdsa,ga, And Sa Non-linear Programming Algorithms And Development Of System-optimal Methodology For Dynamic Pricing On I-95 Express

As urban population across the globe increases, the demand for adequate transportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities. High –Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today’s highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for indepth study of demand patterns and development of an appropriate pricing scheme which reduces congestion. This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution. We perform a comparative evaluation of FDSA, GA and SA non-linear programming algorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric iv forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 201

The improvement research on multi-objective optimization algorithm based on non-dominated sorting

多目标优化问题（MOP）在许多科学研究和工程设计当中普遍存在，此类问题求解十分复杂但又十分重要。尽管传统多目标优化算法已经有了长足的发展，但遗存的问题依然很多，需要改进。 进化多目标优化算法将传统方法中的加权策略改为以种群为单位的进化策略，取得了更理想的优化的效果，NSGA-II就是其中的佼佼者。在此次研究中本人在NSGA-II的基础上提出了一种基于随机交叉算子、变异算子的算法RCVO-NSGA-II（RandomCrossVariationOperator-nondominatedsortinggeneticalgorithmII）用于解多目标优化问题。RCVO-NSGA-II随机采用模拟...Multiobjective optimization problem is common existing in many scientific researches and engineering design and the solution of this kind of problem is very complicated and important. Although the development of the traditional multi-objective optimization algorithm have made great progress, but a lot of problems are need to be improved. Evolutionary multi-objective optimization algorithm change ...学位：工程硕士院系专业：信息科学与技术学院_工程硕士(计算机技术)学号：X201222101

An integration of spreadsheet and project management software for cost optimal time scheduling in construction

Successful performance and completion of construction projects highly depend on an adequate time scheduling of the project activities. On implementation of time scheduling, the execution modes of activities are most often required to be set in a manner that enables in achieving the minimum total project cost. This paper presents an approach to cost optimal time scheduling, which integrates a spreadsheet application and data transfer to project management software (PMS). At this point, the optimization problem of project time scheduling is modelled employing Microsoft Excel and solved to optimality using Solver while organization of data is dealt by macros. Thereupon, Microsoft Project software is utilized for further managing and presentation of optimized time scheduling solution. In this way, the data flow between programs is automated and possibilities of error occurrence during scheduling process are reduced to a minimum. Moreover, integration of spreadsheet and PMS for cost optimal time scheduling in construction is performed within well-known program environment that increases the possibilities of its wider use in practice. An application example is shown in this paper to demonstrate the advantages of proposed approach