A hybrid parallel genetic algorithm for yard crane scheduling

This paper aims at postulating a novel strategy in terms of yard crane scheduling. In this study, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-complete problem regarding the yard crane scheduling, a hybrid algorithm, which employs heuristic rules and parallel genetic algorithm (PGA), is then employed. Then a simulation model is developed for evaluating this approach. Finally, numerical experiments on a specific container terminal yard are used for system illustration. Computational results suggest that the proposed method is able to solve the problem efficiently.     

基于改进的BP-LM在中国人口老龄化预测中的应用

由于人口老龄化率的预测具有高度非线性特征，这与BP神经网络能够处理非线性问题的特征相符合，但BP神经网络算法易使解陷入局部极小。基于L-M算法的改进BP神经网络可以有效克服这一问题，而且收敛速度快。通过具体的仿真及实践结果验证了改进BP的有效性，并对未来五年的中国老龄化率进行了预测。     

Disaster‐Relief Logistics With Limited Fuel Supply

Disaster‐relief logistics consists of providing adequate emergency supplies rapidly to the affected people so as to minimize human suffering and death. This study empirically examines the impact that the shortage of fuel, a commonly encountered problem in many disaster situations, can have on the effectiveness of disaster‐relief logistics operations. We focus on investigating the following two issues: (1) whether the shortage of fuel is more damaging in attaining logistical goals than the equivalent‐sized shortage in emergency supplies themselves, and (2) what types of vehicles should be used when the fuel supply is limited. Results suggest that the shortage of fuel may be more damaging than the shortage of emergency supplies, and that smaller trucks may be preferred to larger trucks when the shortage of fuel becomes severe.     

岩土边坡稳定性的遗传算法分析

非线性优化方法主要缺陷在于当边坡土层为非均质复杂条件时,无法保证搜索到安全系数的全局最小解,而只能搜索到局部极小解。基于遗传算法,建立了搜索岩土边坡稳定性分析最小安全系数和滑移面中心坐标与半径的数值方法。问题的可行解在变量搜索区间内搜索,包括滑弧的圆心坐标和半径。数值模拟结果表明,遗传算法搜索到的边坡稳定最小安全系数与理论解是一致的。     

SWARM INTELLIGENCE: APPLICATION OF THE ANT COLONY OPTIMIZATION ALGORITHM TO LOGISTICS‐ORIENTED VEHICLE ROUTING PROBLEMS

This research evaluates a set of logistics‐oriented vehicle routing problems (VRP) taken from the logistics and supply chain literature under the widely used Clark‐Wright Savings algorithm and the newer metaheuristic method employing a type of swarm intelligence called Ant Colony Optimization (ACO). ACO simulates the decision‐making processes of colonies of ants as they forage for food and is related to other artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Experimentation shows that ACO is successful in finding solutions near the best‐known solutions for problems with up to 20 demand locations. In addition, testing for the affect of spatial patterns suggested by the logistics literature for facility locations appears to make a difference in the quality of the solutions for the two algorithms. Finally, ACO is shown to be superior to the savings algorithm found in software packages and as a result should be tested on even larger, more complex logistics‐oriented vehicle routing problems, representative of those encountered in larger industrial and retail settings.     

APPLYING A GENETIC ALGORITHM TO INTERNATIONAL DIVERSIFICATION OF EQUITY PORTFOLIOS: A SOUTH AFRICAN INVESTOR PERSPECTIVE

This paper uses the genetic algorithm (GA) approach to generate a portfolio optimisation scenario of a South African investor who seeks to maximise return from investing in S&P500, FTSE100, NASDAQ, DOWJONES, CAC40 and the DAX from January 1, 2005, to January 31, 2008, but facing exchange rate risk. The GA searches for the optimal solution in the entire set of financial constraints without looking for partial derivatives of the utility function. Whereas most financial problems require a non‐linear and time‐varying model, the GA, with its survival principle of offspring chromosomes, is better suited to this type of problem than local optimisation methods. The performance of the GA is compared with two non‐linear models, namely the quadratic mean‐variance (QMV), which maximises the portfolio mean‐variance, and the quadratic variance minimisation (QVM), which minimises the portfolio variance. The results show that neither the QMV nor the QVM takes into account the domestic investors' risk attitude towards investing in foreign equities and therefore does not provide any international diversification benefits. In addition, the bootstrapping scenario of 10,000 simulations reveals that neither the QMV nor the QVM outperforms the GA in terms of Sharpe ratio and flexibility in dealing with investors' risk attitude towards investing in foreign equities denominated in foreign currencies.     

How effective are heuristic solutions for electricity planning in developing countries

Heuristic algorithms have been widely used to provide computationally feasible means of exploring the cost effective balance between grid versus off grid sources for universal electrification in developing countries. By definition in such algorithms however, global optimality is not guaranteed. We present a computationally intensive but globally optimal mixed integer non-linear programming (MINLP) model for electricity planning and use it in a Monte Carlo simulation procedure to test the relative performance of a widely used heuristic algorithm due to [28]. We show that the overall difference in cost is typically small suggesting that the heuristic algorithm is generally cost effective in many situations. However we find that the relative performance of the heuristic algorithm deteriorates with increasing degree of spatial dispersion of unelectrified settlements, as well as increasing spatial remoteness of the settlements from the grid network, suggesting that the effectiveness of the heuristic algorithm is context specific. Further, we find that allocation of off grid sources in the heuristic algorithm solution is often significantly greater than in the MINLP model suggesting that heuristic methods can overstate the role of off-grid solutions in certain situations.     

Mark-Choose-Cut Algorithms For Fair And Strongly Fair Division

This paper proposes a new criterion to evaluate algorithms for cake division, by the number of resulting pieces. Then, inspired by the idea of "cut-and-choose", we present "mark-choose-cut" algorithms for fair and strongly fair cake division problems. They are game-theoretic algorithms. The number of resulting pieces is bounded by 2 × 3n-2 + 1 and 4 × 3n-2 + 1, for fair and strongly fair division respectively.     

从文化要素的设计视角谈 旅行社专线产品同质化现象的解决之道

此文从文化要素的角度重新审视了旅行社专线产品,在分析了旅行社专线产品的同质化现状和文化要素的内涵后,明确了注入文化要素在旅行社专线产品设计中的重要性,并面向实践针对性地提出了产品设计中文化要素的选择策略。     

一种实用的域内标记算法及其公平性

本文从6个方面对WTO有关协定的英文文本的语言特点作了分析,结 合翻译实践经验,对其翻译提出些建设性的意见。