基于泊位偏好与岸桥干扰的泊位和岸桥分配

泊位和岸桥是集装箱港口资源中最紧缺的资源,合理的泊位分配和岸桥调度可以提高集装箱港口的资源利用率和港口的运作效率和效益。针对泊位偏离和岸桥工作损失两个因素,文章建立了集装箱港口泊位和岸桥的混合整数线性规划模型;运用采集自宁波某典型集装箱港口的数据,用Gurobi优化软件和两阶段启发式算法对模型进行了求解;对计算结果进行了经济性分析。计算结果表明:该港口的岸线资源利用率为46%时,1000m~1600m基本没被利用;18台岸桥要比16台岸桥的目标值更优,求解时间更短,而且18台岸桥的平均利用率为80%,为此,建议该港口再增加两台岸桥。同时发现:随着船舶规模的增加,Gurobi优化求解的时间增长较快,而两阶段启发式算法仍能在很短时间内求得准优解。     

需要安装时间的平行多功能机排序问题的启发式算法

考虑需要安装时间的平行多功能机排序问题。在该模型中,每个工件对应机器集合的一个子集,其只能在这个子集中的任一台机器上加工,称这个子集为该工件的加工集合;工件分组,同组工件具有相同的加工时间和加工集合,不同组中的工件在同一台机器上连续加工需要安装时间,目标函数为极小化最大完工时间。对该问题NP-难的一般情况设计启发式算法:首先按照特定的规则将所有工件组都整组地安排到各台机器上,然后通过在各机器间转移工件不断改进当前最大完工时间。通过与下界的比较检验算法的性能,大量的计算实验表明,算法是实用而有效的。     

基于资源约束的突发事件应急救援鲁棒性调度优化

突发事件应急救援的调度优化,对于救援活动的顺利实施及应急资源的有效使用具有至关重要的作用。本文研究资源约束下的突发事件应急救援鲁棒性调度优化问题,其中,鲁棒性定义为各活动的时间缓冲与其权重系数乘积的总和,目标是在资源可用量及救援期限的约束下,安排活动开始时间和执行模式以最大化应急救援计划的鲁棒性。作者构建了问题的0-1规划优化模型,针对其NP-hard属性,基于问题特征设计双环路禁忌搜索启发式算法。通过对一个算例的计算分析,得到如下结论:给定网络结构及时间参数,利用权重系数的定义可以将时间缓冲分配到重要活动上,由此提高应急救援计划的鲁棒性;随着资源可用量的增加,计划的鲁棒性呈上升趋势,而当救援期限延长时,计划的鲁棒性单调增加。本文研究可为突发事件应急救援基准计划的制定提供决策支持。     

三维多箱异构货物装载优化及其可视化

针对七种现实约束的集装箱三维多箱异构货物装载优化问题,提出了一种基于 “块”和“空间”的启发式搜索算法。算法采用树搜索策略,根据可用空间,对每一次搜索的货物块进行评估,得到最佳的货物块,直到无可用空间或无可装载的货物为止。基于开放式标准测试数据的计算结果表明,该算法在时间效率和体积利用率上均优于已有的同类研究。并基于Net平台开发了一款3D装箱布局优化可视化软件,已在相关物流企业中得到推广应用,验证了算法的实用性。     

基于不同奖惩机制项目支付进度优化:双重视角

奖惩机制会对合同双方的收益产生重大影响,本文基于承包商和业主的双重视角,对不同奖惩机制下项目支付进度优化问题进行了研究。首先对所研究问题进行界定,并分别基于承包商和业主视角构建了不同奖惩机制下的优化模型;基于模型的属性设计了模拟退火启发式算法;最后通过一个实例对比了承包商和业主在四种不同奖惩机制下收益的优化结果,并对其中的关键参数进行了敏感性分析。结果显示:不同的奖惩机制对承包商和业主的收益有较大影响;不同的奖惩强度也会影响承包商和业主的收益。通过对奖惩机制类型及强度的分析,可以为项目中奖惩机制的设置提供定量化决策支持。     

Constraint Propagation in Graph Coloring

In this paper we propose a method for integrating constraint propagation algorithms into an optimization procedure for vertex coloring with the goal of finding improved lower bounds. The key point we address is how to get instances of Constraint Satisfaction Problems (CSPs) from a graph coloring problem in order to give rise to new lower bounds outperforming the maximum clique bound. More precisely, the algorithms presented have the common goal of finding CSPs in the graph for which infeasibility can be proven. This is achieved by means of constraint propagation techniques which allow the algorithms to eliminate inconsistencies in the CSPs by updating domains dynamically and rendering such infeasibilities explicit. At the end of this process we use the largest CSP for which it has not been possible to prove infeasibility as an input for an algorithm which enlarges such CSP to get a feasible coloring. We experimented with a set of middle-high density graphs with quite a large difference between the maximum clique and the chromatic number.     

A Hybrid Algorithm for the Non-Guillotine Cutting Problem

In this article, a meta-heuristic method to solve the non-guillotine cutting stock problem is proposed. The method is based on a combination between the basic principles of the constructive and evolutive methods. With an adequate management of the parameters involved, the method allows regulation of the solution quality to computational effort relationship. This method is applied to a particular case of cutting problems, with which the computational behaviors is evaluated. In fact, 1000 instances of the problem have been classified according to their combinatorial degree and then the efficiency and robustness of the method have been tested. The final results conclude that the proposed method generates an average error close to 2.18% with respect to optimal solutions. It has also been verified that the method yields solutions for all of the instances examined; something that has not been achieved with an exact constructive method, which was also implemented. Comparison of the running times demonstrates the superiority of the proposed method as compared with the exact method.     

A scatter-search-based learning algorithm for neural network training

In this article, we propose a new scatter-search-based learning algorithm to train feed-forward neural networks. The algorithm also incorporates elements of tabu search. We describe the elements of the new approach and test the new learning algorithm on a series of classification problems. The test results demonstrate that the algorithm is significantly superior to several implementations of back-propagation.     

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

The reliability-redundancy allocation problem is an optimization problem that achieves better system reliability by determining levels of component redundancies and reliabilities simultaneously. The problem is classified with the hardest problems in the reliability optimization field because the decision variables are mixed-integer and the system reliability function is nonlinear, non-separable, and non-convex. Thus, iterative heuristics are highly recommended for solving the problem due to their reasonable solution quality and relatively short computation time. At present, most iterative heuristics use sensitivity factors to select an appropriate variable which significantly improves the system reliability. The sensitivity factor represents the impact amount of each variable to the system reliability at a designated iteration. However, these heuristics are inefficient in terms of solution quality and computation time because the sensitivity factor calculations are performed only at integer variables. It results in degradation of the exploration and growth in the number of subsequent continuous nonlinear programming (NLP) subproblems. To overcome the drawbacks of existing iterative heuristics, we propose a new scaling method based on the multi-path iterative heuristics introduced by Ha (2004). The scaling method is able to compute sensitivity factors for all decision variables and results in a decreased number of NLP subproblems. In addition, the approximation heuristic for NLP subproblems helps to avoid redundant computation of NLP subproblems caused by outlined solution candidates. Numerical experimental results show that the proposed heuristic is superior to the best existing heuristic in terms of solution quality and computation time.     

Using Lagrangian dual information to generate degree constrained spanning trees

In this paper, a Lagrangian-based heuristic is proposed for the degree constrained minimum spanning tree problem. The heuristic uses Lagrangian relaxation information to guide the construction of feasible solutions to the problem. The scheme operates, within a Lagrangian relaxation framework, with calls to a greedy construction heuristic, followed by a heuristic improvement procedure. A look ahead infeasibility prevention mechanism, introduced into the greedy heuristic, allowed us to solve instances of the problem where some of the vertices are restricted to having degrees 1 or 2. Furthermore, in order to cut down on CPU time, a restricted version of the original problem is formulated and used to generate feasible solutions. Extensive computational experiments were conducted and indicate that the proposed heuristic is competitive with the best heuristics and metaheuristics in the literature.