基于有穷损害优先法求解组合拍卖竞胜标问题研究

迄今为止,组合拍卖竞胜标问题并不存在一个多项式时间复杂度的算法,其计算复杂性与拍卖效率之间的矛盾一直是影响组合拍卖广泛应用的主要障碍。它是一个NP难问题,也是组合拍卖机制设计中的难题之一。而有穷损害优先方法是纯粹递归论中的一个十分重要的现代方法,特别对NP难问题求解算法的设计,对研究依复杂度决定的偏序结构的构造是一个很基本的有用工具。因此,本文提出根据组合拍卖的内在特性,将各不同的拍卖商品按照拍卖机制的要求,并结合其自身的协同价值等因素,设定一个优先序,然后采用有穷损害优先法有效有序地解决。     

用嵌套插队算法解决TSP问题

本提出了一种求解TSP问题的近似算法—嵌套插队算法。这种算法结合了启发式算法和随机化算法以及局部寻优的思想。实验结果表明对于较小规模的。TSP问题，直接用插队算法(QJA)就能以很大的概率获得已知最优解。对于规模较大的问题实例。嵌套插队算法(NQJA)能获得质量高于名的启发式算法的解。另外，用嵌套插队算法找到的China144的最短路径优于目前已知的最短路径。嵌套插队算法是专门针对TSP问题而提出的，但其思想也可以给求解其他NP难解的组合优化问题以启发。     

离散变量结构优化设计的组合算法*

本文首先给出了离散变量优化设计局部最优解的定义,然后提出了一种综合的组合算法.该算法采用分级优化的方法,第一级优化首先采用计算效率很高且经过随机抽样性能实验表明性能较高的启发式算法─—相对差商法,求解离散变量结构优化设计问题近似最优解 X ;第二级采用组合算法,在 X 的离散邻集内建立离散变量结构优化设计问题的(-1,0.1)规划模型,再进一步将其化为(0,1)规划模型,应用定界组合算法或相对差商法求解该(0,1)规划模型,求得局部最优解.解决了采用启发式算法无法判断近似最优解是否为局部最优解这一长期未得到解决的问题,提高了计算精度,同时,由于相对差商法的高效率与高精度,以上综合的组合算法的计算效率也还是较高的.     

有时间窗装卸问题的多策略分组编码遗传算法

根据有时间窗装卸问题(PDPTW)的数学模型,设计了多策略分组编码遗传算法,将禁忌思想用于产生可行解的启发式插入算法之中,对计算实例进行了求解,结果表明,此算法可以有效求得有时间窗装卸问题的近似最优解.     

共享单车再平衡问题及其容差插入启发式算法

共享单车再平衡问题是一类NP-难问题,已有启发式求解算法随着问题规模扩大求解速度显著变慢。本文先讨论了该问题的线路可行变换性质,推导证明了插入构造可行解时,被插入位置允许插入客户点的容量区间。在此基础上,提出容差概念,设计了容差插入启发式算法,对该算法应用标准算例测试表明,算法速度快,参数设置简单;算法找到11个测试算例的当前最好解,其中1个为新的当前最好解;算法求解大容量问题的质量优于中、小容量问题。     

充电站选址问题的降阶回溯算法

电动汽车的充电站选址问题是当前社会的热点问题,其实质是组合优化中经典的NP-难问题.文章首先研究了该问题良好的数学性质并给予相应的证明,其中包括可以批量确定某些设施一定开设或一定不开设的性质,利用这些性质降低问题的规模,从而降低问题的求解难度;然后设计了上界子算法,下界子算法,分配子算法以及降阶子算法,基于这些子算法提出了一种可以快速缩小问题规模同时得到最优解的降阶回溯算法;最后通过分析和求解一个示例来进一步阐述文章算法的原理和执行过程,结果表明所提出的算法能够有效地降低时间复杂度.     

求解基于专用道设置的动态交通规划问题的启发式算法

基于专用道设置的策略,该文提出了一个新的动态交通规划问题。大型运动会要求主办方在规定时间内将指定人员从运动员村运送到指定地点。该问题便是源自2010年广州亚运会的交通需求。其要求在保证30分钟内将运动员从运动员村运送到指定场馆的条件下,最小化设置专用通道的总成本。由于该问题的规模较大,本文提出了三种启发式算法用以求解已提出的线性整数规划模型。计算结果表明,通过该文提出的启发式算法得到的解与相对应的采用数学规划软件Lingo8．0得到的解之间的平均误差均小于1．89％。同时,启发式算法的计算时间远小于Lingo8．0所需的计算时间。     

基于改进遗传算法的集合覆盖问题

集合覆盖问题是组合优化中的典型问题,在日常生活中有着广泛的应用.提出了一种改进遗传算法来解决集合覆盖问题.算法对标准遗传算法的改进主要表现在:1)结合启发式算法和随机生成,设计了新的产生初始种群的方法;2)引入修补操作处理不可行解使其转换成可行解;3)对重复个体进行处理再利用;4)对多点交叉进行推广,提出了新的交叉算子;5)针对可行解和不可行解,采取两种自适应多位变异操作.数值实验结果表明该算法对于解决规模较大的集合覆盖问题是有效的.     

一种改进的蚁群算法及其在TSP中的应用

蚁群算法是一种求解复杂组合优化问题的新的拟生态算法,也是一种基于种群的启发式仿生进化算法,属于随机搜索算法的一种,并用于较好地解决TSP问题.然而此算法也有它自己的缺陷,如易于陷入局部优化、搜索时间长等.通过对基本蚁群算法的介绍及相关因素的分析,提出了一种改进的蚁群算法,用于解决TSPLAB问题的10个问题,并与参考文献中的F-W、NCSOM、ASOM算法进行比较,计算机仿真结果表明了改进算法的有效性.如利用改进的蚁群算法解决lin105问题,其最优解为14382.995933(已知最优解为14379),相对误差是0.0209%,计算出的最小值几乎接近于已知最优解.     

考虑多行程与同时取送货的电动车路径问题研究

针对城市物流配送中的电动车辆路径优化问题,考虑电动汽车的充电特性以及车辆多行程和需求点的双向货流,以最小化车辆成本、行驶成本和充电成本为目标,建立考虑多行程与同时取送货的电动车辆路径问题(EVRPMTSPD)模型,并采用列生成算法进行求解.为提高子问题求解速度,提出了基于蚁群算法的启发式寻路算法用以处理较大规模问题,数值实验验证了模型与算法的有效性,表明了考虑多行程和同时取送货能有效降低成本和提高效率.     

An efficient local search algorithm for the winner determination problem

Combinatorial auction, which allows bidders to bid on combinations of items, is an important type of market mechanism. The winner determination problem (WDP) has extensive applications in combinatorial auctions, and attracts more and more attention due to its strong relevance to business. However, this problem is intractable in theory as it has been proven to be NP-hard, and is also a challenging combinatorial optimization problem in practice. This paper is devoted to designing an efficient heuristic algorithm for solving the WDP. This proposed heuristic algorithm dubbed abcWDP is based on an effective yet simple local search framework, and equipped with three novel strategies, i.e., configuration checking, free-bid exploiting, and pseudo-tie mechanism. Extensive computational experiments on a broad range of benchmarks demonstrate that abcWDP performs much better than state-of-the-art algorithms and CPLEX in terms of both revenue and running time. More encouragingly, our abcWDP algorithm as a sequential algorithm even achieves better computational results than the multi-thread implemented algorithm \(\hbox {CA}_\mathrm{RA}\), which confirms its efficiency.     

On Heuristics for Solving Winner Determination Problem in Combinatorial Auctions

The winner determination problem (WDP) in combinatorial auctions is the problem of, given a finite set of combinatorial bids B, finding a feasible subset B of B with a maximum revenue. WDP is known to be equivalent to the maximum weight set packing problem, and hard to approximate by polynomial time algorithms. This paper proposes three heuristic bid ordering schemes for solving WDP; the first two schemes take into account the number of goods shared by conflicting bids, and the third one is based on a recursive application of such local heuristic functions. We conducted several experiments to evaluate the goodness of the proposed schemes. The result of experiments implies that the first two schemes are particularly effective to improve the performance of the resulting heuristic search procedures. More concretely, they are scalable compared with the conventional linear programming (LP) relaxation based schemes, and could quickly provide an optimum solution under optimization schemes such as the branch-and-bound method. In addition, they exhibit a good anytime performance competitive to the LP-based schemes, although it is sensitive to configurable parameters controlling the strength of contributions of bid conflicts to the resultant bid ordering schemes.     

军事物流系统中定位-运输路线安排模型及算法

考虑到战时物资需求的紧迫性和保障资源的有限性,从决策者的角度出发,以军事物流系统总体供应时间最短为目标,构建了两级军事配送网络的定位-运输路线安排模型,并给出一种启发式算法.算法分为两个阶段,首先利用蚁群算法和线性规划的方法解决运输路线安排问题,然后运用贪婪搜索算法解决军事物流配送中心选址问题.最终,将两种算法结合起来进行逐步搜索,从而得到模型的解,并运用实例说明了算法的有效性和可行性.     

建筑废料现场分拣协调调度优化研究

工程项目施工现场废料分拣的效率对施工进度具有重要的影响。通过分析现场废料分拣实施过程,建立了带有限中间缓冲的混合流水车间调度模型。提出了收集阶段作业排序的动态自适应算法和后续设备分配问题的考虑缓冲区的设备分配规则,在此基础上设计了废料分拣模型的启发式算法,平衡各分区工作量,进一步搜索最优解,并推导了问题的一个低界。实验结果表明,所提出算法能很好地对施工现场废料分拣问题进行求解,具有良好的收敛性和较高的时间效率。     

A penalty-based heuristic algorithm for the permutation flowshop scheduling problem with sequence-dependent set-up times

This paper considers the permutation flowshop scheduling problem with sequence-dependent set-up times and develops a penalty-based heuristic algorithm to find an approximately minimum makespan schedule. The proposed algorithm determines the penalty in time associated with a particular sequence and selects the sequence with the minimum time penalty as the best heuristic solution. Computational results comparing the effectiveness and efficiency of the proposed penalty-based heuristic algorithm with an existing savings index heuristic algorithm are reported and discussed.     

产品回收逆向物流网络设计问题的两阶段启发式算法

针对产品回收逆向物流网络设计问题,设计了一种嵌套了模拟退火算法的两阶段启发式算法。第一阶段确定回收点的选址-分配-存储的联合决策;第二阶段确定回收中心的选址-运输的联合决策,两个阶段相互迭代,从而实现最优解的搜索。通过与遗传算法比较,证明了两阶段启发式算法是一种有效的算法。     

An optimization approach for winner determination problem considering transportation cost discounts

Journal of Global Optimization - This study proposes a mixed-integer nonconvex programming (MINP) model for the winner determination problem (WDP) considering two discount functions in a...     

Learning heuristics for basic block instruction scheduling

Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to find a minimum length schedule for a basic block—a straight-line sequence of code with a single entry point and a single exit point—subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic. The heuristic is usually hand-crafted, a potentially time-consuming process. In contrast, we present a study on automatically learning good heuristics using techniques from machine learning. In our study, a recently proposed optimal basic block scheduler was used to generate the machine learning training data. A decision tree learning algorithm was then used to induce a simple heuristic from the training data. The automatically constructed decision tree heuristic was compared against a popular critical-path heuristic on the SPEC 2000 benchmarks. On this benchmark suite, the decision tree heuristic reduced the number of basic blocks that were not optimally scheduled by up to 55% compared to the critical-path heuristic, and gave improved performance guarantees in terms of the worst-case factor from optimality.     

A Savings Index Heuristic Algorithm for Flowshop Scheduling with Sequence Dependent Set-up Times

This paper considers the permutation flowshop scheduling problem with significant sequence dependent set-up times and develops a savings index heuristic algorithm to find an approximately minimum makespan schedule. The proposed algorithm determines the savings in time associated with a particular sequence and selects the sequence with the maximum time savings as the best heuristic solution. Computational experience indicating the effectiveness and efficiency of the proposed savings index heuristic algorithm are reported and discussed.     

A new bidirectional search algorithm with shortened postprocessing

For finding a shortest path in a network bidirectional A^∗ is a widely known algorithm. This algorithm distinguishes between the main phase and the postprocessing phase. The version of bidirectional A^∗ that is considered the most appropriate in literature hitherto, uses a so-called balanced heuristic estimate. This type of heuristic is chosen, as it accounts for a short postprocessing phase. In this paper, we do not restrict ourselves any longer to balanced heuristics. First, we introduce an algorithm containing a new method for the postprocessing phase, reducing this phase considerably for non-balanced heuristics. For a balanced heuristic the new algorithm is nearly equivalent to the existing versions of bidirectional A^∗. An obvious choice for a non-balanced heuristic turns out to be superior in terms of storage space and computation time. Second, we show that the main phase on its own, when using this non-balanced heuristic estimate, is a useful algorithm, which provides us quickly with a feasible approximation.