一类多商品设施选址问题的基于线性松弛解的启发式方法

多商品设施选址问题是众多设施选址问题中一类重要而困难的问题.在这一问题中,顾客的需求可能包含不止一种商品.对于大规模问题,成熟的商业求解器往往不能在满意的时间内找到高质量的可行解.研究了无容量限制的单货源多商品设施选址问题的一般形式,并给出了应用于此类问题的两个启发式方法.这两个方法基于原选址问题的线性规划松弛问题的最优解,分别通过求解紧问题和邻域搜索的方式给出了原问题的一个可行上界.理论分析指出所提方法可以实施于任意可行问题的实例.数值结果表明所提方法可以显著地提高求解器求解此类设施选址问题的求解效率.     

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。     

带准备时间的单机指数时间学习效应排序问题

研究带有准备时间的单机学习效应模型,其中工件加工时间具有指数时间学习效应,即工件的实际加工时间是已经排好的工件加工时间的指数函数。学习效应模型考虑工件的实际加工时间同时依赖于工件本身的加工时间和已加工工件的累计加工时间,目标函数为最小化总完工时间。这个问题是NP-难的,提出了一个数学规划模型来求解该问题的最优解。通过分析几个优势性质和下界,提出分支定界算法来求解此问题,并设计启发式算法改进分支定界算法的上界值。通过仿真实验验证了分支定界算法在求解质量和时间方面的有效性。     

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.     

基于资源受限广义指派问题的分解启发式算法

资源受限广义指派问题(RGAP)是NP-难的,对RGAP问题给出一个分解启发式算法.通过分解目标函数及约束条件,把原问题分解成子问题的集合,并设计分解启发式算法找到该问题的满意解.最后,通过算例说明算法的有效性.     

不可分离凸背包问题的拉格朗日分解和区域分割方法

本文对线性约束不可分离凸背包问题给出了一种精确算法．该算法是拉格朗日分解和区域分割结合起来的一种分枝定界算法．利用拉格朗日分解方法可以得到每个子问题的一个可行解，一个不可行解，一个下界和一个上界．区域分割可以把一个整数箱子分割成几个互不相交的整数子箱子的并集，每个整数子箱子对应一个子问题．通过区域分割可以逐步减小对偶间隙并最终经过有限步迭代找到原问题的最优解．数值结果表明该算法对不可分离凸背包问题是有效的．     

软容量约束带随机需求的设施选址问题的近似算法

文考虑了软容量约束带随机需求的设施选址问题,根据此问题构造出一个无容量约束带随机需求的设施选址问题,通过求解无容量约束情形给出软容量情形的一个可行解,分析出近似比为6。     

考虑应急设施中断风险与防御的可靠性选址模型研究

为提高应急设施运行的可靠性和抵御中断风险的能力, 研究中断情境下的应急设施选址-分配决策问题。扩展传统无容量限制的固定费用选址模型, 从抵御设施中断的视角和提高服务质量的视角建立选址布局网络的双目标优化模型, 以应急设施的建立成本和抵御设施中断的加固成本最小为目标, 以最大化覆盖服务质量水平为目标, 在加固预算有限及最大最小容量限制约束下, 构建中断情境下应急设施的可靠性选址决策优化模型。针对所构建模型的特性利用非支配排序多目标遗传算法(NSGA-Ⅱ)求解该模型, 得到多目标的Pareto前沿解集。以不同的算例分析和验证模型和算法的可行性。在获得Pareto前沿的同时对不同中断概率进行灵敏度分析, 给出Pareto最优解集的分布及应急设施选址布局网络的拓扑结构。     

带学习效应的两台平行机时间表长问题

研究机器带学习效应, 目标函数为时间表长的两台平行机排序问题, 问题是NP-难的. 首先建立了求解该问题最优解的整数规划模型. 其次, 基于模拟退火算法给出了该问题的近似算法SA, 并证明了该算法依概率1 全局收敛到最优解. 最后, 通过数值模拟对所提出的算法进行了性能分析. 数值模拟结果表明, 近似算法SA可以达到最优值的99%, 准确度高, 算法较有效.     

一类半定二次规划逆问题

本文求解了一类半定二次规划的逆问题.具体可描述为在保证一个可行的解是原半定二次规划问题的最优解的前提下,使目标函数中的参数以及约束条件中右端项参数与它们的估计值的距离最小.我们将该逆问题转换为具有线性约束和半正定锥互补约束的问题.再利用对偶理论,又将上述问题转化成只有半正定锥互补约束的问题,但此时也是一个难问题,通过引入一个非光滑的惩罚函数来惩罚互补约束,进而将原问题转化为一个DC问题.再采用序列凸规划方法来求解它,同时给出惩罚方法以及序列凸规划方法的收敛性分析.最后的数值实验表明我们采用的方法对于本文提出的问题求解还是非常有效的.     

A Lagrangian heuristic for concave cost facility location problems: the plant location and technology acquisition problem

We propose a Lagrangian heuristic for facility location problems with concave cost functions and apply it to solve the plant location and technology acquisition problem. The problem is decomposed into a mixed integer subproblem and a set of trivial single-variable concave minimization subproblems. We are able to give a closed-form expression for the optimal Lagrangian multipliers such that the Lagrangian bound is obtained in a single iteration. Since the solution of the first subproblem is feasible to the original problem, a feasible solution and an upper bound are readily available. The Lagrangian heuristic can be embedded in a branch-and-bound scheme to close the optimality gap. Computational results show that the approach is capable of reaching high quality solutions efficiently. The proposed approach can be tailored to solve many concave-cost facility location problems.     

Heuristic algorithms for the inverse mixed integer linear programming problem

The recent development in inverse optimization, in particular the extension from the inverse linear programming problem to the inverse mixed integer linear programming problem (InvMILP), provides more powerful modeling tools but also presents more challenges to the design of efficient solution techniques. The only reported InvMILP algorithm, referred to as Alg^InvMILP, although finitely converging to global optimality, suffers two limitations that greatly restrict its applicability: it is time consuming and does not generate a feasible solution except for the optimal one. This paper presents heuristic algorithms that are designed to be implemented and executed in parallel with Alg^InvMILP in order to alleviate and overcome its two limitations. Computational experiments show that implementing the heuristic algorithm on one auxiliary processor in parallel with Alg^InvMILP on the main processor significantly improves its computational efficiency, in addition to providing a series of improving feasible upper bound solutions. The additional speedup of parallel implementation on two or more auxiliary processors appears to be incremental, but the upper bound can be improved much faster.     

Vehicle routing with split deliveries

This paper considers a relaxation of the classical vehicle routing problem (VRP), in which split deliveries are allowed. As the classical VRP, this problem is NP-hard, but nonetheless it seems more difficult to solve exactly. It is first formulated as an integer linear program. Several new classes of valid constraints are derived, and a hierarchy between these is established. A constraint relaxation branch and bound algorithm for the problem is then described. Computational results indicate that by using an appropriate combination of constraints, the gap between the lower and upper bounds at the root of the search tree can be reduced considerably. These results also confirm the quality of a previously published heuristic for this problem.     

Telecommunication Link Restoration Planning with Multiple Facility Types

To ensure uninterrupted service, telecommunication networks contain excess (spare) capacity for rerouting (restoring) traffic in the event of a link failure. We study the NP-hard capacity planning problem of economically installing spare capacity on a network to permit link restoration of steady-state traffic. We present a planning model that incorporates multiple facility types, and develop optimization-based heuristic solution methods based on solving a linear programming relaxation and minimum cost network flow subproblems. We establish bounds on the performance of the algorithms, and discuss problem instances that nearly achieve these worst-case bounds. In tests on three real-world problems and numerous randomly-generated problems containing up to 50 nodes and 150 edges, the heuristics provide good solutions (often within 0.5% of optimality) to problems with single facility type, in equivalent or less time than methods from the literature. For multi-facility problems, the gap between our heuristic solution values and the linear programming bounds are larger. However, for small graphs, we show that the optimal linear programming value does not provide a tight bound on the optimal integer value, and our heuristic solutions are closer to optimality than implied by the gaps.     

A Lagrangian relaxation approach to large-scale flow interception problems

The paper presents a tight Lagrangian bound and an efficient dual heuristic for the flow interception problem. The proposed Lagrangian relaxation decomposes the problem into two subproblems that are easy to solve. Information from one of the subproblems is used within a dual heuristic to construct feasible solutions and is used to generate valid cuts that strengthen the relaxation. Both the heuristic and the relaxation are integrated into a cutting plane method where the Lagrangian bound is calculated using a subgradient algorithm. In the course of the algorithm, a valid cut is added and integrated efficiently in the second subproblem and is updated whenever the heuristic solution improves. The algorithm is tested on randomly generated test problems with up to 500 vertices, 12,483 paths, and 43 facilities. The algorithm finds a proven optimal solution in more than 75% of the cases, while the feasible solution is on average within 0.06% from the upper bound.     

The Complexity Analysis of the Inverse Center Location Problem

Given a feasible solution, the inverse optimization problem is to modify some parameters of the original problem as little as possible, and sometimes also with bound restrictions on these adjustments, to make the feasible solution become an optimal solution under the new parameter values. So far it is unknown that for a problem which is solvable in polynomial time, whether its inverse problem is also solvable in polynomial time. In this note we answer this question by considering the inverse center location problem and show that even though the original problem is polynomially solvable, its inverse problem is NP–hard.     

A lower bound for weighted completion time variance

We consider a single machine scheduling problem to minimize the weighted completion time variance. This problem is known to be NP-hard. We propose a heuristic and a lower bound based on job splitting and the Viswanathkumar and Srinivasan procedure. The test on more than 2000 instances shows that this lower bound is very tight and the heuristic yields solutions very close to optimal ones since the gap between the solution given by the heuristic and the lower bound is very small.     

An Optimization Problem with a Separable Non-Convex Objective Function and a Linear Constraint

For a class of global optimization (maximization) problems, with a separable non-concave objective function and a linear constraint a computationally efficient heuristic has been developed.The concave relaxation of a global optimization problem is introduced. An algorithm for solving this problem to optimality is presented. The optimal solution of the relaxation problem is shown to provide an upper bound for the optimal value of the objective function of the original global optimization problem. An easily checked sufficient optimality condition is formulated under which the optimal solution of concave relaxation problem is optimal for the corresponding non-concave problem. An heuristic algorithm for solving the considered global optimization problem is developed.The considered global optimization problem models a wide class of optimal distribution of a unidimensional resource over subsystems to provide maximum total output in a multicomponent systems.In the presented computational experiments the developed heuristic algorithm generated solutions, which either met optimality conditions or had objective function values with a negligible deviation from optimality (less than 1/10 of a percent over entire range of problems tested).     

An Improved Lower Bound for the Multimedian Location Problem

We consider the problem of locating, on a network, n new facilities that interact with m existing facilities. In addition, pairs of new facilities interact. This problem, the multimedian location problem on a network, is known to be NP-hard. We give a new integer programming formulation of this problem, and show that its linear programming relaxation provides a lower bound that is superior to the bound provided by a previously published formulation. We also report results of computational testing with both formulations.