震后交通管制下基于用户均衡条件的救灾路径选择

震后如何确定有效的救灾路径是救灾管理部门的一项核心工作。本文综合考虑灾区路网受损以及为避免交通拥挤而实施交通管制情况,进行震后救灾路径选择,以提高救援成效,并尽量减少对非灾民的影响。为此,建立了用户均衡交通量指派条件下以救灾路径旅行时间最短和因交通管制引起的扰民程度最小为目标的多目标救灾路径选择模型,并设计了一种两阶段启发式算例进行求解。最后以Sioux Falls路网为例对模型和算法进行验证,并与NSGA-II算法进行了比较,结果表明:该算法在求解效率上与其他启发式算法相比具有显著的优势。     

回载可分的闭环供应链多配送中心多车辆运输策略

本文从车辆路径的角度研究了具有多个配送中心、多台车辆结合前向物流配送和逆向物流回载的闭环供应链运输策略,考虑回收产品的不同形态和可分批运输的特点,引入库存限制和成本惩罚,建立并分析了问题的数学模型.运用sweep算法把多配送中心转化为单配送中心,引入2σ原则构造了分组的启发式求解方法.算例分析表明该策略的合理有效性.     

双层规划在城市交通污染控制中的一个应用

本文从控制城市交通污染的角度出发,考虑到用户的路径选择行为,采用双层规划模型描述城市交通管理与污染控制问题,并给出了相应的启发式求解算法及算例,在此基础上,提出了城市交通管理与污染控制框架。     

时间依赖型多配送中心带时间窗的开放式车辆路径问题研究

本文研究了时间依赖型多配送中心带时间窗的开放式车辆路径问题,基于道路通行情况,建立车辆行驶速度时间函数;考虑车辆载重、里程限制与客户点服务时间窗的约束,建立混合整数规划模型,目标函数为最小化车辆总成本,具体包括车辆行驶时间成本和车辆固定发车成本;提出了一种二维编码方式的混合遗传算法,针对混合遗传算法设计了多分区段单点交叉策略、动态插入算子及局部搜索策略;最后,基于Solomon VRPTW基准数据集生成27个测试算例,使用混合遗传算法进行求解。数值实验结果表明,考虑道路通行情况,选择合理的出发时间,避免拥堵时段进行配送服务,能明显减少车辆的总行驶时间,且验证了混合遗传算法能够获得问题的满意解。     

震后应急物资多方式供应的模糊动态LRP

研究震后应急物资多方式供应中的多层次设施定位-路线规划问题(LRP),综合考虑应急物流网络中的多周期应急物资模糊需求、时间窗限制、部分路网损毁与动态恢复、车辆随机行驶时间、大需求点采用需求分割策略同时进行运输与配送等特性,以应急物资总供应时间最短为目标,构建了一个震后应急物资多方式供应的多周期模糊LRP优化模型,并根据模型特点设计了一种贪婪算法结合蚁群算法的混合启发式算法予以求解.最后,通过算例验证了本文模型和算法的可行性与有效性.     

一类非线性 TSP 模型及其应用

本文针对海上航标抢修调度问题建立相应的非线性TSP模型,并将其转换成标准的0—1整数规划形式。对大型实际问题给出了一种启发式算法,能进行快速的近似求解。     

考虑差异化服务时间的多车型电动汽车路径优化与充电策略研究

在政府政策大力支持以及社会环境意识不断增长的背景下,电动汽车在物流配送行业快速普及。电动汽车参与的物流配送服务需要物流专员、电动汽车和顾客三方协作完成。因此,在传统车辆配送路径优化的基础上,车辆的多样性、充电策略、人车的匹配以及服务时间差异化等因素都会影响物流运营成本。本文提出了考虑差异化服务成本的多车型电动汽车路径优化与充电策略问题并建立了该问题的整数规划数学模型。其次,提出了混合启发式算法MCWGATS,并通过多组算例验证了算法的有效性。最后,采用多组算例分析了多车型和差异化服务时间对运营成本的影响。实验结果表明,该模型有助于物流企业提高人员、物流车辆、服务时间等资源的利用效率,降低运营成本。     

基于出行时间可靠性的交通配流问题

提出一类由需求随机性所导致的基于出行时间可靠性的交通配流问题.由于每一天交通需求的随机变化,出行者的出行时间不是确定的,而是随机变量.假设出行者在过去经验的基础上能够得知出行时间的随机分布,提出一类新准则去刻画出行者在出行时间不确定情况下的路径选择行为.这种准则可以表示为一种以路径流量为变量的变分不等式模型.对于这类新的模型,给出了解的存在性证明,并且引入一个启发式的算法去求解该问题.数值算例展示了模型在应用上的特性和算法的有效性.     

震后应急物资配送的模糊动态定位—路径问题

进行震后应急物资配送系统优化是提高其配送绩效的重要手段.从系统集成优化的角度,研究应急物资配送中心定位与配送车辆路径安排的联合决策问题.综合考虑应急物资需求的模糊性、动态性和限制期,震后受损路网的动态恢复状况,不同类型有容量限制的配送车辆,以及物资需求分割配送等特点,以各物资需求点的应急物资运达时间之和最小为目标,采用机会约束规划方法建立了一个模糊动态定位—路径问题优化模型,并设计了一种两阶段启发式算法予以求解.最后,通过算例验证了该模型和算法的可行性及有效性.     

求解大规模CVRP问题的快速贪婪算法

为求解大规模具有能力约束的车辆路径问题(Capacitated Vehicle Routing Problem,CVRP),提出了一种快速改进贪婪算法CVRP-IMGR。基于贪婪算法思想设计了求解CVRP问题的贪婪算法CVRP-GR,在此基础上进一步采用K-d tree法和Held Karp模型改进了CVRP-GR的求解速度和求解质量,从而得到CVRP-IMGR。CVRPIMGR的复杂度可以达到O(nlogn),能够快速求解大规模(顾客数量大于500)CVRP问题。为验证CVRP-IMGR的有效性,分别采用CVRP-GR、CVRP-IMGR和经典构建型算法Savings求解了当前24个最大规模的CVRP算例,结果表明:CVRP-IMGR的求解速度远快于复杂度为O(n2logn)的CVRP-GR和Savings;CVRP-IMGR对所有算例的求解质量优于CVRP-GR,并且对18个算例的求解质量优于Savings。     

A hybrid algorithm based on variable neighbourhood for the strip packing problem

This paper addresses the strip packing problem, which has a wide range of real-world applications. Our proposed algorithm is a hybrid metaheuristic that combines an improved heuristic algorithm with a variable neighbourhood search. Different neighbourhoods are constructed based on the concept of block patterns. The proposed algorithm has three interesting features. First, a least-waste strategy is used to improve the constructive heuristics. Second, a better sorting sequence is selected to generate an initial solution. Finally, different neighbourhoods are constructed based on block patterns. The computational results from a diverse set of problem instances show that the proposed algorithm performs better than algorithms reported in the literature for most of the problem sets compared.     

A quasi-human algorithm for the two dimensional rectangular strip packing problem: in memory of Prof. Wenqi Huang

This paper presents a quasi-human algorithm for the rectangular strip packing problem without guillotine constraint. The basic version of the algorithm works according to seven heuristic selection rules, which are designed to select a corner-occupying action. The strengthened version of the algorithm adopts a branching scheme in which the basic version of the algorithm is applied in a heuristic series of parallel branches. Computational tests carried on eight sets of well-known benchmark instances show that the algorithm is efficient for approximately solving the problem. In comparison with the best algorithms in the literature, the algorithm performs better for zero-waste instances and large scale non-zero-waste instances.     

多车场带时间窗车辆路径问题的变邻域搜索算法

多车场带时间窗车辆路径问题是车辆路径问题集合中的一个极为复杂、且仍未得到较好解决的问题。针对这一问题,建立了它的整数规划数学模型,提出了一种改进型变邻域搜索算法。该算法在初始解的构造阶段采用聚类方法完成客户的分配,运用混合算子进行局部搜索,通过后优化过程增强寻优效果,引入模拟退火模型对新解的接受进行控制。最后,在Cordeau提出的标准用例上对改进型变邻域算法进行了实验,实验结果更新了大部分目前该问题的最优解,并在算法的稳定性和求解时间上体现出一定优势。实验表明,该算法是一种求解多车场带时间窗车辆路径问题的有效方法。     

大规模客运专线网络运营优化模型与求解算法

本文在分析铁路运营优化模型的研究进展的基础上,提出了一个适合大规模客运专线网络运营的优化模型,并提出了求解此模型的列生成算法和启发式快速算法。目的是将客运专线网路的开行方案优化与动态收益优化问题结合起来,解决更大、更复杂的客运网络运营优化问题。模型以列车运营总收益最大化为目标。用随机生成数据进行的模型试验表明,模型及算法可以在较短的时间内求解较大规模的收益管理优化问题。     

Multi-start iterated tabu search for the minimum weight vertex cover problem

The minimum weight vertex cover problem (MWVCP) is one of the most popular combinatorial optimization problems with various real-world applications. Given an undirected graph where each vertex is weighted, the MWVCP is to find a subset of the vertices which cover all edges of the graph and has a minimum total weight of these vertices. In this paper, we propose a multi-start iterated tabu search algorithm (MS-ITS) to tackle MWVCP. By incorporating an effective tabu search method, MS-ITS exhibits several distinguishing features, including a novel neighborhood construction procedure and a fast evaluation strategy. Extensive experiments on the set of public benchmark instances show that the proposed heuristic is very competitive with the state-of-the-art algorithms in the literature.     

Multi-neighborhood based path relinking for two-sided assembly line balancing problem

This paper presents a multi-neighborhood based path relinking algorithm (MN-PR) for solving the two-sided assembly line balancing problem. By incorporating an effective local search into a path relinking framework, the proposed MN-PR algorithm integrates a number of distinguishing features, such as a multi-neighborhood based local search procedure, a dedicated path relinking operator to generate new solutions and a strategy to fix an infeasible solution generated by the path relinking procedure to a feasible one. Our proposed MN-PR algorithm is tested on a set of totally 45 public instances widely used in the literature. Comparisons with other reference algorithms show the efficacy of the proposed algorithm in terms of the solution quality. Particularly, the proposed MN-PR algorithm is able to improve the best upper bounds for one instance with 65 tasks and 326 cycle time. This paper also presents an analysis to show the significance of the main components of the proposed algorithm.     

Decomposition algorithms for the integrated process planning and scheduling problem

There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.     

An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.     

考虑充电策略与电池损耗的电动汽车路径优化问题研究

针对目前研究电动物流车辆路径问题的文章未考虑电池损耗对运营成本的影响,且多在充电速率为恒定值的情况下对充电策略进行优化,本文将电动物流车辆在配送货物途中的充电时间和电池损耗成本纳入目标函数并建立了线性规划数学模型,统筹安排车辆行驶路径和充电策略使得物流企业整体运营成本最低。其次,提出了求解该问题的多阶段启发式算法MCWIGALNS。随后,通过多组算例验证了模型和算法的准确性。实验结果表明,考虑充电时间与深度放电成本的模型可以在配送距离不变或略有增加的情况下,较大幅度减少充电时间与电池损耗成本,到达降低运营成本的目的。最后,将算法实验结果与本领域已发表的成果进行比较,证明了MCWIGALNS算法对车辆路径问题具有出色的求解能力,提升了该问题理论成果的实用性。可以为物流企业电动汽车路径策略提供良好借鉴与帮助。     

Scheduling aerial resource operations for the extinction of large-scale wildfires

The significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.