基于人工蜂群算法的配送中心选址问题求解

采用人工蜂群算法对配送中心选址问题进行求解,给出食物源的编码方法,通过整数规范化,使算法能在整数空间内对问题进行求解.应用算法进行了仿真实验,并将结果与其它一些启发式算法进行了比较和分析.计算结果表明人工蜂群算法可以有效求解配送中心选址问题,同时也为算法求解其它一些组合优化问题提供了有益思路.     

乘用车物流运输计划问题的建模研究

首先针对不同类型、数量乘用车的物流运输问题,构建整数线性规划模型,并对模型进行逐层优化求解,通过MATLAB编写通用程序实现计算;在此基础之上,为解决不同目的地的运输要求,采用启发式逐层优化算法进行求解;最后考虑多因素的实际问题,建立分层划分模型,提出构造型分层划分启发式算法求解.计算表明,所建模型计算结果良好,实现了对乘用车物流运输计划问题的优化.     

基于启发式Q学习的汽车涂装车间作业排序优化

针对汽车涂装车间中的作业优化排序问题,提出一种基于启发式Q学习的优化算法。首先,建立包括满足总装车间生产顺序和最小化喷枪颜色切换次数的多目标整数规划模型。将涂装作业优化排序问题抽象为马尔可夫过程,建立基于启发式Q算法的求解方法。通过具体案例,对比分析了启发式Q学习、Q学习、遗传算法三种方案的优劣。结果表明:在大规模问题域中,启发式Q学习算法具有寻优效率更高、效果更好的优势。本研究为机器学习算法在汽车涂装作业优化排序问题的应用提出了新思路。     

基于物料超市的混流装配线准时化物料配送调度

针对混流装配线物料配送优化问题，结合新型的物料超市配送模式，提出了区别于传统周期性配送的准时化配送方法。首先，根据问题描述和相关假设构建了以线边库存最小化为目标的混合整数规划模型。然后，考虑到实际生产的连续性和复杂性，为求解大规模问题，设计出适用于问题的启发式算法，并与遗传算法进行比较，启发式算法能在更短时间内获得满意解。最后，在不同牵引拖车容量下，通过实例分析，与广泛应用的周期性配送进行比较，算例求解结果表明，准时化配送模型能够获得更低的库存水平，减少工作站的空间占用。     

求解资源约束项目调度问题的启发式算法综述

本文综述了求解RCPSP的启发式算法.首先在对各种优先权规则进行归纳的基础上,概述基于优先权规则的RCPSP启发式算法研究现状;其次,综述项目进度的表述方式及常用超启发式策略,汇总求解RCPSP的超启发式算法的研究成果.此外,简要介绍除上述两大类启发式算法之外的其他几种启发式算法;最后,对全文进行总结,并指出该领域几个有希望的研究方向.     

几类元启发式优化算法性能的比较研究

元启发式优化算法包括萤火虫算法、布谷鸟算法、蝙蝠算法及和声搜索算法等.选取20个标准测试函数,统计4种元启发式优化算法的运行结果.以算法运行的精确度、稳定性作为比较指标分析算法的求解性能,提出了3种比较算法优劣性的方法,总结了3种比较方法的优缺点.     

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。     

考虑多阶段共用平行机特征的拖轮作业模式优化

在对拖轮调度问题进行分析的基础之上,将拖轮调度问题归结为一类具有多阶段共用平行机器特征的多处理器任务调度问题。建立了考虑靠泊与停泊两阶段、考虑切换时间的拖轮调度优化模型,并设计了启发式规则与模拟退火相结合的混合算法(HSA)求解该模型。通过运用所设计算法求解仿真算例,并将其结果与三种基于现行调度规则的调度方案进行比较,验证了模型与算法的高效性。基于此,对不同拖轮数量状态下的调度研究结果显示,拖轮总作业时间与平均单艘拖轮作业时间二者存在着一定的矛盾性;并指出采用拖轮适时返回停泊基地的作业模式,可有效地降低拖轮作业时间,进而提高有限拖轮资源的利用率,实现节能减排、绿色运输。     

B2C电子商务仓库拣货路径优化策略应用研究

当前国内B2C电子商务仓库多为人至物的拣货模式,拣货作业成为其核心作业之一,占据仓库大量时间成本和资金成本,拣货路径优化成为企业亟需解决的问题。本文基于TSP对拣货路径进行建模,利用蚁群算法、模拟退火算法和禁忌搜索对该NP-hard问题进行求解,并同当前企业普遍采用的S型启发式策略进行对比,拣货时间节约13.35%。进一步得出当拣货品数量较少时应采用模拟退火算法求解,而当拣货品数量较大时采用蚁群算法仅进行一次迭代,则可以实现短时间得到相对较优的解。所得结果已应用于某大型电子商务企业,效果明显。     

时变挥发率条件下求解Steiner树蚁群优化算法的收敛性

蚁群优化算法是最近提出的求解复杂组合优化问题的启发式算法.在蚁群优化算法中,信息素的更新规则直接影响着算法性能,固定挥发率条件下,虽然也能得到求解Steinei树蚁群优化算法的收敛性结果,但算法的探优能力差,易于陷入局部最优.本文在设计求解最小Steiner树蚁群优化算法时,采用了动态更新信息索挥发率的方法,并给出了时变挥发率条件下算法的收敛性证明.具体的,在时变挥发率条件下,当迭代次数充分大时,该算法能以概率1找到最优解.另外,在动态更新信息素下界的条件下,也能得到类似的收敛性结果.     

A genetic algorithm for robotic assembly line balancing

Flexibility and automation in assembly lines can be achieved by the use of robots. The robotic assembly line balancing (RALB) problem is defined for robotic assembly line, where different robots may be assigned to the assembly tasks, and each robot needs different assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.     

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.     

A branch-and-bound based solution approach for the mixed-model assembly line-balancing problem for minimizing stations and task duplication costs

A common assumption in the literature on mixed-model assembly line balancing is that a task that is common to multiple models must be assigned to a single station. In this paper, we relax this restriction, and allow a common task to be assigned to different stations for different models. We seek to minimize the sum of costs of the stations and the task duplication. We develop an optimal solution procedure based on a backtracking branch-and-bound algorithm and evaluate its performance via a large set of experiments. A branch-and-bound based heuristic is then developed for solving large-scale problems. The heuristic solutions are compared with a lower bound and experiments show that the heuristic provides much better solutions than those obtained by traditional approaches.     

Robust balancing of straight assembly lines with interval task times

This paper addresses the balancing problem for straight assembly lines where task times are not known exactly but given by intervals of their possible values. The objective is to assign the tasks to workstations minimizing the number of workstations while respecting precedence and cycle-time constraints. An adaptable robust optimization model is proposed to hedge against the worst-case scenario for task times. To find the optimal solution(s), a breadth-first search procedure is developed and evaluated on benchmark instances. The results obtained are analysed and some practical recommendations are given.     

A scatter search based hyper-heuristic for sequencing a mixed-model assembly line

We address a mixed-model assembly-line sequencing problem with work overload minimization criteria. We consider time windows in work stations of the assembly line (closed stations) and different versions of a product to be assembled in the line, which require different processing time according to the work required in each work station. In a paced assembly line, products are feeded in the line at a predetermined constant rate (cycle time). Then, if many products with processing time greater than cycle time are feeded consecutively, work overload can be produced when the worker has insufficient time to finish his/her job. We propose a scatter search based hyper-heuristic for this NP-hard problem. In the low-level, the procedure makes use of priority rules through a constructive procedure. Computational experiments over a wide range of instances from the literature show the effectiveness of the proposed hyper-heuristics when compared to existing heuristics. The relevance of the priority rules was evaluated as well.     

具有工作站数量约束的多人工作站混合装配线平衡问题研究

针对装配线设计或改造过程中存在的因场地或成本原因导致的工作站数量不易变更的问题,研究了节拍已知情况下,具有工作站数量约束的多人工作站混合装配线平衡问题,建立以装配线总人数最小、工人负荷量标准差最小、各产品在各工作站装配时间与节拍之间的标准差最小为目标的数学模型,设计了一种结合差分进化的多目标混合遗传算法对该问题求解。通过案例计算以及与其他算法的对比分析表明,本文算法在收敛性和综合性能方面优于NSGAII和DEMO,在装配线人数和工人负荷标准差方面优于Roshani和Nezami提出的算法。     

混流装配线平衡问题的多目标优化方法研究

针对混流装配线存在的工序作业多、平衡难度大等问题,以U型布置的混装线为研究对象,兼顾工作站平均负荷和瞬时负荷平衡,在最小化工作站数的基础上均衡了工作站间和工作站内不同产品的作业负荷,建立U型混流装配线多目标平衡优化模型,同时提出目标法解决联合目标中的目标函数兼容性问题。设计并运用改进的自适应遗传算法求解模型,考虑到交叉、变异操作的随机性,在变异阶段加入强制规则,并对新生成的子代个体进行基因冲突检测,以提高可行解的比率。算例研究的结果表明本文所提的多目标优化方法能够较好地解决混装线平衡问题。     

Shortest-route formulation of mixed-model assembly line balancing problem

A shortest-route formulation of the mixed-model assembly line balancing problem is presented. Common tasks across models are assumed to exist and these tasks are performed in the same stations. The formulation is based on an algorithm which solves the single-model version of the problem. The mixed-model system is transformed into a single-model system with a combined precedence diagram. The model is capable of considering any constraint that can be expressed as a function of task assignments.     

Dynamic task assignment for throughput maximization with worksharing

The traditional assembly line balancing problem involves assigning a set of partially precedence constrained tasks to workstations to maximize efficiency. Each task is assigned to a unique workstation. The case is considered where task sequences are known but the workforce is partially cross-trained and some tasks can alternate between workstations. The flexibility afforded by cross-training allows the line balance to improve. Task times are allowed to be random and small buffers are allowed between workstations. Decision rules are developed and tested for various levels of cross-training between adjacent workers. Cross-training is shown to have significant impact on throughput and easy to administer rules are proven to be effective. The number of decision points for deciding to hold or pass a unit of product is also shown to be important.     

A heuristic solution for fuzzy mixed-model line balancing problem

This paper addresses the mixed-model line balancing problem with fuzzy processing time. A fuzzy binary linear programming model is formulated for the problem. This fuzzy model is then transformed to a mixed zero–one program. Due to the complexity nature in handling fuzzy computation, new approximated fuzzy arithmetic operation is presented. A fuzzy heuristic is developed to solve this problem based on the aggregating fuzzy numbers and combined precedence constraints. The general idea of our approach is to arrange the jobs in a sequence by a varying-section exchange procedure. Then jobs are allocated into workstations based on these aggregated fuzzy times with the considerations of technological constraint and cycle time limit. Promising results are obtained by experiments.