The berth allocation problem with stochastic vessel handling times

In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed. Computational examples show that the proposed approach provides solutions superior to the ones where the expected value of the vessel handling times is used.     

Application of chance-constrained programming for stochastic group shop scheduling problem

In this paper, we study a group shop scheduling (GSS) problem subject to uncertain release dates and processing times. The GSS problem is a general formulation including the other shop scheduling problems such as the flow shop, the job shop, and the open shop scheduling problems. The objective is to find a job schedule which minimizes the total weighted completion time. We solve this problem based on the chance-constrained programming. First, the problem is formulated in a form of stochastic programming and then prepared in a form of deterministic mixed binary integer linear programming such that it can be solved by a linear programming solver. To solve the problem efficiently, we develop an efficient hybrid method. Exploiting a heuristic algorithm in order to satisfy the constraints, an ant colony optimization algorithm is applied to construct high-quality solutions to the problem. The proposed approach is tested on instances where the random variables are normally, uniformly, or exponentially distributed.     

Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm

Manpower scheduling problem is one of the key scheduling problems with extensive applications in manufacturing. This paper presents a mixed-integer programming model with a two-stage heuristic algorithm for solving the manpower scheduling problem in the precision engineering industry. Firstly, a mixed-integer programming formulation is developed to model the manpower scheduling problem in this high-mix low-volume manufacturing environment. Secondly, a two-stage heuristic algorithm is proposed where the first stage is deployed to calculate the skill requirements for each shift by considering the jobs, machines, and their production schedule and the second stage is designed to assign operators to the machines by considering the skill set requirements and the operator's expressed preferences. Lastly, the computational results based on problem instances emulating real-world scenarios demonstrated the feasibility and effectiveness of the proposed heuristic.     

求解楔类接触问题的ANSYS方法评价

选择合适的数值分析方法才能高效、高精度和高可靠性地求解楔类接触问题。介绍了ANSYS中点面接触分析方法和面面接触分析方法,剖析了这两种方法的基本设置及注意事项。然后,在弹性范围内用实例对比其求解楔类接触问题的精度、可靠性、效率及适用范围。分析结果表明：点面接触方法的求解精度和可靠性比面面接触算法高,在计算机硬件资源许可的条件下,建议采用前者;对于楔类接触问题的求解,点面接触方法的求解效率比面面接触方法低;这两种方法不仅适用于平面弹性楔类接触问题,也可推广到塑性和超弹性材料的三维楔类接触问题;点面接触方法在应力集中的区域,能得到较精确的结果,而面面接触方法的求解误差相对较大。     

露天带式输送机驱动打滑改造

宁波港北仑股份有限公司矿石码头主要装卸进口铁矿石，其装卸工艺流程中卸矿和装矿泊位、矿石堆场、铁路装矿楼之间由38条总长约16000多米的输送带连接。大部分带式输送机布置在露天，有些还在海面栈桥上，打滑问题一直是我公司设备管理与生产安排中的隐忧。     

两阶段法求解1.5维切割问题

切割问题亦称为下料问题.多维切割问题是机加工、木材加工和造纸等行业在生产中经常遇见的实际问题.1.5维切割问题是介于一维和二维切割问题之间的一类问题.针对1.5维切割问题的特殊性,采用两阶段方法进行求解.第一阶段是将1.5维切割问题转化为一维切割问题,第二阶段首先利用一种启发式算法得到一组初始解,然后使用粒子群算法对初始解进行优化,得最终解.     

Novel approach to solve the dynamical porous journal bearing problem

Analytical pressure solutions are a simple and robust way to model plain journal bearings in rotordynamics, but cannot be extended to porous journal bearings. Using the method of weighted residuals—viz. Galerkin's method with global shape functions—a novel and fast approach to solve the dynamical porous journal bearing problem is proposed. This approach allows for the influence of rough surfaces on the hydrodynamic pressure implemented through flow-factors, journal misalignment and calculation of stiffness and damping coefficients for finite bearings. The proposed method will be verified using analytical expressions and new results will be shown for porous journal bearings including the influence of rough surfaces.     

运用状态检测技术解决装船机的振动问题

针对装船机俯仰机构存在的不正常振动问题,制定了装船机俯仰机构的振动测试方案,确定了测点位置、数量,通过对振动测试数据的分析处理,揭示了俯仰机构不正常振动的根源,提出了解决方案。     

利用改进的遗传算法解决全局寻优问题

寻找函数的全局最优解是一个很常见的工程应用问题,简单遗传算法是解决此类问题的有力工具.但由于简单遗传算法具有中全局收敛能力差和收敛速度慢的缺点.本文基于对遗传算子的优化,提出一种混合分类选择和定向变异的改进遗传算法来解决全局寻优问题.经仿真结果表明,该算法具有较强的全局收敛能力和较快的收敛速度.     

A two-stage stochastic programming project scheduling approach to production planning

In engineering-to-order (ETO) or manufacturing-to-order (MTO) systems producing highly customized items, the high level of customization, together with long flow times, forces the production plan to be defined before information on items customization, and details on the manufacturing activities are completely disclosed. Due to the partial available information, the production plan must provide a robust schedule of the activities and of the resources utilization, trying to incorporate a certain degree of anticipation of uncertainty. This paper proposes a two-stage stochastic programming project scheduling approach to support production planning in ETO/MTO system. The approach provides a baseline production plan together with a set of revisions of the plan to react to the occurrence of uncertain events. A scenario-based approach is used to model the changes affecting the characteristics of the activities to be processed. The proposed approach is tested on random-generated instances and on a real manufacturing system producing machining centers.     

Psycho-clonal based approach to solve a TOC product mix decision problem

In recent years, theory of constraints (TOC) has emerged as an effective management philosophy that has successfully tackled the problems of profit maximization with known demonstrated bottleneck in traditional as well as modern manufacturing plants. One of the key components of TOC application is to enumerate quantity of the various products to be produced keeping in view the system constraints and this is termed as the TOC product mix decision problem. It is a well-known computationally complex problem and thus warrants the application of heuristics technique or AI based optimization tools to achieve near optimal solutions in real time. To accomplish this objective a new algorithm, Psycho-Clonal has been proposed that works on the principle of artificial immune system and behavioral theory, namely Maslow's need hierarchy theory. Intensive computational experiments have been carried out and superiority of proposed heuristic on a given dataset is established. It is observed that results obtained are better compared to what have been achieved by the TOC heuristic, revised theory of constraint heuristic (RTOC), integer linear programming (ILP), and tabu search based approaches.     

改进蚁群算法解决 UUV 集群任务规划问题

针对水下无人航行器(UUV)集群在有限续航力和负载约束条件下求解广泛且稀疏分布区域勘察任务规划问题时常规 算法存在收敛性差、解质量不高的不足,提出了一种改进的蚁群优化算法。 首先,通过分析个体 UUV 平台能力和集群任务的约 束条件,建立 UUV 集群任务规划的约束模型和优化模型;其次,基于任务点间距离与平均距离之差设计初始信息素浓度的非均 等分配方法,提出优化模型的最佳与最差阈值对蚂蚁进行分类并对应完成信息素更新,在状态转移规则中创新加入可随迭代进 程动态改变的“引力系数”来增加算法前中期次优节点被选中的概率;再次,设置对照统计实验完成算法优化项的有效性分析, 依据最优解出现的次数和平均收敛值优化算法参数;最后,以经典文献案例仿真,对比分析基本蚁群算法、精英蚁群算法与提出 算法,相较于前两种算法,算法在 50 次统计实验中找到近似最优解的百分比分别提升 78% 和 66% ,平均在第 40 代实现收敛,表 明出很好的全局寻优能力和收敛性能。 通过设计具有一定规模的 UUV 集群任务规划典型案例,验证了算法求解 UUV 集群广 泛且稀疏分布区域任务规划问题的快速性和有效性。     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

An interactive approach to solve the operation sequencing problem using simulated annealing

The problem of operation sequencing is affected by geometrical parameters such as tool compatibility, feature symmetry, feature accessibility, feature orientation and non-geometrical parameters such as dimensional tolerance, geometric tolerance, location tolerance and surface finish. Operation sequences are determined after meeting the objective functions such as minimum setup changeover and tool changeover, maximum tool motion continuity and maximum loose precedence among features. Because of the conflicting nature of the objectives and constraints, it is a tedious task to formulate a single objective function that can meet the requirements of the problem. Thus in this paper, an attempt has been made to address this issue to an extent by developing operation sequencing rating index (OSRI) which is the weighted sum of four indices: setup changeover index, tool changeover index, motion continuity index and loose precedence index. Determination of setup changeover index involves datum selection and sequencing in addition to grouping features into setup. Owing to the combinatorial nature of the problem, the simulated annealing (SA) based algorithm has been employed to determine the optimal/near-optimal operation sequence by maximising OSRI. In the proposed methodology, a perturbation scheme named as modified shifting scheme (MSS) has been devised to generate a feasible neighbourhood sequence that minimizes the search space and helps the algorithm to escape from local optima. A new approach for temperature variation in the SA algorithm is also incorporated in which the temperature is assumed to be parabolic. The advantage and effectiveness of the proposed methodology in terms of its algorithmic implementation have been verified on four test parts.     

感知器求解逻辑分类问题方法研究

本文研究在MATLAB环境下．利用感知器求解逻辑分类问题。首先阐述单层感知器求解逻辑。与”和逻辑“或”问题．其次研究了多层感知器对“异或”问题的求解。结论表明：在MATLAB环境下．利用感知器对逻辑“与”、“或”、。异或”进行求解时，网络训练时间短．仿真效果好．而且能够实现逻辑。与”和逻辑“或”输出的0误差。     

Multi-objective collaborative optimization using linear physical programming with dynamic weight

The multi-objective collaborative optimization problem with multi-objective subsystems has a bi-level optimization architecture, that consists of the system and subsystem levels. Combining the multi-objective optimization algorithm with a bi-level optimization structure can obtain a satisfactory solution. Given that the preference-based algorithm requires minimal running time, the Linear physical programming (LPP) method, one of the typical preference-based algorithms, is adopted. Considering that setting the preference values for the incompatibility function is difficult, the weighted incompatibility function is added to the piecewise linear function of the LPP model. An expression of dynamic weight is also presented according to the inconsistency among the subsystems, which is caused by the sharing and auxiliary variables relative to the different subsystems. Using an engineering example, this study reveals that the interdisciplinary consistency is satisfactory when the dynamic weight is used in the LPP model, which thereby demonstrates the effectiveness of the presented method.

     

An integrated approach to machine selection and operation allocation problem

Machine selection and operation allocation is a multi-criteria decision-making problem which involves the consideration of both qualitative and quantitative factors. Thus, a hybrid model integrating the knowledge-based expert system and the genetic algorithm may be effectively applied to the decision problem. This paper proposes a two-step approach where suitable machines for every operation in a work center is selected and optimized as a whole to obtain the optimum machine park. The first step of the model determines the suitability of each machine type for every operation using the knowledge-based expert system. The second stage searches through the solution space to find the optimal machine park with the use of a genetic algorithm. A real-life case study at an outdoor advertisement manufacturing company demonstrates the applicability of the model.     

Application of a hybrid simulated annealing-mutation operator to solve fuzzy capacitated location-routing problem

In the field of supply chain management and logistics, using vehicles to deliver products from depots to customers is one of the major operations. Before using vehicles, optimizing the location of depots is necessary in a location-routing problem (LRP). Also, before transportation products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. In this paper, the mathematical modelling of LRP is developed according to the existing condition and constraint in the real world. Maximum travelling time constraint is added, and we apply fuzzy numbers to determine customer demands, travelling time and drop time. The objective is to open a subset of depots to assign customers to these depots and to design vehicle routes, in order to minimize both the cost of open depots and the total cost of the routes. The proposed problem is modelled as a fuzzy linear programming (FLP), by applying the fuzzy ranking function method; the proposed FLP is converted to an exact linear programming (LP). A Lingo solver is used to solve this LP model in very small size. LRP is an non-deterministic polynomial-time hard (NP-Hard) problem, and because of the limitation of Lingo solver in solving medium, and large-size numerical examples, a hybrid algorithm including simulated annealing and mutation operator is proposed to solve these numerical examples. Also, a heuristic algorithm is proposed to find a suitable initial solution which is used in hybrid algorithm. At the end, a different analysis of the applied algorithm and a proposed model are introduced.