Topological and dimensional synthesis of planar linkages for multiple kinematic tasks

This paper presents the combined use of two systematic methods for the synthesis of planar linkage mechanisms satisfying multiple kinematic tasks. First, a Graph Theory-based method is used to exhaustively enumerate the topological alternatives for a given problem. Then each feasible alternative is automatically dimensioned using the Precision Position Method; this computation includes space and design constraints. The existing methods to synthesize multiple tasks solve, in sequence, a decomposition of the problem into single kinematic tasks. The task decomposition and the topology selection for each task are usually performed by hand. This process leads to topologies with a repeated pattern and could lead to ignoring potentially desirable topologies. This paper analyzes a design strategy for the simultaneous solution of multiple kinematic tasks. This strategy has two advantages: (i) it eliminates the need for task decomposition, and (ii) it allows the exhaustive exploration of all non-isomorphic topologies up to a defined number of links. An example of simultaneous synthesis for a double rigid-body guidance task with application to a flap-tab mechanism is shown to illustrate the methodology.     

The manpower allocation problem with time windows and job-teaming constraints: A branch-and-price approach

In this paper, we consider the manpower allocation problem with time windows, job-teaming constraints and a limited number of teams (m-MAPTWTC). Given a set of teams and a set of tasks, the problem is to assign to each team a sequential order of tasks to maximize the total number of assigned tasks. Both teams and tasks may be restricted by time windows outside which operation is not possible. Some tasks require cooperation between teams, and all teams cooperating must initiate execution simultaneously. We present an integer programming model for the problem, which is decomposed using Dantzig–Wolfe decomposition. The problem is solved by column generation in a branch-and-price framework. Simultaneous execution of tasks is enforced by the branching scheme. To test the efficiency of the proposed algorithm, 12 realistic test instances are introduced. The algorithm is able to find the optimal solution in 11 of the test instances. The main contribution of this article is the addition of synchronization between teams in an exact optimization context.     

An improved LNS algorithm for real-time vehicle routing problem with time windows

This paper studies the dynamic vehicle routing problem with hard time windows (DVRPTW). The main study course of this problem was briefly reviewed. The solving strategy and algorithm of the problem are put forward. First of all, DVRPTW problem is decomposed into a series of static VRPTW. When and how to decompose the DVRP is the issue, that must be addressed. An event-trigger mechanism has been proposed and used to decompose the DVRPTW into a series of system delay-snapshots. The trigger event to be adopted is a new request arrival during the stable operation. And each snapshot is regarded as a static VRPTW. Whether each static VRPTW can quickly and efficiently be solved within a given time or a shorter time, i.e. the solving time is another issue for the DVRPTW. In the solving process, how to merge the latest requirement to the current solution is the third issue that must be solved. An improved large neighborhood search (LNS) algorithm is proposed to solve the static problem. Utilizing the remove-reinsert process of the LNS, the latest request nodes are regarded as a part of the removed nodes; these nodes can be inserted into the original or current solution in good time in the reinsertion process; meanwhile, its computing speed is high and effective for it does not need to resolve primal problem each time. Computational results of a large number of test problems, which cited from Solomon's static benchmarks and Lacker’s dynamic data set, show that our method is superior to other methods in most instances.     

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.     

Scheduling hybrid flowshop with parallel batching machines and compatibilities

This paper considers a two-stage hybrid flowshop problem in which the first stage contains several identical discrete machines, and the second stage contains several identical batching machines. Each discrete machine can process no more than one task at time, and each batching machine can process several tasks simultaneously in a batch with the additional feature that the tasks of the same batch have to be compatible. A compatibility relation is defined between each pair of tasks, so that an undirected compatibility graph is obtained which turns out to be an interval graph. The batch processing time is equal to the maximal processing time of the tasks in this batch, and all tasks of the same batch start and finish together. The goal is to make batching and sequencing decisions in order to minimize the makespan. Since the problem is NP-hard, we develop several heuristics along with their worst cases analysis. We also consider the case in which tasks have the same processing time on the first stage, for which a polynomial time approximation scheme (PTAS) algorithm is presented.     

Hierarchical control approach to a composite inventory-marketing problem

A composite inventory-marketing problem is considered and treated for the case where there exist distributed time delays in the supply and/or the advertisement process, as well as product losses at constant rates. Since in a real large-scale situation a centralized solution may be impractical, two hierarchical coordinated control algorithms are presented which are based on a proper decomposition of the overall problem. Backlogging is not permitted and the delays are assumed fixed and known. The first algorithm makes use of the dual optimization technique, whereas the second algorithm is based on a decentralized procedure which allows each of the company's departments to solve its problem individually, and then with the coordination of an upper level to obtain the overall solution. Numerical examples are included which illustrate the effectiveness of the two algorithms.     

工作流技术在虚拟企业订单分解上的应用

虚拟企业被认为是在21世纪的竞争、合作、动态环境中最具竞争力的企业运作方式。而选择适当的合作伙伴,对成功地组建虚拟企业具有重要的意义。订单分解在进行合适的伙伴选择中起着重要的作用。但是如何正确地分解订单是一个复杂的问题。该文对将工作流技术应用于订单分解问题的可行性与有效性方面进行分析的基础上,提出了基于工作流的订单分解方法。在这个方法中,订单和组成订单的每个子任务被表示为过程和过程中的活动这样的层次结构。活动之间的关系使用过程建模技术来描述。与此同时,每个活动都具有一定的属性,如时间、质量、成本与服务。使用每个活动的性能,可以获得整个过程(即订单)的性能。也就是说,通过保证每个活动(子任务)的性能,就可以保证订单过程的性能。另外,它也为伙伴选择过程中与其他企业在这些性能指标上的协商提供了方法论。最后,使用一个实例验证了如何使用该方法,并验证了方法的有效性。     

求解SAT问题的拟人退火算法

该文利用一个简单的变换，将可满足性（SAT）问题转换为一个求相应目标函数最小值的优化问题，提出了一种用于跳出局部陷阱的拟人策略，基于模拟退火算法和拟人策略，为SAT问题的高效近注解得出了拟人退火算法（PA），该方法不仅具有模拟退火算法的全局收敛性质，而且具有一定的并行性，继承性。数值实验表明，对于本文随机产生的测试问题例，采用拟人策略的模拟退火算法的结果优于局部搜索算法，模拟退火算法以及近来国际上流行的WALKSAT算法，因此拟人退火算法是可行的和有效的。     

Two-sided assembly line balancing considering the relationships between tasks

Previous studies of the two-sided assembly line balancing problem assumed equal relationships between each two tasks assignable to a side of the line. In practice, however, this relationship may be related to such factors as the distance between the implementation place and the tools required for implementation. We know that the more relationships exist between the tasks assigned to each station, the more efficient will be the assembly line. In this paper, we suggest an index for calculating the value of the relationship between each two tasks, and define a performance criterion called ‘assembly line tasks consistency’ for calculating the average relationship between the tasks assigned to the stations of each solution. We propose a simulated annealing algorithm for solving the two-sided assembly line balancing problem considering the three performance criteria of number of stations, number of mated-stations, and assembly line tasks consistency. Also, the simulated annealing algorithm is modified for solving the two-sided assembly line balancing problem without considering the relationships between tasks. This modification finds five new best solutions for the number of stations performance criterion and ten new best solutions for the number of mated-stations performance criterion for benchmark instances.     

平行结构类问题的分解与任务分布

本文给出平行结构类问题及其求解系统的形式化描述,讨论了此类问题的分解与任务分布,并提出了一种IPD算法(Improved Problem Decomposition).该算法从规模上将问题分解为若干性质相同的任务,按就近原则将任务预分布到系统中各结点上,并通过启发式状态空间查找方法进行负载调整,使系统负载平衡.试验表明:IPD算法的分解分布结果负载平衡,系统潜在协作量小.     

Market-Based Model Predictive Control for Large-Scale Information Networks: Completion Time and Value of Solution

There are several important properties of modern software systems. They tend to be large-scale with distributed and component-based architectures. Also, dynamic nature of operating environments leads them to utilize alternative algorithms. However, on the other hand, these properties make it hard to provide appropriate control mechanisms due to the increased complexity. Components are sharing resources and each component can have alternative algorithms. As a result, the behavior of a software system can be controlled through resource allocation, as well as algorithm selection. This novel control problem is worthy of investigation in order to double the benefits of those properties. In this paper, we design a control mechanism for such systems. The quality-of-service we are considering is a product of the value of solution and the time for generating solution for a given problem. We build a mathematical programming model that trades off these two conflicting objectives, and decentralize the model through an auction market. By periodically opening the auction market for each existing system state, a closed-loop policy is formed. Though similar problems can be found in multiprocessor scheduling literature, they have limitations in addressing this control problem. They commonly consider so-called workflow applications in which each component only has to process one task after all of its predecessors complete their tasks. In contrast, a component in the networks under consideration processes multiple tasks in parallel with its successors or predecessors.     

Problem representation for refinement

In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples.     

A constraint-based approach for the shift design personnel task scheduling problem with equity

This paper presents an industrial problem which arises in a company specialized in drug evaluation and pharmacology research. The aim is to build employee timetables covering the demand given by a set of fixed tasks. The optimality criterion concerns the equity of the workload sharing. A solution to this problem is the assignment of all tasks whose resulting working shifts respect tasks requirements as well as legal and organizational constraints. Scheduling problems usually consider a fixed set of shifts which have to be assigned to a given number of employees whereas in our problem shifts are not fixed and are deduced from the task assignment. In the following, we refer to this problem as the shift-design personnel task scheduling problem with an equity criterion (SDPTSP-E), in reference to the shift minimization personnel task scheduling problem (SMPTSP). Even if the SDPTSP-E is related to several problems, none of them allow to grasp its full complexity. Consequently, we propose a dedicated method based on constraint programming. Several branching and exploration strategies are proposed and tested.     

An efficient approach for type II robotic assembly line balancing problems

In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.     

A decomposition approach to non-preemptive real-time scheduling

Consider the problem of scheduling a set ofn tasks on a uniprocessor such that a feasible schedule that satisfies each task's time constraints is generated. Traditionally, researchers have looked at all the tasks as a group and applied heuristic or enumeration search to it. We propose a new approach called thedecomposition scheduling where tasks are decomposed into a sequence of subsets. The subsets are scheduled independently, in the order of the sequence. It is proved that a feasible schedule can be generated as long as one exists for the tasks. In addition, the overall scheduling cost is reduced to the sum of the scheduling costs of the tasks in each subset.Simulation experiments were conducted to analyze the performance of decomposition scheduling approach. The results show that in many cases decomposition scheduling performs better than the traditional branch-and-bound algorithms in terms of scheduling cost, and heuristic algorithms in terms of percentage of finding feasible schedules over randomly-generated task sets.     

基于自适应蚁群算法的车辆路径问题研究

车辆路径问题(VRP)是物流研究领域中一个具有重要理论和现实意义的问题．蚁群算法是一种新型的模拟进化算法,可以很好地解决旅行商问题(TSP)．在分析VRP与TSP区别的基础上,构造了求解VRP的自适应蚁群算法．指出可行解问题是蚁群算法的关键问题,并重点对该问题进行了研究,提出了近似解可行化等解决策略．实验结果表明,自适应蚁群算法性能优良,能够有效地求解VRP问题．     

A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.     

基于任务的Cholesky分解多核并行化研究

针对Cholesky分解算法采用OpenMP并行程序设计时的并行性开销增大和线程负载不平衡的问题,利用并行性能分析工具对串行程序进行热点分析,提出了一种基于任务的Cholesky分解多核并行算法。该算法将大循环问题划分成各个相互独立的小任务,并运用任务窃取技术和动态负载均衡算法使多个任务能够并行完成。采用ParallelAmplifier对并行程序进行调试和优化,实验结果表明,其性能得到较大幅度的提升。     

Cooperative management of a net of intelligent surveillance agent sensors

The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system‐level tasks that need to be fulfilled. For each system task, indications about its system‐level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor‐level tasks to be executed by its sensor, providing also the sensor‐level priority and performance objectives for each task. The problem of sensor(s)‐to‐task(s) assignment (including decomposition of system‐level tasks into sensor‐level tasks and translation of system‐level performance requirements to sensor‐level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom‐up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system‐level and sensor‐level tasks will be inferred and applied during the negation process. © 2003 Wiley Periodicals, Inc.