Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.     

Algorithms for on-line order batching in an order picking warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these – indivisible – customer orders into picking orders. With respect to order batching, two problem types can be distinguished: in off-line (static) batching, all customer orders are known in advance; in on-line (dynamic) batching, customer orders become available dynamically over time. This paper considers an on-line order batching problem in which the maximum completion time of the customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for off-line order batching can be modified in order to deal with the on-line situation. In a competitive analysis, lower and upper bounds for the competitive ratios of the proposed algorithms are presented. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the maximum completion time.     

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.     

A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system

An order storage assignment problem (SAP) is to find an effective way to locate products in a warehouse in order to improve the operational efficiency of order picking. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed. Most of previous researches focused on picker-to-parts warehousing systems or automated storage and retrieval systems. However, pick-and-pass systems play an important role for the faster delivery of small and frequent orders of inventory with the rise of e-commerce and e-business in the global supply chain. Two factors lead to idle time of pickers in a pick-and-pass system: picking line imbalance and shortage replenishment of products. This paper develops a genetic based heuristic method to solve SAP for a pick-and-pass system with multiple pickers to determine the appropriate storage space for each product and balance the workload of each picking zone so that the performance of the system can be improved. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm and compare the throughput for different storage assignment methods as well. The results indicate that the proposed heuristic policy outperforms existing assignment methods in a pick-and-pass system.     

Supporting rescheduling using CSP, RMS and POB—an example application

This paper introduces an innovative approach to the problem of rescheduling within manufacturing industry. An example of a manufacturing context that requires rescheduling capability is given (tyre production). The meaning of rescheduling, possible metrics for assessment of rescheduling and the advantages of applying the new techniques are reviewed. Of particular importance is the notion that the technology for providing rescheduling and explanation capabilities is to a large degree problem and context insensitive. The manner in which an original schedule has been created is irrelevant to the use of the technology described, allowing the advantages of the approach to be realized as an add-on facility to any existing scheduling system that fulfills a minimal set of requirements. These advantages are due to the use of a constraint based approach to new schedule creation used in tandem with dependency analysis techniques based on reason maintenance systems (de Kleer, 1986) and partial order backtracking (Ginsberg and McAllister, 1995; Spragg and Kelleher, 1996).     

Application of an iterative framework for real-time railway rescheduling

Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe the application of an iterative framework in which all these three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This case study shows that the framework and the existing rescheduling approaches can be of great value in practice.     

资源动态组织支持的网格依赖任务重调度机制

针对网格依赖任务重调度所面临的效率低与触发频繁的问题,提出资源动态组织支持的网格依赖任务调度机制.该机制以资源的动态组织为核心,基于资源动态性度量结果对资源进行性能聚类分析,并过滤性能相似资源中的强动态性资源,以在减少资源数量的同时提高重调度备选资源的稳定性.实验表明基于该机制的重调度算法保持了静态调度策略在动态网格环境下相对于动态调度策略的性能优势,从而验证了该机制解决网格依赖任务重调度所面临问题的有效性.     

Algorithms for burst rescheduling in WDM optical burst switching networks

Wavelength division multiplexing (WDM) is a promising technology for realizing terabit networks. Optical burst switching (OBS) is a way to efficiently support bursty traffic on WDM-based optical Internet networks. In OBS networks, the control (header) and payload (data) components of a burst are sent separately with a time gap. The control packet first traverses the burst switching nodes and reserves suitable wavelengths on the links for the corresponding data burst by using a scheduling algorithm. Our work is motivated from the observation that the existing scheduling algorithms either have low computational complexity or high performance in terms of burst dropping probability, but not both simultaneously. Since the arrival of bursts is dynamic, it is highly desirable that the scheduling is done as quickly as possible. We develop scheduling algorithms which integrate the merits of both low computational complexity and high burst dropping performance. The key idea is to reschedule an existing burst by assigning a new wavelength to it keeping the burst arrival and leaving time unchanged in order to accommodate the new burst. We propose computationally simple rescheduling algorithms called on-demand burst rescheduling and aggressive burst rescheduling. The effectiveness of the proposed algorithms and the signaling overhead are studied through simulation experiments.     

A Constraint-Based Interactive Train Rescheduling Tool

In this paper, we report the design and implementation of a constraint-based interactive train rescheduling tool, a project in collaboration with the International Institute for Software Technology, United Nations University (UNU/IIST), Macau. We formulate train rescheduling as constraint satisfaction and describe a constraint propagation approach for tackling the problem. Algorithms for timetable verification and train rescheduling are designed under a coherent framework. Formal correctness properties of the rescheduling algorithm are established. We define two optimality criteria for rescheduling that correspond to minimizing the number of station visits affected and passenger delay respectively. Two heuristics are then proposed to speed up and direct the search towards optimal solutions. The feasibility of our proposed algorithms and heuristics are confirmed with experimentation using real-life data.     

Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

Improved filtering for weighted circuit constraints

We study the weighted circuit constraint in the context of constraint programming. It appears as a substructure in many practical applications, particularly routing problems. We propose a domain filtering algorithm for the weighted circuit constraint that is based on the 1-tree relaxation of Held and Karp. In addition, we study domain filtering based on an additive bounding procedure that combines the 1-tree relaxation with the assignment problem relaxation. Experimental results on Traveling Salesman Problem instances demonstrate that our filtering algorithms can dramatically reduce the problem size. In particular, the search tree size and solving time can be reduced by several orders of magnitude, compared to existing constraint programming approaches. Moreover, for medium-size problem instances, our method is competitive with the state-of-the-art special-purpose TSP solver Concorde.     

移动自组网中一种网络生存时间最优的广播算法

移动自组网中广播操作的网络生存时间问题一直是备受关注的热点研究问题.现有的研究已经证明,基于最小生成树算法的广播算法能够最优地解决网络生存时间问题.但是,这些研究工作都是基于静止的网络拓扑,从而不适用于一些实际的网络拓扑动态变化的应用场景,如军事通信应用等.因此,针对节点移动导致的网络拓扑变化的场景,本文提出了一种移动...     

Alattin: mining alternative patterns for defect detection

To improve software quality, static or dynamic defect-detection tools accept programming rules as input and detect their violations in software as defects. As these programming rules are often not well documented in practice, previous work developed various approaches that mine programming rules as frequent patterns from program source code. Then these approaches use static or dynamic defect-detection techniques to detect pattern violations in source code under analysis. However, these existing approaches often produce many false positives due to various factors. To reduce false positives produced by these mining approaches, we develop a novel approach, called Alattin, that includes new mining algorithms and a technique for detecting neglected conditions based on our mining algorithm. Our new mining algorithms mine patterns in four pattern formats: conjunctive, disjunctive, exclusive-disjunctive, and combinations of these patterns. We show the benefits and limitations of these four pattern formats with respect to false positives and false negatives among detected violations by applying those patterns to the problem of detecting neglected conditions.     

Item-associated cluster assignment model on storage allocation problems

Warehouse management is currently facing fierce competition. By integrating information systems, retailers order more frequently with multiple items, but each order has smaller quantities. The situation becomes more stressful in a disintermediation supply–demand system. A good example is in the Business-to-Customer (B2C) online retailing business in which warehouses have to fulfill divergence orders directly. This study proposes a two-stage Clustering-Assignment Problem Model (CAPM) for the customized-orders picking problem. For multi-item-small-quantity orders, the CAPM targets a between-item association rather than the traditional group clustering to reduce the picking distance.The first stage of CAPM draws item association indices, based on between-item support, from customers’ orders. It then develops a mathematical programming model to search for the maximum total item support. The second stage applies assignment techniques to locate the clustered group in the storage place so as to minimize picking distance. We use Lingo commercial software to help the solution-finding procedures. By emphasizing the item association, CAPM is suitable for orders with multiple items and smaller quantities in the modern retailing sector. It also more effectively shortens the picking distance compared with popular frequency-based and random assignment storage methods. In the example of the drug distribution center studied herein, CAPM proves more effective as it reduces over 45% of the picking distances versus the current set-up.     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题，为了提高拣货作业效率，提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先，建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型；然后，考虑双重优化的复杂性，设计了一种嵌套遗传算法对模型进行求解，外层不断优化订单分批结果，内层根据外层订单分批结果优化拣货路径。算例结果表明，与传统的订单分步优化、分批分步优化策略相比，所提策略的拣货时间分别减少了45.6%、6%，基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能，分别对10、20、50张订单规模的算例进行仿真实验，结果表明，随着订单量的增加，整体拣货距离和时间进一步减少，拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题，为配送中心拣选系统的优化提供依据。     

Metaheuristics for order batching and sequencing in manual order picking systems

Order picking involves the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems is the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid shipment or production delays. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow order picking systems to operate more efficiently. Solutions can be improved by 46% on average, compared to those obtained with standard constructive heuristics such as the Earliest Due Date rule.     

基于嵌套遗传算法的拣货作业联合优化

针对物流配送中心拣货作业过程中传统订单分批和拣货路径分步优化难以获得整体最优解的问题，为了提高拣货作业效率，提出了一种基于嵌套遗传算法的订单分批和路径优化的联合拣货策略。首先，建立了以拣货总时间最短为目标函数的订单分批与拣货路径联合优化模型；然后，考虑双重优化的复杂性，设计了一种嵌套遗传算法对模型进行求解，外层不断优化订单分批结果，内层根据外层订单分批结果优化拣货路径。算例结果表明，与传统的订单分步优化、分批分步优化策略相比，所提策略的拣货时间分别减少了45.6%、6%，基于嵌套遗传算法的联合优化模型得出的拣货路径更短、拣货时间更少。为验证该算法对不同规模订单均有较优性能，分别对10、20、50张订单规模的算例进行仿真实验，结果表明，随着订单量的增加，整体拣货距离和时间进一步减少，拣货时间的减少从6%增加到7.2%。基于嵌套遗传算法的拣货作业联合优化模型和其求解算法可以有效解决订单分批与拣货路径联合优化问题，为配送中心拣选系统的优化提供依据。     

Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids

As computational Grids are increasingly used for executing long running multi-phase parallel applications, it is important to develop efficient rescheduling frameworks that adapt application execution in response to resource and application dynamics. In this paper, three strategies or algorithms have been developed for deciding when and where to reschedule parallel applications that execute on multi-cluster Grids. The algorithms derive rescheduling plans that consist of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. Using large number of simulations, it is shown that the rescheduling plans developed by the algorithms can lead to large decrease in application execution times when compared to executions without rescheduling on dynamic Grid resources. The rescheduling plans generated by the algorithms are also shown to be competitive when compared to the near-optimal plans generated by brute-force methods. Of the algorithms, genetic algorithm yielded the most efficient rescheduling plans with 9–12% smaller average execution times than the other algorithms.     

Vehicle routing problem with time windows considering overtime and outsourcing vehicles

The vehicle routing problem with time windows (VRPTW) is an important problem in third-party logistics and supply chain management. We extend the VRPTW to the VRPTW with overtime and outsourcing vehicles (VRPTWOV), which allows overtime for drivers and the possibility of using outsourced vehicles. This problem can be applied to third-party logistics companies for managing central distributor-local distributors, local distributor-retailers (or customers), and manufacturers. We developed a mixed integer programming model, a genetic algorithm (GA), and a hybrid algorithm based on simulated annealing. The computational results demonstrate the efficiency of the developed algorithms. We also develop a decision support system for the VRPTWOV that is equipped with a vehicle route rescheduling function for realistic situations based on the GA.     

Hybrid Evolutionary Algorithm for job scheduling under machine maintenance

The job scheduling problem (JSP) belongs to the well-known combinatorial optimization domain. After scheduling, if a machine maintenance issue affects the scheduled processing of jobs, the delivery of jobs must be delayed. In this paper, we have first proposed a Hybrid Evolutionary Algorithm (HyEA) for solving JSPs. We have then analyzed the effect of machine maintenance, whether preventive or breakdown, on the job scheduling. For the breakdown maintenance case, it is required to revise the algorithm to incorporate a rescheduling option after the breakdown occurs. The algorithm has been tested by solving a number of benchmark problems and thence comparing them with the existing algorithms. The experimental results provide a better understanding of job scheduling and the necessary rescheduling operations under process interruption.