Resource-Oriented Petri Net for Deadlock Avoidance in Flexible Assembly Systems

In many flexible assembly systems, base components are transported with pallets; parts to be mounted onto the base ones are transported by trays with no pallets. When an assembly operation is performed by using some parts in a tray but not all, the tray with the remaining parts still occupies a buffer space. In this way, an assembly/disassembly material flow is formed. In such a material flow, deadlock can occur both in the base component and part flow. Furthermore, the assembly operations can also result in a deadlock. Thus, it is a great challenge to tackle deadlocks in such processes. This paper models them using resource-oriented Petri nets. Based on the models, a deadlock control policy is proposed and proved to be computationally efficient and less conservative than the existing policies in the literature. An industrial case study is used to show the results.     

What Java developers know about compatibility,and why this matters

Real-world programs are neither monolithic nor static—they are constructed using platform and third party libraries, and both programs and libraries continuously evolve in response to change pressure. In case of the Java language, rules defined in the Java Language and Java Virtual Machine Specifications define when library evolution is safe. These rules distinguish between three types of compatibility—binary, source and behavioural. We claim that some of these rules are counter intuitive and not well-understood by many developers. We present the results of a survey where we quizzed developers about their understanding of the various types of compatibility. 414 developers responded to our survey. We find that while most programmers are familiar with the rules of source compatibility, they generally lack knowledge about the rules of binary and behavioural compatibility. This can be problematic when organisations switch from integration builds to technologies that require dynamic linking, such as OSGi. We have assessed the gravity of the problem by studying how often linkage-related problems are referenced in issue tracking systems, and find that they are common.     

Truck scheduling at zero-inventory cross docking terminals

Handling freight at cross docking terminals constitutes a complex planning task which comprises several executive steps as shipments delivered by inbound trucks are to be unloaded, sorted according to their designated destinations, moved across the dock and finally loaded onto outbound trucks for an immediate delivery elsewhere in the distribution system. To enable an efficient synchronization of inbound and outbound flows, a careful planning of operations, e.g. by computerized scheduling procedures, becomes indispensable. This work treats a special truck scheduling problem arising in the (zero-inventory) cross docks of the food industry, where strict cooling requirements forbid an intermediate storage inside the terminal, so that all products are to be instantaneously loaded onto refrigerated outbound trucks. The problem is formalized such that different operational objectives, i.e. the flow time, processing time and tardiness of outbound trucks, are minimized. To solve the resulting truck scheduling problem suited exact and heuristic solution procedures are presented.     

Anyone but him: The complexity of precluding an alternative

Preference aggregation in a multiagent setting is a central issue in both human and computer contexts. In this paper, we study in terms of complexity the vulnerability of preference aggregation to destructive control. In particular, we study the ability of an election's chair to, through such mechanisms as voter/candidate addition/suppression/partition, ensure that a particular candidate (equivalently, alternative) does not win. And we study the extent to which election systems can make it impossible, or computationally costly (NP-complete), for the chair to execute such control. Among the systems we study—plurality, Condorcet, and approval voting—we find cases where systems immune or computationally resistant to a chair choosing the winner nonetheless are vulnerable to the chair blocking a victory. Beyond that, we see that among our studied systems no one system offers the best protection against destructive control. Rather, the choice of a preference aggregation system will depend closely on which types of control one wishes to be protected against. We also find concrete cases where the complexity of or susceptibility to control varies dramatically based on the choice among natural tie-handling rules.     

Tissue P systems with channel states

We consider tissue-like P systems with states associated with the links (we call them synapses) between cells, controlling the passage of objects across the links. We investigate the computing power of such devices for the case of using—in a sequential manner—antiport rules of small weights. Systems with two cells are proved to be universal when having arbitrarily many states and minimal antiport rules, or one state and antiport rules of weight two. Also the systems with arbitrarily many cells, three states, and minimal antiport rules are universal. In contrast, the systems with one cell and any number of states and rules of any weight only compute Parikh sets of matrix languages (generated by matrix grammars without appearance checking); characterizations of Parikh images of matrix languages are obtained for such one-cell systems with antiport rules of a reduced weight.     

Paint batching problem on M-to-1 conveyor systems

An M-to-1 conveyor system consists of multiple upstream conveyors and a single downstream conveyor. In this paper, we investigate the paint batching problem on M-to-1 conveyor systems with the objective of minimizing setup costs. Our research is motivated by a vehicle re-sequencing problem at a major Korean automotive manufacturer. Setup costs are incurred when two consecutive jobs in the downstream conveyor do not share the same feature. Re-sequencing flexibility is limited by the precedence relationship among jobs in the upstream conveyors. First, we develop a mixed integer linear programming model and propose an efficient dynamic programming (DP) algorithm for a 2-to-1 conveyor system. However, because the suggested DP cannot guarantee optimality in general settings, we propose two efficient genetic algorithms (GAs) to find near optimal solutions. Specifically, we design the reordering operation for making offspring to satisfy the precedence condition. We show that the proposed GAs perform prominently with respect to optimality gap and computation time; thus, they are amenable to environments where solutions must be obtained within tight time constraints.     

Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage

Cross-docking is an approach in inventory management which can reduce inventories, lead times and customer response time. In this strategy, products and shipments are unloaded from inbound trucks, sorted and categorized based on their characteristics, moved and loaded onto outbound trucks for delivery to demand points in a distribution network. The important fact is that, the items are stored in the inventory for a period which is primarily less than the actual time allocated to keep these items in a typical warehouse. Therefore, total cost and space requirement for inventory can be cut down. One of the most important targets in such systems is to establish coordination between the performance of inbound and outbound trucks in that these trucks can be scheduled, and the product items can be allocated to trucks effectively. This paper addresses some meta-heuristics to find the best sequence of inbound and outbound trucks, so that the objective, minimizing the total operation time called makespan, can be satisfied. Furthermore, not only the efficiency and capability of the algorithms’ parameters are assessed and analyzed by some performance measures, but also these meta-heuristics are compared with each other in order to find out the set of homogeneous algorithms among all proposed algorithms. By this analysis, it can be shown that the suitability of these meta-heuristics is quite sensible especially for the cross-docking systems with large sizes in which a high volume of inbound or outbound trucks transmit the product items.     

SPAR analysis of LDEF vibration characteristics

This paper presents a structural dynamic modeling of the Long Duration Exposure Facility (LDEF), which is a Space Shuttle payload of passive scientific experiments contained in trays mounted on a large cylindrically shaped structure. Special detailed finite element modeling, using the SPAR system of computer programs was required to obtain good agreement between analytical and test vibrations modes. Experimental trays contributed significantly to overall LDEF stiffnesses, and these contributions were realistically represented for each tray by the stiffness matrix of an equivalent orthotropic panel in the overall LDEF SPAR model. Orthotropic stiffnesses for this panel were obtained from finely detailed statically loaded tray SPAR models in which stiffness coupling was accounted for along with partial relative sliding allowed by the tray clamping attachments. Joint boundary conditions were also significant in the structural dynamic modeling of LDEF, and static data proved valuable in assessing modeling of local end fittings.     

Three-stage hybrid-flowshop model for cross-docking

This paper deals with the optimization of a cross-docking system. It is modeled as a three-stage hybrid flowshop, in which shipments and orders are represented as batches. The first stage corresponds to the receiving docks, the second stage corresponds to the sorting stations, and the third stage corresponds to the shipping docks. The objective of the problem is to find a schedule that minimizes the completion time of the latest batch. In order to obtain good quality feasible solutions, we have developed several heuristic schemes depending on the main stage considered, and several rules to order the batches in this stage. Then, we propose a branch-and-bound algorithm that takes into account the decomposition of the problem into three stages. To evaluate the heuristics and to reduce the tree size during the branch-and-bound computation, we also propose lower bounds. Finally, the computational experiments are presented to demonstrate the efficiency of our heuristics. The results show that the exact approach can solve instances containing up to 9–10 batches in each stage (i.e., up to 100 jobs). In addition, our heuristics were evaluated over instances with up to 3000 jobs, and they can provide good quality feasible solutions in a few seconds (i.e., less than 2 s per heuristic).     

Redistributing stock in library systems with a depot

Public library organizations often utilize depots for carrying out shipments to libraries in case of stock-outs and for storing low demand rental items at low cost. Similar systems may be employed by rental companies for other rental products such as tools, DVDs, and jewelry. Since shipments deplete the depot’s inventory, stock must be taken back from the libraries in order to deal with future shipment requests. These shipment and take-back operations are carried out periodically, e.g. daily or weekly. This work focuses on optimizing the decisions for shipments and take-backs. We model the system by means of a Markov decision process and investigate its optimal policy for various problem instances. For the take-back decision, we distinguish between so-called threshold, reactive, and preventive take-backs. We use the insights from the MDP to develop a three-phase take-back heuristic. In experiments, our heuristic performs within 1% on average from the optimal solution. For settings with a large number of libraries, it is shown that an acceptable performance can be achieved by setting a base-stock level at the depot and taking back sufficient stock from the libraries to achieve this level.     

Remarks on the cellular automaton global synchronisation problem: deterministic versus stochastic models

In the global synchronisation problem, one is asked to find a cellular automaton which has the property that every initial condition evolves into a homogeneous blinking state. We study this simple inverse problem for the case of one-dimensional systems with periodic boundary conditions. Two paradoxical observations are made: (a) despite the apparent simplicity of finding rules with good statistical results, there exist no perfect deterministic solutions to this problem, (b) if we allow the use of randomness in the local rule, constructing “perfect” stochastic solutions is easy. For the stochastic case, we give some rules for which the mean time of synchronisation varies quadratically with the number of cells and ask if this result can be improved. To explore more deeply the deterministic rules, we code our problem as a SAT problem and use SAT solvers to find rules that synchronise a large set of initial conditions (in appendix).

     

Fit for purpose: engineering principles for selecting an appropriate type of data exchange standard

Data standards are a powerful, real-world tool for enterprise interoperability, yet there exists no well-grounded methodology for selecting among alternative standards approaches. We focus on a specific sub-problem within a community’s data sharing challenge and identify four major standards-based approaches to that task. We present characteristics of a data sharing community that one should consider in selecting a standards approach—such as relative power, motivation level, and technical sophistication of different participants—and illustrate with real-world examples. These characteristics and other factors are then analyzed to develop decision rules for selecting among the four approaches. Independent of the data exchange problem, we suggest two general practices in choosing a standards approach: (1) vertical decomposition of interoperability issues, in order to define a narrow, formal, tractable problem, and (2) option-exclusion rules, as they are much simpler than stating optimal-choice rules.     

Study on visual machine-learning on the omnidirectional transporting robot

We present a computer vision solution integrated to an omnidirectional transporting robot to perform the position tracking of multiple trays moving on its planar acrylic plate surface. The trays were designed to carry lightweight materials on top of their surface so that the mechanism could be implemented as an automated transporting system for applications that require the displacement of products and/or materials in any given space. One hurdle faced by the visual system for suitable detection was the partial occlusion of the image of a tray when placing arbitrary objects on its surface. Our strategy to overcome this challenge consisted on the implementation of machine learning algorithms, such as Support Vector Machines (SVM), using datasets of images containing trays with different occlusion patterns for fast object detection through rigorous training. The results of experimental tests validate the implementation of our proposal as a reliable approach for the object tracking of multiple trays on the robotic device, even under partial occlusion. We also studied the accuracy of the position measurements performed by our visual system with respect to the position measurements taken by the OPTITRACK motion capture system and evaluated the processing time per frame required by the software implementation.     

Generalized rules for combination and joint training of classifiers

Classifier combination has repeatedly been shown to provide significant improvements in performance for a wide range of classification tasks. In this paper, we focus on the problem of combining probability distributions generated by different classifiers. Specifically, we present a set of new combination rules that generalize the most commonly used combination functions, such as the mean, product, min, and max operations. These new rules have continuous and differentiable forms, and can thus not only be used for combination of independently trained classifiers, but also as objective functions in a joint classifier training scheme. We evaluate both of these schemes by applying them to the combination of phone classifiers in a speech recognition system. We find a significant performance improvement over previously used combination schemes when jointly training and combining multiple systems using a generalization of the product rule.     

Minimization strategies for maximally parallel multiset rewriting systems

Maximally parallel multiset rewriting systems (MPMRS) give a convenient way to express relations between unstructured objects. The functioning of various computational devices may be expressed in terms of MPMRS (e.g., register machines and many variants of P systems). In particular, this means that MPMRS are Turing universal; however, a direct translation leads to quite a large number of rules. Like for other classes of computationally complete devices, there is a challenge to find a universal system having the smallest number of rules. In this article we present different rule minimization strategies for MPMRS based on encodings and structural transformations. We apply these strategies to the translation of a small universal register machine (Korec (1996) [9]) and we show that there exists a universal MPMRS with 23 rules. Since MPMRS are identical to a restricted variant of P systems with antiport rules, the results we obtained improve previously known results on the number of rules for those systems.     

基于NSGA-II的扩展置信规则库激活规则多目标优化方法

针对扩展置信规则库（extended belief rule base,EBRB）系统在不一致的激活规则过多时推理准确性不高的问题,引入带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ）,提出一种基于NSGA-Ⅱ的激活规则多目标优化方法。该方法首先将激活权重大于零的规则（即激活规则）进行二进制编码,把最终参与合成推理的激活规则集合的不一致性以及激活权重和作为多目标优化问题的目标函数,通过带精英策略的快速非支配排序遗传算法求解不一致性更小的激活规则集合,从而降低不一致激活规则对于EBRB系统推理准确性的影响。为了验证本文方法的有效性和可行性,引入非线性函数和输油管道检漏实例进行测试。实验结果表明,基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法能够有效提高EBRB系统的推理能力。     

Production systems with negation as failure

We study action rule-based systems with two forms of negation, namely classical negation and "negation as failure to find a course of action". We show, by means of several examples, that adding negation as failure to such systems increases their expressiveness in the sense that real-life problems can be represented in a natural and simple way. Then we address the problem of providing a formal declarative semantics to these extended systems by adopting an argumentation-based approach which has been shown to be a simple unifying framework for understanding the declarative semantics of various nonmonotonic formalisms. In this way, we naturally define the grounded (well-founded), stable and preferred semantics for production systems with negation as failure. Next, we characterize the class of stratified production systems, which enjoy the properties that the above-mentioned semantics coincide and that negation as failure to find a course of action can be computed by a simple bottom-up operator. Stratified production systems can be implemented on top of conventional production systems in two ways. The first way corresponds to the understanding of stratification as a form of priority assignment between rules. We show that this implementation, though sound, is not complete in the general case. Hence, we propose a second implementation by means of an algorithm which transforms a finite stratified production system into a classical one. This is a sound and complete implementation, though it is computationally hard     

Multilevel analysis of classical conditioning in a behaving real world artifact

The explanation of behavior requires a multilevel approach taking into account the properties of the nervous system, the phenotype (e.g., body structure) and the environment. In this paper we present an illustration of such an approach, synthetic neural modeling. We will analyse the behavior of Darwin IV, which is comprised of a real world artifact (NOMAD) and a simulated nervous system, during a block sorting task. In this study we focus on the behavioral implications of different rules governing synaptic plasticity. More specifically the concept of value-dependent learning and the role of diffuse ascending systems will be addressed. Some properties of value-dependent learning will be demonstrated by comparing it to two simulated mechanisms for synaptic change derived from Hebbian principles. Differences in performance are explained in terms of differences in the mechanisms governing the potentiation and depression of synaptic strength. The present results form a small part of a larger project focused on understanding the brain mechanism involved in classical conditioning.     

Evaluation of criteria for selecting temperature control trays in distillation columns

The use of tray temperatures to infer compositions is widespread in distillation control. A number of criteria have been proposed for selecting which trays to hold at constant temperature. The most commonly used are (1) choosing a tray where there are large changes in temperature from tray to tray (“slope” of the temperature profile), (2) finding the tray where there is the largest change in temperature for a change in the manipulated variable (“sensitivity”), (3) using singular value decomposition (SVD) analysis, (4) selecting the tray where the temperature does not change as feed composition changes while producing the desired distillate and bottoms purities and (5) choosing the tray that produces the smallest changes in product purities when it is held constant in the face of feed composition disturbances.This paper provides a quantitative comparison of the effectiveness of these five alternative criteria. Several systems are considered, ranging from ideal binary to azeotropic multi-component. Results show that SVD analysis provides a simple and effective method for selecting temperature control tray location.     

Maximal consistent extensions of information systems relative to their theories

Knowledge encoded in information systems can be represented by different sets of rules generated by these systems. One can consider sets of deterministic, nondeterministic or probabilistic rules. Such sets of rules can be treated as theories of information systems. Any such a theory generated from a given information system corresponds to a subjective view on knowledge encoded in this information system. Such theories can be used for solving different problems. For example, the maximal consistent extensions of information systems were studied for synthesis of concurrent processes specified by information systems. In this approach, the maximal consistent extension of a given information system consists of all objects perceived by means of attributes which are consistent with the theory including all the so called true and realizable deterministic rules extracted from the original information system. In this paper, we report results on the maximal consistent extensions of information systems relative to some other theories of information systems, e.g., theories consisting of rules such as true and realizable inhibitory rules, true inhibitory rules, and true deterministic rules. We also discuss algorithmic problems related to the maximal consistent extensions. In particular, from the obtained results it follows that solutions based on these new sets of rules, e.g., on inhibitory rules can be of higher quality than in the case of deterministic rules.