Controlling lead times and minor ordering costs in the joint replenishment problem with stochastic demands under the class of cyclic policies

In this paper, we consider the periodic review joint replenishment problem under the class of cyclic policies. For each item, the demand in the protection interval is assumed stochastic. Moreover, a fraction of shortage is lost, while the other quota is backordered. We suppose that lead times and minor ordering costs are controllable. The problem concerns determining the cyclic replenishment policy, the lead times, and the minor ordering costs in order to minimize the long‐run expected total cost per time unit. We established several properties of the cost function, which permit us to derive a heuristic algorithm. A lower bound on the minimum cost is obtained, which helps us to evaluate the effectiveness of the proposed heuristic. The heuristic is also compared with a hybrid genetic algorithm that is specifically developed for benchmarking purposes. Numerical experiments have been carried out to investigate the performance of the heuristic.     

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

Nowadays, developing effective techniques able to deal with data coming from structured domains is becoming crucial. In this context kernel methods are the state-of-the-art tool widely adopted in real-world applications that involve learning on structured data. Contrarily, when one has to deal with unstructured domains, deep learning methods represent a competitive, or even better, choice. In this paper we propose a new family of kernels for graphs which exploits an abstract representation of the information inspired by the multilayer perceptron architecture. Our proposal exploits the advantages of the two worlds. From one side we exploit the potentiality of the state-of-the-art graph node kernels. From the other side we develop a multilayer architecture through a series of stacked kernel pre-image estimators, trained in an unsupervised fashion via convex optimization. The hidden layers of the proposed framework are trained in a forward manner and this allows us to avoid the greedy layerwise training of classical deep learning. Results on real world graph datasets confirm the quality of the proposal.     

Markovian agent modeling swarm intelligence algorithms in wireless sensor networks

Wireless Sensor Networks (WSN) are large networks of tiny sensor nodes that are usually randomly distributed over a geographical region. The network topology may vary in time in an unpredictable manner due to many different causes. For example, in order to reduce power consumption, battery operated sensors undergo cycles of sleeping–active periods; additionally, sensors may be located in hostile environments increasing their likelihood of failure; furthermore, data might also be collected from a range of sources at different times. For this reason multi-hop routing algorithms used to route messages from a sensor node to a sink should be rapidly adaptable to the changing topology. Swarm intelligence has been proposed for this purpose, since it allows the emergence of a single global behavior from the interaction of many simple local agents. Swarm intelligent routing has been traditionally studied by resorting to simulation. The present paper aims to show that the recently proposed modeling technique, known as Markovian Agent Model (MAM), is suited for implementing swarm intelligent algorithms for large networks of interacting sensors. Various experimental results and quantitative performance indices are evaluated to support this claim. The validity of this approach is given a further proof by comparing the results with those obtained by using a WSN discrete event simulator.     

On the role of tests in test-driven development: a differentiated and partial replication

Background: Test-Driven Development (TDD) is claimed to have positive effects on external code quality and programmers’ productivity. The main driver for these possible improvements is the tests enforced by the test-first nature of TDD as previously investigated in a controlled experiment (i.e. the original study). Aim: Our goal is to examine the nature of the relationship between tests and external code quality as well as programmers’ productivity in order to verify/ refute the results of the original study. Method: We conducted a differentiated and partial replication of the original setting and the related analyses, with a focus on the role of tests. Specifically, while the original study compared test-first vs. test-last, our replication employed the test-first treatment only. The replication involved 30 students, working in pairs or as individuals, in the context of a graduate course, and resulted in 16 software artifacts developed. We performed linear regression to test the original study’s hypotheses, and analyses of covariance to test the additional hypotheses imposed by the changes in the replication settings. Results: We found significant correlation (Spearman coefficient = 0.66, with p-value = 0.004) between the number of tests and productivity, and a positive regression coefficient (p-value = 0.011). We found no significant correlation (Spearman coefficient = 0.41 with p-value = 0.11) between the number of tests and external code quality (regression coefficient p-value = 0.0513). For both cases we observed no statistically significant interaction caused by the subject units being individuals or pairs. Further, our results are consistent with the original study although there were changes in the timing constraints for finishing the task and the enforced development processes. Conclusions: This replication study confirms the results of the original study concerning the relationship between the number of tests vs. external code quality and programmer productivity. Moreover, this replication allows us to identify additional context variables, for which the original results still hold; namely the subject unit, timing constraint and isolation of test-first process. Based on our findings, we recommend practitioners to implement as many tests as possible in order to achieve higher baselines for quality and productivity.     

STUDYING THE EFFECTS OF STRESS ON NEGOTIATION BEHAVIOR

Negotiation is a collaborative activity that requires the participation of different parties whose behaviors influence the outcome of the whole process. The work presented here focuses on the identification of such behaviors and their impact on the negotiation process. The premise for this study is that identifying and cataloging the behavior of parties during a negotiation may help to clarify the role that stress plays in the process. To do so, an experiment based on a negotiation game was implemented. During this experiment, behavioral and contextual information about participants was acquired. The data from this negotiation game were analyzed in order to identify the conflict styles used by each party and to extract behavioral patterns from the interactions, useful for the development of plans and suggestions for the associated participants. The work highlights the importance of the knowledge about social interactions as a basis for informed decision support in situations of conflict.     

Effectiveness of a multidevice 3D virtual environment application to train car service maintenance procedures

This paper reports a study which demonstrates the advantages of using virtual-reality-based systems for training automotive assembly tasks. Sixty participants were randomly assigned to one of the following three training experiences to learn a car service procedure: (1) observational training through video instruction; (2) an experiential virtual training and trial in a CAVE; and (3) an experiential virtual training and trial through a portable 3D interactive table. Results show that virtual trained participants, after the training, can remember significantly better (p < .05) the correct execution of the steps compared to video-trained trainees. No significant differences were identified between the experiential groups neither in terms of post-training performances nor in terms of proficiency, despite differences in the interaction devices. The relevance of the outcomes for the automotive fields and for the designers of virtual training applications are discussed in light of the outcomes, particularly that virtual training experienced through a portable device such as the interactive table can be effective, as can training performed in a CAVE. This suggests the possibility for automotive industries to invest in advanced portable hardware to deliver effectively long-distance programs of training for car service operators placed all over the world.     

Robust explicit model predictive control via regular piecewise-affine approximation

This paper proposes an explicit model predictive control design approach for regulation of linear time-invariant systems subject to both state and control constraints, in the presence of additive disturbances. The proposed control law is implemented as a piecewise-affine function defined on a regular simplicial partition, and has two main positive features. First, the regularity of the simplicial partition allows one to efficiently implement the control law on digital circuits, thus achieving extremely fast computation times. Moreover, the asymptotic stability (or the convergence to a set including the origin) of the closed-loop system can be enforced a priori, rather than checked a posteriori via Lyapunov analysis.     

Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search

International patent corpus is a gigantic source containing today about 80 million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO.This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.