Traditionally, permutation scheduling in Flow Shop problems have been used as maintaining the same task order across all the machines. However, this definition becomes ambiguous when operations are missing [16]. Alternative definitions, such as the FIFO-based definition introduced by Pinedo [18], are more suitable in those contexts. The present study first analyzes Pinedo’s definition in Flow Shop problems with missing operations, then extends this approach to Job Shop scheduling, examining its implications with respect to the quality of the solutions.

Research on the shortest path in networks to maintain connectivity in the Internet of Things (IoT) remains an important issue for determining minimal routes, especially in terms of time and distance, between two devices at distinct points (i.e., nodes) of the network. Many constraints exist for IoT smart devices for transmitting a large amount of information and data, such as limited resources, energy, and time consumption, as well as the potential for overwhelmed communication traffic. Several algorithms were designed and implemented to address these problems that can be simulated and considered as information message passing. The search space is often modeled by a graph, where each node corresponds to a location of a smart device, and the edges represent the paths or links that carry messages, while the absence of a path between two nodes designates a communication breakdown or obstacle. Existing pathfinding algorithms are incorporated in applications, such as Google Maps, rescue people, video games, online packet routing, and rescue applications used in harsh environments. For these latter scenarios, the infrastructure for various technologies of communication becomes vulnerable and dysfunctional, so maintaining connectivity and finding the shortest path becomes a priority. Our goal is to remedy this problem by taking advantage of modernized peer-to-peer wireless technologies, such as Wi-Fi Direct, which can be improved through autonomous wireless technology kits like Lopy 4 of Pycom, and through two alternatives of moving devices (nodes) or service drones. This paper investigates several shortest path algorithms and identifies three case studies to maintain connectivity in harsh environments.

Historical data contained in event logs can reveal important insights about the execution of a business process. In particular, the trends computed by processing and analyzing the sequence of events generated by multiple instances of the same process serve as the basis to produce forecasts about current executions of the process. In this paper, we join the concepts of event stream processing and machine learning to create a framework that allows the computation of various kinds of predictions on event logs. The proposed framework is generic: by providing different definitions to a handful of event functions, multiple different types of predictions can be computed using the same basic workflow. The approach has been implemented and experimentally evaluated by extending an existing event stream processing engine.

Information systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow parameters are defined. This process has been implemented and tested in a real-world event stream processing tool, called BeepBeep. Experimental results show that deviations from a reference trend can be detected in realtime for streams producing up to thousands of events per second.

We present here new results and algorithms for the Linear Arrangement Problem (LAP).We first propose a new lower bound, which links LAP with the Max Cut Problem, and derive a LIP model as well as a branch/bound algorithm for the general case.Then we focus on the case of interval graphs: we first show that our lower bound is tight for unit interval graphs, and derive an efficient polynomial time approximation algorithm for general interval graphs.I.