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Manufacturing companies typically use sophisticated production planning systems optimizing production steps, often delivering near-optimal solutions. As a downside for delivering a near-optimal schedule, planning systems have high computational demands resulting in hours of computation. Under normal circumstances this is not issue if there is enough buffer time before implementation of the schedule (e.g. at night for the next day). However, in case of unexpected disruptions such as delayed part deliveries or defectively manufactured goods, the planned schedule may become invalid and swift replanning becomes necessary. Such immediate replanning is unsuited for existing optimal planners due to the computational requirements. This paper proposes a novel solution that can effectively and efficiently perform replanning in case of different types of disruptions using an existing plan. The approach is based on the idea to adhere to the existing schedule as much as possible, adapting it based on limited local changes. For that purpose an agent-based scheduling mechanism has been devised, in which agents represent materials and production sites and use local optimization techniques and negotiations to generate an adapted (sufficient, but non-optimal) schedule. The approach has been evaluated using real production data from Huawei, showing that efficient schedules are produced in short time. The system has been implemented as proof of concept and is currently reimplemented and transferred to a production system based on the Jadex agent platform. [ABSTRACT FROM AUTHOR]

PID controllers are one of the most popular types of controllers found in the industry; they require determining three real values to minimize the error over time and to deal with specific process requirements. Finding such values has been subjected to extensive research, and many popular algorithms and methods exist to accomplish this. One of these methods is Simulated Annealing. In this paper, we study the use of the re-annealing characteristic of Adaptive Simulated Annealing (ASA) for PID tuning in 20 benchmark systems. This adaptive version gives special treatment to each parameter of the search space. We compare the results of ASA with a simple SA algorithm. An extra comparison, with a Particle Swarm Optimization algorithm, was made to provide some information on how ASA behaves compared against another optimization based method. The results show that using an adaptive algorithm effectively improves the performance of the tested systems. [ABSTRACT FROM AUTHOR]

Rapidly changing detrimental safety conditions on an evacuation route might cause casualties and provoke panic. This is why we need to take into account these conditions for real-time route optimization. The objective of this paper is to facilitate efficient people evacuation through dynamic optimization of evacuation routes based on the safety conditions on the route and stress-related evacuees' responses to the same. We propose a multi-agent based evacuation route optimization architecture for smart space networks that considers the influence of stress on human reactions to the recommended routes. This architecture is meant as a real-time decision support in route selection that recommends routes to evacuees, possibly through smart phone or smart building displays. The functioning of the architecture is shown on an example of a simple smart space network. [ABSTRACT FROM AUTHOR]

Weighted Partial MaxSAT (WPMS) is a well-known optimization variant of Boolean Satisfiability (SAT) that finds a wide range of practical applications. WPMS divides the formula in two sets of clauses: The hard clauses that must be satisfied and the soft clauses that can be falsified with a penalty given by their associated weight. However, some applications may require each original constraint to be modeled as a set or group of clauses. Then, the cost or penalty of falsifying one or several clauses of the same group is exactly the same. The resulting formalism is referred to as Group MaxSAT. This paper overviews Group MaxSAT, and shows how several optimization problems can be modeled as Group MaxSAT. Several encodings from Group MaxSAT to standard MaxSAT are formalized and refined. A comprehensive empirical study compares the performance of several MaxSAT solvers with the proposed encodings. The results indicate that, depending on the underlying MaxSAT solver and problem domain, the solver may perform better with a given encoding than with the others. [ABSTRACT FROM AUTHOR]

This paper heuristically tackles a challenging scheduling problem arising in the field of hydraulic distribution systems in case of a contamination event, that is, optimizing the scheduling of a set of tasks so that the consumed volume of contaminated water is minimized. Each task consists of manually activating a given device, located on the hydraulic network of the water distribution system. In practice, once contamination has been detected, a given number of response teams move along the network to operate each device on site. The consumed volume of contaminated water depends on the time at which each device is operated, according to complex hydraulic laws, so that the value associated to each schedule must be evaluated by a hydraulic simulation.We explore the potentials of Genetic Algorithms as a viable tool for tackling this optimization-simulation problem. We compare different encodings and propose ad hoc crossover operators that exploit the combinatorial structure of the feasible region, featuring hybridization with Mixed Integer Linear Programming.Extensive computational results are provided for a real life hydraulic network of average size, showing the effectiveness of the approach. Indeed, we greatly improve upon common sense inspired solutions which are commonly adopted in practice. [ABSTRACT FROM AUTHOR]