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Structured Approach to Fuzzy Reasoning
Published in Konar Amit, Artificial Intelligence and Soft Computing, 2018
The algorithm for cycle-detection consists of several procedures. Procedure Find-places-on-cycle determines the set of places Sk that lie on cycles with k transitions. Procedure Find-IRS-places saves in Lk the connectivity between pairs of immediately reachable set of places, lying on cycles with k transitions. Procedure Find-connected-places-on-cycles determines the list of places ordered according to their immediate reachability on cycles with k transitions and saves them in Newlistkk. Procedure Put-transitions positions appropriate transitions in the list of places in Newlistk, so that places preceding and following a transition in the modified list Finallistk are its input and output places on a cycle with k-transitions. The variable ‘cycles’ in procedure Cycle-detection denotes the list of cycles.
Introduction
Published in Joseph Y.-T. Leung, Handbook of SCHEDULING, 2004
After a cycle is implemented and the solution is updated, 3 -opt is applied to maintain within route optimality. Now, the improvement graph needs to be regenerated based on the updated solution. The practice of only updating the improvement graph rather than reconstructing it all over again is evaluated. Although it would save some time, it was insignificant comparing with the implementation complications it would invoke. Since our graph is quite big and many routes are involved in a single iteration, updating instead of reconstructing it is not very beneficial. Furthermore, since the computational time spent on constructing the improvement graph is of the same order as the time spent on finding a negative cycle, it would probably not help much even if the time on generating the improvement graph can be cut down, unless a better algorithm is also used for negative cycle detection. For problems of smaller sizes, where the number of routes that are affected in any particular iteration are much smaller than the total number of routes, updating the improvement graph rather than reconstructing it can save significant amount of time.
Genetic algorithms for planning and scheduling engineer-to-order production: a systematic review
Published in International Journal of Production Research, 2023
Anas Neumann, Adnene Hajji, Monia Rekik, Robert Pellerin
Three main classes of genetic algorithms are used for scheduling problems: Permutation-based Genetic Algorithm (PGA), Priority Value-based Genetic Algorithm (PVGA), and Priority Rule based Genetic Algorithm (PRGA) (Hartmann 1998). According to the studied problem, permutation-based encoded chromosomes can be seen either as the actual sequence of activities or as a combined and flattened sequence that contains the operations of several resources. The individuals of a PVGA encode only the relative priority of each activity or operation (used in 5 reviewed papers). Finally, PRGA individuals encode a priority rule (like Shortest Processing Time first (SPT) or Earliest Due Date first (EDD)) or a list of priority rules instead of the operations. Hence, the actual sequences are often obtained during the decoding stage. Executed at least once for each generated solution, the decoding procedure must be relatively fast (Whitley 1994). Permutation and priority value-based encoding formats are the most used (Katoch, Chauhan, and Kumar 2021; Singh, Panchal, and Singh 2018) and PRGAs are not used in the reported papers. While some PGAs encoding formats (like AS, used in 12 papers) model the complete list of operations to execute (Hartmann 1998, 2002), others represent only the related job or product number to avoid deadlocks. Designed for scheduling problems with operations having at most one predecessor, one successor, and finish-start precedence relations without overlapping or synchronisation (otherwise known as ‘serial precedence relations’), the so-called OS format represents all operations and resources in a single sequence. Only the job number appears and is repeated according to its number of operations. The main advantage is to obtain a cycle-free sequence and avoid time-consuming cycle detection (X. Li and Gao 2016). The OS format is used by 3 of the reviewed algorithms.
Maritime cognitive radio spectrum sensing based on multi-antenna cyclostationary feature detection
Published in International Journal of Electronics, 2020
Jingbo Zhang, Feng Ran, Da Liu
In (Sadeghi & Azmi, 2008), a multi-antenna receiver diversity scheme using SUM-MSDF algorithm is proposed. This scheme first makes independent soft decisions on each antenna, and then converts the decision results of each antenna into a yield problem for fusion decision. The results show that the scheme can improve the reliability of PU signal detection. Since each individual antenna unit needs to adopt a multi-cycle detection algorithm, the implementation complexity of the scheme is relatively high.
CyDER – an FMI-based co-simulation platform for distributed energy resources
Published in Journal of Building Performance Simulation, 2019
Thierry S. Nouidui, Jonathan Coignard, Christoph Gehbauer, Michael Wetter, Jhi-Young Joo, Evangelos Vrettos
For the initialization of the coupled system, the master algorithm supports initialization based on graph cycle detection (Andersson 2015), which detects cycles by analysing the dependency information between inputs and outputs of the coupled system. These input–output dependencies can be optionally provided by the FMUs. The graph cycle detection results in a schedule for propagating outputs to inputs and invoking the FMU to produce new outputs.