Managing the tabu list length using a fuzzy inference system: an application to examination timetabling

Pais, Tiago C.; Amaral, Paula

doi:10.1007/s10479-011-0867-6

Cited by 14 publications

(5 citation statements)

References 55 publications

(65 reference statements)

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“…Pais and Amaral [22] implemented an improved tabu search for the examination timetabling. Here, tabu list is automatically tuned by a fuzzy inference rule-based system (FIRBS), and this improves tabu search in exploring a promising area of solution space.…”

Section: Related Workmentioning

confidence: 99%

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Mandal

Kahar

2020

IJEEI

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Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing a feasible examination timetable, while meta-heuristics are applied for improving the solution quality. In this paper, we present the combination of graph heuristics and major trajectory metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristics with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill-climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 datasets, respectively. Experimental results indicate that hybrid graph heuristics produce the best initial solutions across both these benchmark problems. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark datasets. Moreover, the performance of our method is comparable with other approaches in the scientific literature.

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Section: Related Workmentioning

confidence: 99%

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Mandal

Kahar

2020

IJEEI

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“…Conventionally, fixed length tabu lists are usually implemented, but in [10], it was shown that fixed length list cannot always prevent cycling as such variable length list may also be considered. In [16], a fuzzy inference rule based system (FIRBS), based on frequency and inactivity, was developed and used to manage the tabu list length for examination timetabling, resulting in improved performance.…”

Section: Tabu Search (Ts)mentioning

confidence: 99%

Tabu Search with Explicit Adaptive Guiding Heuristic for the Examination Timetabling Problem

Ezike¹

2020

NIPES

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Tabu Search algorithm (TS) is one of the leading methods for solving the Examination Timetabling Problem (ETP). The challenge with TS which uses only one solution in searching is that it can easily miss some areas of the search space and chose the best solution based on the current search step and position without taking the future steps into consideration. To address these problems, an explicit and adaptive guiding heuristic was proposed for the standard TS algorithm with the goal of enhancing its performance. The standard TS and the TS with the explicit adaptive guiding mechanism, the TS_G algorithm were implement using the Java programing language (version 8.92) with the MySQL database and experimented with data from Bells University of Technology, Ota. Performance of the two algorithms were evaluated using first Order Conflict Counts (OCC), Second OCC and third OCC for students and invigilators respectively, in addition to memory consumption. The TS and TS_G yielded an average first OCC 0.0 and 0.0 students and 0.0 and 0.0 for invigilators. Similarly, TS and TS_G yielded average second OCC of 18.8 and 10.5 for students and, 0.0 and 0.0 for invigilators respectively. Furthermore, TS and TS_G yielded average third OCC of 33375.0 and 9922.1 for students and 0.0 and 0.0 for invigilators, respectively. The TS_G algorithm outperformed the standard TS algorithm with lower conflicts violations. Both algorithms however recorded similar memory consumption.

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“…One last question to be solved in order to have specified all the elements of the model (8) is to determine the b j , i.e., the proportion allocated to each mental account. To do so, we propose to use fuzzy inference systems (Mamdani and Assilian 1975;Takagi and Sugeno 1985;Jang 1993;Pais and Amaral 2012).…”

Section: Determining the Aspiration Levels For The Financial Objectivmentioning

confidence: 99%

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

et al. 2015

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The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if-then rules.

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Managing the tabu list length using a fuzzy inference system: an application to examination timetabling

Cited by 14 publications

References 55 publications

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Tabu Search with Explicit Adaptive Guiding Heuristic for the Examination Timetabling Problem

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

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