Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem

Nogareda, Ana Maria; Camacho, David

doi:10.1007/s00500-016-2375-8

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The ACO meta‐heuristic is based on the foraging behaviour of ants, which deposit pheromones on the paths they use (Dorigo et al, 2006; Nogareda and Camacho, 2016; Stützle and Hoos, 2000). In ACO, virtual ants deposit pheromones on the solution they find.…”

Section: Aco and Ga Approaches For The Rvrpmentioning

confidence: 99%

On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

et al. 2020

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This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta-heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as main approach and GA as local search. GA-ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO-GA approach outperforms the others. Considering the cost, ACO, GA, and GA-ACO provide better results. Regarding computation time, GA and GA-ACO have been found the most competitive among the benchmark.

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Section: Aco and Ga Approaches For The Rvrpmentioning

confidence: 99%

On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

et al. 2020

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“…For example, when seats are allocated to students in a university, students may submit preferences for the courses, in which case, the simple objective might be to maximize students' satisfaction [8]. If professors have also preferences for students, because of their background or their grades, then both types of preferences, students' and professors', have to be considered in order to find a good solution to the problem.…”

Section: Introductionmentioning

confidence: 99%

“…If any of those constraints is not fully satisfied in a solution, then this solution will not be feasible. We can find for example capacity constraints [5,7,8], or skills constraints [4].…”

Section: Introductionmentioning

confidence: 99%

“…In the early years of this metaheuristic, most of the problems where ACO was used could be represented with a graph, as the Travelling Salesman Problem [26], but very quickly ACO algorithms spread and were used in many different fields, for example an ant colony system has been used to define the location of hubs in a network and to allocate each node of this network to one of the hubs [27]. ACO algorithms have also been adapted to solve a MORAP [28], a timetabling problem [29] or a course allocation problem [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Constraint-Based Approach for Classes Setting-Up Problems in Secondary Schools

Nogareda¹,

Camacho²

2017

Int. j. simul. model.

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In this paper, we present the problem of allocating students to classes in Swiss secondary schools, where students have different profiles due to their level in some fields or to the options they attend. The pedagogical objective is to have a high diversity of profiles within a class and similarity between classes. In order to achieve this goal, the problem is modelled as a resource allocation problem (RAP), where students are resources, using a constraint satisfaction optimisation approach (CSOP). The RAP is then solved in two different ways, with a solver for CSOP, and with an ant colony optimisation algorithm (ACO). Eight real datasets are used to compare their performance. The ACO algorithm provides better solutions than the CSOP solver in a shorter time. Results show that the pheromones used in the ACO help to find better solutions in a much smaller amount of time. The short computation time enables the school's directors to simulate different compositions of their future classes before having the final results of the last exams.

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EduBot: A Proof-of-Concept for a High School Motivational Agent

Faria

Barbosa²,

Veloso³

et al. 2022

Intelligent Data Engineering and Automated Learning – IDEAL 2022

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Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem

Cited by 4 publications

References 18 publications

On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

On the design of hybrid bio‐inspired meta‐heuristics for complex multiattribute vehicle routing problems

A Constraint-Based Approach for Classes Setting-Up Problems in Secondary Schools

EduBot: A Proof-of-Concept for a High School Motivational Agent

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