scholarly journals A New Constructive Heuristic for the Fm|block|ΣT

2014 ◽  
pp. 285-291
Author(s):  
Ramon Companys Pascual ◽  
Imma Ribas Vila
Keyword(s):  
Constructive Heuristic

An efficient constructive heuristic for flowtime minimisation in permutation flow shops

Omega ◽  
2003 ◽  
Vol 31 (4) ◽  
pp. 311-317 ◽  
Author(s):  
J.M. Framinan ◽  
R. Leisten
Keyword(s):  
Flow Shops ◽  
Constructive Heuristic

An efficient constructive heuristic for optimizing frequency allocation in cellular network

2017 ◽  
Author(s):  
Saiful Izwan Suliman ◽  
Ismail Musirin ◽  
Roslina Mohamad ◽  
Murizah Kassim ◽  
Mohd Faizul Md Idros
Keyword(s):  
Cellular Network ◽  
Frequency Allocation ◽  
Constructive Heuristic

A Novel Constructive Heuristic for Flowshop Group Scheduling Problems to Minimize Total Flow Time

2021 ◽  
Author(s):  
Xuan He ◽  
Yu-Fan Liu ◽  
Quan-Ke Pan ◽  
Yang Yu ◽  
Yuan-Zhen Li
Keyword(s):  
Flow Time ◽  
Group Scheduling ◽  
Scheduling Problems ◽  
Total Flow ◽  
Total Flow Time ◽  
Constructive Heuristic

scholarly journals Mathematical models and a constructive heuristic for finding minimum fundamental cycle bases

2005 ◽  
Vol 15 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Leo Liberti ◽  
Edoardo Amaldi ◽  
Francesco Maffioli ◽  
Nelson Maculan
Keyword(s):  
Linear Programming ◽  
Total Cost ◽  
Fundamental Cycle ◽  
Cycle Basis ◽  
Cpu Time ◽  
Constructive Heuristic ◽  
Cycle Bases ◽  
Time Required ◽  
Application Fields

The problem of finding a fundamental cycle basis with minimum total cost in a graph arises in many application fields. In this paper we present some integer linear programming formulations and we compare their performances, in terms of instance size, CPU time required for the solution, and quality of the associated lower bound derived by solving the corresponding continuous relaxations. Since only very small instances can be solved to optimality with these formulations and very large instances occur in a number of applications, we present a new constructive heuristic and compare it with alternative heuristics.

Integrating Individual and Aggregate Diversity in Top-N Recommendation

2020 ◽  
Author(s):  
Ethem Çanakoğlu ◽  
İbrahim Muter ◽  
Tevfik Aytekin
Keyword(s):  
Information Overload ◽  
Programming Model ◽  
User Behavior ◽  
Nonlinear Integer Programming ◽  
Data Sets ◽  
Objective Functions ◽  
Web Technologies ◽  
User Interests ◽  
Constructive Heuristic ◽  
Main Components

Recommender systems have become one of the main components of web technologies that help people to cope with information overload. Based on the analysis of past user behavior, these systems filter items according to users’ likes and interests. Two of the most important metrics used to analyze the performance of these systems are the accuracy and diversity of the recommendation lists. Whereas all the efforts exerted in the prediction of the user interests aim at maximizing the former, the latter emerges in various forms, such as diversity in the lists across all user recommendation lists, referred to as aggregate diversity, and diversity in the lists of individuals, known as individual diversity. In this paper, we tackle the combination of these three objectives and justify this approach by showing through experiments that handling these objectives in pairs does not yield satisfactory results in the third one. To that end, we develop a mathematical model that is formulated using multiobjective optimization approaches. To cope with the intractability of this nonlinear integer programming model, its special structure is exploited by a decomposition technique. For the solution of the resulting formulation, we propose an iterative framework that is composed of a clique-generating genetic algorithm, a constructive heuristic, and an improvement heuristic. The former is designed to incorporate all objective functions into the generated cliques and specifically impose a certain level of individual diversity, whereas the latter chooses one clique for each user such that the desired aggregate diversity level is fulfilled. We conduct experiments on three data sets and show that the proposed modeling approach successfully handles all objectives according to the needs of the system and that the proposed methodology is capable of yielding good upper bounds.

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