Heuristics for the Periodic Mobile Piston Pump Unit Routing Problem

Andrade, Marcos R. Q. de; Ochi, Luiz Satoru; Martins, Simone L.

doi:10.4018/ijncr.2015010101

Cited by 1 publication

(1 citation statement)

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“…The application of meta-heuristics for solving these optimization problems presents a new paradigm, because it does not guarantee the optimal solution but achieves good feasible solutions with reasonable computational effort. An optimization problem widespread in the literature is the scheduling one and it can be modeled as a combinatorial problem (Andrade et al, 2015). Thus, this work focuses on optimizing the scheduling of oil products distribution network problem.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary computation methods for the schedule optimisation of pipeline networks

Krause

Lopes

2016

IJICA

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Two evolutionary computation methods are presented in this paper, both variants of the Differential Evolution (DE) algorithm. Their main difference is the encoding process (binary and continuous) and both methods were successfully applied to the pipeline network schedule problem. A binary mathematical model is proposed to represent the flow of oil products in a 48 hours horizon period. In this paper, we introduce new benchmarks of the pipeline scheduling problem for testing the proposed evolutionary algorithms on a specific network topology, but with different products and demands. Although computationally expensive, a Mixed Integer Linear Programming (MILP) approach is used to obtain optimal solutions so as to compare results with the evolutionary methods. MILP results achieved optimal solutions for nine out of the fifteen benchmarks proposed, but it requires far more computational effort than the DE-variants. Even though it is a real-parameter algorithm, the DE can be consider as a good heuristic, which is an alternative for the discrete problem studied. The overall comparison of results between the proposed DE-variants and MILP supports the efficiency, robustness and convergence speed of DE algorithm suggesting its usefulness to real-world problems of limited complexity.

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Section: Introductionmentioning

confidence: 99%