Large-Scale Refinery Crude Oil Scheduling by Integrating Graph Representation and Genetic Algorithm

Abstract

Scheduling is widely studied in process systems engineering and is typically solved using mathematical programming. Although popular for many other optimization problems, evolutionary algorithms have not found wide applicability in such combinatorial optimization problems with large numbers of variables and constraints. Here we demonstrate that scheduling problems that involve a process network of units and streams have a graph structure which can be exploited to offer a sparse problem representation that enables efficient stochastic optimization. In the proposed structure adapted genetic algorithm, SAGA, only the subgraph of the process network that is active in any period is explicitly represented in the chromosome. This leads to a significant reduction in the representation, but additionally, most constraints can be enforced without the need for a penalty function. The resulting benefits in terms of improved search quality and computational performance are established by studying 24 different crude oil operations scheduling problems from the literature.

A novel application of genetic algorithm for synthesizing optimal water reuse network with multiple objectives

“…Among the various stochastic optimization methods, Genetic Algorithms (GAs) have been commonly used (Tsai and Chang, 2001;Prakotpol and Srinophakun, 2004;Lavric et al, 2005;Tudor and Lavric, 2010) although other techniques such as adaptive random search (Jeżowski et al, 2003;Poplewski and Jeżowski, 2007) have also been proposed. While GA is often computationally slower than deterministic methods and may not guarantee a global optima, they are more robust in the sense that they are able to handle discontinuous functions and obtain good quality solutions even for large-scale combinatorial problems within reasonable computational times (Ramteke and Srinivasan, 2012). Another advantage of GAs over deterministic methods is that GA techniques are inherently more suitable for multi-objective problems.…”

“…These chromosome values are then used to calculate the objective function(s) of the TWN problem. These objective function(s) are next converted to a fitness function which serves as an indicator of the suitability of each chromosome population as a solution to the problem (Ramteke and Srinivasan, 2012). Subsequently, these chromosome values are further assessed using the concept of non-domination for generating the next generation of chromosomes.…”

“…complex superstructures involving four PUs and above, we could obtain solutions that are very close to the global optima (<1% difference). Such a shortfall in obtaining the global optima is common with stochastic approaches and can be attributed to their slower convergence as they approach an optimum (Ramteke and Srinivasan, 2012). Next, we will illustrate our approach by solving two multi-objective case studies.…”

Efficient Two-Level Hybrid Algorithm for the Refinery Production Scheduling Problem Involving Operational Transitions

“…As a category of stochastic optimization algorithms, meta-heuristics are effective for problems with large search space and can obtain satisfactory solutions within reasonable times. The popular metaheuristics include genetic algorithm, 37 particle swarm optimization (PSO), 38 ant colony optimization, 39 cuckoo search algorithm, 40 etc. However, meta-heuristics cannot guarantee the quality of final solutions obtained and have difficulty in representing complex constraints to find feasible solutions.…”

Otimização Multiobjetivo Da Programação De Petróleo Em Refinaria Por Programação Genética Em Linguagem Específica De Domínio

“…Estudos foram e continuam sendo realizados utilizando a técnica de algoritmos genéticos (AG) para otimização da atividade de programação da produção [3,8,9,10]. Entretanto, um fator representa um desafio sobre essa modelagem: a complexidade do problema, que implica em indivíduos (cromossomos) de tamanho muito grande cuja representação compromete o desempenho do sistema.…”