A comparison of three metaheuristics for the workover rig routing problem

Ribeiro, Glaydston Mattos; Laporte, Gilbert; Mauri, Geraldo Regis

doi:10.1016/j.ejor.2012.01.031

Cited by 32 publications

(21 citation statements)

References 19 publications

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“…While the OMM uses the variables S imt and S jmh in constraints (3) and (4), the RMM and the DMM do not use the index j of the formulation and use the variable S imh in constraint (20) and the variable S ih in constraint (24), that is, they use fewer constraints. The same number of variables is used in the OMM and the RMM, and fewer are used in the DMM compared to them because it is a mathematical model that does not consider the problem of allocating wells to rigs.…”

Section: Comparison Between the Omm The Rmm And The Dmmmentioning

confidence: 99%

“…Constraint (23) ensures that the start of the intervention in each well occurs only once by a rig at a specific time. Constraint (24) ensures that a maximum of N interventions are performed at a specific time and that there is no interference between the allocations of the wells to rigs. Constraint (25) defines the domain of the decision variables and ensures that the start of the interventions in the wells is within the time window.…”

Section: Decomposed Mathematical Modelmentioning

confidence: 99%

See 1 more Smart Citation

A New Mathematical Model for the Workover Rig Scheduling Problem

2016

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ABSTRACT.One of the most important activities in the oil and gas industry is the intervention in wells for maintenance services, which is necessary to ensure the constant production of oil. These interventions are carried out by workover rigs. Thus, the Workover Rig Scheduling Problem (WRSP) consists of finding the best schedule to service the wells while considering the limited number of rigs with the objective of minimizing the total production loss. In this study, a 0-1 integer linear programming model capable of efficiently solving the WRSP with a homogeneous fleet of rigs is proposed. Computational experiments were carried out using instances based on real cases in Brazil to compare the results obtained by the proposed model with the results reported by other methods. The proposed model was capable of solving all instances considered in a reduced computational time, including the large instances for which only approximate solutions were presently known.

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Section: Comparison Between the Omm The Rmm And The Dmmmentioning

confidence: 99%

Section: Decomposed Mathematical Modelmentioning

confidence: 99%

A New Mathematical Model for the Workover Rig Scheduling Problem

2016

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“…Aloise et al 4 proposed a variable neighborhood search heuristic and tested it on real-world cases in Brazil. Ribeiro et al 11 proposed the clustering search and adaptive large neighborhood search heuristics using instances generated by Neves. 12 Ribeiro et al 13 proposed a mathematical model and a branch-price-and-cut algorithm and generated a group of random instances.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Workover Rig Fleet Sizing and Scheduling Using Deterministic and Stochastic Programming Models

Perez

Oliveira

Hamacher

2018

Ind. Eng. Chem. Res.

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We present deterministic and stochastic programming models for the workover rig problem, one of the most challenging problems in the oil industry. In the deterministic approach, an integer linear programming model is used to determine the rig fleet size and schedule needed to service wells while maximizing oil production and minimizing rig usage cost. The stochastic approach is an extension of the deterministic method and relies on a two-stage stochastic programming model to define the optimal rig fleet size considering uncertainty in the intervention time. In this approach, different scenario-generation methods are compared. Several experiments were performed using instances based on real-world problems. The results suggest that the proposed methodology can be used to solve large instances and produces quality solutions in computationally reasonable times.

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“…Ribeiro et al [33] present a CS to the workover rig routing problem that is a variant of the vehicle routing problem with time windows. The objective is to minimize the total lost production in the operations of onshore oil fields.…”

Section: Cs Related Applicationsmentioning

confidence: 99%

Clustering search

2013

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ABSTRACT. This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization problems. Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into clusters. The CS and its applications are reviewed and a case study for a problem of capacitated clustering is presented.

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A comparison of three metaheuristics for the workover rig routing problem

Cited by 32 publications

References 19 publications

A New Mathematical Model for the Workover Rig Scheduling Problem

A New Mathematical Model for the Workover Rig Scheduling Problem

Optimizing Workover Rig Fleet Sizing and Scheduling Using Deterministic and Stochastic Programming Models

Clustering search

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