Robust Optimization for a Maritime Inventory Routing Problem

Agra, Agostinho; Christiansen, Marielle; Hvattum, Lars Magnus; Rodrigues, Filipe

doi:10.1287/trsc.2017.0814

Cited by 59 publications

(30 citation statements)

References 46 publications

(73 reference statements)

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“…Any diversion of the inventory from this range of safety stock limits was then penalized. Most recently, some authors adopted optimization under uncertainty techniques to address uncertainty in production/consumption rates or in travel time through the adoption of inventory safety levels in inventory planning (Halvorsen-Weare et al, 2013;Agra et al, 2015;Zhang et al 2015;Assis and Campnogara, 2016;Agra et al, 2016;Zhang et al, 2018;Agra et al, 2018).…”

Section: Uncertainty Aspectsmentioning

confidence: 99%

“…To address the uncertain aspects of sailing times and production levels, Halvorsen-Weare et al 2013 as a two-stage stochastic mixed-integer program and proposed a two-phase solution approach that considers disruptions in the planned delivery routes. Agra et al (2018) investigated the use of adaptive robust optimization, where the sailing times are assumed to belong to the well-known budget polytope uncertainty set. In their recourse model, the routing, the order of port visits, and the quantities to load and unload are fixed before the uncertainty is revealed, while the visit time to ports and the stock levels can be adjusted after the uncertainty is observed.…”

Section: Mir Problem Under Uncertaintymentioning

confidence: 99%

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A robust optimization model for the maritime inventory routing problem

2018

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Uncertainty is a highly important aspect of maritime transportation. Unforeseen occurrences related to environmental conditions, poor weather, vessel reliability, or port congestion are frequent and have a non-negligible impact on the total time required for vessels to perform (un)loading operations at ports. We study a special case of maritime transportation named the maritime inventory routing (MIR) problem, in which one must determine the routings of vessels while keeping the inventory levels at ports within the operational limits. In this paper, we propose a robust optimization approach that considers the uncertainty in the total time spent by vessels at the ports. This approach allows the trade-off between the risk of infeasibility (i.e., violating inventory limits at ports) and the increase in operational costs due to the protection against uncertainty events to be assessed. To test the proposed methodology, we used a real-world instance based on the MIR problem faced by a Brazilian petroleum company. In this problem, violating the inventory limits at ports causes considerable financial losses due to consequent interruptions in crude oil production. Our approach supports the decision maker to devise more robust plans in which the risk of violating inventory limits is acceptable. In other words, despite the increase in the operational costs associated with more robust solutions, the approach enables the decision maker to avoid much larger potential costs. For the problem considered, we observed that the probability of infeasibility of the proposed solution may be reduced from 87% to 2%, depending on the level of robustness adopted by the decision maker. However, this increased protection causes an increase of up to 13% in the overall costs.

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Section: Uncertainty Aspectsmentioning

confidence: 99%

Section: Mir Problem Under Uncertaintymentioning

confidence: 99%

A robust optimization model for the maritime inventory routing problem

2018

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“…One of those lines consists of searching for better solutions by exploring neighborhoods of given reference solutions. Some examples are large-neighborhood search (Shaw 1998), iterated local search (Agra et al 2016(Agra et al , 2018aFischetti and Lodi 2003), relaxation induced neighborhood search (Danna et al 2005), feasibility pump (Fischetti et al 2005), proximity search (Fischetti and Monaci 2014), and other methods based on a large-neighborhood search framework such as Rothberg (2007).…”

Section: Introductionmentioning

confidence: 99%

Weighted proximity search

et al. 2021

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Proximity search is an iterative method to solve complex mathematical programming problems. At each iteration, the objective function of the problem at hand is replaced by the Hamming distance function to a given solution, and a cutoff constraint is added to impose that any new obtained solution improves the objective function value. A mixed integer programming solver is used to find a feasible solution to this modified problem, yielding an improved solution to the original problem. This paper introduces the concept of weighted Hamming distance that allows to design a new method called weighted proximity search. In this new distance function, low weights are associated with the variables whose value in the current solution is promising to change in order to find an improved solution, while high weights are assigned to variables that are expected to remain unchanged. The weights help to distinguish between alternative solutions in the neighborhood of the current solution, and provide guidance to the solver when trying to locate an improved solution. Several strategies to determine weights are presented, including both static and dynamic strategies. The proposed weighted proximity search is compared with the classic proximity search on instances from three optimization problems: the p-median problem, the set covering problem, and the stochastic lot-sizing problem. The obtained results show that a suitable choice of weights allows the weighted proximity search to obtain better solutions, for 75$$\%$$ % of the cases, than the ones obtained by using proximity search and for 96$$\%$$ % of the cases the solutions are better than the ones obtained by running a commercial solver with a time limit.

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“…The second factor is the reliability associated with the effect of transportation uncertainty on the oil storage capability level. This is because during the transportation of fossil energy, the transportation system often encounters some unexpected situations, which delay the system operation and affects the reliability of the transportation system [15]. In practice, Industries had to balance multiple conflicting performance criteria to determine optimal scheduling [16].…”

Section: Introductionmentioning

confidence: 99%

Coastal Shuttle Tanker Scheduling Model Considering Inventory Cost and System Reliability

et al. 2020

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The near-sea offshore oil extraction and transportation system use heterogeneous fleets to transfer crude oil from the floating production storage and offloading to the land-based oil storage port. Based on the characteristics of this system, the short sea inventory routing problem is investigated considering the shuttle tanker fleet and inventory management. In order to minimize the total operation cost and maximize the system reliability, a semi-continuous model for the shuttle tanker scheduling problem is established. The model optimizes the tanker scheduling plan and the design of the tanker fleet. To solve the complex model, this paper proposes an improved non-dominated sorting genetic algorithm with differential evolution operator to solve the optimization of the multi-objective model. This research also uses public vessel operation data to test the modeling and optimizing efficiency. The Pareto Fronts associated with the total operation cost and the system reliability from the optimization outcome is analyzed to provide scheduling priority advice. The results indicate that proposed optimization algorithms are effective, and the operation could be optimized with the proposed model and algorithm. INDEX TERMS Maritime inventory routing problem, non-dominated sorting genetic algorithm (NSGA-II), offshore oil transportation, semi-continuous model, shuttle tanker, system reliability.

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Robust Optimization for a Maritime Inventory Routing Problem

Cited by 59 publications

References 46 publications

A robust optimization model for the maritime inventory routing problem

A robust optimization model for the maritime inventory routing problem

Weighted proximity search

Coastal Shuttle Tanker Scheduling Model Considering Inventory Cost and System Reliability

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