An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

Gounaris, Chrysanthos E.; Repoussis, Panagiotis P.; Tarantilis, Christos D.; Wiesemann, Wolfram; Floudas, Christodoulos A.

doi:10.1287/trsc.2014.0559

Cited by 55 publications

(39 citation statements)

References 81 publications

(89 reference statements)

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“…The goal is to determine a single set of vehicle routes as well as the associated fleet size and composition such that the total demand served on any route is less than the associated vehicle capacity, under any realization of the demands in a prespecified uncertainty set. Our paper generalizes the works of [28,27] for the robust CVRP along multiple directions. First, our work addresses not only the CVRP, but also all major variants of the HVRP that have been considered in the literature.…”

Section: Our Contributionsmentioning

confidence: 62%

Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

Subramanyam

Repoussis

Gounaris

2020

INFORMS Journal on Computing

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This paper studies robust variants of an extended model of the classical Heterogeneous Vehicle Routing Problem (HVRP), where a mixed fleet of vehicles with different capacities, availabilities, fixed costs and routing costs is used to serve customers with uncertain demand. This model includes, as special cases, all variants of the HVRP studied in the literature with fixed and unlimited fleet sizes, accessibility restrictions at customer locations, as well as multiple depots. Contrary to its deterministic counterpart, the goal of the robust HVRP is to determine a minimum-cost set of routes and fleet composition that remains feasible for all demand realizations from a pre-specified uncertainty set. To solve this problem, we develop robust versions of classical node-and edge-exchange neighborhoods that are commonly used in local search and establish that efficient evaluation of the local moves can be achieved for five popular classes of uncertainty sets. The proposed local search is then incorporated in a modular fashion within two metaheuristic algorithms to determine robust HVRP solutions. The quality of the metaheuristic solutions is quantified using an integer programming model that provides lower bounds on the optimal solution. An extensive computational study on literature benchmarks shows that the proposed methods allow us to obtain high quality robust solutions for different uncertainty sets and with minor additional effort compared to deterministic solutions.

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Section: Our Contributionsmentioning

confidence: 62%

Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

Subramanyam

Repoussis

Gounaris

2020

INFORMS Journal on Computing

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“…(ii) SPIRF-W, corresponds to Option (W) for the inclusion of valid inequalities discussed in Section 3.2. (iii) SPIRF-A, this strategy corresponds to Option (A) applied to inequalities (22), (23), and (24) and Option (W) applied to inequalities (25). Notice that Option (A), aggregation of the inequalities for all the scenarios, applied to inequalities (25) leads to very weak inequalities.…”

Section: Inclusion Of Valid Inequalitiesmentioning

confidence: 99%

“…Based on this concept, a very general metaheuristic focused on the exploitation of strategic memory components was proposed by Glover [22]. Such a metaheuristic, known as Adaptive Memory Programming (AMP), has been applied over the years to solve several hard-combinatorial optimization problems, such as vehicle routing problems [23,41], stochastic production distribution network design [15], and supplier selection problems [42]. Another closely related concept is the Concentration Set (CS) introduced by Rosing and ReVelle [36] to solve the deterministic p -median problem heuristically.…”

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confidence: 99%

An adjustable sample average approximation algorithm for the stochastic production‐inventory‐routing problem

2017

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We consider a stochastic single item production‐inventory‐routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first‐stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances.

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“…Afterwards, this semideviation is substituted for robust measurement of combinatorial semideviation in their robustness optimization method. Gounaris et al [288] proposed an adaptive memory programming (AMP) metaheuristic to tackle the CVRP with uncertain customer demand. Similar to population-based evolutionary algorithm, the AMP starts with initialization phase where some high-quality solutions are generated to construct the reference set.…”

Section: Optimization With Uncertaintymentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

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An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

Cited by 55 publications

References 81 publications

Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

Robust Optimization of a Broad Class of Heterogeneous Vehicle Routing Problems Under Demand Uncertainty

An adjustable sample average approximation algorithm for the stochastic production‐inventory‐routing problem

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

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