Solving the vehicle routing problem with adaptive memory programming methodology

Tarantilis, Christos D.

doi:10.1016/j.cor.2004.03.005

Cited by 110 publications

(29 citation statements)

References 36 publications

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“…Similar approaches for the control exploration of feasible and infeasible regions are widely used, and as reported in Vidal et al (2012), they are found to enhance the performance of the search process. Finally, it is worth to note that the proposed mechanisms for selecting solution components of varying size generalize those of earlier AMP heuristics applied for the deterministic CVRP in Tarantilis and Kiranoudis (2002) and Tarantilis (2005), while they provide more consistent performance.…”

Section: Article Submitted Tomentioning

confidence: 99%

“…As mentioned earlier, it is important during the initialization phase to ensure an adequate level of diversity among the solutions in the initial reference set (Tarantilis 2005). For this purpose, we use a generalized savings construction heuristic to generate initial solutions (Lines 4 and 5 in Algorithm 1) coupled with a probabilistic mechanism, similar to that proposed in .…”

Section: Greedy Randomized Savings Heuristicmentioning

confidence: 99%

“…AMP is a general-purpose metaheuristic framework that focuses on the exploitation of strategic memory components (Glover 1997). It has been successfully applied to a range of difficult combinatorial optimization problems, in particular in the vehicle routing domain , Rochat and Taillard 1995, Tarantilis and Kiranoudis 2002, Tarantilis 2005. Based on the intuition that high-quality locally optimal solutions share common features and components (such as common customer visiting sequences), the goal of AMP is to exploit a set of long term memories for the iterative construction of new provisional solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Exact solution methods for the CVRP are typically based on extensions of branch-and-cut (Lysgaard et al 2004), branch-and-cut-and-price (Fukasawa et al 2006) or set partitioning approaches (Baldacci et al 2008). Due to the computational challenges involved in solving the CVRP, a number of heuristic methods have been developed, such as iterative improvement local search algorithms Vigo 2003, Xu andKelly 1996), evolutionary algorithms (Prins 2004, Reimann et al 2004) and hybrid metaheuristic schemes, such as Memetic Algorithms (Nagata and Bräysy 2009) and Adaptive Memory Programming (Tarantilis 2005). …”

Section: Introductionmentioning

confidence: 99%

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

Gounaris

Repoussis

Tarantilis

et al. 2016

Transportation Science

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We present an Adaptive Memory Programming (AMP) metaheuristic to address the Robust Capacitated Vehicle Routing Problem under demand uncertainty. Contrary to its deterministic counterpart, the robust formulation allows for uncertain customer demands, and the objective is to determine a minimum cost delivery plan that is feasible for all demand realizations within a prespecified uncertainty set. A crucial step in our heuristic is to verify the robust feasibility of a candidate route. For generic uncertainty sets, this step requires the solution of a convex optimization problem, which becomes computationally prohibitive for large instances. We present two classes of uncertainty sets for which route feasibility can be established much more efficiently. While we discuss our implementation in the context of the AMP framework, our techniques readily extend to other metaheuristics. Computational studies on standard literature benchmarks with up to 483 customers and 38 vehicles demonstrate that the proposed approach is able to quickly provide high quality solutions. In the process, we obtain new best solutions for a total of 123 benchmark instances.

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Section: Article Submitted Tomentioning

confidence: 99%

Section: Greedy Randomized Savings Heuristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gounaris

Repoussis

Tarantilis

et al. 2016

Transportation Science

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“…Swapping mechanism [35]: this method swaps two parts selected randomly; the following figure (Fig. 5) As a custom training function the proposed adaptation used simulated annealing method [14], [31], it is used when the current solution is blocked in the local minima; it helps to accept some movement and escape from the local optimum.…”

Section: Adaptation Of Gbsa Algorithmmentioning

confidence: 99%

Hybrid Algorithm for Solving the Quadratic Assignment Problem

Riffi¹,

Sayoti²

2019

IJIMAI

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T HE quadratic assignment problem (QAP) is one of the known classical combinatorial optimization problems, in 1976 Sahni and Gonzalez [1] proved that the QAP belongs to the class of NP-hard problems [1]. It was introduced for the first time by Koopmans and Beckmann in 1957 [2]; its purpose is to assign n facilities to n fixed locations with a given flow matrix of facilities and distance matrix of locations in order to minimize the total assignment cost. This problem is applied in various fields such as hospital layout [3], scheduling parallel production lines [4] and analyzing chemical reactions for organic compounds [5].Many recent hybrid approaches have improved performance in solving QAP such as genetic algorithm hybridized with tabu search method [6], ant colony optimization mixed with local search method [7] and ant colony optimization combined with genetic algorithm and local search method [8]. Recently the hybrid algorithms are much proposed and used by many researchers to find optimal or near optimal solutions for the QAP.In this paper we propose a new competitive approach when compared with other existing methods in the literature. The golden ball algorithm mixed with simulated annealing (GBSA) is considered here as a hybrid metaheuristic to apply in the quadratic assignment problem.This work presents an efficient adaptation of GBSA algorithm to the quadratic assignment problem (QAP). This algorithm is based on the concept of soccer; it guides the search by simulated annealing [9] to escape from the local optima. The suggested technique has never been proposed or tested with QAP. In this research we use some small, medium and large test problems for comparing our approach to other recent methods from literature. Our approach is able to explore effectively the search space; it reaches the known optimal solutions in less time.The rest of this paper is structured as follows: In section I, Introduction. In section II, Quadratic assignment problem formulation. In section III, Methods. In section IV, Results and discussion. In section V, Conclusion. KeywordsCombinatorial Optimization, Golden Ball Algorithm, Simulated Annealing, Quadratic Assignment Problem. AbstractThe Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literatu...

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Bibliography

2016

Metaheuristics for Vehicle Routing Problems

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Solving the vehicle routing problem with adaptive memory programming methodology

Cited by 110 publications

References 36 publications

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

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

Hybrid Algorithm for Solving the Quadratic Assignment Problem

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