Hybrid Algorithm for Solving the Heterogeneous Fixed Fleet Vehicle Routing Problem

Takan, Melis Alpaslan; Kasımbeyli, Refail

doi:10.1109/ismsit.2019.8932935

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…They proposed a hybrid genetic algorithm based on MSGA. Takan and Kasimbeyli [11] developed a new hybrid subgradient algorithm for solving the capacitated vehicle routing problem. In another recent study conducted by Bulbul and Kasimbeyli [12], a new version of the aircraft maintenance routing problem is addressed.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Modified Subgradient Algorithm That Self-Determines the Proper Parameter Values

Saraç

TUTUMLU

Özer

2023

Journal of Scientific Reports-A

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A successful solution algorithm for non-convex optimization problems is the Modified Subgradient Algorithm (MSGA), which solves dual problems based on the sharp augmented lagrangian function. However, its performance highly depends on its parameter values, and determining the appropriate parameter values is difficult as they can be completely different for each problem. In this study, a new hybrid solution approach that a tabu search algorithm to find the appropriate MSGA parameter values and the MSGA algorithm run together is proposed. Although it seems like a contradiction to use an algorithm that also has its parameters to determine the most appropriate parameter values of an algorithm, this contradiction is eliminated by fixing the parameter values of the tabu search algorithm. The proposed algorithm does not need appropriate values of any algorithm parameter. It can find appropriate parameter values for each problem itself starting with the same fixed initial values. To show the success of the developed algorithm, especially on 0-1 quadratic problems, it is compared with the classical MSGA algorithm by using the quadratic knapsack test instances taken in the literature. According to the obtained solutions, the superiority of the hybrid algorithm has been demonstrated.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Modified Subgradient Algorithm That Self-Determines the Proper Parameter Values

Saraç

TUTUMLU

Özer

2023

Journal of Scientific Reports-A

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“…Detailed information about these algorithms can be reached from Gasimov [17] and Kasimbeyli et al [18]. MSG and FMSG algorithms were utilized in the literature by many researchers to solve different nonconvex optimization problems such as quadratic assignment problem [19], quadratic knapsack problem [20], cell formation problem [21], multi-period facility layout problem [22], generalized quadratic multiple knapsack problem [23], capacitated vehicle routing problem [24] and aircraft maintenance routing problem [25]. Since the performances of the both MSG and FMSG algorithms depend on the performance of the solution method used for solving the sub problem at any iteration, some researchers hybridized these algorithms with metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

“…Since the performances of the both MSG and FMSG algorithms depend on the performance of the solution method used for solving the sub problem at any iteration, some researchers hybridized these algorithms with metaheuristics. Ozcelik and Saraç [21] and Takan and Kasımbeyli [24] hybridized MSG with genetic algorithm, Saraç and Sipahioglu [23] hybridized FMSG with GA and Bulbul and Kasımbeyli [25] with ant colony optimization algorithm. When the hybrid algorithms in which MSG/FMSG algorithms and metaheuristic algorithms work together in the literature are examined, it has been observed that these studies have been applied to constrained problems, whose constraints are hard to handle with metaheuristics, and successful results have been obtained for these.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm for Flow Shop Scheduling Problem with Unavailable Time Periods and Additional Resources

Özçelik

Saraç

2023

Gazi University Journal of Science

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In machine scheduling problems, not all machines are always available. They may be interrupted periodically for reasons such as planned maintenance, lunch and rest breaks. However, this situation is often ignored in the literature or these time periods are considered as fixed. In this study, flow shop scheduling problem with unavailable time periods is discussed. In the considered problem, unavailable time periods are not fixed, they can be pulled forward to prevent the idle times. A MIP model and a genetic algorithm have been developed for the considered problem. The performance of the proposed solution methods is demonstrated using randomly generated test problems. With the developed genetic algorithm, GAMS results are improved by an average of 26,82%.

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A Comparative Study for Determining the Vehicle Routing Optimal Path

Aditi¹,

Dhinakar²,

Sowmiya³

2022

Communication, Software and Networks

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Hybrid Algorithm for Solving the Heterogeneous Fixed Fleet Vehicle Routing Problem

Cited by 3 publications

References 14 publications

A Hybrid Modified Subgradient Algorithm That Self-Determines the Proper Parameter Values

A Hybrid Modified Subgradient Algorithm That Self-Determines the Proper Parameter Values

A Hybrid Algorithm for Flow Shop Scheduling Problem with Unavailable Time Periods and Additional Resources

A Comparative Study for Determining the Vehicle Routing Optimal Path

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