An adaptive large neighborhood search heuristic for solving a robust gate assignment problem

Yu, Chuhang; Zhang, Dong; Lau, Hon‐Shiang

doi:10.1016/j.eswa.2017.04.050

Cited by 48 publications

(27 citation statements)

References 22 publications

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“…Adaptive large neighborhood search (ALNS) has been applied to a broad range of routing [6-8, 13, 18, 20, 25, 27, 29, 33, 39, 41, 48, 51, 56-58, 66, 69-71, 74, 90-93, 97, 101, 105, 106, 113, 125] and scheduling problems [4, 11, 15, 22, 46, 87, 97, 98, 100-102, 104, 119, 129], but also to the location problem [17], lot sizing [89], assignment problem [53,133], or a combination of (the aforementioned) problems: inventory routing [5,44,86,116,117,132], production routing [1], location routing [62], or routing and scheduling [10].…”

Section: A In Alns: the Adaptive Layermentioning

confidence: 99%

“…The following 25 articles were excluded because the weight adjustment mechanism was not described in detail: [9,10,17,20,29,36,40,41,44,45,47,57,61,65,77,86,90,106,112,116,117,119,122,130,132]. An additional 5 articles [1,22,92,98,133] were excluded because the adaptive layer cannot be removed by setting a parameter to a certain value, and [58] was excluded as a roulette wheel principle is not employed (rather, a heuristic with the highest weight is chosen). [82] was excluded because there is no ALNS introduced in the paper (rather, solutions from a population are chosen in an adaptive fashion).…”

Section: Identification Selection and Main Properties Of Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Turkeš

Sörensen

Hvattum

2021

European Journal of Operational Research

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Research on metaheuristics has focused almost exclusively on (novel) algorithmic development and on competitive testing, both of which have been frequently argued to yield very little generalizable knowledge. The main goal of this paper is to promote meta-analysis-a systematic statistical examination that combines the results of several independent studies-as a more suitable way to obtain problem-and implementation-independent insights on metaheuristics. Meta-analysis is widely used in several scienti c domains, most notably the medical sciences (e.g., to establish the e cacy of a certain treatment). To the best of our knowledge this is the rst meta-analysis in the eld of metaheuristics.

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Section: A In Alns: the Adaptive Layermentioning

confidence: 99%

Section: Identification Selection and Main Properties Of Studiesmentioning

confidence: 99%

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Turkeš

Sörensen

Hvattum

2021

European Journal of Operational Research

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“…Since then, ALNS has been successfully used to solve various combinatorial optimizations. Yu et al [23] applied ALNS to solve a robust gate assignment problem; the results were compared with the benchmark algorithm and showed the competitiveness of the proposed ALNS in solving the considered problem. Carvalho and Santos [24] also used ALNS to solve the electronic circuits design problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Location Decision Making and Transportation Route Planning Considering Fuel Consumption

Theeraviriya

Pitakaso

Sillapasa

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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This study presents the Location Routing Problem (LRP) for which we have created a model for the integration of locating facilities and vehicle routing decisions to solve the problem. The case study is the Palm Oil Collection Center, which is also important for the supply chain system. A mathematical model was made to minimize the total cost of a facility-opening cost, fixed cost of vehicle uses and fuel consumption cost. The fuel consumption cost relies on the distance and road conditions, in case of poor physical condition of a road, and its width, which can be affected the speed of the vehicle as well as the used fuel. Thus, we propose an Adaptive Large Neighborhood Search (ALNS) based on heuristic for solving the LRP. The ALNS method was tested with three datasets of samples divided into small, medium and large problems. Then, the results were compared with the results from the exact method by the Lingo program. The computational study indicated that the ALNS algorithm was competitive to the results of the Lingo for all instance sizes. Moreover, the ALNS was more effective than the exact method; approximately 99% in terms of processing time. We extended this approach to solve the case study, which was considered to be the largest problem, and the ALNS algorithm was efficient with acceptable solutions and short processing time. Therefore, the proposed method provided an effective solution to manage location routing decision of the palm oil collection center.

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“…Sinclair et al [20] implemented the VLSN search algorithm for an integrated aircraft and passenger recovery problem. Yu et al [21] and Yagiura et al [22] used this algorithm to solve assignment problems. Majid et al [23] used this algorithm to solve supply chain network design problems.…”

Section: Introductionmentioning

confidence: 99%

Very Large-Scale Neighborhood Search for Steel Hot Rolling Scheduling Problem With Slab Stack Shuffling Considerations

Shi

Liu

2021

IEEE Access

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We study a steel hot rolling scheduling (HRS) problem considering slab stack shuffling (SSS), which is a kind of key issues in the steel strip production. The HRS problem is to select suitable slabs for a predefined sequence of hot rolling slots, from their respective candidate slab sets so that the number of shuffling operations of slabs is minimized. Different from the most researches, we consider a situation where there are intersections among candidate slab sets and shuffled slabs will not return original stacks. Basing on a special index method of slabs, we present an integer linear programming (ILP) to compute approximation solutions of the problem. In order to improve the solutions obtained by the ILP model, we propose a very large-scale neighborhood (VLSN) search algorithm. In the VLSN search process, the solutions of HRS problems are improved by an iteration procedure that partial solutions are interactively destroyed and repaired. In each iteration, partitioned hot rolling slots correspond to a sub-problem. In the repair operations, we propose an efficient branch-and-bound algorithm for solving sub-problem. The computational results on a number of different scale simulated instances show that the VLSN search algorithm is efficient for solving this kind of HRS problems.INDEX TERMS Branch-and-bound algorithm, local search, steel hot rolling schedule, slab stack shuffling, very large-scale neighborhood search.

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An adaptive large neighborhood search heuristic for solving a robust gate assignment problem

Cited by 48 publications

References 22 publications

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search

Location Decision Making and Transportation Route Planning Considering Fuel Consumption

Very Large-Scale Neighborhood Search for Steel Hot Rolling Scheduling Problem With Slab Stack Shuffling Considerations

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