Planning a selective delivery schedule through Adaptive Large Neighborhood Search

Palomo-Martínez, Pamela J.; Salazar-Aguilar, M. Angélica; Laporte, Gilbert

doi:10.1016/j.cie.2017.08.037

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…The ALNS algorithm was first proposed by Pisinger and Ropke [29]. It performs particularly well on problems with order acceptance features, such as the orienteering problem [30], the satellite scheduling problem [31] and the pickup and delivery problem with selective requests [32].…”

Section: Related Workmentioning

confidence: 99%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling.This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. A novel memetic algorithm for this problem, called Sparrow, comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to stateof-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.Adaptive large neighbourhood search, Order acceptance and scheduling, Sequence-dependent setup times 2. We compare Sparrow with state-of-the-art algorithms on a set of standard benchmark instances from the literature. The proposed algorithm obtains better-quality solutions with comparable running time.3. We study the correlation of the problem properties and the algorithm performance and find that the congestion ratio, the length of time windows, and the correlation of processing time and revenue of orders are highly related to the difficulty of the problem. 2 4. We further generate new instances that are more representative of real problem instances in satellite scheduling (more congestion), commerce (high correlation between revenue and processing time), and the travelling repairman problem (short processing times and long time windows), and compare the performance of multiple state-of-the-art algorithms on these new instances. The remainder of this article is summarized as follows: Section 2 provides 1 This algorithm combines a population-based genetic algorithm with adaptive large neighbourhood search. Each individual in a "swarm" thus has a bird's eye view of the search space -hence Sparrow. 2 These terms are defined in Section 4.3. Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max Min Avg Max 0.10 0.10

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Section: Related Workmentioning

confidence: 99%

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Guijt

Weerdt

et al. 2019

Computers & Industrial Engineering

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“…The large neighborhood search (LNS) algorithm, originally proposed by Shaw (1998), attempts at improving a given solution by alternately destroying and repairing it (Mancinia and Steccab (2018)). The ALNS is an extension of the LNS that has been first proposed by Ropke and Pisinger (2006) and since then has obtained successful results for a large variety of optimization problems (Palomo-Martínez et al 2017;Alinaghian and Shokouhi 2018). The ALNS allows the use of multiple destroy and repair algorithms within the same search, and adopts an adaptive selection mechanism to decide which operators should be used.…”

Section: Adaptive Large Neighborhood Searchmentioning

confidence: 99%

Solution of minimum spanning forest problems with reliability constraints

Ahani¹,

Salari²,

Hosseini³

et al. 2019

Preprint

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We propose the reliability constrained k-rooted minimum spanning forest, a relevant optimization problem whose aim is to find a k-rooted minimum cost forest that connects given customers to a number of supply vertices, in such a way that a minimum required reliability on each path between a customer and a supply vertex is satisfied and the cost is a minimum. The reliability of an edge is the probability that no failure occurs on that edge, whereas the reliability of a path is the product of the reliabilities of the edges in such path. The problem has relevant applications in the design of networks, in fields such as telecommunications, electricity and transports. For its solution, we propose a mixed integer linear programming model, and an adaptive large neighborhood search metaheuristic which invokes several shaking and local search operators. Extensive computational tests prove that the metaheuristic can provide good quality solutions in very short computing times.

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A robust multi-project scheduling problem under a resource dedication-transfer policy

Zhao,

Hu,

Wang

et al. 2024

Ann Oper Res

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Planning a selective delivery schedule through Adaptive Large Neighborhood Search

Cited by 10 publications

References 18 publications

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

Solution of minimum spanning forest problems with reliability constraints

A robust multi-project scheduling problem under a resource dedication-transfer policy

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