Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming

Nguyen, Su; Zhang, Mengjie; Johnston, Mark

doi:10.1007/978-3-662-44303-3_11

Cited by 4 publications

(2 citation statements)

References 11 publications

(36 reference statements)

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“…Nguyen et al [7] proposed an exact branch-and-bound (B&B) with genetic programming (GP) to discover good ordering rules. Their method could find optimal solutions for instances with up to 20 orders.…”

Section: Related Workmentioning

confidence: 99%

“…The standard benchmark set used is that by Cesaret et al [5], which is the most common benchmark set used by many articles [5,[7][8][9][10][11][12][13][14][15][16][17]. This benchmark set has n, τ and r as parameters, where n is the number of orders n = 25, 50, 100, and τ and R are the tardiness factor and the due time range factor, having the values of 0.1, 0.3, 0.5, 0.7, and 0.9.…”

Section: Parameter Settings For Sparrowmentioning

confidence: 99%

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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%

Section: Parameter Settings For Sparrowmentioning

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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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

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220

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem

Chaurasia

Singh

2017

Applied Soft Computing

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Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming

Cited by 4 publications

References 11 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

Genetic programming for production scheduling: a survey with a unified framework

Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem

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