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

Chaurasia, Sachchida Nand; Singh, Alok

doi:10.1016/j.asoc.2016.09.051

Cited by 39 publications

(17 citation statements)

References 32 publications

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“…Oguz et al (2010) proposed the OAS problem with a penalty for late completion, time windows and sequence-dependent setup times. The problem was approached by MIP (Cesaret et al 2012), TS (Cesaret et al 2012), genetic algorithm (Nguyen et al 2015;Chen et al 2014;Chaurasia and Singh 2017), artificial bee colony algorithm (Lin and Ying 2013;Chaurasia and Kim 2019), hyper-heuristic based methods (Nguyen 2016) and iterated local search (Silva et al 2018). Recently, Silva et al (2018) used Lagrangian relaxation and column generation to find tight upper bounds of problem instances.…”

Section: Domain Instancesmentioning

confidence: 99%

Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

Weerdt

Yorke-Smith

2019

J Intell Manuf

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In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and typically have an earliest and latest possible start time. We introduce and analyze four algorithmic ideas for this class of time/sequence-dependent over-subscribed scheduling problems with time windows: a novel hybridization of adaptive large neighbourhood search (ALNS) and tabu search (TS), a new randomization strategy for neighbourhood operators, a partial sequence dominance heuristic, and a fast insertion strategy. Through factor analysis, we demonstrate the performance of these new algorithmic features on problem domains with varying properties. Evaluation of the resulting general purpose algorithm on three domains-an order acceptance and scheduling problem, a real-world multi-orbit agile Earth observation satellite scheduling problem, and a time-dependent orienteering problem with time windows-shows that our hybrid algorithm robustly outperforms general algorithms including a mixed integer programming method, a constraint programming method, recent state-of-the-art problem-dependent meta-heuristic methods, and a two-stage hybridization of ALNS and TS.

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Section: Domain Instancesmentioning

confidence: 99%

Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

Weerdt

Yorke-Smith

2019

J Intell Manuf

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“…ABC [9], HSSGA [16], and EA/G-LS [16]. Note that the performance of MIP solved by ILOG CPLEX has been tested by Cesaret et al [5] and its performance is bad for large instances with more than 25 orders.…”

Section: Comparison With State-of-the-art Algorithmsmentioning

confidence: 99%

“…Besides Sparrow, we include two other algorithms which correspond to the state of the art: ILS by Silva et al [8] and HSSGA by Chaurasia and Singh [16].…”

Section: The Performances Of Different Algorithms On New Instancesmentioning

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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“…Silva et al (2018) considered the OA&S with sequence-dependent setup times on a single machine. Chaurasia and Singh (2017) considered the same problem by considering release dates and sequence dependent setup times. This problem, with customer class set up, was investigated by Xie and Wang (2016).…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 summarizes the assumptions and features of some of recently closely related papers of OA&S and LS. Emami et al, 2016)     Lagrangian relaxation (Chaurasia & Singh, 2016)     GA (Ou & Zhong, 2016)     Heuristic (Nguyen, 2016)     GA (Lei & Guo, 2015)     PNS (C. Chen et al, 2014)     GA (Wang et al, 2013)      (Cesaret et al, 2012)     TS (Noroozi et al, 2017)      GA+PSO (Lalitha et al, 2017)    Heuristic (Zhang et al, 2017)   MBO (Han et al, 2016)   NSGA-II (Mukherjee et al, 2017)    Optimal properties (Nejati et al, 2016)   GA&SA (Ming Cheng et al, 2016)   Heuristic (Sang et al, 2015)   IWO As can be seen in the Table 1, the OA&S or LS studies only focused on determining schedules of the orders to minimize the production cost without taking account of the distribution costs and revenue of the orders. While to achieve business goals integrating the production and distribution scheduling is critical (Chen, 2010).…”

Section: Introductionmentioning

confidence: 99%

Coordinating order acceptance and integrated lot streaming-batch delivery scheduling considering third party logistics

Noroozi¹,

Mazdeh²,

Heydari³

et al. 2019

10.5267/j.uscm

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Inspired by the industries such as food and beverage, metal and steel, as well as petroleum and petrochemical ones, the current study addresses a joint order acceptance and scheduling, lot streaming in a flexible flow shop and batch delivery problem. For maximizing a profit objective function with trading off between the revenue of the accepted orders and the costs incurred, a novel mixed integer linear programming is proposed. This paper develops a hybrid metaheuristic algorithm based on the Genetic Algorithm. In the developed algorithm, (1) a heuristic, (2) a local search, and (3) a restart phase is proposed. To set the appropriate parameters of the algorithms, Taguchi experimental design was applied. The obtained results reveal the appropriate performance of the hybrid genetic algorithm.ensee Growing Science, Canada by the authors; lic 9 © 201

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

Cited by 39 publications

References 32 publications

Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm

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

Coordinating order acceptance and integrated lot streaming-batch delivery scheduling considering third party logistics

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