The time-dependent orienteering problem with time windows: a fast ant colony system

Verbeeck, Cédric; Vansteenwegen, Pieter; Aghezzaf, El‐Houssaine

doi:10.1007/s10479-017-2409-3

Cited by 35 publications

(41 citation statements)

References 36 publications

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Order By: Relevance

“…Therefore when inserting one order into the current solution at a position, it is possible to create more space for the candidate order by postponing some orders in the solution. In order to determine how much one order can be postponed, we adopt the time slack idea from Verbeeck et al [2]. We further propose the due time slack heuristic for this problem with tardiness penalty.…”

Section: Fast Insertion Algorithmmentioning

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: Fast Insertion Algorithmmentioning

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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“…Therefore, the transition time directly depends on the observation look angles, and thus depends on the observation start times. This problem characteristic is widely known as "time-dependent travel time" in the Vehicle Routing Problem (VRP) [12] or the Orienteering Problem (OP) [13]. In previous works, in order to verify the transition time constraint, the real transition time for each pair of successive tasks was calculated, which requires a lot of (online) computation time.…”

Section: B Minimal Transition Timementioning

confidence: 99%

“…In order to solve large instances in an acceptable computation time, a pre-process method, inspired by a recent work [13], is proposed to reduce the number of unnecessary insertion attempts. For each task t i on the same orbit, we define its preceding neighbour set V p i and its succeeding neighbour set V s i .…”

Section: B Pre-processingmentioning

confidence: 99%

An Iterated Local Search Algorithm for Agile Earth Observation Satellite Scheduling Problem

Peng

Vansteenwegen

Liu

et al. 2018

2018 SpaceOps Conference

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The scheduling problem of an Agile Earth Observation Satellite is to schedule a subset of observation tasks among a set of candidates during their visible time windows in order to gain a maximum collected profit. Each pair of two consecutive tasks should satisfy the "time-dependent" transition time constraint, which contributes to the difficulty of solving the problem. To tackle this problem, a concept of "minimal transition time" which only depends on the observation start time of the previous task, is introduced. On this basis, we develop a fast and effective Iterated Local Search algorithm. A local search insert procedure is specifically designed to deal with time-dependent transition times and time windows. The experimental results on a variety of instances show that our algorithm performs very well in terms of solution quality and computation time and outperforms the state of the art.

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“…This makes the problem become an over-subscribed scheduling problem, consisting of simultaneously selecting a subset of jobs to be processed as well as the associated schedule. This problem is important because it represents a class of real-world problems including the Earth observation satellite scheduling problem (Augenstein et al 2016;Akturk and KiliÇ 1999), the order acceptance and scheduling problem (Oguz et al 2010;Wang et al 2017), the orienteering problem (Verbeeck et al 2017), and selective maintenance scheduling (Duan et al 2018). Many real-world instances in this class have time windows (e.g., from the time when the factory receives the raw material to the user-specified deadline (Rebai et al 2012)) and time/sequence-dependent setup times (e.g., the time to prepare next batches of products in an intelligent manufacturing system): the scheduled start time of each job must be in its time window, and the setup time between every two jobs depends on the specific pair of jobs (Mirsanei et al 2011) or their scheduled start times (Dong et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…For the varying problem instances in this class with time windows and time/sequence-dependent setup times, general approaches such as mixed integer programming do not perform very well (Cesaret et al 2012;Verbeeck et al 2017;Liu et al 2017). There are also problem-specific methods that use highly specialised subroutines (Liu et al 2017;Silva et al 2018;Poggi et al 2010).…”

Section: Introductionmentioning

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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The time-dependent orienteering problem with time windows: a fast ant colony system

Cited by 35 publications

References 36 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

An Iterated Local Search Algorithm for Agile Earth Observation Satellite Scheduling Problem

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

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