Online Integrated Allocation of Berths and Quay Cranes in Container Terminals with 1-Lookahead

Pan, Jiayin; Xu, Yinfeng

doi:10.1007/978-3-319-21398-9_32

Lecture Notes in Computer Science

2015

DOI: 10.1007/978-3-319-21398-9_32

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Online Integrated Allocation of Berths and Quay Cranes in Container Terminals with 1-Lookahead

Jiayin Pan

Yinfeng Xu

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Cited by 3 publications

References 13 publications

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Semi-online scheduling on two uniform parallel machines with initial lookahead

Zheng,

Chen,

Liu

2024

RAIRO-Oper. Res.

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This work studies the problem of semi-online scheduling on two uniform parallel machines with speeds 1 and s≥2, respectively. We introduce a novel concept of initial lookahead in which any deterministic online algorithm has the full knowledge of the first k jobs at the beginning, while the remaining jobs are released one-by-one in the online over-list mode. The objective of the considered problem is to minimize the makespan. We focus on the case where the first k jobs are of a total processing time not less than (s+1)Δ where Δ is the largest job length, and it is assumed that s is an integer. We prove a lower bound of (s2+s+1)/(s2+s), and propose a deterministic online algorithm with competitive ratio of (s+1)2/(s2+s+1). The ratio is at most 9/7 and much less than that of 1.618 for the basic case without initial lookahead (Cho and Sahni [7]). Our results demonstrate that a finite ability of initial lookahead can greatly improve the competitiveness of online algorithms.

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Semi-online scheduling on two uniform parallel machines with initial lookahead

Zheng,

Chen,

Liu

2024

RAIRO-Oper. Res.

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Semi-Online Scheduling on Two Identical Parallel Machines with Initial-Lookahead Information

Zheng¹,

Chen²,

Liu³

et al. 2023

Asia Pac. J. Oper. Res.

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This work investigates a new semi-online scheduling problem with lookahead. We focus on job scheduling on two identical parallel machines, where deterministic online algorithms only know the information of [Formula: see text] initial jobs (i.e., the initial-lookahead information), while the following jobs still arrive one-by-one in an over-list fashion. We consider makespan minimization as the objective. The study aims at revealing the value of knowing [Formula: see text] initial jobs, which are used to improve the competitive performance of those online algorithms without such initial-lookahead information. We provide the following findings: (1) For the scenario where the [Formula: see text] initial jobs are all the largest jobs with length [Formula: see text], we prove that the classical LIST algorithm is optimal with competitive ratio [Formula: see text]; (2) For the scenario where the total length of these [Formula: see text] jobs is at least [Formula: see text], we show that any online algorithm has a competitive ratio at least 3/2, implying that the initial-lookahead knowledge is powerless since there exists a 3/2-competitive online algorithm without such information; (3) For the scenario where the total length of these [Formula: see text] jobs is at least [Formula: see text] ([Formula: see text]), we propose an online algorithm, named as LPT-LIST, with competitive ratio of [Formula: see text], implying that the initial-lookahead information indeed helps to improve the competitiveness of those online algorithms lacking such information.

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Algorithm Design for an Online Berth Allocation Problem

Chen,

Wang,

Pan

et al. 2024

JMSE

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In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that the widely used Greedy algorithm has a very poor theoretical guarantee; specifically, the competitive ratio of the Greedy algorithm for this problem is lower bounded by Ω(log⁡m/log⁡log⁡m), which increases with the number of berths m. On account of this, we borrow an idea from algorithms for the online strip packing problem and provide a comprehensive theoretical analysis of the Revised Shelf (RS) algorithm as applied to our berth allocation problem. We prove that the competitive ratio of RS for our problem is 5, improving on the original competitive ratio of 6.66 for the online strip packing problem. Through numerical studies, we examine the RS algorithm and Greedy algorithm in an average case. The numerical simulation of competitive ratios reveals distinct advantages for different algorithms depending on job size. For smaller job sizes, the Greedy algorithm emerges as the most efficient, while for medium-sized jobs, the RS algorithm proves to be the most effective.

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Online Integrated Allocation of Berths and Quay Cranes in Container Terminals with 1-Lookahead

Cited by 3 publications

References 13 publications

Semi-online scheduling on two uniform parallel machines with initial lookahead

Semi-online scheduling on two uniform parallel machines with initial lookahead

Semi-Online Scheduling on Two Identical Parallel Machines with Initial-Lookahead Information

Algorithm Design for an Online Berth Allocation Problem

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