A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem

Recent trends in the management of supply chains have witnessed an increasing implementation of the cross-docking strategy. The cross-docking strategy, being the one that can potentially improve supply chain operations, has received a lot of attention from researchers in recent years, especially over the last decade. Cross-docking involves the reception of inbound products, deconsolidation, sorting, consolidation, and shipping of the consolidated products to the end customers. The number of research efforts, aiming to study and improve the cross-docking operations, increases every year. While some studies discuss cross-docking as an integral part of a supply chain, other studies focus on the ways of making cross-docking terminals more efficient and propose different operations research techniques for various decision problems at cross-docking terminals. In order to identify the recent cross-docking trends, this study performs a state-of-the-art review with a particular focus on the truck scheduling problem at cross-docking terminals. A comprehensive evaluation of the reviewed studies is conducted, focusing on the major attributes of the cross-docking operations. These attributes include terminal shape considered, doors considered, door service mode considered, preemption, internal transportation mode used, temporary storage capacity, resource capacity, objectives considered, and solution methods adopted. Based on findings from the review of studies, some common issues are outlined and future research directions are proposed.

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“…Fonseca et al [39] focused on the truck scheduling problem at a parallel-dock CDT. The objective of the study was to minimize the makespan.…”

Section: General Cdt Truck Schedulingmentioning

confidence: 99%

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

et al. 2019

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“…In this paper, we deal with an exact dynamic programming approach. For some further recent general approaches for the solution of different types of single and multiple criteria scheduling problems, the interested reader is referred to [23][24][25][26][27][28][29] which highlight the importance of developing advanced scheduling approaches. This concerns both the identification of new polynomially solvable problems as well as new MILP models and metaheuristic or hybrid algorithms.…”

Section: Literature Overviewmentioning

confidence: 99%

Two-Machine Job-Shop Scheduling with Equal Processing Times on Each Machine

Gafarov

Werner

2019

Mathematics

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In this paper, we consider a two-machine job-shop scheduling problem of minimizing total completion time subject to n jobs with two operations and equal processing times on each machine. This problem occurs e.g., as a single-track railway scheduling problem with three stations and constant travel times between any two adjacent stations. We present a polynomial dynamic programming algorithm of the complexity O ( n 5 ) and a heuristic procedure of the complexity O ( n 3 ) . This settles the complexity status of the problem under consideration which was open before and extends earlier work for the two-station single-track railway scheduling problem. We also present computational results of the comparison of both algorithms. For the 30,000 instances with up to 30 jobs considered, the average relative error of the heuristic is less than 1 % . In our tests, the practical running time of the dynamic programming algorithm was even bounded by O ( n 4 ) .

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“…A Memetic Algorithm was developed to minimize the total vessel service cost for the vessel diversion decision making [25]. Dulebenets et al [26] proposed a hybrid method based on a Lagrangian relaxation technique through the volume algorithm. Using information from the Lagrangian multipliers, constructive heuristics with local search procedures were utilized to reach the objective of minimizing the makespan in cross-docking.…”

Section: Related Workmentioning

confidence: 99%

Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Kang

Zheng

2019

Algorithms

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A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

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A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem

Cited by 49 publications

References 42 publications

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

Two-Machine Job-Shop Scheduling with Equal Processing Times on Each Machine

Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

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