Integrated truck and workforce scheduling to accelerate the unloading of trucks

Tadumadze, Giorgi; Boysen, Nils; Emde, Simon; Weidinger, Felix

doi:10.1016/j.ejor.2019.04.024

Cited by 46 publications

(20 citation statements)

References 41 publications

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“…In terms of the processing time of a job is a bounded linear function or convex function of the amount of the resource allocated to the processing of it, this kind of problems is divided into two categories: scheduling with linear resource‐dependent processing times and scheduling with convex resource‐dependent processing times. The body of literature on this topic is vast, and we refer the reader to the recent studies of Shabtay and Steiner (2007a)), Sun, Geng, Wang, and Liu (2019), Tadumadze, Boysen, Emde, and Weidinger (2019), and Yang, Lai, and Yang (2014), and the survey by Shabtay and Steiner (2007b). Since our paper focuses on scheduling with linear resource‐dependent processing times, in what follows we review some related studies following this line of research.…”

Section: Literature Reviewmentioning

confidence: 99%

Two‐agent scheduling with linear resource‐dependent processing times

Wang

Qiu

et al. 2020

Naval Research Logistics

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This paper considers a two‐agent scheduling problem with linear resource‐dependent processing times, in which each agent has a set of jobs that compete with that of the other agent for the use of a common processing machine, and each agent aims to minimize the weighted number of its tardy jobs. To meet the due date requirements of the jobs of the two agents, additional amounts of a common resource, which may be in discrete or continuous quantities, can be allocated to the processing of the jobs to compress their processing durations. The actual processing time of a job is a linear function of the amount of the resource allocated to it. The objective is to determine the optimal job sequence and resource allocation strategy so as to minimize the weighted number of tardy jobs of one agent, while keeping the weighted number of tardy jobs of the other agent, and the total resource consumption cost within their respective predetermined limits. It is shown that the problem is NP‐hard in the ordinary sense, and there does not exist a polynomial‐time approximation algorithm with performance ratio unless P=NP; however it admits a relaxed fully polynomial time approximation scheme. A proximal bundle algorithm based on Lagrangian relaxation is also presented to solve the problem approximately. To speed up convergence and produce sharp bounds, enhancement strategies including the design of a Tabu search algorithm and integration of a Lagrangian recovery heuristic into the algorithm are devised. Extensive numerical studies are conducted to assess the effectiveness and efficiency of the proposed algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Two‐agent scheduling with linear resource‐dependent processing times

Wang

Qiu

et al. 2020

Naval Research Logistics

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“…Truck scheduling problems from the literature almost without exceptions typically ignore the workforce scheduling aspect. However, Tadumadze et al (2019) propose integrated truck and workforce scheduling problem but their workforce is dedicated to the intra-terminal operations (i.e., unloading goods from trailers at dock door) while spotter scheduling treats operations in the yard, i.e., outside of the terminal. The only other papers treating spotter scheduling are the ones by Berghman et al (2014) and Berghman and Leus (2015).…”

Section: Trailer Yard Operations and Literature Reviewmentioning

confidence: 99%

“…It is well-known that the truck scheduling problem can be modeled as a parallel machine scheduling problem (Tadumadze et al 2019(Tadumadze et al , 2020. Thus, we generate arrival times and (un-)loading times of trucks similarly to the instance generation scheme proposed by Hall and Posner (2001).…”

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confidence: 99%

Robust spotter scheduling in trailer yards

2020

Self Cite

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Spotters (also denoted as switchers) are specialized terminal tractors, which are dedicated to the rapid maneuvering of semitrailers between parking lot and dock doors in large trailer yards. This paper is dedicated to spotter scheduling, i.e., the assignment of predefined trailer movements to a given fleet of spotters. The limited number of dock doors for loading and unloading is often the scarce resource during trailer processing, so that idle time of the bottleneck, e.g., caused by unforeseen delay in the yard, is to be avoided. In this setting, we aim to insert time buffers between any pair of subsequent jobs assigned to the same spotter, so that small delays are not propagated and subsequent jobs can still be executed in a timely manner. We formalize two versions of the resulting robust spotter scheduling problem and provide efficient algorithms for finding optimal solutions in polynomial time. Furthermore, we simulate delays during the execution of spotter schedules and show that the right robustness objective can greatly improve yard performance.

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“…Peng and Zhou (2019) proposed a hybrid Grey Wolf Optimizer (GWO) to address a biobjective truck scheduling problem in automotive industry environment. Tadumadze et al, (2019) developed an integrated truck and workforce scheduling problem for unloading trucks and proposed a set of heuristics to solve it. Yi et al, (2019) proposed a scheduling appointment system for container truck arrivals considering their effects on congestion and solved this complex problem by PSO.…”

Section: -Introduction and Literature Reviewmentioning

confidence: 99%

Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

Fathollahi-Fard

Ranjbar-Bourani

Cheikhrouhou

et al. 2019

Computers & Industrial Engineering

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The truck scheduling problem is one of the most challenging and important types of scheduling with a large number of real-world applications in the area of logistics and cross-docking systems. This problem is formulated to find an optimal condition for both receiving and shipping trucks sequences. Due to the difficulty of the practicality of the truck scheduling problem for large-scale cases, the literature has shown that there is a chance, even with low possibility, for a new optimizer to outperform existing algorithms for this optimization problem. The paper introduces modified versions of the Social Engineering Optimizer (SEO), an algorithm inspired by social engineering phenomena to solve the truck scheduling problem. To validate these optimizers, they are evaluated by solving a set of standard benchmark functions. All the algorithms have been calibrated by the Taguchi experimental design approach to further enhance their optimization performance. In addition to some truck scheduling benchmarks , a real case study is addressed to show the high-efficiency of the developed optimizers in a real situation. The results indicate that the proposed modifications of SEO considerably outperform the state of the art algorithms for the truck scheduling problem.

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Integrated truck and workforce scheduling to accelerate the unloading of trucks

Cited by 46 publications

References 41 publications

Two‐agent scheduling with linear resource‐dependent processing times

Two‐agent scheduling with linear resource‐dependent processing times

Robust spotter scheduling in trailer yards

Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

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