A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility

Dulebenets, Maxim A.

doi:10.3390/su10051333

Cited by 42 publications

(44 citation statements)

References 48 publications

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“…Dulebenets [34] proposed an MIP model for the CDT truck scheduling problem, aiming to minimize the total truck service cost, including the total handling cost, the total waiting cost, and the total delayed departure cost. A Diploid EA (DEA) was proposed as a solution approach and was based on the observed drawbacks of the previous solution algorithms that used the haploidy concept.…”

Section: General Cdt Truck Schedulingmentioning

confidence: 99%

“…Some of the reviewed studies proposed the CDT truck scheduling models, where the modeled CDTs had the I-shapes [34,35,38,44,57,59]. The I-shape is considered as one of the most commonly used shapes for CDTs [21,34,35]. However, the majority of the reviewed studies proposed generic mathematical models and/or solution methodologies that can be applied for truck scheduling at CDTs with different shapes (e.g., the T-shape, the L-shape, and the X-shape).…”

Section: Cdt Shape Consideredmentioning

confidence: 99%

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

et al. 2019

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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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Section: General Cdt Truck Schedulingmentioning

confidence: 99%

Section: Cdt Shape Consideredmentioning

confidence: 99%

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

et al. 2019

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“…Then, a comparative analysis was performed on the proposed models on benchmark instances from the literature. For solving approaches, many meta-heuristics were suggested: Population based meta-heuristics (Differential Evolution [28,29], Diploid Differential Evolution [30], Genetic algorithm [25], Particle Swarm Optimization [31], etc.) and single solution based meta-heuristics (Tabu search [32], Variable Neighborhood Search [33], Simulated Annealing [33,34], etc.…”

Section: Classical Cross-docking Terminalsmentioning

confidence: 99%

Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

et al. 2019

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In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

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“…The parameter values for the BSPSR mathematical model were selected based on the available BSP literature and relevant online sources [7][8][9][10][11][12][14][15][16][17][18][19][27][28][29][30][31][32][33][34][35][36]. The adopted parameter values are presented in Table 2.…”

Section: Input Data Generationmentioning

confidence: 99%

A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

et al. 2018

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Since ancient times, maritime transportation has played a very important role for the global trade and economy of many countries. The volumes of all major types of cargo, which are transported by vessels, has substantially increased in recent years. Considering a rapid growth of waterborne trade, marine container terminal operators should focus on upgrading the existing terminal infrastructure and improving operations planning. This study aims to assist marine container terminal operators with improving the seaside operations and primarily focuses on the berth scheduling problem. The problem is formulated as a mixed integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. A self-adaptive Evolutionary Algorithm is proposed to solve the problem, where the crossover and mutation probabilities are encoded in the chromosomes. Numerical experiments are conducted to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. Results indicate that all the considered solution algorithms demonstrate a relatively low variability in terms of the objective function values at termination from one replication to another and can maintain the adequate population diversity. However, application of the self-adaptive parameter control strategy substantially improves the objective function values at termination without a significant impact on the computational time.Algorithms 2018, 11, 100 2 of 35 of waterborne trade increased by 33.6% from 7.7 billion tons in 2006 to 10.3 billion tons in 2016 [5]. Moreover, the volumes of all major types of cargo, transported by vessels, have significantly increased over the last ten years. Specifically, containerized trade increased by 59.9%, while major bulk cargo, dry bulk cargo, and oil/gas increased by 74.9%, 10.8%, and 13.2%, respectively, from 2006 to 2016. The general consumption goods and high-value cargo are typically transported in containerized form. Containers are transferred among different continents by vessels, which are served at marine container terminals (MCTs). Rapid growth of waterborne trade requires MCT operators upgrading the existing terminal infrastructure and improving operations planning.MCT operations can be categorized in the following three types [1,6]: (1) seaside operations, which focus on service of arriving vessels (i.e., loading containers on vessels and unloading containers from vessels); (2) marshaling yard operations, which focus on temporary storage of containers in yard blocks of the MCT marshaling yard; and (3) landside operations, which focus on pick-up and delivery of containers by the inland transportation modes (generally, on-dock rail and/or drayage trucks). Efficient seaside operations are critical for MCT performance, as disruptions in the seaside operations may significantly delay service of the arriving v...

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A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility

Cited by 42 publications

References 48 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

Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

A Self-Adaptive Evolutionary Algorithm for the Berth Scheduling Problem: Towards Efficient Parameter Control

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