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Journal articles on the topic 'Docking a truck'

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Author: Grafiati

Published: 10 December 2022

Last updated: 19 February 2023

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1

Kim, Byung Soo, and Cheol Min Joo. "Scheduling Trucks in Multi-Door Cross Docking Systems: An Adaptive Genetic Algorithm with a Dispatching Rule." Asia-Pacific Journal of Operational Research 32, no. 03 (June 2015): 1550016. http://dx.doi.org/10.1142/s0217595915500165.

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One of the most important operational management problems of a cross docking system is the truck scheduling problem. Cross docking is a logistics management concept in which products delivered to a distribution center by inbound trucks are immediately sorted out, routed and loaded into outbound trucks for delivery to customers. The truck scheduling problem in a multi-door cross docking system considered in this paper comprises the assignment of trucks to dock doors and the determination of docking sequences for all inbound and outbound trucks in order to minimize the total operation time. A mathematical model for optimal solution is derived, and the genetic algorithms (GAs) and the adaptive genetic algorithms (AGAs) as solution approaches with different types of chromosomes are proposed. The performance of the meta-heuristic algorithms are evaluated using randomly generated several examples.

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2

Küçükoğlu, İlker, and Nursel Öztürk. "A mathematical model for truck-door assignment and product placement problem in cross-docking center." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 135–42. http://dx.doi.org/10.18844/gjbem.v7i1.1408.

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In today’s cross-docking offers a significant cost benefits to companies because of the small orders and frequent deliveries in logistical environment. Cross-docking is a relatively new logistics strategy in which items are moved from suppliers to customers through cross-docking centers without putting them into long-term storage. One of the fundamental issues in cross-docking facility is to assign the incoming and outgoing trucks to the doors in order to minimize movement of the products. This paper addresses the truck-door assignment problem of cross-docking centers with product placement plans and proposes a mixed integer mathematical model to solve problem. The objective of the mathematical model is to find best truck-door assignment and product placement plans that minimize total travelling distance of the products. The proposed model is tested on a problem set which are created by using a real-life data of a logistics company. The computational results show that the proposed mathematical model can be carried out for the real-life operations to minimize movement costs of the products in cross-docking centers. Keywords: Cross-docking, truck-door assignment, product placement, mathematical modelling;

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3

Vahdani, Behnam, and Shayan Shahramfard. "A truck scheduling problem at a cross-docking facility with mixed service mode dock doors." Engineering Computations 36, no. 6 (July 8, 2019): 1977–2009. http://dx.doi.org/10.1108/ec-08-2018-0355.

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Purpose The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap. Design/methodology/approach In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented. Findings The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison. Research limitations/implications There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future. Originality/value In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

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4

Correa Issi, Gustavo, Rodrigo Linfati, and John Willmer Escobar. "Mathematical Optimization Model for Truck Scheduling in a Distribution Center with a Mixed Service-Mode Dock Area." Journal of Advanced Transportation 2020 (November 27, 2020): 1–13. http://dx.doi.org/10.1155/2020/8813372.

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Cross-docking is a logistics strategy in which products arriving at a distribution center are unloaded from inbound trucks and sorted for transfer directly to outbound trucks, reducing costs and storage and product handling times. This paper addresses a cross-docking problem by designing a mixed-integer linear programming (MILP) model to determine a schedule for inbound and outbound trucks in a mixed service-mode dock area that minimizes the time from when the first inbound truck arrives until the last outbound truck departs (makespan). The model is developed using AMPL software with the CPLEX and Gurobi solvers, which provide results for different instances, most of these with actual shift data from an integrated distribution center of a multinational food company located in Concepción, Chile. The results obtained from the case study are notable and show the effectiveness of the proposed mathematical model.

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5

Zhou, Binghai, and Shi Zong. "Adaptive memory red deer algorithm for cross-dock truck scheduling with products time window." Engineering Computations 38, no. 8 (March 8, 2021): 3254–89. http://dx.doi.org/10.1108/ec-05-2020-0273.

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Purpose The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies. Originality/value For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

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6

Yu, Wooyeon, Chunghun Ha, and SeJoon Park. "A Hybrid Genetic Algorithm for Integrated Truck Scheduling and Product Routing on the Cross-Docking System with Multiple Receiving and Shipping Docks." Mathematical Problems in Engineering 2021 (March 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/2026834.

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In this research, a truck scheduling problem for a cross-docking system with multiple receiving and shipping docks is studied. Until recently, single-dock cross-docking problems are studied mostly. This research is focused on the multiple-dock problems. The objective of the problem is to determine the best docking sequences of inbound and outbound trucks to the receiving and shipping docks, respectively, which minimize the maximal completion time. We propose a new hybrid genetic algorithm to solve this problem. This genetic algorithm improves the solution quality through the population scheme of the nested structure and the new product routing heuristic. To avoid unnecessary infeasible solutions, a linked-chromosome representation is used to link the inbound and outbound truck sequences, and locus-pairing crossovers and mutations for this representation are proposed. As a result of the evaluation of the benchmark problems, it shows that the proposed hybrid GA provides a superior solution compared to the existing heuristics.

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7

Serrano, Christian, Javier Moral, Xavier Delorme, and Alexandre Dolgui. "Cross-docking Operation Scheduling: Truck Arrivals, Shop-Floor Activities and Truck Departures." IFAC-PapersOnLine 49, no. 12 (2016): 1353–58. http://dx.doi.org/10.1016/j.ifacol.2016.07.751.

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8

Kuo, Yiyo. "Optimizing truck sequencing and truck dock assignment in a cross docking system." Expert Systems with Applications 40, no. 14 (October 2013): 5532–41. http://dx.doi.org/10.1016/j.eswa.2013.04.019.

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9

Hengmeechai, Piya, Takashi Irohara, and Warisa Wisittipanich. "Truck scheduling problems in the cross docking network." International Journal of Logistics Systems and Management 33, no. 3 (2019): 420. http://dx.doi.org/10.1504/ijlsm.2019.10022658.

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Wisittipanich, Warisa, Takashi Irohara, and Piya Hengmeechai. "Truck scheduling problems in the cross docking network." International Journal of Logistics Systems and Management 33, no. 3 (2019): 420. http://dx.doi.org/10.1504/ijlsm.2019.101164.

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11

Chiarello, Antonino, Manlio Gaudioso, and Marcello Sammarra. "Truck synchronization at single door cross-docking terminals." OR Spectrum 40, no. 2 (February 16, 2018): 395–447. http://dx.doi.org/10.1007/s00291-018-0510-x.

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12

Boysen, Nils. "Truck scheduling at zero-inventory cross docking terminals." Computers & Operations Research 37, no. 1 (January 2010): 32–41. http://dx.doi.org/10.1016/j.cor.2009.03.010.

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13

Theophilus, Oluwatosin, Maxim A. Dulebenets, Junayed Pasha, Olumide F. Abioye, and Masoud Kavoosi. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review." Sustainability 11, no. 19 (September 25, 2019): 5245. http://dx.doi.org/10.3390/su11195245.

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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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14

Özden, Gökçe, and İnci Sarıçiçek. "Solution approaches for truck scheduling problem in cross-docking." Pamukkale University Journal of Engineering Sciences 25, no. 1 (2019): 82–88. http://dx.doi.org/10.5505/pajes.2018.80034.

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15

Yu, Hongnian, and Gyan Bahadur Thapa. "A Cross-Docking Framework for Supply Chain Logistics in Production Scheduling." Journal of Institute of Science and Technology 19, no. 1 (November 8, 2015): 39–44. http://dx.doi.org/10.3126/jist.v19i1.13825.

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Synchronization of all partners along a supply chain logistics system is one of the challenges in most of the business operations. In this paper, we present a brief literature review of cross-docking approaches for supply chain logistics problem. On describing the problem, we formulate the supply chain logistics problem as a truck sequencing problem to minimize the discrepancies of operation times of inbound and outbound trucks. The problem is decomposed in two sub-problems as inbound and outbound parts as a solution strategy.Journal of Institute of Science and Technology, 2014, 19(1): 39-44

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16

Pan, Fei, Tijun Fan, Xinyi Qi, Jingyi Chen, and Chong Zhang. "Truck Scheduling for Cross-Docking of Fresh Produce with Repeated Loading." Mathematical Problems in Engineering 2021 (June 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/5592122.

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Due to mismanagement of supply chain operations, fresh produce, which deteriorates highly depending on time and operating environment (including temperature and humidity), will suffer huge losses in transit, resulting in substantial monetary losses. Cross-docking, as an efficient logistics operation strategy, has been widely used in fresh produce distribution in the cold supply chain, whereas it has not received adequate attention in the scientific literature. In order to improve the efficiency of fresh produce distribution, this study formulates a novel mixed-integer mathematical formulation model that allows repeated loading of outbound trucks to minimize the total deterioration (TD) of all the fresh produce in the cross-docking center. To solve this problem, an advanced genetic algorithm is proposed based on a constructional mixed chromosome with two parts and three levels. The numerical analyses are conducted on 10 typical instances under different combinations of parameters. Results show that our proposed model based on the repeated loading mode can effectively decrease the total deterioration compared with the traditional nonrepeated loading mode. And this superiority becomes more significant, as the value of truck changeover time and lot loading quantity (called lot size in the text) decrease. In particular, when the truck changeover time equals 0, the total deterioration obtained under repeated loading mode will be more than 31.8% on average smaller than that under nonrepeated loading mode.

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17

Wang, Zhanzhong, Yue Lu, Liying Zhao, and Ningbo Cao. "Improved Harmony Search Algorithm for Truck Scheduling Problem in Multiple-Door Cross-Docking Systems." Discrete Dynamics in Nature and Society 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/7913256.

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The key of realizing the cross docking is to design the joint of inbound trucks and outbound trucks, so a proper sequence of trucks will make the cross-docking system much more efficient and need less makespan. A cross-docking system is proposed with multiple receiving and shipping dock doors. The objective is to find the best door assignments and the sequences of trucks in the principle of products distribution to minimize the total makespan of cross docking. To solve the problem that is regarded as a mixed integer linear programming (MILP) model, three metaheuristics, namely, harmony search (HS), improved harmony search (IHS), and genetic algorithm (GA), are proposed. Furthermore, the fixed parameters are optimized by Taguchi experiments to improve the accuracy of solutions further. Finally, several numerical examples are put forward to evaluate the performances of proposed algorithms.

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18

Boysen, Nils, Dirk Briskorn, and Martin Tschöke. "Truck scheduling in cross-docking terminals with fixed outbound departures." OR Spectrum 35, no. 2 (December 6, 2012): 479–504. http://dx.doi.org/10.1007/s00291-012-0311-6.

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19

Naderi, B., Shadi Rahmani, and Shabnam Rahmani. "A Multiobjective Iterated Greedy Algorithm for Truck Scheduling in Cross-Dock Problems." Journal of Industrial Engineering 2014 (May 8, 2014): 1–12. http://dx.doi.org/10.1155/2014/128542.

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The cross-docking system is a new distribution strategy which can reduce inventories, lead times, and improve responding time to customers. This paper considers biobjective problem of truck scheduling in cross-docking systems with temporary storage. The objectives are minimizing both makespan and total tardiness. For this problem, it proposes a multiobjective iterated greedy algorithm employing advance features such as modified crowding selection, restart phase, and local search. To evaluate the proposed algorithm for performance, it is compared with two available algorithms, subpopulation particle swarm optimization-II and strength Pareto evolutionary algorithm-II. The comparison shows that the proposed multiobjective iterated greedy algorithm shows high performance and outperforms the other two algorithms.

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20

Meidute-Kavaliauskiene, Ieva, Nihal Sütütemiz, Figen Yıldırım, Shahryar Ghorbani, and Renata Činčikaitė. "Optimizing Multi Cross-Docking Systems with a Multi-Objective Green Location Routing Problem Considering Carbon Emission and Energy Consumption." Energies 15, no. 4 (February 18, 2022): 1530. http://dx.doi.org/10.3390/en15041530.

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Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.

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Ribeiro, Pedro, André Frank Krause, Phillipp Meesters, Karel Kural, Jason van Kolfschoten, Marc-André Büchner, Jens Ohlmann, Christian Ressel, Jan Benders, and Kai Essig. "A VR Truck Docking Simulator Platform for Developing Personalized Driver Assistance." Applied Sciences 11, no. 19 (September 24, 2021): 8911. http://dx.doi.org/10.3390/app11198911.

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Professional truck drivers frequently face the challenging task of manually backwards manoeuvring articulated vehicles towards the loading bay. Logistics companies experience costs due to damage caused by vehicles performing this manoeuvre. However, driver assistance aimed to support drivers in this special scenario has not yet been clearly established. Additionally, to optimally improve the driving experience and the performance of the assisted drivers, the driver assistance must be able to continuously adapt to the needs and preferences of each driver. This paper presents the VISTA-Sim, a platform that uses a virtual reality (VR) simulator to develop and evaluate personalized driver assistance. This paper provides a comprehensive account of the VISTA-Sim, describing its development and main functionalities. The paper reports the usage of VISTA-Sim through the scenario of parking a semi-trailer truck in a loading bay, demonstrating how to learn from driver behaviours. Promising preliminary results indicate that this platform provides means to automatically learn from a driver’s performance. The evolution of this platform can offer ideal conditions for the development of ADAS systems that can automatically and continuously learn from and adapt to an individual driver. Therefore, future ADAS systems can be better accepted and trusted by drivers. Finally, this paper discusses the future directions concerning the improvement of the platform.

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22

Hengmeechai, Piya, Takashi Irohara, and Warisa Wisittipanich. "Particle Swarm Optimization for Truck Scheduling Problem in Cross Docking Network." International Journal of Industrial and Systems Engineering 35, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijise.2020.10025370.

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23

Wisittipanich, Warisa, Takashi Irohara, and Piya Hengmeechai. "Particle swarm optimisation for truck scheduling problem in cross docking network." International Journal of Industrial and Systems Engineering 35, no. 3 (2020): 345. http://dx.doi.org/10.1504/ijise.2020.107778.

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24

Olteanu, Sergiu, Florin Valentin Rusca, Mircea Augustin Rosca, and Eugen Rosca. "The Optimal Allocation of Unloading/Loading Ramps in Distribution Centers." Applied Mechanics and Materials 809-810 (November 2015): 1366–71. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.1366.

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Warehousing enables the consolidation of shipments to clients by assembling full truck loads from goods stored in warehouse or distribution center. Storage can be efficiently replenished by ordering full truck loads from suppliers. In order to reduce freight storage and handling costs from distribution centers, cross-docking technique represents a logistic solution which is more and more used. Thus, the optimization of simultaneous unloading/loading operations constitutes an important issue for the distribution center owner/administrator. This paper presents a model based on genetic algorithms optimization techniques for solving the problem of unloading/loading ramps allocation. The efficiency of a cross-docking center is conditioned practically by finding an allocation and an optimal arrangement of ramps. This implies the allocation of destinations to unloading/loading ramps of distribution centers to minimize the total distance performed by the the cargo handling equipment. The case study presents a cross-docking platform with single handling equipment which is assigned to 10 unloading/loading ramps. By applying the solution of the ramp allocation generated through simulation, the total distance performed by the handling equipment is reduced which means lower handling cost for the distribution center owner/administrator.

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25

Taghizadeh, Mahziar, Amir Abbas Shojaie, Amir Homayoun Sarfaraz, and Sadigh Raissi. "A Multiobjective Mathematical Model for Truck Scheduling Problem in Multidoor Cross-Docking System." Discrete Dynamics in Nature and Society 2022 (July 31, 2022): 1–15. http://dx.doi.org/10.1155/2022/2171305.

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Cross-docking is the main operation of unloading products from incoming trucks, regrouping products in relation to their destination, and loading directly onto shipping trucks, reducing warehousing, picking, transportation costs, and delivery times. This is the intended logistics technology. In this paper, we present a new bi-objective mixed-integer mathematical model for truck scheduling problems in cross-docking systems. The goal of the proposed mixed-integer mathematical model is to minimize the total operation time (makespan) and cost of moving cargo within the terminal. The performance of the proposed model is compared with that of the available model to solve small instances. The results showed that in solving small size of problem, the proposed model in this study is more efficient and we found better solutions. An evolutionary algorithm called the nondominated sorting genetic algorithm (NSGA-II) has been proposed to solve larger instances due to computational complexity. To evaluate the proposed algorithm, a comparative analysis of benchmark instances was performed and the efficiency of the above algorithm was compared to the nondominated ranked algorithm (NRGA) based on the index designed in the literature. The statistical hypothesis testing (t-test) is used for determining the best algorithm based on the average runtime and average number of Pareto solutions. Using the Taguchi method, the proposed algorithms are tuned. Considering a temporary storage space and the multiple receiving and shipping docks is the main contribution of the paper. Finally, for evaluating algorithms, multicriteria decision-making (MCDM) technique and statistical method are used. The results show the suitable performance of presented model.

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26

Wu, Gen-Han, Yi-Ting Chen, and Ke-Hsuan Chen. "Hybrid Algorithms for Inbound and Outbound Truck Scheduling in Cross-Docking Systems." Applied Sciences 12, no. 21 (October 28, 2022): 10931. http://dx.doi.org/10.3390/app122110931.

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A cross-docking system is a kind of facility design for the purpose of enhancing the time efficiency of a distribution center. In this study, we attempt to consider a cross-docking system without temporary storage and obtain great system performance by addressing the scheduling problem of inbound and outbound trucks, in which the total operation time of trucks is minimized. In order to reduce computational efforts, three hybrid metaheuristic approaches based on particle swarm optimization, simulated annealing, and a variable neighborhood search are proposed. By the computational experiments, the three optimized approaches are analyzed and compared with each other. The experimental results show that all of these three approaches can obtain pretty good solutions, even in the large-scale examples. Moreover, one of these approaches—a hybrid metaheuristic with particle swarm optimization and a variable neighborhood search—can usually obtain the best solutions.

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Movassaghi, Mitra. "Cross-docks scheduling with multiple doors using fuzzy approach." European Transport/Trasporti Europei 79, ET.2020 (September 2020): 1–18. http://dx.doi.org/10.48295/et.2020.79.3.

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One of the most important practices in logistics is Cross-Docking which sets its goals as inventory reduction and customer satisfaction increase. Customers receive goods through docks. Docks are responsible to provide a place for goods before being delivered to the customers. Then, these materials are directly loaded into outbound trucks with little or no storage in between to send to customers in the shortest possible time. This paper is mainly aimed at introducing a mixed integer, non-linear programming model to solve scheduling several cross-docking problems. The proposed model is highly facilitated to allocate the most optimal destinations to storage doors and truck scheduling in docks while selecting the collection and delivery routes. Using optimization approaches at uncertainty conditions is also of great importance. Mathematical programming techniques vividly fail to solve transportation problems that include fuzzy objective function coefficients. A fuzzy multi-objective linear programming model is proposed to solve the transportation decision-making with fuzzy objective function coefficients.

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Amini, Alireza, and Reza Tavakkoli-Moghaddam. "A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks." Computers & Industrial Engineering 96 (June 2016): 180–91. http://dx.doi.org/10.1016/j.cie.2016.03.023.

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29

Yu, Wooyeon. "Truck scheduling for cross docking systems with multiple receiving and shipping docks." International Journal of Shipping and Transport Logistics 7, no. 2 (2015): 174. http://dx.doi.org/10.1504/ijstl.2015.067850.

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30

Ghobadian, Ehsan, Reza Tavakkoli-Moghaddam, Hassan Javanshir, and Mahdi Naderi-Beni. "Scheduling trucks in cross docking systems with temporary storage and dock repeat truck holding pattern using GRASP method." International Journal of Industrial Engineering Computations 3, no. 5 (October 1, 2012): 777–86. http://dx.doi.org/10.5267/j.ijiec.2012.08.001.

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Ghobadian, Ehsan, Reza Tavakkoli-Moghaddam, Mahdi Naderi-Beni, and Hassan Javanshir. "Scheduling trucks in cross docking systems with temporary storage and dock repeat truck holding pattern using genetic algorithm." Management Science Letters 3, no. 2 (February 1, 2013): 603–12. http://dx.doi.org/10.5267/j.msl.2012.12.009.

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32

Dulebenets, Maxim. "A Diploid Evolutionary Algorithm for Sustainable Truck Scheduling at a Cross-Docking Facility." Sustainability 10, no. 5 (April 25, 2018): 1333. http://dx.doi.org/10.3390/su10051333.

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Nurprihatin, Filscha, Elvina, Glisina Dwinoor Rembulan, Kevin Christianto, and Henny Hartono. "Decision support system for truck scheduling in logistic network through cross-docking strategy." Journal of Physics: Conference Series 1811, no. 1 (March 1, 2021): 012009. http://dx.doi.org/10.1088/1742-6596/1811/1/012009.

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34

Chargui, Tarik, Abdelghani Bekrar, Mohamed Reghioui, and Damien Trentesaux. "Multi-objective Truck Scheduling in a Physical Internet Road-Road Cross-docking Hub." IFAC-PapersOnLine 54, no. 1 (2021): 647–52. http://dx.doi.org/10.1016/j.ifacol.2021.08.175.

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35

Wisittipanich, Warisa, and Piya Hengmeechai. "Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization." Computers & Industrial Engineering 113 (November 2017): 793–802. http://dx.doi.org/10.1016/j.cie.2017.01.004.

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Amini, Alireza, Reza Tavakkoli-Moghaddam, and Aschkan Omidvar. "Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes." Production & Manufacturing Research 2, no. 1 (January 2014): 784–804. http://dx.doi.org/10.1080/21693277.2014.955217.

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Theophilus, Oluwatosin, Maxim A. Dulebenets, Junayed Pasha, Yui-yip Lau, Amir M. Fathollahi-Fard, and Arash Mazaheri. "Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations." Computers & Industrial Engineering 156 (June 2021): 107240. http://dx.doi.org/10.1016/j.cie.2021.107240.

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Molavi, Danyar, Amin Shahmardan, and Mohsen S. Sajadieh. "Truck scheduling in a cross docking systems with fixed due dates and shipment sorting." Computers & Industrial Engineering 117 (March 2018): 29–40. http://dx.doi.org/10.1016/j.cie.2018.01.009.

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Liao, T. W., P. J. Egbelu, and P. C. Chang. "Simultaneous dock assignment and sequencing of inbound trucks under a fixed outbound truck schedule in multi-door cross docking operations." International Journal of Production Economics 141, no. 1 (January 2013): 212–29. http://dx.doi.org/10.1016/j.ijpe.2012.03.037.

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Lo, Shih-Che. "A Particle Swarm Optimization Approach to Solve the Vehicle Routing Problem with Cross-Docking and Carbon Emissions Reduction in Logistics Management." Logistics 6, no. 3 (September 1, 2022): 62. http://dx.doi.org/10.3390/logistics6030062.

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Background: The logistics network design with cross-docking operations enables shipping service providers to integrate the physical flow of products between vendors and dealers in logistics management. The collective goal is to synchronize the goods in both pickup and delivery operations concurrently to reduce the handling cost, inventory cost, and operation cost generated. Therefore, the optimal vehicle routing plan is crucial to generate a truck routing schedule with minimal total cost, fulfilling the purchasing requirements and the distribution demand. Global warming and climate change are important topics due to increasing greenhouse gas emissions. Sustainable logistics management with optimized routes for trucks can assist in reducing greenhouse gas emissions and easing the effects of temperature increases on our living environment. Methods: A heuristic approach based on Particle Swarm Optimization, called ePSO, was proposed and implemented in this paper to solve the vehicle routing problems with cross-docking and carbon emissions reduction at the same time. Results: Performance comparisons were made with the Genetic Algorithm (GA) through the experiments of several vehicle routing problems with pickup and delivery benchmark problems to validate the performance of the ePSO procedure. Conclusions: Experimental results showed that the proposed ePSO approach was better than the GA for most cases by statistical hypothesis testing.

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Yazdani, Mehdi, Bahman Naderi, Shabnam Rahmani, and Shadi Rahmani. "Truck routing and scheduling for cross-docking in the supply chain: model and solution method." RAIRO - Operations Research 51, no. 3 (July 2017): 833–56. http://dx.doi.org/10.1051/ro/2016067.

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Tavana, Madjid, Kaveh Khalili-Damghani, Francisco J. Santos-Arteaga, and Mohammad-Hossein Zandi. "Drone shipping versus truck delivery in a cross-docking system with multiple fleets and products." Expert Systems with Applications 72 (April 2017): 93–107. http://dx.doi.org/10.1016/j.eswa.2016.12.014.

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Xu, Wan Hong, Cheng Shuang Zhang, and Shou Yan Wang. "Design and Simulating Analysis of Compressed Garbage Transport Truck Lifting Mechanism Based on ADAMS." Applied Mechanics and Materials 215-216 (November 2012): 1224–27. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.1224.

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The different lifting mechanisms of compressed garbage transport trunks are analyzed and compared, and the impact of characters and parameters on the functions of the machines is also analyzed. Finally, a kind of lifting mechanism on the basis of the principle of double docking bars is put forward. Creating 3D models of lifting mechanism based on the Pro/E, and using ADAMS to simulating analysis about motion process of mechanism, then the dynamic curve about acceleration of waste bin, press of hydraulic cylinder and other parameters can be gotten, which is valided of optimality of the mechanism and provided reference for a more comprehensive analysis and optimization of similar institutions later.

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Nogueira, Thiago Henrique, Felipe Provezano Coutinho, Rafael Pereira Ribeiro, and Martín Gómez Ravetti. "Parallel-machine scheduling methodology for a multi-dock truck sequencing problem in a cross-docking center." Computers & Industrial Engineering 143 (May 2020): 106391. http://dx.doi.org/10.1016/j.cie.2020.106391.

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Liao, T. W., P. J. Egbelu, and P. C. Chang. "Two hybrid differential evolution algorithms for optimal inbound and outbound truck sequencing in cross docking operations." Applied Soft Computing 12, no. 11 (November 2012): 3683–97. http://dx.doi.org/10.1016/j.asoc.2012.05.023.

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Kusumakar, Rakshith, Lejo Buning, Frank Rieck, Peter Schuur, and Frans Tillema. "INTRALOG – intelligent autonomous truck applications in logistics; single and double articulated autonomous rearward docking on DCs." IET Intelligent Transport Systems 12, no. 9 (November 1, 2018): 1045–52. http://dx.doi.org/10.1049/iet-its.2018.0083.

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Chargui, Tarik, Abdelghani Bekrar, Mohamed Reghioui, and Damien Trentesaux. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption." Sustainability 11, no. 11 (June 3, 2019): 3127. http://dx.doi.org/10.3390/su11113127.

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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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Ye, Yan, Jingfeng Li, Kaibin Li, and Hui Fu. "Cross-docking truck scheduling with product unloading/loading constraints based on an improved particle swarm optimisation algorithm." International Journal of Production Research 56, no. 16 (April 25, 2018): 5365–85. http://dx.doi.org/10.1080/00207543.2018.1464678.

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Golshahi-Roudbaneh, Amir, Mostafa Hajiaghaei-Keshteli, and Mohammad Mahdi Paydar. "Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center." Knowledge-Based Systems 129 (August 2017): 17–38. http://dx.doi.org/10.1016/j.knosys.2017.05.006.

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Assadi, Mohammad Taghi, and Mohsen Bagheri. "Differential evolution and Population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems." Computers & Industrial Engineering 96 (June 2016): 149–61. http://dx.doi.org/10.1016/j.cie.2016.03.021.

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