A Review on Quantitative Approaches for Dock Door Assignment in Cross-Docking

Cross docking is a relatively new technique in supply chain operations. It offers limited storage time to maximize the efficiency of goods transshipment. Efficient operation of a cross docking system requires an appropriate coordination of inbound and outbound flows, accurate planning and dynamic scheduling.  The planning strategies at cross docking terminals, which are receiving growing attention today, are the truck-to-door assignment and destination to door assignment problems. This paper provides a comprehensive literature review of quantitative approaches in dock door assignment problems of cross docking planning. The contributions of this paper are to identify the gap of knowledge in operational levels mainly in dock door assignment and to point out the future research direction in cross docking

An Optimization Model for Scheduling Problems with Two-Dimensional Spatial Resource Constraint

Traditional scheduling problems involve determining temporal assignments for a set of jobs in order to optimize some objective. Some scheduling problems also require the use of limited resources, which adds another dimension of complexity. In this paper we introduce a spatial resource-constrained scheduling problem that can arise in assembly, warehousing, cross-docking, inventory management, and other areas of logistics and supply chain management. This scheduling problem involves a twodimensional rectangular area as a limited resource. Each job, in addition to having temporal requirements, has a width and a height and utilizes a certain amount of space inside the area. We propose an optimization model for scheduling the jobs while respecting all temporal and spatial constraints

An Ant Colony Optimization for the Multi-Dock Truck Scheduling Problem with Cross-Docking

Cross-docking operation is a new distribution strategy for synchronizing inbound and outbound trucks at the terminal. Products move directly from inbound dock to shipping dock without being stored in the distribution center. In this paper, we consider the truck scheduling problem which simultaneously determines dock assignment and truck scheduling of both inbound and outbound trucks for a multi-door cross-docking operation. The objective is to minimize total holding cost at the cross-docking terminal. A mixed integer programming model is first formulated for the problem. Since both dock assignment and truck scheduling problems are NP-hard, this truck scheduling problem is more difficult to solve. Thus we propose an ant colony optimization (ACO) algorithm for the problem. To evaluate the proposed ACO, 24 instances are generated and tested. The computational results and comparison with Gurobi optimizer solutions show that the ACO is competitive

Usulan Konseptual Sistem Distribusi Cross Docking untuk Meminimumkan Biaya Distribusi pada Industri Retail

For retail industry, the distribution system is the main challenge that must be faced by the company. Choosing the right strategy will save unnecessary costs and reduce distribution time. The distribution time is needed to get products to the warehouse and to get them ready for shipping at the distribution centre. If the distribution time is felt to be too long, it might add costs. The objective of the study was to design a conceptual cross docking distribution system to minimize distribution costs and reduce distribution time by focusing on the product distribution inside the distribution center until the products are delivered to each outlet. Designing a conceptual cross docking distribution system consists of transportation costs, retain costs, the amoun to truck necessities, truck schedules, and the safety stock planning. Results from our calculation showed that the company's distribution costs werereduced. That is, the distribution costs were down by 62% compared to the current distribution costs. Based on the result, it is concluded that the conceptual cross docking distribution system designed in the present research successfully answered the existing problems. Kata kunci : Cross Docking, Truck Scheduling, Simulatio

Dock Assignment and Truck Scheduling Problems at Cross-docking Terminals

In this paper, we consider the integration of dock assignment and truck scheduling problem at cross-docking terminals. The problem is first formulated as a 0-1 integer programming model. Since both dock assignment and truck scheduling problems are NP-hard, its integration is more difficult to solve. Thus we propose reduced variable neighborhood search (RVNS) algorithms to solve the problem. Computational experiments are carried out on four set of instances. The results show that RVNS is capable of finding good solutions in a much shorter computation time when it is compared with optimization solver Gurobi’s solutions

Practical solutions for a dock assignment problem with trailer transportation.

We study a distribution warehouse in which trailers need to be assigned to docks for loading or unloading. A parking lot is used as a buffer zone and transportation between the parking lot and the docks is performed by auxiliary resources called terminal tractors. Each incoming trailer has a known arrival time and each outgoing trailer a desired departure time. The primary objective is to produce a docking schedule such that the weighted sum of the number of late outgoing trailers and the tardiness of these trailers is minimized; the secondary objective is to minimize the weighted completion time of all trailers, both incoming and outgoing. The purpose of this paper is to produce high-quality solutions to large instances that are comparable to a real-life case. We implement several heuristic algorithms: truncated branch and bound, beam search and tabu search. Lagrangian relaxation is embedded in the algorithms for constructing an initial solution and for computing lower bounds. The different solution frameworks are compared via extensive computational experiments.Dock assignment; Multicriteria scheduling; Branch and bound; Beam search; Lagrangian relaxation; Tabu search;

Cargo Consolidation and Distribution Through a Terminals-Network: A Branch-And-Price Approach

Less-than-truckload is a transport modality that includes many practical variations to convey a number of transportation-requests from the origin locations to their destinations by using the possibility of goods-transshipments on the carrier?s terminals-network. In this way logistics companies are required to consolidate shipments from different suppliers in the outbound vehicles at a terminal of the network. We present a methodology for finding near-optimal solutions to a less-than-truckload shipping modality used for cargo consolidation and distribution through a terminals-network. The methodology uses column generation combined with an incomplete branch-and-price procedure.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors

Improving the throughput of molecular docking, a computationally intensive phase of the virtual screening process, is a highly sought area of research since it has a significant weight in the drug designing process. With such improvements, the world might find cures for incurable diseases like HIV disease and Cancer sooner. Our approach presented in this paper is to utilize a multi-core environment to introduce Data Level Parallelism (DLP) to the Autodock Vina software, which is a widely used for molecular docking software. Autodock Vina already exploits Instruction Level Parallelism (ILP) in multi-core environments and therefore optimized for such environments. However, with the results we have obtained, it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times. This will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi-core to many-core of the current era. Therefore, we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases.Comment: Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference o

Simulation Modeling of Alternative Staffing and Task Prioritization in Manual Post-Distribution Cross Docking Facilities

Many supply chains have grown increasingly complex, which has led to the development of different facility types. One such facility is known as a post-distribution cross docking system (Post-C). In these facilities, bulk sorted product is received from various suppliers. Each product has its own destination, so the bulk package is broken, sorted by destination, and staged by destination. Typical processing includes: sort received goods by product type; break bulk and sort out goods by destination; move palletized goods to the staging areas of their respective destinations. This paper compares a global staffing policy (in which all workers may perform any task) to a dedicated staffing policy (in which groups of workers are assigned specific tasks). Through comparisons of the two models, it was found the dedicated worker model’s benefits from reduced change-over outweigh the lower worker utilization it experiences

A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks