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The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today's digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented. [ABSTRACT FROM AUTHOR]

A class of strong lower bounds on the solution value of a Linearised Integer Programming reformulation is introduced for the binary quadratic optimisation model to assign origin and destination nodes to strip and stack doors, resp., in a cross-dock infrastructure. The goal is to minimise the transportation cost of the commodities to be handled at the cross-dock. Strip- and stack-related decomposition submodels are developed by taking benefit of the Integer Linearisation Property that appears in the new model. A linear search heuristic is also provided for obtaining feasible solutions by exploiting the special structure of the problem. We present an extensive computational study on a testbed of 55 instances to show that the proposed joint scheme for lower bounding and feasible solution providing is very efficient. The results obtained by our proposal are similar to those obtained by CPLEX in the 50 instances taken from the literature, but usually the time is much smaller for the large instances. The comparison with CPLEX is particularly good for the new five largest instances, which make it very promising for its application in real-life cases. [ABSTRACT FROM AUTHOR]

So-called "cross-docking" is the prediction of the bound configuration of small-molecule ligands that differ from the cognate ligand of a protein co-crystal structure. This is a much more challenging problem than re-docking the cognate ligand, particularly when the new ligand is structurally dissimilar from prior known ones. We have updated the previously introduced PINC ("PINC Is Not Cognate") benchmark which introduced the idea of temporal segregation to measure cross-docking performance. The temporal set encompasses 846 future ligands for ten targets based on information from the earliest 25% of X-ray co-crystal structures known for each target. Here, we extend the benchmark to include thirteen targets where the bound poses of 128 macrocyclic ligands are to be predicted based on knowledge from structures of bound non-macrocyclic ligands. Performance was roughly equivalent for both the temporally-split non-macrocyclic ligand set and the macrocycle prediction set. Using standard and fully automatic protocols for the Surflex-Dock and ForceGen methods, across the combined 974 non-macrocyclic and macrocyclic ligands, the top-scoring pose family was correct 68% of the time, with the top-two pose families achieving a 79% success rate. Correct poses among all those predicted were identified 92% of the time. These success rates far exceeded those observed for the alternative methods AutoDock Vina and Gnina on both sets. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics procedure implemented in a warehouse to achieve a competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. Today, one of the challenges related to cross-docking for both practitioners and researchers is handling the uncertainty. Robust cross-docking solutions bring a part of the answer to this challenge. This paper proposes an overview of robust and real-time models for cross-dock problems with a focus on scheduling problems, notably in the road-to-road cross-dock environment. To this end, the conducted systematic literature review addresses the collection, identification, screening, eligibility, and inclusion steps to extract the most relevant literature. The gaps in the literature are identified, and some perspectives to support future studies are proposed. [ABSTRACT FROM AUTHOR]

This study aims to develop a sustainable freight transportation network considering capacitated cross-docks for minimising the overall supply chain costs, including carbon emission cost. The problem is inspired by a major retail company based in India, which would like to expand its product portfolio in the new region. A mathematical model is developed to minimise total costs encompassing transportation cost, pipeline and retailers inventory cost, fixed cost of cross-dock and carbon emission costs. The deterministic time dependant demand, multiple products and multiple sourcing and distribution are some of the challenges faced by the retail industry. A two-level self-adaptive variable neighbourhood search algorithm is applied to solve a computationally complex problem. The results based on a two-level self-adaptive variable neighbourhood search algorithm are compared with the variable neighbourhood search algorithm to test the robustness of the developed model. Results reveal that an increase in retailers over suppliers significantly influences the number of open cross-docks. A multiple-case scenario approach captures the implications of varying capacity on the number of open cross-docks; thus, supporting the freight distribution managers in making sustainability-driven decisions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors. Design/methodology/approach: Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Findings: Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods. Originality/value: This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions. [ABSTRACT FROM AUTHOR]

This study describes, characterises and analyses the optimisation of truck entry and exit time windows, which includes a cross-docking centre planning strategy, using the simulation environment of the AnyLogic software environment. The article describes the use of software integers (IP*) and heuristics (IP1 and IP2) for the time windows of a cross-docking centre. Based on a mathematical model using an integer number program (IP*) and heuristics (IP1 and IP2), a scheduling problem is investigated. This program consists in minimizing the penalties for violation of the time windows for inbound and outbound trucks. It also enables to calculate the operation time of time windows on an ongoing basis. The cross-docking-center model is a layout, which includes logistic elements of technological processes, created using the AnyLogic software. It is found out by using order diagrams that after unloading arriving trucks, the goods are collected in four stages, the first two of which (queuing and waiting for collection) determine the loading rate of goods in the trucks departing from the cross-dock. After determining the main stages of order collecting using the cross-docking strategy, the dependences of the average time of unloading and loading goods are studied. The average unloading and loading time are found to be 30 and 25 minutes, respectively. Based on the time dependences, a diagram of the average time of a truck in the dock, being about an hour - an hour and a half, is created. [ABSTRACT FROM AUTHOR]

Cross-Docking (CD) is a modern distribution strategy in a supply chain. The optimal scheduling of vehicle routing, known as the Vehicle Routing Problem (VRP), is one of the influential factors of the efficiency of a supply chain. In recent years, researchers and business consultants in different organizations have been interested in integrating the VRP with CD (VRPCD). Since VRPCD is a NP-hard problem, heuristic or meta-heuristic methods are always recommended to solve large-scale VRPCD. The Genetic Algorithm (GA) is a population based meta-heuristic algorithm and also, it is based on the principles of genetic and natural selections. The GA is capable of finding near optimal solutions to large-scale optimization problems which are extremely difficult to solve using traditional optimization algorithms. Therefore, in this study, a meta-heuristic approach based on the GA is proposed to solve the vehicle routing problem with moving shipments at the cross-docking facility (VRPCD&MS). The data are extracted from benchmark instances in the literature. The optimum solutions obtained to small-scale instances by the GA are compared with the exact solutions obtained by the Branch and Bound (BB) algorithm, which is a traditional algorithm to solve problems of this nature. The GA and BB algorithms are respectively coded in MATLAB and LINGO. The results reveal that the relative difference between the exact solution and the near-optimal solution is below 5%. Therefore, it can be concluded that the proposed GA is a better alternative method, considering its overall performance, to solve the VRPCD&MS models. Moreover, since the computational time is low, the proposed GA can be used to schedule the vehicles to the routes of VRPCD&MS at the last moment prior to the start of the time horizon. [ABSTRACT FROM AUTHOR]

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions. [ABSTRACT FROM AUTHOR]

Optimal warehouse design is the most substantial issue for many companies, because of increasing their efficiency and productivity. Nowadays, a warehouse is just undefined as a site for goods storage and delivery but is taken into account as a unit interacting with other sectors. Recently, cross-docking warehouses have been widely used since they differ from traditional warehouses in terms of good deposition volume, storage and delivery times, mitigating procurement, exploitation of resources, and accelerating service pace. This study proposes a mathematical model for transit warehouse layout and design to minimize travel distances, downsize the free space of the warehouse and make retailers satisfied. Small-sized problems are solved by the augmented epsilon-constraint and the weighted sum methods, while NSGA-II and MOGWO algorithms are proposed for large-sized problems. Comparison between algorithms indicates higher efficiency of NSGA-II rather than MOGWO algorithm to solve cross-docking warehouse design for allocating stores to floor locations. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics process in which products are unloaded through receiving docks and then transferred to shipping docks with almost no storage in between. In this paper, a mixed integer linear programming model (MILP) is proposed to optimise the scheduling, storage, assignment and sequencing of trucks at receiving and shipping docks for a problem inspired from a multiple-door cross-dock facility of an industrial partner with multiple temporary storage zones. The multiple storage zones are separated and located in the centre of the cross-dock handling different types of products. The objective is to minimise the total tardiness of inbound and outbound trucks. A heuristic (H) is proposed to find an initial solution. Then, three meta-heuristics are developed, namely Random Search (RS), Tabu Search (TS) and Simulated Annealing (SA) to improve the scheduling of trucks in order to minimise the tardiness of inbound and outbound trucks. Experimental results indicate that the three meta-heuristics (RS, TS and SA) are able to find good quality results within reasonable computational times. Finally, since SA showed the best performance compared to RS and TS, it was chosen to be compared to the current manual method using discrete event simulation. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the innovative concept of a smart warehouse is defined as a progressive development in logistics and operations management. Numerous advanced techniques are recommended for integration, including wearable devices, communication, loading/unloading, optimization, robotics, and automated transportation, among others. Currently, the growing demand for swift cargo distribution, cost‐effective labour utilization, and enhanced productivity stands as an urgent topic for both enterprises and scholars. This paper presents an EtherCAT‐based model of automated guided vehicle (AGV)‐manipulation introduced within the Unity platform for smart warehouse management. The working environment selected for this model is a cross‐docking warehouse with a chessboard layout. Initially, theoretical computations pertaining to mechanical design are established to provide accurate parameters. Subsequently, the structure of the TwinCAT Human–Machine Interface Server and the configuration within the Unity environment are outlined. Following that, the software architecture, encompassing data exchange and a set of motion commands, is meticulously explained. To validate the proposed approach, several laboratory tests are conducted under identical conditions. This research offers several contributions: firstly, it introduces the innovative use of real‐time technology to represent AGV systems; secondly, it elucidates the potential connection between virtual reality and the physical model; and thirdly, it employs fluid motion techniques to illustrate and visualize driving performance. These findings hold significant promise not only for the advancement of smart warehouse management but also for the broader landscape of digital trade. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated material flow optimization problem of cross-docking satellites, in which the transportation problem, the truck-door assignment problem with material placement plans, and the two-dimensional truck loading problem are taken into account. The study aims to find the best distribution and operational plans for the cross-docking satellites to minimize the total transportation cost of the materials. To solve the considered problem, a hybrid genetic algorithm (HGA) is developed, which integrates simulated annealing (SA) algorithm within a genetic algorithm (GA). In this way, a new individual with a low solution quality is rejected by using the stochastic solution acceptance feature of the SA. Moreover, the HGA is enhanced with an advanced two-dimensional loading-check procedure and a rule-based material placement procedure to obtain efficient solutions. The proposed loading-check procedure reduces the processing time of the HGA by avoiding duplicate examinations for the truck loading plans. Likewise, the rule-based material placement procedure prevents unnecessary searches for the assignment plans of the products in a temporary storage area. In computational studies, the performance of the HGA is tested on two different problem sets by comparing HGA with the SA and GA. Furthermore, the HGA is applied to a problem set that is formed by using real-life data of a logistics company. The computational results show that the HGA introduces effective solutions and outperforms both the SA and GA. In addition, the results of the real-life application denote that the HGA can be employed to find effective material flow plans in real situations of cross-docking operations. [ABSTRACT FROM AUTHOR]

Manufacturing and service companies in different sectors are trying to green their supply chains in two ways: by optimizing processes and by using green technologies such as electric vehicles. Cross-docking offers a good solution for optimizing supply chain processes. The aim of this research is to investigate how cross-docking supply with electric vehicles can lead to energy efficient solutions and emission reduction. The scientific novelty of this research is the integration of a process improvement solution and the application of advanced technology through optimization. The presented functional model, mathematical model and its solution algorithm are suitable to demonstrate the economic feasibility of applying a crossdocking facility and solving the transportation tasks with e-trucks for a conventional supply chain. Based on the numerical studies presented, it can be concluded, that the proposed solution can significantly contribute to the optimization of local and global supply chains, reducing the ecological footprint of supply chains both locally and globally. As the analysis of case studies shows, it is possible to reach an energy efficiency improvement of about 40%, which can also lead to a significant GHG emission up to 30%. [ABSTRACT FROM AUTHOR]

Nowadays cross-docking operations play a significant role in the cold-chain logistics. This paper addresses the cold-chain cross-docking truck scheduling problem where two types of products, i.e. refrigerated and frozen ones, demand separate trucks and storage areas with distinct temperature settings during their storage and transportation. We present a mixed-integer linear programming model with the objective of minimising the total operational costs that consist of inbound truck arrival penalties for violating contracted time windows, product delivery tardiness penalties, inventory costs and outbound truck transportation costs. Due to the strong NP-hardness of the considered problem, we solve it in two phases where the inbound truck arrival schedule and the schedule of outbound truck departure together with product processing are produced, respectively. Four heuristic algorithms are proposed to generate complete solutions of the considered two-stage problem, which are the combinations of two solution frameworks for the first stage and two methods for the second stage. Computational experiments are carried out to verify the effectiveness and efficiency of the proposed heuristic algorithms in terms of the solution quality and running time, respectively. [ABSTRACT FROM AUTHOR]

Purpose: An important phenomenon often observed in supply chain, known as the bullwhip effect, implies that demand variability increases as we move up in the supply chain. On the other hand, the cross-docking is a distribution strategy that eliminates the inventory holding function of the retailer distribution center, where this latter functions as a transfer point rather than a storage point. The purpose of this paper is to analyze the impact of cross-docking strategy compared to traditional warehousing on the bullwhip effect. Design/methodology/approach: The authors quantify this effect in a three-echelon supply chain consisting of stores, retailer and supplier. They assume that each participant adopts an order up to level policy with an exponential smoothing forecasting scheme. This paper demonstrates mathematically the lower bound of the bullwhip effect reduction in the cross-docking strategy compared to traditional warehousing. Findings: By simulation, this paper demonstrates that cross-docking reduces the bullwhip effect upstream the chain. This reduction depends on the lead-times, the review periods and the smoothing factor. Research limitations/implications: A mathematical demonstration cannot be highly generalizable, and this paper should be extended to an empirical investigation where real data can be incorporated in the model. However, the findings of this paper form a foundation for further understanding of the cross-docking strategy and its impact on the bullwhip effect. Originality/value: This paper fills a gap by proposing a mathematical demonstration and a simulation, to investigate the benefits of implementing cross-docking strategy on the bullwhip effect. This impact has not been studied in the literature. [ABSTRACT FROM AUTHOR]

In a cross-docking system, doors can be used exclusively for receiving or sending operations, or they can be flexible enough to be used for both. Flexible doors have gained attention for improving cross-docking system performance, but their implementation incurs additional costs due to the need for dual-function equipment to handle both receiving and sending operations. Despite the advantages of flexible doors, applying them is usually associated with additional costs due to the need for dual-function equipment to receive and send. Moreover, the distance that goods move within crossdocking facilities, from receiving to sending dock doors, significantly affects how well these facilities work. This paper presents a new bi-objective mixed-integer linear programming model for scheduling the inbound and outbound trucks in a cross-docking facility. The model objectives are to minimize total system costs, including additional costs of applying flexible doors and transshipment (maximize efficiency) and to minimize outbound trucks’ tardiness from predetermined due dates (maximize responsiveness). As a result of the uncertainties in cross-dock truck scheduling problems, the parameters are considered triangular interval-valued fuzzy (IVF) numbers. Moreover, a new IVF-uncertain solution approach based on the max-min operator and compromise programming concepts is proposed to solve multi-objective mathematical programming problems with triangular IVF numbers. The proposed model and IVF-solution approach are used for cross-docking operations planning at a well-known food manufacturing group. The results are also analyzed according to their sensitivity to changes in key parameters of the cross-docking problem. Comparison of the proposed approach with other known fuzzy methods for solving multi-objective models demonstrates the superior performance of the proposed approach in the real case study. [ABSTRACT FROM AUTHOR]

Purpose: Collaborative Logistics (CL) and merging operations are crucial strategies for reducing costs and improving service in transportation companies. This study proposes a model for optimizing efficiency in supply chain networks through inbound and outbound Collaborative Logistics implementation among the carriers in centralized, coordinated networks with cross-docking. Design/methodology/approach: A mixed-integer non-linear programming model is developed to determine the optimal truck-goods assignment while gaining economies of scale through mixing multiple less-than-truckload (LTL) products with different weight-to-volume ratios. Unlike the previous studies that have considered Collaborative Logistics from the cost and profit-sharing perspective, the proposed model seeks to determine an appropriate form of Collaborative Logistics in the VRP. Findings: This article shows that in a three-echelon supply chain consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. This approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of collaborative logistics among the carriers was discussed. In a three-echelon SC consisting of a set of suppliers, a set of customers and a cross-docking terminal, partial collaboration among the inbound carriers and outbound carriers outperforms no/complete collaboration. Using a combination of experimental analysis and optimization process, it was recommended that managers be cautious that too much (full or complete) or no collaboration can result in SC performance deterioration. Originality/value: The suggested approach enhances the supply chain efficiency by minimizing the total transportation costs, the total transportation miles and the total number of trucks and maximizing fleet utilization. While addressing the four points, the role of Collaborative Logistics among the carriers was discussed. [ABSTRACT FROM AUTHOR]

Space and labor are the two internal resources within a warehouse or cross-dock center which seek attention. Meaningful efforts in optimizing these two resources can reduce the operational cost or time of the goods delivery. The timely allocation of resources to order picking not only reduces the makespan and operational time but can also evade delay. In decentralized settings, where all the information is not properly shared between the players of the supply chain, miscommunication results in delays in product delivery. In this study, efforts were made to determine the pallet quantity of different product types in an order quantify when there is a gap in information shared and, based on that, the allocation of material handling devices or pickers was conducted. Each handling device is bounded by a workload to eliminate the option of idle resources and ensure it is utilized properly. A mixed integer linear programming model was formulated for this study and was solved using Lingo. Numerical experiments were performed under varying resource numbers and pallet quantities to investigate the circumstances where the number of pallet types and allocation of machines have the highest benefit. The results confirm that a change in the pallet quantity of the products increases the total picking time. However, an increase in the number of handling devices minimizes the level of over-utilization of a particular machine. [ABSTRACT FROM AUTHOR]

The present consumer behavior is manipulated by "fast fashion", where purchasing new, trendy, affordable clothes is preferred over recycling old ones. This changing mannerism has escalated the GHG emissions from the fashion industry. Energy-intensive raw material production, preparation, and processing contribute to considerable emissions. The management of the returned goods from the primary market and further processing through the secondary outlets indulge in reverse logistics. In this paper, efforts are made to minimize the total cost and the carbon emission amount during the process of managing the return articles from the primary market to the reverse distribution center, further processing of the articles at the secondary outlet, and the return of the unsold or excess articles from the secondary outlet. Reverse cross-docking has been implemented in managing the return articles, while environmental concerns over GHG emissions have been addressed by investing in green technology under a strict carbon cap policy. In this research, return articles from the primary and secondary markets, rework of the returned articles, and disposal of the impaired returned articles have been considered. The carbon emission cost at all stages of transportation, rework, or disposal has also been incorporated into this model. A constrained mixed integer linear programming model is proposed and solved considering green investment. A numerical example has been formulated to investigate the effect of green technology on the total cost. The results portray that, though the total cost increases by nearly 2% due to investment in green technology, it ensures a considerable drop of 23% in the carbon emission amount. Also, the result is successful in establishing that reverse cross-docking is a better option than traditional warehousing in terms of minimizing the cost. [ABSTRACT FROM AUTHOR]

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate a specific truck scheduling problem at cross-docks in the postal service industry on an operational level aiming to maximise the number of duly parcels assuming fixed departure times of the outbound trucks. The inbound gates and the conveyors as means of transportation inside the hub constitute the bottleneck resources. As a novel extension, we propose flexible unloading speeds to efficiently utilise the scarce resources. We formalise the problem with a mixed integer program and explicitly incorporate controllable unloading speeds of the inbound trucks. We determine the computational complexity and develop a genetic algorithm to efficiently solve the problem. Our investigation focuses on both the performance of the genetic algorithm and the applicability of the results in a real-world environment by implementing scheduling policies in a simulation model that considers individual parcel interactions. Based on our experimental results, we can state that especially in problem settings with scarce conveyor capacities, our approach to incorporate controllable unloading speeds has the potential of significantly increasing the number of duly parcels. [ABSTRACT FROM AUTHOR]

The absence of designated remedies for coronavirus disease 19 (Covid-19) and the lack of treatment protocols drove scientists to propose new small molecules and to attempt to repurpose existing drugs against various targets of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in order to bring forward efficient solutions. The main protease (Mpro) is one of the most promising drug targets due to its crucial role in fighting viral replication. Several antiviral drugs have been used in an attempt to overcome the pandemic, such as hydroxychloroquine (HCQ). Despite its perceived positive outcomes in the beginning of the disease, HCQ was associated with several drawbacks, such as insolubility, toxicity, and cardiac adverse effects. Therefore, in the present study, a structure-based virtual screening approach was performed to identify structurally modified ligands of the chloroquinoline (CQ) scaffold with good solubility, absorption, and permeation aimed at eventually suggesting a more dependable alternative. PDB ID:7BRP Mpro was chosen as the most reliable receptor after cross-docking calculation using 30 crystal structures. Then, a SiteMap analysis was performed and a total of 231,456 structurally modified compounds of the CQ scaffold were suggested. After Lipinski criteria filtration, 64,312 molecules were docked and their MM-GBSA free binding energy were calculated. Next, ADME descriptors were calculated, and 12 molecules with ADME properties better than that of HCQ were identified. The resulting molecules were subjected to molecular dynamics (MD) simulation for 100 ns. The results of the study indicate that 3 molecules (CQ_22; CQ_2 and CQ_5) show better interactions and stability with the Mpro receptor. Binding interaction analysis indicates that GLU143, THR26, and HIS41 amino acids are potential binding hot-spot residues for the remaining 3 ligands. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper deals with a truck scheduling and transshipment problem in a cross-docking terminal, with one inbound gate and one outbound gate. Each inbound truck carries one or more load units possibly of different commodities, and each outbound truck must be loaded with different commodities as well. The problem is to define the sequence of trucks at the inbound and outbound gates and the transshipment plan, so as to minimize the makespan of the whole process. We propose a Lagrangian relaxation scheme and a dual ascent method to optimize the Lagrangian dual that encloses three heuristics aimed at recovering feasible solutions for the problem at hand from the solution of the Lagrangian problem. Computational experiments are also provided, in order to evaluate the effectiveness of the Lagrangian approach. [ABSTRACT FROM AUTHOR]

In today's consumer society, there is a growing need for companies to optimise their sourcing processes, as without this, the performance of the entire supply chain can be severely compromised. An important step in this process is the integrated design of production and logistics processes to create an optimal value chain that lead to efficient operations. Conventional optimisation methods for blending technologies are focusing mainly on the optimisation of process parameters and neglect the logistics aspects that can further improve the efficiency of the overall system. Within the frame of this article, the author describes a novel model of supplier selection of production processes using blending technologies which makes it possible to analyse the impact of logistics constraints on the cost efficiency of the solution. The analysed supply chain is a multi-supplier, cross-docking facility based single user supply chain. As the numerical examples and computational results show, the new integrated approach makes it possible to increase the cost efficiency of the supply of blending technologies. [ABSTRACT FROM AUTHOR]

In an emergency logistics operation, quick delivery and fair distribution of relief items are of extreme importance. Using direct shipments from suppliers to the affected areas for distributing relief items may be challenging because of scarcity of trucks after a disaster and underutilization of limited trucks due to direct shipments (i.e., full truckload shipments may not be achieved). A warehouse or a cross-dock may help overcome these challenges. Warehouses are essential for storing a predetermined level of goods for disasters. However, as a distribution center, they lack agility as there is a need for sorting, storage and order picking. In this paper, we incorporate the concept of opportunistic cross-docking (sending items directly from incoming to outgoing vehicles whenever possible) into emergency logistics operations as a means of more efficient delivery of relief items. Unlike the traditional cross-docking, where temporary inventory should be zero at the end of each period, the opportunistic cross-docking allows storage at the cross-dock facility. We compare cross-docking approaches for emergency logistics operations. After formulating the integrated cross-docking and distribution problems, we discuss several solution methods, namely: fix-and-relax, fix-and-relax combined with local search, local branching, and local branching combined with fix-and-relax. Computational experiments performed on three sets of randomly generated instances and a case study of Tehran's district #1 data yield results in favor of opportunistic cross-docking. Furthermore, the solution methods are compared to each other and a commercial solver. [ABSTRACT FROM AUTHOR]

Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers' demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers' requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test. [ABSTRACT FROM AUTHOR]

Purpose: Cross-docking deals with the goals of lean supply chain management. It is a distribution strategy. In this structure, goods are shipped from inbound to outbound trailers on the same day or overnight without putting them into storage. Cross-docking can realize transport efficiencies at reduced material handling and storage costs by eliminating the storage and order-picking activities from the main warehouse operations. These advantages make cross-docking an interesting logistic strategy that can give companies considerable competitive advantages. This paper aims to introduce cross-docking, its characteristics and its problem types, comprehensively. The focus of this study is on distinguishing research gaps. Design/methodology/approach: Systematic review is "review research by explicit, rigorous and accountable methods". Reviews and their findings can vary on many aspects of the 'dimensions of different'. One such review is the 'review of reviews'. A systematic review of primary research is the second level of research analysis. A review of reviews (sometimes called 'overview' or 'umbrella' review) is a tertiary level of analysis. It is a systematic map and/or synthesis of previous reviews. The 'data' for such reviews of reviews are previous reviews rather than primary research studies. This can be an efficient method for examining previous research. In this paper, cross-docking literature systematic review (review of reviews method) has been discussed by introducing, clustering crossdocking decision problems, and drawing future research directions. Findings: Bibliography, cross-dockings, and classified characteristics of cross-docking based on the decision levels (strategic, tactical and operational levels) were the major findings of this study. Also, the cross-docking problem types were addressed and the gaps in the field of cross-docking were determined. This study highlighted two frequent gaps that would need to be filled by focusing on the research areas of taking deadlines for the trucks into account and dealing with uncertainty. Research limitations/implications: Book chapters and conference papers were excluded from this research. Reviewed papers were published in a certain period and the English language. Practical implications: Today firms use different types of distribution strategies. Cross-docking with eliminating the storage and order-picking activities from the main warehouse operations is an interesting logistics strategy. products with predictable, high cubic volume flow, and perishable products are ideal candidates for cross-docking. Cross-docking is nowadays used by many companies and industries such as the retail industry, automotive industry, telecommunications and electronics industries. Social implications: Cross-docking results in smaller volumes of more visible inventories that are delivered faster and more frequently. Several other advantages of cross-docking are addressed in the literature compared to traditional distribution centres. Some advantages are reduced cost (warehousing costs, inventory-holding costs, handling costs, labour costs), shorter delivery lead time (from supplier to customer), improved customer service, reduced storage space, faster inventory turnover, fewer overstocks, and reduced risk for loss and damage. Originality/value: Due to the advantages and extensive applications of cross-docking, in this paper a "review of reviews" was performed, and after introducing cross-docking terminals and their problem types, distinguish research gaps were determined for the future research study. Therefore, researchers were suggested to concentrate on the addressed gaps in their models to get closer to real-life constraints. [ABSTRACT FROM AUTHOR]

Cross-docking is used as logistics strategy, where the goods are removed from the incoming vehicles to be forward directly to the outgoing vehicles. Based on the logistics processes are need more attention to minimalize the storage time or completely neglecting it. It can be used by several different companies around the world due to its economic and environmental benefits. Perhaps the most popular example is the two giant companies of American owners, Amazon and Walmart, whose main activity has a major role to play in the success of a well-planned and operated distribution logistics. With its use, the transportation costs can be highly minimized, products from several suppliers can be combined to a single shipment for economy, and lead times are reduced, and inventory costs are improved. [ABSTRACT FROM AUTHOR]

With the constant growth and integration of technologies in the economic, political, social, cultural and environmental fields that allow framing globalization through trade and financial flows, organizations and companies are implementing tools to improve the efficient use of resources based on business policies that lead to economic growth. This article describes the circular economy and Cross Docking models that allow the correct use and implementation of techniques in business logistics, reducing times in the supply chain and increasing efficiency in business productivity, taking into account the needs of customers in the management of business resources, which can be done through the system called Enterprise Resource Planning (ERP Enterprise Resource Planning), the ERP system integrates and relates the Circular Economy and Cross Docking as in logistics. Methodology: Research was conducted in different educational platforms such as: IEEE Xplore, Web of Science, Publindex, Google Academy among others, in order to obtain and compare articles involving Circular Economy and Cross Docking Method in enterprise resource management. [ABSTRACT FROM AUTHOR]

This document talks about the management of business resources, specifying the circular economy and Croos Docking models, for which an analysis of its different characteristics was carried out through an investigation of several documents and scientific articles published, related to the subject. With this in mind, an overview is provided explaining the different concepts and their rationale, the key features, and the most important tools. The analysis of the different strategies proposed in the implementation of the different models highlights the incredible leadership in different countries where these processes are getting better and better. Interested readers will be able to deepen their understanding of the subject from the references provided. It should be noted that in all organizations and companies there are many processes that need to be managed for their correct development, such as finances, customer service, different inventories, as well as sales and human resources. The existing challenge in this area is to effectively control and manage all the company's resources. Therefore, the maximum demand must be satisfied with the minimum effort, and for this the effort of all the components of the organization must be properly managed. Within this order of ideas, it is there that we note the importance of the circular economy and Cross Docking models, which have become paradigms in the search for sustainable development, proposing different strategies throughout the chain. production and use of products and services. With this in mind, we mention different models and strategies to properly manage business resources. [ABSTRACT FROM AUTHOR]

In recent decades, economic growth, industrial development and modern technology have brought a new level of comfort and prosperity to our lives, resulting in increased consumption of goods and services. The following article presents a study of the circular economy, which aims to maintain and strengthen socioeconomic development for current and future generations, including the creation of jobs in a sustainable manner, without compromising ecosystem functions and conserving natural resources. The purpose of the circular economy concept definition is to fundamentally change the current linear system of production and consumption, linking the use of natural resources with negative external consequences for well-being and development. It is about replacing a linear economy based on extraction, production, consumption and disposal with a circular model in which waste materials are used to produce new products or raw materials. Material reduction, reuse, recycling and recycling are essential processes in this planning. As for the Cross Docking model, this is a distribution system where goods received at a warehouse or distribution center are not stored, but are immediately prepared for the next shipment. Cross docking has therefore become one of the most efficient distribution systems for logistics transport. This translates into significant savings for companies, as they do not have to allocate resources to inventory and do not have to worry about inventory. Therefore, cross-docking in distribution is considered an effective strategy that requires cooperation between the agents involved in the process (i.e., customers and suppliers). According to the research of each topic, several conclusions will be drawn at the end, so that the issues raised will be considered as known. [ABSTRACT FROM AUTHOR]

Cross-docking is important for logistics because it reduces inventory, lead-times, and shipments. However, dynamic imbalances between supply and demand usually results in some inventory being warehoused in cross-docks. Therefore, flexible automation using robots, such as automated guided vehicles (AGV), can be used to improve performance of cross-docking, a trend in industry. This paper focuses on mathematical modeling of cross-docking operations when packages are moved in AGVs. A model based on Mean Value Analysis (MVA) is developed for determining the number of AGVs and estimating the service rate. This service rate is used as a parameter in a fork/join queuing model to characterize the performance of cross-docks in which outbound trucks get their packages directly from in-bound trucks as well as warehouses by estimating the queue lengths and mean sojourn times. The efficacy of the combined MVA and fork/join analytical models is verified using a discrete-event simulation as a case study, which shows that the models are largely in agreement in a test range with difference of 28% at the lowest throughput tested. Future study could use advances in sensors and Industry 4.0 for estimating parameters in the proposed models and improve performance and sustainability. [ABSTRACT FROM AUTHOR]

Cross-docking is a very useful logistics technique that can substantially reduce distribution costs and improve customer satisfaction. A key problem in its success is truck scheduling, namely, decision on assignment and docking sequence of inbound/outbound trucks to receiving/shipping dock doors. This paper focuses on the problem with the requirement of unloading/loading products in a given order, which is very common in many industries, but is less concerned by existing researches. An integer programming model is established to minimise the makespan. An improved particle swarm optimisation (ωc-PSO) algorithm is proposed for solving it. In the algorithm, a cosine decreasing strategy of inertia weight is designed to dynamically balance global and local search. A repair strategy is put forward for continuous search in the feasible solution space and a crossover strategy is presented to prevent the algorithm from falling into local optimum. After algorithm parameters are tuned using Taguchi method, computational experiments are conducted on different problem scales to evaluate ωc-PSO against genetic algorithm, basic PSO and GLNPSO. The results show that ωc-PSO outperforms other three algorithms, especially when the number of dock doors, trucks and product types is great. Statistical tests show that the performance difference is statistically significant. [ABSTRACT FROM AUTHOR]

This study proposed an algorithm called selective strategy differential evolution (SSDE) to handle the complexity of the stochastic internal task scheduling problem in cross-docking terminals. The aims of this study are to assign workers and transfer equipment to internal operations and sequence those operations under randomness and uncertainty with the purpose to minimise total tardiness. The main feature of SSDE is its ability to adapt itself in order to execute the best search strategy. The proposed algorithm was tested on 16 instances using generated data based on real-case scenarios of a pharmaceutical distribution centre. The results showed the significant performance of SSDE to other existing algorithms in terms of solution quality and computational time. The key success factors of SSDE are the use of various search strategies in a single run and the application of suitable termination conditions. [ABSTRACT FROM AUTHOR]

A novel self-learning differential evolution (SLDE) algorithm for addressing large-scale internal tasks scheduling problems in cross-docking is proposed herein. The goal is to obtain an optimal schedule for working teams and transferring equipment for handling incoming containers at the inbound area and patient orders at the outbound area to minimise the total tardiness. The proposed SLDE aims to increase the search capability of its original differential evolution (DE). The key concept of SLDE is to allow a DE population to learn the capabilities of different search strategies and automatically adjust itself to potential search strategies. The performance of the proposed algorithms is evaluated on a set of generated data based on a real-case scenario of a medical product distribution centre; subsequently, the performance results are compared with results obtained from other metaheuristics. Numerical results demonstrate that the proposed SLDE outperforms other algorithms in terms of solution quality and convergence behaviour by providing superior solutions using fewer function evaluations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cross-docking is a kind of process that products are unloaded in front of the inbound doors, consolidated based on the downstream demand, and then directly transferred to the outbound doors without a long storage process during the transportation. In this paper, a multi-door cross-dock truck scheduling problem is investigated in which the scheduling and sequencing assignment of trucks need to be considered, with the objectives of minimizing the inner transportation cost in the cross-dock and the total truck waiting cost. The major contribution of this paper is that a novel product-related time window constraint and the temporary storage area are firstly introduced to adapt to different physical conditions of goods considering real-world requirements. Then, a Lagrangian relaxation algorithm is proposed which aims to decompose the relaxed problem into several easy-to-be-solved sub-problems. Besides, a subgradient algorithm is used at each iteration to further deal with these sub-problems. Finally, theory analysis and simulation experiments of different problem scales are carried out during the comparison with a Greedy algorithm to evaluate the performance of the proposed algorithm. Results indicate that the Lagrangian relaxation algorithm is able to achieve more satisfactory near-optimal solutions within an acceptable time. [ABSTRACT FROM AUTHOR]

The paper addresses the location-allocation and transportation problems in designing a cross-docking distribution network that consists of suppliers, cross-docks, and plants. A developed mixed-integer non--linear model is proposed for a post-distribution cross-docking strategy with multiple cross-docks and products that cross-docks can be connected. The objective function is to minimise the total cost comprising the cost of established cross-docks and transportation costs. To obtain this model, first, two models are introduced and compared (basic non-linear model 1 and non-linear model 2 with the possibility of connections between cross-docks). Results indicate that the total cost is decreased when the connection between cross-docks exists. So, model 2 is more efficient and suitable than model 1. Then, consolidation of plant orders is added to model 2, and the developed model is formulated. Finally, some problems with different sizes are generated randomly and solved by GAMS software to evaluate the model accuracy. [ABSTRACT FROM AUTHOR]

Firms have realised that they cannot compete successfully in a marketplace without considering their customers. The impact of Supply Chain Innovative Practices (SCIPs) on customer retention in manufacturing SMEs is not well recognized. This paper aims to examine the extent to which manufacturing SMEs embrace SCIPs and their impact on customer retention. A simple random sampling technique and cross-sectional research design were used. Data were collected from 168 SME owners in Mwanza-Tanzania using a questionnaire survey to test the relevance of transactional cost theory in ascertaining the impact of SCIPs on customer retention. To determine the relationship between SCIPs and customer retention, Multiple Linear Regression was used. The study spotted e-supply chain, customisation, just in time, collaborative SC, reverse logistics, SC configurations, green supply chain, outsourcing and cross-docking as the most SCIPs adopted in manufacturing SMEs. The spotted SCIPs showed a positive link and significant impact on customer retention in Tanzania's manufacturing SMEs. Only reverse logistics had a nonsignificant impact. SCIPs are important tools for customer retention. Therefore, all actors along the supply chain of manufacturing SMEs should aim at adopting SCIPs to improve performance, satisfy and retain customers. The study enriches the literature by documenting the role of SCIPs in customer retention in manufacturing SMEs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The main reason for the development of this research refers to the increased attention of businesses to the CLSC concept due to the social responsibilities, strict international legislations and economic motives. Hence, this study investigates the issue of optimizing a CLSC problem involving multiple manufacturers, a hybrid cross-dock/collection center, multiple retailers and a disposal center in deterministic, multi-product and multi-period contexts. The bi-objective MILP model developed here is to simultaneously minimize total costs and total processing time of CLSC. Both strategic and tactical decisions are considered in the model where retailer demands and capacity constraints are satisfied. Since the presented model is NP-hard, NSGAII and MOPSO are hired to find near-to-optimal results for practical problem sizes in polynomial time.Then, to increase the accuracy of solutions by tuning the algorithms' parameters, the Taguchi method is applied. The practicality of the developed [ABSTRACT FROM AUTHOR]