Logistics Distribution Center Research Articles

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

Determination of Logistics Distribution Centers: A Combination of Spatial Analysis and Analytical Hierarchy Process

Determination of Logistics Distribution Centers: A Combination of Spatial Analysis and Analytical Hierarchy Process

A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection

PurposeThis study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.Design/methodology/approachThis study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.FindingsThe optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.Originality/valueThis study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

A multi-objective optimization model for the location of cold chain logistics distribution center based on trust domain optimization algorithm

Cold chain transportation refers to a logistics method that transports fresh, perishable, and perishable items from the place of production, processing or warehouse to the place of consumption under certain temperature conditions. As consumers have higher and higher requirements for food safety and quality, the cold chain logistics industry has also developed rapidly. However, the high cost of cold chain logistics, the difficulty of technology, the difficulty of ensuring service quality and other problems have also arisen, and how to optimize cold chain transportation has become a hot issue in the cold chain logistics industry. Cold chain transportation optimization refers to the premise of ensuring the quality and safety of goods, through reasonable transportation paths, transportation methods, temperature control and other measures, to minimize the cost of cold chain transportation, improve efficiency and service quality. In order to achieve the optimization of cold chain transportation, it is necessary to establish a reliable cold chain logistics distribution center to ensure the safety and quality of goods during transportation. In order to solve the problem of location selection of cold chain logistics distribution center, this paper first defines the service reliability and calculation method of cold chain distribution center. Then, the location model of the cold chain logistics distribution center and the trust domain optimization algorithm model were established. The model aims to minimize the total cost, while considering multiple factors such as transportation distance, temperature control, facility construction, etc., to ensure the reliability and operational efficiency of the cold chain logistics distribution center. Finally, this paper introduces an example for calculation to prove the feasibility and universality of the model in the location problem of cold chain logistics distribution center. Through the calculation of examples, it can be seen that the model can effectively reduce the cost of cold chain transportation and improve the service quality and efficiency of cold chain logistics distribution center. In summary, this paper proposes a reliable location model and trust domain optimization algorithm model for cold chain logistics distribution centers to solve the challenges faced by the cold chain logistics industry. This model can provide guidance for cold chain logistics companies to gain a greater advantage in a highly competitive market.

Analysis of a logistics flow int the light of VSM 4.0: a case study of in a Distribution Center (DC) in Brazil

This article presents an application of Value Stream Mapping 4.0 (VSM 4.0) developed by Meudt et al. (2017) in the detailed identification of activities in information flows in a logistics Distribution Center (DC). The order generation, order picking, checking, consignor checking, and shipping flows were mapped. The research method applied was a case study in a distribution center of a large Brazilian industry in the metallurgical sector that manufactures consumer goods for retail. In this practical application, the approach produced satisfactory results as it was possible to identify and measure the informational indicators of VSM 4.0: the digitization/automation rate (DR) was 9%, the data utilization (DU) rate was 0%, and data availability (DA) rate of 16%. From this data, it was possible to verify that the company does not have a proprietary information flow, making this information sparse and management difficult, as 84% of the information is manual, that is, without the use of digital media, with low ERP use. The practical applicability of VSM 4.0 was verified in a logistics environment where information flow is significant. Furthermore, we observed that VSM 4.0 could contribute to the identification of waste in operations management and provide information for digital transformation inflows and processes, which are so crucial for the migration to Industry 4.0.

Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center

In recent years, global warming and environmental problems have become more serious due to greenhouse gas (GHG) emissions. Harvesting solar energy for production and logistic activities in supply chains, including factories and distribution centers, has been promoted as an effective means to reduce GHG emissions. However, it is difficult to balance the supply and demand of solar energy, owing to its intermittent nature, i.e., the output depends on the daylight and season. Moreover, the use of large-capacity solar power generation systems and batteries incurs higher installation costs. In order to maintain low costs, demand-to-supply management of solar energy, based on appropriate seasonal analysis of power generation and consumption and the capacity planning for power generation and the storage battery, is necessary. In this study, the on-demand cumulative control method is applied to actual power consumption data and solar power generation data estimated at a distribution center. Moreover, the monthly, seasonal, and temporal characteristics of power generation and consumption at the distribution center are analyzed. Additionally, the total amount of power purchased is investigated for solar energy demand-to-supply management.

An improved genetic algorithm with density-guiding operator based on the location selection model of emergency logistics center

In disaster relief efforts, establishing logistical channels with the affected population is crucial. This necessitates the construction of temporary logistics distribution centers. However, rapidly selecting suitable locations for these temporary logistics centers presents a considerable challenge. An inability to swiftly provide viable relief strategies can have significant negative implications for both the government’s reputation and the overall effectiveness of the relief efforts. In terms of problem modeling, the majority of researchers base their models on economic variables, with few considering the issue of panic among the disaster-affected populace. However, it is critical to address not only the widespread panic but also to focus on proactive rescue operations for those who are excessively panic-stricken or less mobile due to the disaster. In this article, we create an improved genetic algorithm with a density-guiding operator based on the location selection model of emergency logistics centers to address these issues. During variation iteration, the algorithm introduces population density as a guide and makes the algorithm’s parameters adaptive and dynamic, which can successfully choose a superior option. Finally, numerical tests are run on the Jiading District of Shanghai data set to confirm the algorithm’s efficacy. The final improvement of the adaptive algorithm is 13.21%.

Environmental cold chain distribution center location model in the semiconductor supply chain: A hybrid arithmetic whale optimization algorithm

This study addresses the high cost and high carbon emissions associated with the transportation sector. An environmental cold chain logistics distribution center location model has been designed by considering various constraints such as demand, time windows, and cost factors associated with cargo damage, refrigeration of cold chain cargo, and carbon emissions. An environmental cold chain logistics distribution center location model is designed for the raw materials of low-temperature storage and transportation in the semiconductor supply chain to achieve sustainable development by reducing carbon emissions. This study effectively reduces transportation costs and carbon emissions. To address the issues of slow convergence speed and susceptibility to local optima in the conventional whale optimization algorithm (WOA), a hybrid arithmetic whale optimization algorithm (HAWOA) is proposed by combining arithmetic optimization and the honey badger algorithm. Simulation analysis shows that the HAWOA is capable of obtaining the location scheme with the minimum distribution cost under the same number of iterations, which provides a substantial advantage compared to other algorithms.

Cost-Effectiveness of Introducing Autonomous Trucks: From the Perspective of the Total Cost of Operation in Logistics

This study explored the economic influence of introducing fully autonomous trucks (ATs) on the total cost of operation (TCO) from the viewpoint of freight transport operators. We utilized the cost–benefit analysis and scenario planning frameworks using a case study of a middle-mile route between a port and a large logistics distribution center in South Korea, where an autonomous truck pilot project has been running for several years. We employed a data source obtained from Hanjin Transportation Co., LTD., which enables us to reflect the actual expenses of operating freight transportation in the context of the study route. The findings of this study indicate that ATs would attain substantial operation cost savings for freight transport operators across all five scenarios, ranging from the most pessimistic to the most optimistic. Specifically, results of the base scenario reveal that monthly TCO decreases by 56.25, 47.04, 42.97, and 41.73 percent for 1-, 5-, 12-, and 25-ton trucks, respectively. The cost reduction per month was USD 2399 for a 1-ton truck, USD 2891 for a 5-ton truck, and USD 3438 for a 12-ton truck. Even in the most pessimistic scenario, the operation cost savings for operating with ATs in the route are USD 1415, USD 1642, USD 1985, and USD 2222 per month for 1-, 5-, 12-, and 25-ton trucks, respectively. When factoring in changes in cost, the wage reduction for vehicle drivers was substantial (USD 2430, USD 2835, USD 3240, and USD 3645 for 1-, 5-, 12-, and 25-ton trucks, respectively). However, remote operation was a new cost factor that increased TCO, which increased by USD 243,384,357 in the base case. This study’s findings have multiple implications, including (1) informing economic efficiency and productivity of operating autonomous vehicles (AVs), (2) providing insights on indicative cost estimates for AV operations, (3) providing valuable information for making informed investment decisions, and (4) supporting the notion that freight transport is an especially attractive market for AVs.

A Novel Risk Assessment for Cable Fires Based on a Hybrid Cloud-Model-Enabled Dynamic Bayesian Network Method

The fire risk of cables constantly changes over time and is affected by the materials and working conditions of cables. To address its internal timing property, it is essential to use a dynamic analysis method to assess cable fire risk. Meanwhile, data uncertainty resulting in the deviation of risk values must also be considered in the risk assessment. In this regard, this study proposes a hybrid cloud model (CM)-enabled Dynamic Bayesian network (DBN) method to estimate the cable fire risk under uncertainty. In particular, the CM is initially applied to determine the membership degrees of the assessment data relative to different states of the root nodes; then, these degrees are considered the prior probabilities of DBN, where the dynamic risk profiles are reasoned. Subsequently, the Birnbaum and Fussell–Vesely importance measures are constructed to identify the key nodes for risk prevention and control, respectively. Moreover, a case study of the Chongqing Tobacco Logistics Distribution Center is conducted, the computational results of which indicate the proposed method’s decision-making effectiveness. Finally, a comparison of the reasoning results between the proposed and traditional methods is performed, presenting strong evidence that demonstrates the reliability of the proposed method.

The Application of the Improved Jellyfish Search Algorithm in a Site Selection Model of an Emergency Logistics Distribution Center Considering Time Satisfaction.

In an emergency situation, fast and efficient logistics and distribution are essential for minimizing the impact of a disaster and for safeguarding property. When selecting a distribution center location, time satisfaction needs to be considered, in addition to the general cost factor. The improved jellyfish search algorithm (CIJS), which simulates the bionics of jellyfish foraging, is applied to solve the problem of an emergency logistics and distribution center site selection model considering time satisfaction. The innovation of the CIJS is mainly reflected in two aspects. First, when initializing the population, the two-level logistic map method is used instead of the original logistic map method to improve the diversity and uniform distribution of the population. Second, in the jellyfish search process, a Cauchy strategy is introduced to determine the moving distance of internal motions, which improves the global search capability and prevents the search from falling into local optimal solutions. The superiority of the improved algorithm was verified by testing 20 benchmark functions and applying them to site selection problems of different dimensions. The performance of the CIJS was compared to that of heuristic algorithms through the iterative convergence graph of the algorithm. The experimental results show that the CIJS has higher solution accuracy and faster solution speed than PSO, the WOA, and JS.

Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye

Abstract This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the Best-Worst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.

Parallel Binary Rafflesia Optimization Algorithm and Its Application in Feature Selection Problem

The Rafflesia Optimization Algorithm (ROA) is a new swarm intelligence optimization algorithm inspired by Rafflesia’s biological laws. It has the advantages of high efficiency and fast convergence speed, and it effectively avoids falling into local optimum. It has been used in logistics distribution center location problems, and its superiority has been demonstrated. It is applied to solve the problem of continuity, but there are many binary problems to be solved in the actual situation. Thus, we designed a binary version of ROA. We used transfer functions to change continuous values into binary values, and binary values are used to symmetrically represent the meaning of physical problems. In this paper, four transfer functions are implemented to binarize ROA so as to improve the original transfer function for the overall performance of the algorithm. In addition, on the basis of the algorithm, we further improve the algorithm by adopting a parallel strategy, which improves the convergence speed and global exploration ability of the algorithm. The algorithm is verified on 23 benchmark functions, and the parallel binary ROA has a better performance than some other existing algorithms. In the aspect of the application, this paper adopts the datasets on UCI for feature selection. The improved algorithm has higher accuracy and selects fewer features.

TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection.

At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately.Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics centersite selection (MLCSS) is given to validate the proposed method.

Exploring a new development direction of the Belt and Road Initiative in the transitional period towards the post-COVID-19 era

Since the outbreak of COVID-19, its impacts have been envisaged in multi-dimensional ways, including global supply chains, international logistics, and transportation. Owing to a series of virus variants since 2020, several Belt and Road Initiative (BRI) projects have been halted along the Belt and Road (B&R), and their implementation progress has been affected. In addition, China, which initiated the BRI in 2013, has been facing challenges which are caused by its economic, socio-demographic, and international political structural changes. Moreover, growing conflicts and tensions in international trade and politics, such as the war between Russia and Ukraine, China-US trade conflicts, foreign companies’ reshoring the production lines from China, and diplomatic disputes between China and Australia, have been interwoven with the impacts of COVID-19 on the supply chains, international trade, and transportation in the world. Having considered the above, this study explores a new research-driven approach to reignite the BRI momentum in the transitional period towards the post-COVID-19 era from a Chinese economic perspective. In doing so, this paper proposes nine research agendas, such as the optimization network of transportation and logistics distribution centers (LDCs) along the B&R, priority development and performance of LDCs, greening the B&R with green shipping corridors, revisiting port devolution continuum, humanitarian logistics in association with COVID-19, security and risk analysis of China’s energy supply chains, and export of the 6th Generation Ports (6GP) model with smart ports to major container ports along the port supply chains. Each research agenda is addressed with its motivation, significance, and applicable and representative methods.

Logistics Distribution Centers in Multimodal Transport Operations

Logistics Distribution Centers in Multimodal Transport Operations

Site-selection method of agricultural products logistics distribution centre based on blockchain

In order to overcome the problems of low on-time delivery rate and high distribution cost existing in the existing location methods of agricultural products logistics distribution centre, this paper proposes a new location method of agricultural products logistics distribution centre based on blockchain. Based on the analysis of the basic problems affecting the location of agricultural products logistics distribution centre, combined with the blockchain technology, the location model of agricultural products logistics distribution centre based on input-output ratio was constructed. Combining the idea of mountain climbing algorithm and particle swarm optimisation algorithm, the hybrid particle algorithm is used to solve the location model of agricultural products' logistics distribution centre, and the optimal location scheme is obtained. The experimental results show that the proposed method can effectively improve the on-time delivery rate, customer satisfaction, and reduce the logistics distribution costs. The maximum on-time delivery rate is 97.4%.

Design of logistics distribution center location method based on particle swarm optimization

At present, the traditional method of logistics distribution center location is mainly achieved through dynamic planning of location. When the scale of the problem is large, it is easy to produce large deviation, and the optimization effect is not ideal. For this reason, a method design of logistics distribution center location based on particle swarm optimization is proposed. This method effectively improves the learning factor and location formula of particle swarm optimization, and establishes a mathematical model of logistics distribution center location. The experimental results show that the proposed method has higher convergence and better search ability.

Location Optimization of Environmental Cold Chain Logistics Distribution Center Based on Hybrid Arithmetic Whale Optimization Algorithm

Location Optimization of Environmental Cold Chain Logistics Distribution Center Based on Hybrid Arithmetic Whale Optimization Algorithm

Site-selection method of agricultural products logistics distribution centre based on blockchain

Site-selection method of agricultural products logistics distribution centre based on blockchain