A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction

To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. .

Remanufacturing closed-loop supply chain network design based on genetic particle swarm optimization algorithm

As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

A dynamic optimization model of an integrated coal supply chain system and its application

Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

E-Commerce Supply Chain Process Optimization Based on Whole-Process Sharing of Internet of Things Identification Technology

With in-depth development of the Internet of Things(IoT)in various industries,the informatization process of various industries has also entered the fast lane.This article aims to solve the supply chain process problem in e-commerce,focusing on the specific application of Internet of Things technology in e-commerce.Warehousing logistics is an important link in today’s e-commerce transactions.This article proposes a distributed analysis method for RFID-based e-commerce warehousing process optimization and an e-commerce supply chain management process based on Internet of Things technology.This article first introduces the advantages and disadvantages of shared IoT identification technology and the IoT resource sharing platform based on the three-layer abstract data model and representational state transfer(REST)style.Combining actual IoT applications and the characteristics of an existing platform,a REST-based IoT resource sharing platform is proposed.Combined with actual projects,a REST-based IoT resource sharing platform was built,and key technology experiments were conducted for verification.Finally,optimizing the e-commerce supply chain management process under Internet of Things technology and explaining the advantages of optimized e-commerce supply chain management are discussed.Research on this subject provides a theoretical basis for the application of the Internet of Things in e-commerce and has practical significance and practical value for managing service capabilities and service levels in e-commerce.

Green Investment Cost Optimization Model in the Supply Chain

The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green management using a company’s supply chain information. To formulate this model, we first define and analyze a green supply chain in a multi-dimensional and quantitative manner. The green investment alternatives considering in our model are as follows: 1) purchasing eco-friendly raw materials that cost more than conventional raw materials but whose use in production results in lower CO2 emissions;2) replacing current facilities with new eco-friendly facilities that have the capability to reduce CO2 emissions;and 3) changing modes of transport from less eco-friendly to more eco-friendly modes. We propose a green investment cost optimization (GICO) model that enables us to determine the optimal investment points. The proposed GICO model can support decision-making processes in green supply chain management environments.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

Network topology optimization of supply chain information systems

Network topology optimization has been widely researched. Since market competition has gradually developed into competition among the supply chain information systems, the network to- pology optimization of supply chain information systems has been in urgent need. However, the net- work topology optimization of supply chain information systems is still in its early stages and still has some challenges. So a description of typical seven network topologies for various supply chain infor- mation systems has been given. The generic characteristics of each network topology can be summa- rized. To analyze the optimization of network topology optimization of supply chain information sys- tems, a numeric model has been established based on these general characteristics. A genetic algo- rithm is applied in the network topology optimization of supply chain information systems model to a- chieve the minimum cost and shortest path. Finally, our experiment results are provided to demon- strate the robustness and effectiveness of the proposed model.

A Mathematical Model Based on Supply Chain Optimization for International Petrochemical Engineering Projects

Based on the study of supply chain(SC) and SC optimization in engineering projects, a mixed integer nonlinear programming(MINLP) optimization model is developed to minimize the total SC cost for international petrochemical engineering projects. A steam cracking project is selected and analyzed, from which typical SC characteristics in international engineering projects in the area of petrochemical industry are summarized. The MINLP model is therefore developed and applied to projects with detailed data. The optimization results are analyzed and compared by the MINLP model, indicating that they are appropriate to SC management practice in engineering projects, and are consistent with the optimal priceeffective strategy in procurement. As a result, the model could provide useful guidance to SC optimization of international engineering projects in petrochemical industry, and improve SC management by selecting more reliable and qualified partner enterprises in SC for the project.

Three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants

In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.

Optimization Agricultural Supply Chain:A Case Study of Fertilizer Supplier Selection

The 21st century is associated with the IndustrialRevolution 4.0 and the organic agriculture trend,making the utilization of high-quality fertilizers,abundant nutritional content,economical,and no affect to environment pollution.According to the new concept,clean agricultural production and organic agricultural products are not allowed to excessively use synthetic chemicals such as chemical fertilizers,and plant protection drugs,but priority is to use manure,organic fertilizers,and natural mineral fertilizers.Fertilizer must meet the balanced nutritional requirements of crops,maintain,and improve the fertility of the ground,protect the surrounding ecosystem,and leave harmful effects in agricultural products,products with high quality,safe for users and high economic efficiency for producers.To achieve the above goal,the selection of a fertilizer supplier is an important decision,supporting the supply chain’s sustainable development,fertilizer supplier selection is a multicriteria decision making model,the decision maker must assess all qualitative and quantitative factors.In this paper,the author proposed an integer decision making model including Fuzzy Analytic Hierarchy Process(FAHP)and Complex Proportional Assessment of Alternatives(COPRAS)for fertilizer supplier selection.The weightings of the criteria are calculated by using FAHP,COPRAS is then applied for ranking some potential fertilizer suppliers.The efficiency of the proposed models is proved by a case study conducted in a farm located in the south of Vietnam.This research is the first fertilizer supplier evaluation and se-lection model in Vietnam by interviewing experts and reviewing the literature.Re-search result is to provide a case study on evaluating supplier in agricultural supply chain utilizing the model proposed by the combination of FAHP and COPRAS models.

Supply Chain Production-distribution Cost Optimization under Grey Fuzzy Uncertainty

Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.

Optimization of Medical Supply Chains and Forward Store Locations for Recurrent Homecare Patient Demand with Periodic Interruptions

Robust and cost-effective distribution is critical to any home delivery network growing company, both to meet demand under normal conditions and to adapt to temporary disruptions. Home healthcare is anticipated to be a rapidly growing modality of healthcare, itself the largest industry in the US and rife with optimization needs in areas such as logistics, scheduling, and supply chains. We develop two mixed integer programming models to optimize forward storage locations in the supply chain of a national consumable medical supplies company with consistent monthly repeating demand, temporary disruption of facility operations, and remote international manufacturers. Modified p-median single and multi-echelon models are used to determine optimal locations of warehouses and distribution facilities that minimize total transportation cost, with 13% savings in one application (approximately $1.4 million annually). Sensitivity analyses to a range of scenarios suggest that the optimal solution is robust across a number of potential scenarios.

An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.

Optimization Analysis of Agricultural Product Supply Chain From the Perspective of Internet

With the development of our agricultural modernization process accelerated, the agricultural products supply chain is also facing the demand of further optimization and improvement. And the development of the Intemet makes it become an important means to optimize the agricultural products supply chain. Firstly, it is based on the concept explanation of ＂Internet ＋＂. Secondly, according to the present situation which includes traditional models dominated, the main bodies on the chain are diversified, farmers＇ position is lower and the problems which include the chain is too long, logistics nodes are scattered, infi＇astructure lags behind, circulation of information is poor, and risk monitoring is difficult of the supply chain of agricultural products are facing in China, clarifying the role of the Internet in the agricultural products supply chain which includes making the agricultural information network more perfect, adjusting and optimizing the supply chain of agricultural products. Finally, authors put forward some measures to optimize the supply chain of agricultural products and make conclusions.

The evolution of the cold chain logistics vehicle routing problem:a bibliometric and visualization revie

This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.

Optimization of Candida tropicalis Fermentation Conditions to Produce Long-chain Dibasic Acids

[Objectives] The fermentation conditions of Candida tropicalis 1798-pxa1 were optimized to further improve its yield of long-chain dibasic acids. [Methods] The strains used in the study were C. tropicalis 1798, C. tropicalis 1798-pxa1 and C. tropicalis 1798-pxa1 p2. First, through single factor experiments, the activated three fungi were cultured in different carbon sources, and the absorbance was measured every 2 h. The growth curve was drawn by software, and finally the most suitable substance for the substrate was selected, i.e., dodecane. Then, the composition of the fermentation medium and the fermentation process parameters were determined through the PB experiment of the single-deletion C. tropicalis 1798-pxa1. [Results] The experiment determined the important components affecting the synthesis of long-chain dibasic acids, namely(NH_4)_2SO_4, NaCl and dodecane. After optimization of the culture conditions, the yield of long-chain dibasic acids of C. tropicalis 1798-pxa1 increased from 5.6 to 10.1 g/L. [Conclusions] The scheme has been verified to be capable of greatly increasing the yield of the corresponding long-chain dibasic acids of the C. tropicalis engineering strain.