Traditional Problems Research Articles

The Multi-Objective Transportation Problem Solve with Geometric Mean and Penalty Methods

The traditional (classical) Transportation Problem (TP) can be viewed as a specific case of the Linear Programming (LP) problem, as well as its models are used to find the best solution for the problem of predetermined how many units of a good or service need to be shipped from one source to multiple locations, with the goals being to reduce time or expense. Classical TP has one objective but when there are two or more objectives to be optimized for in a TP, the strategies used to optimize a single objective are inapplicable. The term “Multi-Objective Transportation Problem (MOTP)” refers to situations in which there are two or more objectives in a TP. The specific extension of the transportation problem is the MOTP. This work provided a novel alternative algorithm that uses geometric means along with the penalty technique to address MOTP. Specifically, analyzed data by comparing our method with numerical examples and presenting the results in a line graph. Our analysis shows that our approach yields better solutions than existing methods, demonstrating the novelty and effectiveness of our approach. The comparison with numerical examples provides a clear and intuitive way of presenting the superiority of our method, making it accessible to practitioners and researchers in the field. These findings have important implications for improving the accuracy and reliability of solutions to the problem at hand. Overall, our study contributes to the advancement of the field by providing a novel and effective method for solving the problem.

A no-tardiness job shop scheduling problem with overtime consideration and the solution approaches

In make-to-order manufacturing environments, overtime work is one of the effective and commonly used resources for expanding production capacity to ensure orders on-time delivery. However, if overtime work is not used reasonably and optimally, not only it cannot expedite the order completion, but also may lead to an increase in manufacturing costs. In order to use overtime work optimally, this paper presents a no-tardiness job shop scheduling problem with overtime work consideration (NTJSSP-OW) to minimize the total earliness inventory and overtime work costs simultaneously. A mathematical model is formulated and a hybrid genetic algorithm with simulated annealing (GASA) is proposed to solve it. Nine algorithms are also selected for performance comparisons. Unlike the traditional job shop scheduling problem, when solving NTJSSP-OW by heuristics and meta-heuristics, no-tardiness constraint is more likely to lead to infeasible solutions. So a multi-stage decoding scheme with a reconstructing rule is developed to ensure feasible solution. In order to extend the search space, a dispatching rule-based population initialization procedure and a repairing mechanism are provided. Comprehensive experiments are conducted on 14 modified benchmark problems, and non-parametric statistical tests like the Friedman test and post-hoc Nemenyi test are performed for the experimental results. Further systematic analyses indicate that GASA has significantly faster convergence on 90% of all test instances, and its global search ability outperforms other competing algorithms for 12 out of 14 instances.

A Novel Quasi-Oppositional Learning-Based Chaos-Assisted Sine Cosine Algorithm for Hybrid Energy Integrated Dynamic Economic Emission Dispatch

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an significant role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms.

Evaluation of a two-dimensional Moire-free antiscatter grid for cone-beam computed tomography.

Scatter radiation is a traditional problem in cone-beam computed tomography (CBCT). Accordingly, numerous methods have been researched for scatter reduction in terms of software- or hardware-based solutions. The concept of a two-dimensional antiscatter grid (2D-ASG) has been shown to provide a solution to the scatter reduction in CBCT. However, the use of an ASG makes it challenging to use in clinical CBCT systems because it causes Moire artifacts of theimage. We have developed a Moire-free 2D-ASG that was designed to solve the Moire artifact and the scatter radiation problems. We provide the experimental results pertaining to the image quality measurements from the evaluation of the 2D-ASG compared with those obtained from a one-dimensional antiscatter grid (1D-ASG) and no antiscatter grid (No-ASG) to demonstrate the quantitative extent of the improvements. The 2D-ASG, fabricated based on a sawing process with a graphite body, was prepared to evaluate image quality improvements. Projection images for Pro-CT MK II phantom were acquired using the CBCT testbed of sample rotation type and reconstructed by Feldkamp-Davis-Kress (FDK) algorithm without any filters. We measured the contrast-to-noise ratio (CNR) and uniformity index for the cupping artifacts of the 2D-ASG, 1D-ASG, and No-ASG cases. 2D-ASG considerably reduced the cupping artifacts owing to the scatter reduction compared with the 1D-ASG and No-ASG cases. The cupping artifacts were reduced by 85% in the 2D-ASG compared with No-ASG, while the cupping artifacts were reduced by 63% in 1D-ASG compared with No-ASG. The 2D-ASG also yielded a CNR improvement. The average CNR improvements for eight insert materials were 47% in the 2D-ASG compared with No-ASG, while the CNR was improved by 36% in the 1D-ASG compared withNo-ASG. We demonstrated that the Moire-free 2D-ASG improved the image quality by removing scatter radiation in CBCT compared with 1D-ASG and No-ASG. We believe that the Moire-free 2D-ASG can become one of the effective ways to solve the scatter radiation problem in CBCT images because it provides usability and has the potential to have synergistic effects on other methods, such as bow-tie filter and scattercorrection.

A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.

The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing DR through color fundus images requires a skilled clinician to spot lesions, but this can be challenging, especially in areas with a shortage of trained experts. As a result, there is a push to create computer-aided diagnosis systems for DR to help reduce the time it takes to diagnose the condition. The detection of diabetic retinopathy through automation is challenging, but convolutional neural networks (CNNs) play a vital role in achieving success. CNNs have been proven to be more effective in image classification than methods based on handcrafted features. This study proposes a CNN-based approach for the automated detection of DR using Efficientnet-B0 as the backbone network. The authors of this study take a unique approach by viewing the detection of diabetic retinopathy as a regression problem rather than a traditional multi-class classification problem. This is because the severity of DR is often rated on a continuous scale, such as the international clinical diabetic retinopathy (ICDR) scale. This continuous representation provides a more nuanced understanding of the condition, making regression a more suitable approach for DR detection compared to multi-class classification. This approach has several benefits. Firstly, it allows for more fine-grained predictions as the model can assign a value that falls between the traditional discrete labels. Secondly, it allows for better generalization. The model was tested on the APTOS and DDR datasets. The proposed model demonstrated improved efficiency and accuracy in detecting DR compared to traditional methods. This method has the potential to enhance the efficiency and accuracy of DR diagnosis, making it a valuable tool for healthcare professionals. The model has the potential to aid in the rapid and accurate diagnosis of DR, leading to the improved early detection, and management, of the disease.

Multi-objective integrated optimization study of prefabricated building projects introducing sustainable levels

As construction becomes greener, people have higher and higher requirements for engineering project management, which makes it necessary to deeply study the comprehensive optimization of schedule, cost and sustainability level. Adhering to the concept of low carbon and green, the article takes carbon emission factor into the total cost of building construction and improves the traditional cost objective of engineering projects; then quantitatively analyzes the economic, environmental and social impacts of assembled buildings from the perspective of sustainability, and introduces the sustainability objective into the traditional duration-cost problem study, taking the duration of each job in the double code arrow diagram as the independent variable to construct the duration -cost-sustainability level multi-objective optimization model. In order to solve the type effectively, a series of Pareto optimal solutions are obtained using the NSGA-II algorithm, and the efficacy coefficient method is used for program decision making. The results show that the Pareto solution set can provide effective support for the project manager’s decision making, and the NSGA-II algorithm is effective in solving the multi-objective optimization model.

The perfect match? Assessment of excess commute and transport externalities using an agent-based transport model

Excess commute (EC) refers to the sub-optimal allocation of workers to jobs in an urban region in terms of minimal commute distances. This article investigated EC for the Munich metropolitan area using a microscopic agent-based modeling suite. We first considered the optimization strategy, comparing the traditional zone-based transportation problem to a microscopic routine. Zone-based optimization is computationally efficient, but it underestimates EC due to spatial biases, even when the zone system is highly disaggregate. We then applied the optimized job-worker assignment to a microscopic travel demand model, assigning workers to jobs of the same job sector at a shorter distance from their home, accounting for multi-worker households. With shorter commutes, there was a substantial shift toward active modes. However, we only observed moderate impacts on total travel demand, with the overall effect dampened due to compensatory behavior between mandatory and discretionary travel. Finally, the outputs from the demand model were used by a microscopic assignment model to calculate externalities attributable to EC. Because of the complexity of travel demand, we conclude that the EC indicator alone would overestimate the benefits of policies targeting jobs-housing balance.

Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets

Problem definition: We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available, but also the status of each seat. Methodology/results: We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time and propose efficient approximation algorithms for general cases. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a “re-solving a dynamic primal” policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of our proposed policies on resource-allocation efficiency. Managerial implications: The results of this study reveal connections between our problem and traditional network revenue management problems. Particularly, we demonstrate that by adaptively using our proposed methods, the impact of the assign-to-seat restriction becomes limited both in theory and practice. Funding: S. Liu’s research is partly supported by the National Natural Science Foundation of China (NSFC) [Grant NSFC-72072117]. Z. Wang’s research is partly supported by the NSFC [Grant NSFC-72150002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1188 .

A city is not a static tree: understanding urban areas through the lens of real-time behavioral data

Cities are the main ground on which our society and culture develop today and will develop in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. Apart from measuring or monitoring such problems in a more comprehensive way, the analysis of those large datasets using modern machine learning techniques or causality detection permits to unveil of the behavioral roots behind them. As a result, only by incorporating real-time behavioral data can we design more efficient policies or interventions to improve such critical societal issues in our urban areas.

Dual-Sourcing Inventory Routing Problem with Route-Dependent Lead Times in Rolling Horizon Framework

Traditional inventory routing problems ignore the time consumption in transportation. In this paper, an inventory routing problem with air–land transportation and route-dependent lead times is studied. The model is based on the rolling horizon framework which can serve as a “here-and-now” approximation for multi-period inventory routing problems. A planning horizon crossing strategy is proposed to consider the effect of the single-period decision on long-term planning. The box uncertainty set is used to depict demands. A tractable closed-form robust solution for optimal replenishment quantity is derived. An adaptive variable neighborhood search algorithm is developed for this problem. A novel shaking phase is proposed, and the performance of shaking operators is evaluated in numerical experiments. Results also validate the effectiveness of the robust solution and the planning horizon crossing strategy.

BIM-based steel reinforcing bar detail construction design and picking optimization

Steel reinforcing bar (SRB) engineering is a very professional, complex, massive, and neglected project, which affects the structural performance and cost of building. Traditional SRB engineering is a labor-intensive project, often accompanied by the quality of SRB construction and the waste of material. At present, information technology is undergoing a revolution. With the rise and optimization of Building Information Modeling (BIM) technology and the continuous improvement of artificial intelligence-based optimization algorithms, new solutions have been put forward to solve these traditional SRB engineering problems. This study combines two different fields of BIM and optimization algorithms to establish a process of BIM-based SRB detail construction design and picking optimization that provides a reference for traditional SRB engineering. It is also an exploration and application of BIM technology to practical engineering.This study classified and analyzed the SRB crash nodes problems classified into four crash nodes classification and proposed a BIM-based SRB designing process. For the problem of the SRB connection position, a general segmental formula for SRB (for example, Longitudinal SRB on columns and beams) was studied, and a set of processes was established to determine the unknowns of the segment formula to locate the connection position of SRB. Besides, this study utilizes BIM’s coherent information transfer and visualization technologies that select Revit software to create new information such as “SRB shape family,“ ”component family,“ ”annotated family,“ and so on. The application of BIM in SRB is carried out through the transfer, transformation, and expression of information in the forms of SRB design drawings, SRB processing schedules, SRB construction schedules, and SRB construction partition charts, and so on. in addition, the problem of one-dimensional blanking of single-size and multiple-size raw materials is discussed with the minimum number of SRB consumed as the objective function. The commonly used classical solution method is analyzed in detail, and Particle Bee Algorithm (PBA) was introduced. Through calculation and comparison of examples, the performance of PBA on the SRB problem was evaluated.The study preliminary proposed a BIM based on SRB detail construction design and picking optimization and secondary development system by a particle engineering project. The results showed that this study had a certain reference contribution for the SRB detail construction design and picking optimization.

Human trafficking interdiction with decision dependent success

This paper presents a bi-level network interdiction model to increase the effectiveness of attempting to disrupt a human trafficking network under a resource constrained environment. To model the behavior of the trafficker, we present a new interpretation of the traditional maximum flow network problem in which the arc capacity parameter serves as a proxy for the trafficker’s desirability to travel along segments of the network. The objective for the anti-human trafficking stakeholder is to invest resources in detection and intervention efforts throughout the network in a manner that minimizes the trafficker’s expected maximum desirability of operating on the network. Interdictions are binary, and their effects are stochastic (i.e., there is a positive probability that a disruption attempt is unsuccessful). We present a multi-stage version of the model, which incorporates the effect of interdictions becoming more or less successful over time. Using a genetic algorithm that uses a pseudo-utility ratio for the repair operation, we solve multiple problem instances for a case study of the road network in the Eastern Development Region of Nepal and multiple grid networks. We then discuss observations regarding the impact of probabilistic interdiction success and the implications it has for optimal policies to disrupt a human trafficking network with limited resources.

Assessment of a Side-Row Continuous Canopy Shaking Harvester and Its Adaptability to the Portuguese Cobrançosa Variety in High-Density Olive Orchards.

The olive tree is an important crop in Portugal, where different levels of intensification coexist. The traditional olive orchards present profitability problems, mainly due to harvesting, so there has been a drastic reconversion towards high-density or super-high-density olive orchards. The latter present major constraints due to very specific needs for their use, being practically destined for new orchards. Consequently, the possibility of using systems based on canopy shakers in high-density olive orchards with local varieties is promising. The objective of this work is to evaluate a prototype canopy shaker for the harvesting of high-density olive orchards of the Portuguese variety 'Cobrançosa'. The evaluation is based on the study of canopy shaking in order to adapt canopy training and the adaptability of the machine. For this purpose, the vibration of 72 points of the tree canopy was recorded and a qualitative assessment of the harvest was carried out. Differences were found between the different zones according to the direction of the forward movement of the harvester and the distance to the trunk. These differences were associated with the values obtained for fruit detachment, and a greater quantity of fruit was harvested in the areas of the canopy in contact with the rods.

Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning.

The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future. This paper proposes a machine learning model with high predictive accuracy and stable prediction ability, namely, the back propagation neural network cross-support vector regression model (BPCSVR). By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs, and the stable prediction ability of the model was improved by integrating multiple models and correcting similar samples. We used leave-one-out cross-validation on 3038 samples from six data sets. The coefficient of determination, root mean square error, and absolute error were used as the evaluation parameters. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets, and the prediction accuracy is higher than other comparison models.

CSMA/PJ: A Protective Jamming Based MAC Protocol to Harmonize the Long and Short Links

WiFi-based Long Distance (WiLD) networks are promising to cover the rural and remote regions. But the explosive short-range WiFi deployments result in the long-short coexistence. Due to CSMA is ignorance of propagation delay, its carrier sensing is too short to detect long links, leading to the temporal hidden terminal problem that causes serious performance degradation and even starvation of long links. Existing methods for traditional hidden terminal problem are inefficient to cope with this problem because of the different causes. In this paper, we propose CSMA with Protective Jamming (CSMA/PJ), a new WiLD MAC protocol that solves the temporal hidden terminal problem with the minimized influence on uncontrollable short links. The key is generating protective jamming at the WiLD receiver that is sensible to the short links. By leveraging the asymmetric propagation delay of the WiLD transmitter and receiver, we make the jamming protective rather than destructive. We precisely control the jamming right before the arrivals of WiLD packets to set aside channel time for short links. We implement and evaluate CSMA/PJ on commercial devices. The experimental results show that CSMA/PJ can improve the throughput of the WiLD link by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5 \times $ </tex-math></inline-formula> compared with the CSMA/CA and RTS/CTS methods.

A study on uncertain fixed charge bi-objective 4-dimensional transportation problems under budget constraints

&lt;span lang="EN-US"&gt;Generally, in traditional transportation problems, single items are transported from a set of origins to a set of destinations in such a way that the transportation cost is minimized. To face the challenges that arise in modelling transportation problem various parameters are to be considered. In this work to provide an optimal transportation policy to the decision maker, we have studied a fixed-charge bi-objective multi-product &lt;br /&gt; 4-dimensional transportation problem with vehicle speed under budget constraints under uncertain environment. The objective of the proposed model is to have the maximum profit with the minimum time taken of transporting the goods. An equivalent deterministic model of the proposed model is obtained to deal the uncertain variable using expected value-chance constraint properties on uncertainty theory and its compromise solution is obtained by using the goal programming technique. A numerical example is discussed to provide more clarity on the proposed model.&lt;/span&gt;

Blok Zincir Uygulamaları ile Kapalı Döngü Tedarik Zincirinde Bilgi Yönetiminin Değeri

Purpose: Rapidly developing information technology increases expectations for obtaining much more efficient structures by providing accurate, sufficient and secure information sharing in supply chains as well as in many other fields. Traceability is a critical element in supply chain management, especially in security-sensitive sectors such as food, medicine, etc. Blockchain is a decentralized record platform that provides traceability, transparency and security, and it shows promise in alleviating traditional supply chain management problems and making a positive contribution. This article aims to improve the relevant literature by revealing the level of impact of blockchain technology on supply chain management.Methodology: First of all, a traditional closed-loop supply chain (CLSC) network was designed and modeled. Then, the main model was developed by creating four more scenarios for different applications of the blockchain technology to the existing model and the application results were analyzed.Findings: This paper demonstrated the implementation of blockchain technology in forward and reverse flows activities coordination for effective and efficient supply chain management, and the resulting gains by developing appropriate models through explanatory scenarios. The implications showed that blockchain technology could significantly reduce supply chain costs.Originality: While blockchain technology is gaining attention, there are very few studies focused on its integration into the supply chain. Apart from the applications in the field of finance, the most important contribution and originality of the study is the application of the blockchain to CLSC from different aspects as partial, full, only forward flow and only reverse flow.

Optimal control by deep learning techniques and its applications on epidemic models

We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity analysis is applied to train the neural networks embedded in differential equations. This method can not only be applied in classic epidemic control problems, but also in epidemic forecasting, discovering unknown mechanisms, and the ideas behind can give new insights to traditional mathematical epidemiological problems.

Public health in Kosovo after five difficult years of independence

Kosovo is undergoing a rapid process of transformation to an independent state,which was formally proclaimed in 2008, after almost a decade under United Nationsadministration. Regarding the health status, five years after independence, compared with other European countries, post-war transitional Kosovo is still characterized by higher mortality rates including traditional public health problems pertinent to infant mortality and maternal deaths. In parallel, however, Kosovo is undergoing a rapid process of epidemiological transition characterized by an aging trend which is inevitably coupled with high cardiovascular and cancer mortality and morbidity along with an excess mortality in external causes of death and injuries among the adult population. Adoption of the new Health Law in December 2012 by the Kosovo Assembly aims the transition from centralized health care system established under emergency conditions of the post-war period towards a contemporary modern health care system with a clear purchaser-provider split based on a high transparency and accountability of the health care providers and their contractors. The health care reform, leading eventually to significant changes within the health sector in Kosovo, consists of two main pillars: (i) structural and functional reorganization of the health care system through establishment of Kosovo Health Service (an autonomous and non-for-profit public enterprise at central level of the health care sector), and; (ii) establishment for the first time of the public health insurance system with a Health Insurance Fund as its main body. Nevertheless, five years after declaration of independence, Kosovo, the newest state in Europe consisting of the youngest population, is currently facing a particularly difficult socioeconomic and political transition and is additionally struggling and mainstreaming all energies and efforts in order to get full international recognition.

Simplified Approach to the Parameterization of the NRTL Model for Partially Miscible Binary Systems: Tττ LLE Methodology

A new approach to parameterizing the NRTL model for binary liquid–liquid equilibria (LLE) is presented. It allows for the incorporation of a priori knowledge into the parameterization procedure rather than using the “black box” approach often employed. The method first converts the compositional Txx data to a set of unique binary interaction parameters, Tττ data. Thereafter, the second step improves the parameterization of frequently used temperature-dependent parameters (TDPs) by reducing the traditional nonlinear regression problem to simpler linear regression. This method is less susceptible to poor initial guesses, finding local minima, and significantly reduces computational requirements, with comparable/improved performance to published parameters. A total of 29 binary systems, including upper and/or lower critical solution temperatures, were evaluated using the approach to provide generalized recommendations and understanding of the TDP requirements for each system type. Inclusion of such information and use of the approach will significantly simplify multicomponent LLE model parameterization.