International Journal of Industrial Engineering & Production Research

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Shifts in freight transport demand, both locally and internationally, have significantly increased cargo flows to and from logistics centers in recent years. As a result, it is essential to develop effective methods for assessing freight accessibility to road corridors designated for land cargo transportation. This paper proposes a methodology that facilitates the freight accessibility analysis to a road corridor for land cargo transportation. The accessibility analysis considers variables such as mobilized tons, state of the roads, length of the routes that are connected to the corridor, and origin-destination nodes in terms of productive chains mobilized by this means of transport. This methodology is applied to a case study in Colombia. The results reveal areas necessitating infrastructure investments to enhance road corridor accessibility and promote the efficient transportation of goods. Furthermore, it offers valuable insights into characterizing areas with significant cargo generation and reception, enabling targeted improvements in transportation industry responsiveness.

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Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that all the calculations can be obtained from one main moment multi dimensional integral whose expression is obtained through some particular change of variables. Indeed, we consider that this calculus technique for improper integral has its own importance .

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  Managing products’ end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to solve the proposed NP-hard problem and find the best possible design for different facilities. In order to test the applicability of proposed GA, we suppose a tire reverse logistic case and solve the problem. The results show that the least cost will be achieved by using the free space of distribution centers and integrating collection and inspection centers within them. In addition, we suggest using hybrid algorithm in future allocation problems to obtain best solutions .

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Discrete facility location,   Distribution center,   Logistics,   Inventory policy,   Queueing theory,   Markov processes,

The distribution center location problem is a crucial question for logistics decision makers. The optimization of these decisions needs careful attention to the fixed facility costs, inventory costs, transportation costs and customer responsiveness. In this paper we study the location selection of a distribution center which satisfies demands with a M/M/1 finite queueing system plus balking and reneging. The distribution center uses one for one inventory policy, where each arrival demand orders a unit of product to the distribution center and the distribution center refers this demand to its supplier. The matrix geometric method is applied to model the queueing system in order to obtain the steady-state probabilities and evaluate some performance measures. A cost model is developed to determine the best location for the distribution center and its optimal storage capacity and a numerical example is presented to determine the computability of the results derived in this study .

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As functional integration has increased in hand-held consumer devices features such as Global Positioning System (GPS) receivers have been embedded in increasingly more devices in recent years. For example, the train positioning system based on GPS provides an integrated positioning solution which can be used in many rail applications without a cost intensive infrastructure. The network built in the GPS receiver has the advantage of determining the exact location and time of the train. The objective of this research was to develop a system which accepts the location from the GPS receiver mounted on the train and extracts its local time. This is implemented using Altera SOPC builder in the NIOS – II environment. Nios II is a 32 bit soft-core embedded-processor architecture designed specifically for the Altera family of FPGAs. The signal received using the GPS receiver is given to the DE2 board through the UART port and converted it in to local time and displayed on the NIOS II console. A working system was developed, which accepts the location from the GPS receiver and extracted its local time.

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Design of a logistics network in proper way provides a proper platform for efficient and effective supply chain management. This paper studies a multi-period, multi echelon and multi-product integrated forward-reverse logistics network under uncertainty. First, an efficient complex mixed-integer linear programming (MILP) model by considering some real-world assumptions is developed for the integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then, the stochastic counterpart of the proposed MILP model is used to measure the conditional value at risk (CVaR) criterion, as a risk measure, that can control the risk level of the proposed model. The computational results show the power of the proposed stochastic model with CVaR criteria in handling data uncertainty and controlling risk levels.

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In many industrial and non-industrial applications the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variables. This relationship is referred to as profile. In the past decade, profile monitoring has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable. In this paper, the focus is especially on the binary response followed by the bernoulli distribution due to its application in many fields of science and engineering. Some methods have been suggested to monitor such profiles in phase I, the modeling phase however, no method has been proposed for monitoring them in phase II, the detecting phase. In this paper, two methods are proposed for phase II logistic profile monitoring. The first method is a combination of two exponentially weighted moving average (EWMA) control charts for mean and variance monitoring of the residuals defined in logistic regression models and the second method is a multivariate T2 chart to monitor model parameters. The simulation study is done to investigate the performance of the methods.

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Today, companies need to make use of appropriate patterns such as supply chain management system to gain and preserve a position in competitive world-wide market. Supply chain is a large scaled network consists of suppliers, manufacturers, warehouses, retailers and final customers which are in coordination with each other in order to transform products from raw materials into finished goods with optimal placement of inventory within the supply chain and minimizing operating costs in the face of demand fluctuations. Logistics is the management of the flow of goods between the point of origin and the point of consumption. One issue in Logistics management is the presence of possible long delays in goods transportation. In order to handle long delays, there are two possible solutions proposed in this paper. One solution is to use Model Predictive Controllers (MPCs) using orthonormal functions (Laguerre functions) and the other is to change supply chain model in which an integrator is imbedded. To this end, the two mentioned solutions will be implemented on a supply chain with long logistics delays and the results will be compared to classical MPC without using orthonormal basis and augmented model for different type of customer demand (constant, pulse and random demand).

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Recently, urbanization has been expanded rapidly in the world and a number of metropolitan areas have been appeared with a population of more than 10 million people. Because of dense population in metropolitan and consequently increasing the delivery of goods and services, there has been a lot of problems including traffic congestion, air pollution, accidents and high energy consumption. This made some complexities in distribution of urban goods; Therefore, it is essential to provide creative solutions to overcome these complexities. City logistics models can be effective in solving these complexities.

In this paper, concepts and definitions related to city logistics are explained to provide a mathematical model in order to design city logistics distribution network aim at minimizing response times. This objective is effective for goods and emergency services, especially in times of crisis and also for goods that are delivered as soon as possible. This is a three-level network and has been used in modeling of queuing theory. To validate the model, a numerical example has been established and results of the model have been explained using BARON solver in Gams software. Finally, conclusions and recommendations for future research are presented.

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The frequency of chronic diseases such as cardiovascular diseases has significantly increased in recent years. This study is a developmental research which is categorized as descriptive-survey in terms of data collection method. The aim of this study is to prioritize 22 districts of Tehran for the purpose of prevention from cardiovascular diseases. In the present study, after extraction of the effective factors on the prevalence of cardiovascular diseases from previous studies, the weight of each factor with their specific data for each 22 districts of Tehran (collected from relevant organizations) is obtained using two levels of Fuzzy Delphi method and one level of fuzzy best-worst method, for confirming or denying factors and weighting them based on the opinion of 25 cardiologists, respectively, and transferred to Arc GIS software for prioritizing 22 districts of Tehran.Using a combination of fuzzy best-worst method, which is one of the newest methods for making multi-criteria decision, and GIS, for weighting parameters and prioritizing 22 districts of Tehran, gives an acceptable worth to the present study.Our results-after classification, drawing, and combination of maps- indicated that the 8^th district (except a little part in the west) is the best district, and 16^th and 19^th districts (approximately whole district) are in the last priority for prevention of cardiovascular diseases. Other districts respectively placed in the second to 21^th places.

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In the era of digitization and globalization, national higher education systems face a number of challenges of the exogenous nature. Intensification of the competition in the educational services market necessitates the search for new ways of increasing the level of the competitiveness of universities and higher education systems as a whole. Development of theoretical, methodological and applied foundations of the formation and implementation of the integrated model of the competitive higher education becomes relevant. Application of the interdisciplinary approach to the research allows combining tools and techniques of different sciences. Economic, psycho-pedagogical, legal and managerial blocks are structural components of the proposed model of the competitive higher education. The effective implementation of such a model requires the involvement of a wide range of stakeholders and the impact of changing factors in the exogenous environment. Successful implementation of the model requires the existence of a developed regulatory framework harmonized with the provisions of the EU legislation. Practical implementation of the model concept proposed in this article will increase the competitiveness of the national higher education system in a highly competitive global scientific and educational area.

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The logistical problems that companies must face tend to have conflicting interests between customers and service providers, which makes them difficult to solve. In turn, when the activities involve the transport of hazardous materials, the problem becomes critical in security terms, and makes logistics operations even more difficult. In the hazardous materials transportation literature, problems related to the routing of vehicles and the geographic location of supply or service centres are often addressed. However, there are not many studies related to the study of the loading, unloading and weighing operations of trucks that handle hazardous materials within industrial plants. That is why this work presents a case study of the installation of a new truck balance in an industrial plant in Argentina. To do this, the internal logistics operation and the current state of the plant's infrastructure are analyzed. A detailed study of the alternatives for the location of the balance was carried out following the criteria set by the company's management and the problem was solved using an empirical weighting method coordinated with the heads of the Supply Chain Department. A satisfactory solution was obtained.

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Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

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Diagnosis of diseases is a critical problem that can help for more accurate decision-making regarding the patients’ health and required treatments. Machine learning is a solution to detect and understand the symptoms related to heart disease. In this paper, a logistic regression model is proposed to predict heart disease based on a dataset with 299 people and 13 variables and to evaluate the impact of different predictors on the outcome. In this regard, at first, the effect of each predictor on the precise prediction of the outcome has been evaluated and analyzed by statistical measurements such as AIC scores and p-values. The logit models of different predictors have also been analyzed and compared to select the predictors with the highest impact on heart disease. Then, the combined model that best fits the dataset has been determined using two statistical approaches. Based on the results, the proposed model predicts heart disease with a sensitivity and specificity of 84.21% and 90.38%, respectively. Finally, using normal probability density curves, the likelihood ratios have been established based on classes 1 and 0. The results show that the likelihood ratio classifier performs as satisfactorily as the logistic regression model.

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