Bulk wheat transportation and storage problem of public distribution system

This research investigates the multi-period multi-modal bulk wheat transportation and storage problem in a two-stage supply chain network of Public Distribution System (PDS). The bulk transportation and storage can significantly curtail the transit and storage losses of food grains, which leads to substantial cost savings. A mixed integer non-linear programming model (MINLP) is developed after studying the Indian wheat supply chain scenario, where the objective is to minimize the transportation, storage and operational cost of the food grain incurred for efficient transfer of wheat from producing states to consuming states. The cost minimization of Indian food grain supply chain is a very complex and challenging problem because of the involvement of the many entities and their constraints such as seasonal procurement, limited scientific storages, varying demand, mode of transportation and vehicle capacity constraints. To address this complex and challenging problem of food grain supply chain, we have proposed the novel variant of Chemical Reaction Optimization (CRO) algorithm which combines the features of CRO and Tabu search (TS) and named it as a hybrid CROTS algorithm (Chemical reaction optimization combined with Tabu Search). The numerous problems with different sizes are solved using the proposed algorithm and obtained results have been compared with CRO. The comparative study reveals that the proposed CROTS algorithm offers a better solution in less computational time than CRO algorithm and the dominance of CROTS algorithm over the CRO algorithm is demonstrated through statistical analysis

Optimization of Managerial, Organizational and Technological Solutions of Grain Storages Construction and Reconstruction

The work is devoted to the important problem of optimization of managerial, organizational and technological solutions of construction and reconstruction of separate grain storages and the management of specialized building enterprise as a whole. Models of the company for the grain storages construction and renovation were designed, analyzed and described: multidimensional organizational structure, computer model of the enterprise. The results of a two-stage construction products cost optimization were presented. The recommendations for the adoption of optimal organizational and technological solutions were developed. The method for justification of financial income level for the grain storages construction and reconstruction using project management principles and provisions of the existing regulations was proposed

Green food supply chain design considering risk and post-harvest losses: a case study

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results

Modelling supply chain network for procurement of food grains in India

The procurement of food grains from farmers and their transportation to regional level has become decisive due to increasing food demand and post-harvest losses in developing countries. To overcome these challenges, this paper attempts to develop a robust data-driven supply chain model for the efficient procurement of food grains in India. Following the data collected from three leading wheat producing Indian regions, a mixed-integer linear programming model is formulated for minimising total supply chain network costs and determining number and location of procurement centres. The NK Hybrid Genetic Algorithm (NKHGA) is employed to cluster the villages, along with a novel density-based approach to optimise the supply chain network. Sensitivity analysis indicates that policymakers should focus on creating an adequate number of procurement centres in each surplus state, well before the start of the harvesting season. The study is expected to benefit food grain supply chain stakeholders such as farmers, procurement agencies, logistics providers and government bodies in making an informed decision

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables

Optimasi Interest Income dengan Penetapan Interest Rate Fasilitas Revolving Credit Line

Perbankan memiliki peranan strategis sebagai lembaga intermediasi yang akan menyalurkan dana dari pihak yang memiliki kelebihan dana (savers) dengan kedudukan sebagai penabung ke pihak yang memerlukan dana (borrowers). Hal tersebut sejalan dengan kebutuhan dari perusahaan untuk mendapatkan pendanaan untuk menunjang kegiatan perusahaan. Perbankan akan menyediakan pendanaan bagi perusahaan berupa credit line yang dikenal juga sebagai revolving credit line untuk memenuhi kebutuhan likuiditas jangka pendek serta juga menyediakan kredit berjangka untuk pembiayaan investasi jangka panjang. Fasilitas revolving credit line memberikan keleluasaan dari perusahaan sebagai debitur untuk menggunakan fasilitas kredit dengan batas atas sesuai dengan maksimum kredit yang diberikan dengan kewajiban untuk melakukan pembayaran beban bunga sesuai dengan nilai penggunaan fasilitas revolving credit line. Fleksibiltas tersebut menyebabkan tingkat utilitas penggunaan fasilitas revolving credit line belum optimal sehingga akan mempengaruhi pencapaian interest income. Penelitian ini dilakukan untuk mengetahui pengaruh karakteristik credit line, perusahaan sebagai debitur, dan perbankan sebagai kreditur terhadap utilitas fasilitas revolving credit line serta dilakukan permodelan regresi untuk prediksi utilitas credit line (UCL) debitur. Berdasarkan persamaan yang dihasilkan diketahui bahwa Line Age (LAGE), Asset, Return of Asset (ROA), dan Equity to Asset (ETA) berpengaruh negatif terhadap UCL. Sedangkan rasio Non Performing Loan (NPL) berpengaruh positif terhadap UCL. Optimasi interest income dilakukan dengan data UCL berdasarkan prediksi dari persamaan regresi tersebut. Metode yang digunakan dalam optimasi interest income adalah metode linear programming. Hasil optimalisasi tersebut menjadi acuan kreditur untuk menetapkan interest rate kepada debitur. Adapun hasil optimasi yang dilakukan terhadap asset eksisting mampu mencapai interest income sebesar Rp.241,63 M dan target yield yang ditetapkan sebesar 9,00%. Optimasi tersebut dapat pula dilakukan guna perencanaan ekspansi penyaluran kredit sehingga perbankan memiliki acuan range maksimum penyaluran kredit dalam setiap tiering suku bunga dan juga acuan penetapan interest rate atas fasilitas kredit yang disalurkan kepada debitur. ===================================================================================================================================== Banking has a strategic role as an intermediary institution that will fund from parties who have excess funds (savers) with a position as savers to those who need funds (borrowers). This is in line with the needs of companies to obtain funding to support company activities. The bank will provide funding for companies in the form of a credit line, also known as a revolving credit line to meet short-term liquidity needs and also provide term loans for long-term investment financing. Revolving credit line provides flexibility of the company as a debtor to use credit facilities depends on maximum credit limit and the company must pay interest expense which is calculated based on utility of credit line. This flexibility causes utility of credit line facilities to be suboptimal so that it will affect the achievement of interest income. This study was conducted to determine the effect of credit line characteristics, companies as debtors, and banks as creditors on the utility of revolving credit line facilities and create regression modeling to predict debtor credit line (UCL) utilities. Based on the resulting equation it is known that Line Age (LAGE), Asset, Return of Asset (ROA), and Equity to Asset (ETA) have a negative effect on UCL. While the Non Performing Loan (NPL) ratio has a positive effect on UCL. Interest income optimization used UCL based on prediction model by linear regression. The method used in optimizing interest incoms is linear programming method. The results of this optimization become a reference for creditor to set interest rates for debtors. Optimization of existing assets are able to achieve interest income at IDR 241,63 billion and achieve yield target at 9,00%. Optimization interest income can be applied to plan credit expansion so creditor has reference the maximum range of credit distribution and also determine of interest rate’s debtors