Deriving the cost function of an integrated dyadic supply chain with uncertainty in the supply

2012 ◽  
Vol 11 (1/2) ◽  
pp. 154
Author(s):  
S. Mehdi Sajadifar ◽  
Behrooz Pourghannad
Keyword(s):  
2020 ◽  
pp. 77-90
Author(s):  
V.D. Gerami ◽  
I.G. Shidlovskii

The article presents a special modification of the EOQ formula and its application to the accounting of the cargo capacity factor for the relevant procedures for optimizing deliveries when renting storage facilities. The specified development will allow managers to take into account the following process specifics in the format of a simulated supply chain when managing inventory. First of all, it will allow considering the most important factor of cargo capacity when optimizing stocks. Moreover, this formula will make it possible to find the optimal strategy for the supply of goods if, also, it is necessary to take into account the combined effect of several factors necessary for practice, which will undoubtedly affect decision-making procedures. Here we are talking about the need for additional consideration of the following essential attributes of the simulated cash flow of the supply chain: 1) time value of money; 2) deferral of payment of the cost of the order; 3) pre-agreed allowable delays in the receipt of revenue from goods sold. Developed analysis and optimization procedures have been implemented to models of this type that are interesting and important for a business. This — inventory management systems, the format of which is related to the special concept of efficient supply. We are talking about models where the presence of the specified delays for the outgoing cash flows allows you to pay for the order and the corresponding costs of the supply chain from the corresponding revenue on the re-order interval. Accordingly, the necessary and sufficient conditions are established based on which managers will be able to identify models of the specified type. The purpose of the article is to draw the attention of managers to real opportunities to improve the efficiency of inventory management systems by taking into account these factors for a simulated supply chain.


2013 ◽  
pp. 532-538 ◽  
Author(s):  
Muhammad Kadwa ◽  
Carel N Bezuidenhout

The Eston Sugar Mill is the newest in the South African KwaZulu-Natal sugar belt. Like most other mills, it can be argued that there are inefficiencies in the supply chain due to systematic issues, which reduce optimum performance. It was alleged that mill processes are slowed, or stopped, on Sundays, Mondays, as well as some Tuesdays and Wednesdays, due to pay-weekends, because of the associated cutter absenteeism. This increases the length of the milling season (LOMS), increases milling costs and reduces the average cane quality for the season. Data on cane deliveries to the Eston Mill, over a period of five seasons, were analysed to study the magnitude of the problem. It was statistically verified that cane shortages occur immediately after payweekends and it was conservatively estimated that cutter absenteeism occurs between 25–29 days per season, which increases the LOMS by six to ten days. The associated cost of this problem equated to an average of US$159,500 (approximately EUR120,000) per milling season. In this paper, an alternative harvesting system scenario is suggested, assuming that mechanical harvesters be used after a pay-weekend, to mitigate the impacts of cutter shortages. However, the solution is calculated to be risky. When the cost of new equipment was considered, only two of the five seasons were able to justify the associated costs.


Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2263
Author(s):  
Mahmood Ebadian ◽  
Shahab Sokhansanj ◽  
David Lee ◽  
Alyssa Klein ◽  
Lawrence Townley-Smith

In this study, an inter-continental agricultural pellet supply chain is modeled, and the production cost and price of agricultural pellets are estimated and compared against the recent cost and price of wood pellets in the global marketplace. The inter-continental supply chain is verified and validated using an integration of an interactive mapping application and a simulation platform. The integrated model is applied to a case study in which agricultural pellets are produced in six locations in Canada and shipped and discharged at the three major ports in Western Europe. The cost of agricultural pellets in the six locations is estimated to be in the range of EUR 92–95/tonne (CAD 138–142/tonne), which is comparable with the recent cost of wood pellets produced in small-scale pellet plants (EUR 99–109/tonne). The average agricultural pellet price shipped from the six plants to the three ports in Western Europe is estimated to be in a range of EUR 183–204 (CAD 274–305/tonne), 29–42% more expensive that the average recent price of wood pellets (EUR 143/tonne) at the same ports. There are several potential areas in the agricultural pellet supply chains that can reduce the pellet production and distribution costs in the mid and long terms, making them affordable supplement to the existing wood pellet markets. Potential economic activities generated by the production of pellets in farm communities can be significant. The generated annual revenue in the biomass logistics system in all six locations is estimated to be about CAD 21.80 million. In addition, the logistics equipment fleet needs 176 local operators with a potential annual income of CAD 2.18 million.


2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.


2021 ◽  
pp. 1-12
Author(s):  
Zou Xiaohong ◽  
Chen Jinlong ◽  
Gao Shuanping

The shared supply chain model has provided new ideas for solving contradictions between supply and demand for large-scale standardized production by manufacturers and personalized demands of consumers. On the basis of a platform network effect perspective, this study constructs an evolutionary game model of value co-creation behavior for a shared supply chain platform and manufacturers, analyzes their evolutionary stable strategies, and uses numerical simulation analysis to further verify the model. The results revealed that the boundary condition for manufacturers to participate in value co-creation on a shared supply chain platform is that the net production cost of the manufacturers’ participation in the platform value co-creation must be less than that of nonparticipation. In addition, the boundary condition for the shared supply chain platform to actively participate in value co-creation is that the cost of the shared supply chain platform for active participation in value co-creation must be less than that of passive participation. Moreover, value co-creation behavior on the shared supply chain platform is a dynamic game interaction process between players with different benefit perceptions. Finally, the costs and benefits generated by the network effect can affect value co-creation on shared supply chain platforms.


2020 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Alexsandro Oliveira Alexandrino ◽  
Carla Negri Lintzmayer ◽  
Zanoni Dias

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations. The traditional approach is to consider that any rearrangement has the same probability to happen, and so, the goal is to find a minimum sequence of operations which sorts the permutation. However, studies have shown that some rearrangements are more likely to happen than others, and so a weighted approach is more realistic. In a weighted approach, the goal is to find a sequence which sorts the permutations, such that the cost of that sequence is minimum. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about the lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for five versions of this problem involving reversals and transpositions. We also give bounds for the diameters concerning these problems and provide an improved approximation factor for simple permutations considering transpositions.


2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.


2000 ◽  
Vol 25 (2) ◽  
pp. 209-227 ◽  
Author(s):  
Keith R. McLaren ◽  
Peter D. Rossitter ◽  
Alan A. Powell

2017 ◽  
Vol 6 (2) ◽  
pp. 136 ◽  
Author(s):  
Mohamed Ali Wahdan ◽  
Mohamed Ashraf Emam

This paper presents the impact of applying the supply chain management (SCM) on the agribusiness field to optimize productivity and decreasing cost which will have a direct impact on the net income of the organization. The main two research questions are: is there a significant impact of supply chain management on financial performance? and is there a significant relationship between supply chain management and financial performance as well as responsibility accounting? To answer the research questions, data was collected from financial statements of agribusiness case from Egypt and the survey was conducted. The findings of the study indicated that there is a significant impact of supply chain management on financial performance through enhancing the productivity, decreasing the cost and improving profitability. Moreover, applying the efficient supply chain management can improve the use of responsibility accounting through the efficient usage for the budget of the crop.


2012 ◽  
Vol 472-475 ◽  
pp. 3273-3276
Author(s):  
Qing Ying Zhang ◽  
Ying Chi ◽  
Yu Liu ◽  
Qian Shi

The main target of supply chain management is to control inventory of each node enterprise effectively with the minimum cost. In this paper, the control strategies and methods of inventory based on supply chain management are put forward, which are significant for saving the cost of supply chain and improving the overall benefits of the whole chain.


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