Growing concern about the supply of goods under the COVID pandemic due to border restrictions and community lockdown has made us aware of the limitations of the global supply chain. Fertilizers are ...pivotal for the growth and welfare of humankind, and there is more than a century of history in industrial technology. Ammonia is the key platform chemical here which can be chemically diversified to all kinds of fertilizers. This article puts a perspective on production technologies that can enable a supply of ammonia locally and on-demand in Australia, for the farmers to produce resilient and self-sustained fertilizers. To assess the validity of such a new business model, multiobjective optimization has to be undergone, and computing is the solution to rank the millions of possible solutions. In this lieu, an economic optimization framework for the Australian ammonia supply chain is presented. The model seeks to address the economic potential of distributed ammonia plants across Australia. Different techniques for hydrogen and related ammonia production such as thermal plasma, nonthermal plasma, and electrolysis (all typifying technology disruption), and mini Haber–Bosch (typifying scale disruption) are benchmarked to the central mega plant on a world-scale using conventional technology, verifying that “Moore’s Law” (Mack, C. A. IEEE Trans. 2011, 24 (2), 202–207) of growing bigger and bigger is not the only path to sustainable agriculture. Results show that ammonia can be produced at $317/ton at a regional scale using thermal plasma hydrogen generation which could be competitive to the conventional production model, if credit in terms of lead time and carbon footprint could be taken into account.
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IJS, KILJ, NUK, PNG, UL, UM
As rapid movement of Information technology, the amount of data and unexpected factors impact on forecasting are gradually uncontrollable, thus, traditional method may be not enough efficient to deal ...with this issue. Therefore, the development of AI is advantageous for forecasting when data extends significantly. In this paper, machine learning model was developed to estimate energy load based on the characteristics of building design. This determination helps business and engineer reduce energy consumption cost and environmental impact. The data set was collected from energy simulation in the study of Tsanas, A., & Xifara, A. (2012) including 768 observations with 8 inputs and 2 outputs (heating load and cooling load). The energy loads were achieved through innovative methods such as Artificial Neural Network, Support Vector Machine or Random Forest (nonlinear) and Multilinear Regression (linear) with the support of Interactions. The performance of result from neural network technique was quite overfitting with dataset and better than linear as 20% in RMSE. So, it is proposed that using ANN forecasting combine with the predictor variable interaction of Multilinear Regression helps the user analyze the predictive value coordinate with input adjustment. Generally, the research supports the feasibility of machine learning in building energy forecast based on historical data and the building design parameter as well as the possibility to apply to another dataset for prediction purpose.
Inefficient routing leading to an increase in cost for outbound is a common problem many distribution centers encounter. This paper introduces an approach to establish a routing system to be cost ...saving and sustainable, ensuring better utilization of truckload comparing to the current situation as well as strictly following retailer time for receiving orders. Vehicle Routing Problem (VRP) would be reviewed and a mathematical model will be proposed in order to find optimal set of routes with minimum total cost. To satisfy the requirements of orders, VRP with time window (VRPTW) is applied to consider upper time and lower time constraints, and capacity of vehicles constraints. However, this model follows a NP-hard problem, the new approach is introduced divided into 2 stages to reduce size of the model. The approach includes a heuristic-based clustering algorithm which was applied to group of locations into a certain number of clusters, and then VRPTW is used to solve for each cluster to find optimal set of routes.
Recently, reverse supply chain (RSC)has received significant attention from researchers, governments and industries. Most current studies assumed that parameters in RSC are deterministic or known in ...advance. In addition, risk and incentive elements in RSC models are neglected. Risk factors normally come from transportation and treatment processes which can lead to the increasing cost of the RSC system. Incentive fee can support consumers to return their end-of-life products. This research aims to minimize the total cost of an RSC model incorporating risk and incentive factors while some key parameters in the model are represented by fuzzy numbers. Fuzzy mathematical programming is utilized to solve the problem and find the optimal locations of facilities as well as the flow of materials in the network. A numerical example of electronic waste (e-waste)is investigated to demonstrate the usefulness of the proposed model. This study can provide significant implications since it can support managers to determine important strategic decisions in the design of an effective RSC model towards sustainable development.
The reverse supply chain (RSC) recently attracted many Vietnamese authorities, enterprises and academia owing to the rise of concern on the environment and regulations of waste process. Along with ...rapid development, Vietnamese manufacturing network has become tightly strained when the end-of-life (EOL) items are not taken back by their manufacturers but end up being processed disorderly in different local businesses. A distressing example is the waste of imported solar panels in Vietnam. Since the number of solar panels has grown steadily in Vietnam recently, we speculate that the network flows of EOL solar panel of Vietnam will be very large and complex in a few years. In order to help Vietnamese government establish efficiently RSC, our paper will apply the mixed-integer linear programming (MILP) and demonstrate an optimized solution for the RSC of EOL solar panel in Ho Chi Minh City. Indeed, via our collected data from current financial market of Ho Chi Minh city, our MILP shows that the optimal cost-reduction is 11219 USD, even with limited constraints of only two landfills and very few collection facilities in Ho Chi Minh city at the moment. This result of our proposed RSC demonstrates that a significant profit is definitely possible when the number of collection facilities in Ho Chi Minh city increase in the future. Also, our MILP approach is flexible for decision-makers to achieve a satisfactory solution.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
The rapid development of the electrical and electronic products (EEPs) market in recent years has created both positive and negative impacts on the environment and humans. In order to achieve ...sustainable development, the most critically negative impacts - i.e., waste EEP (WEEP) - must be properly handled, such as disposal, remanufacturing, recycling, reusing, to name a few. Among the suggested solutions for solving these issues, closed-loop supply chain management has proven to be a highly potential candidate due to its efficiency, effectiveness, and economics. This paper presents a model of multi-EEP closed-loop supply chain (CLSC) systems with fuzzy parameters. The proposed model is transformed into the equivalent auxiliary crisp model by appropriate approach, and the final preferred compromise solutions are also found.
碩士
國立臺灣科技大學
工業管理系
99
Bass model is a very successful model in forecasting the diffusion process of new product. Its wide applications in managing the customer demand and controlling the inventory ...level have been studied by several scholars. This thesis gives a synthesis view of using Bass model in supply chain management. Similar other common supply chain, the supply chain in this thesis is divided into 3 stages, namely: retailer, supplier and manufacturer. The retailer is the stage handling the customer demand which is assumed following the Bass diffusion process. Information in this model is assumed being fully shared among stages. Based on this information, each stage tries to maximize their profit. By using appropriate approach- in this thesis, MATLAB software is used to program and solve the problem-this study gives suggestion, e.g. to order, the number of order, order quantities and manufacturing plans, for determining the inventory policies for each stage.
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