scholarly journals Quasi-Optimal Sizing of a Vehicle Fleet Considering Environmental Impact, Maintenance, and Eventual Containment Measures

2021 ◽  
Vol 13 (8) ◽  
pp. 4384
Author(s):  
Malek Mechlia ◽  
Jérémie Schutz ◽  
Sofiene Dellagi ◽  
Anis Chelbi
Keyword(s):  
Environmental Impact ◽  
Electric Vehicles ◽  
Preventive Maintenance ◽  
Cost Model ◽  
Optimal Number ◽  
Health Crisis ◽  
Containment Measures ◽  
Vehicle Fleet ◽  
The Cost ◽  
Maintenance Plan

In this paper, N types of vehicles having different environmental impacts and different failure rates are considered to perform a set of missions during a predefined period. The sizing problem of the fleet of vehicles is typically based on the literature for the environmental impact of each type of vehicle. This work intends to develop a model that allows considering not only the extent of recourse to non-polluting vehicles but also the preventive maintenance (PM) policy to be adopted for each of the N types of vehicles. More specifically, the objective of this work consists in determining simultaneously the quasi-optimal number of vehicles of each type to be used, the duration of their use, and their average usage rate as well as the period according to which each type of vehicle should be submitted to preventive maintenance. A mathematical model is developed to express and optimize the expected total cost, which includes the costs related to acquisition, operating, maintenance, and environmental impact in addition to considering the resale value. Then, the situation of using the acquired vehicle fleet in a context of a health crisis with containment measures is considered. The latter make it impossible to perform preventive maintenance actions during the containment period. For such situations, given the accumulated degradation in absence of preventive maintenance, the cost model is modified to generate a new preventive maintenance plan to be applied for each vehicle after the containment exit. Numerical results related to fuel and electric vehicles of two brands (Renault and Nissan) are presented and discussed.

Integrated Production and Preventive Maintenance Decision Making Using Option-Based Cost Model

2009 ◽  
Author(s):  
Xiaoning Jin ◽  
Lin Li ◽  
Jun Ni
Keyword(s):  
Decision Making ◽  
Production System ◽  
Preventive Maintenance ◽  
Demand Uncertainty ◽  
Cost Model ◽  
Stochastic Demand ◽  
Optimal Number ◽  
Integrated Production ◽  
Maintenance Decision ◽  
Cost Parameters

This paper presents an analytical, option-based cost model for an integrated production and preventive maintenance decision making with stochastic demand. The determination of preventive maintenance times and their schedule during a production period is converted to an option problem through maximizing the profit of the production per unit time. The optimal number of preventive maintenance actions is obtained and some further discussions on how the cost parameters affect the optimal results are also derived. The resulting option-based model is found to add flexibility to the production system and thus reduce the risk of shortage when the production system is faced with stochastic demand. A comparisons between the basic model (without option) and the option-based preventive maintenance model has shown that the option model is a more flexible under demand uncertainty and results in at least as much profit as the basic one.

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

scholarly journals A METHODOLOGY TO SUPPORT COMPANIES IN THE FIRST STEPS TOWARDS DE-MANUFACTURING

2021 ◽  
Vol 1 ◽  
pp. 131-140
Author(s):  
Federica Cappelletti ◽  
Marta Rossi ◽  
Michele Germani ◽  
Mohammad Shadman Hanif
Keyword(s):  
Environmental Impact ◽  
End Of Life ◽  
Design Stage ◽  
Life Strategies ◽  
Disassembly Sequence ◽  
The Status ◽  
The Cost ◽  
Technical Solutions ◽  
First Time ◽  
Complex Activities

AbstractDe-manufacturing and re-manufacturing are fundamental technical solutions to efficiently recover value from post-use products. Disassembly in one of the most complex activities in de-manufacturing because i) the more manual it is the higher is its cost, ii) disassembly times are variable due to uncertainty of conditions of products reaching their EoL, and iii) because it is necessary to know which components to disassemble to balance the cost of disassembly. The paper proposes a methodology that finds ways of applications: it can be applied at the design stage to detect space for product design improvements, and it also represents a baseline from organizations approaching de-manufacturing for the first time. The methodology consists of four main steps, in which firstly targets components are identified, according to their environmental impact; secondly their disassembly sequence is qualitatively evaluated, and successively it is quantitatively determined via disassembly times, predicting also the status of the component at their End of Life. The aim of the methodology is reached at the fourth phase when alternative, eco-friendlier End of Life strategies are proposed, verified, and chosen.

scholarly journals Economic burden of childhood diarrhea in Burundi

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Fulgence Niyibitegeka ◽  
Arthorn Riewpaiboon ◽  
Sitaporn Youngkong ◽  
Montarat Thavorncharoensap
Keyword(s):  
Economic Burden ◽  
Cost Model ◽  
Primary Treatment ◽  
Management Program ◽  
Community Based ◽  
Factors Affecting ◽  
Hospitalization Costs ◽  
Illness Study ◽  
The Cost ◽  
Substantial Economic Burden

Abstract Background In 2016, diarrhea killed around 7 children aged under 5 years per 1000 live births in Burundi. The objective of this study was to estimate the economic burden associated with diarrhea in Burundi and to examine factors affecting the cost to provide economic evidence useful for the policymaking about clinical management of diarrhea. Methods The study was designed as a prospective cost-of-illness study using an incidence-based approach from the societal perspective. The study included patients aged under 5 years with acute non-bloody diarrhea who visited Buyenzi health center and Prince Regent Charles hospital from November to December 2019. Data were collected through interviews with patients’ caregivers and review of patients’ medical and financial records. Multiple linear regression was performed to identify factors affecting cost, and a cost model was used to generate predictions of various clinical and care management costs. All costs were converted into international dollars for the year 2019. Results One hundred thirty-eight patients with an average age of 14.45 months were included in this study. Twenty-one percent of the total patients included were admitted. The average total cost per episode of diarrhea was Int$109.01. Outpatient visit and hospitalization costs per episode of diarrhea were Int$59.87 and Int$292, respectively. The costs were significantly affected by the health facility type, patient type, health insurance scheme, complications with dehydration, and duration of the episode before consultation. Our model indicates that the prevention of one case of dehydration results in savings of Int$16.81, accounting for approximately 11 times of the primary treatment cost of one case of diarrhea in the community-based management program for diarrhea in Burundi. Conclusion Diarrhea is associated with a substantial economic burden to society. Evidence from this study provides useful information to support health interventions aimed at prevention of diarrhea and dehydration related to diarrhea in Burundi. Appropriate and timely care provided to patients with diarrhea in their communities and primary health centers can significantly reduce the economic burden of diarrhea. Implementing a health policy to provide inexpensive treatment to prevent dehydration can save significant amount of health expenditure.

Use of Sugarcane Bagasse Ashes as Raw Material in the Replacement of Fluxes for Applications on Porcelain Tile

2016 ◽  
Vol 881 ◽  
pp. 383-386 ◽  
Author(s):  
Raimundo J.S. Paranhos ◽  
Wilson Acchar ◽  
Vamberto Monteiro Silva
Keyword(s):  
Mechanical Properties ◽  
Environmental Impact ◽  
Physical Properties ◽  
Sugarcane Bagasse ◽  
Sintering Temperature ◽  
Raw Materials ◽  
Raw Material ◽  
The Cost ◽  
Potential Use ◽  
Porcelain Tile

This study evaluated the potential use of Sugarcane Bagasse Ashes (SBA) as a flux, replacing phyllite for the production of enamelled porcelain tile. The raw materials of the standard mass components and the SBA residue were characterized by testing by XRF, XRD, AG, DTA and TGA. Test samples were fabricated, assembled in lots of 3 units and sintered at temperatures of 1150 ° C to 1210 ° C. The results of the physical properties, mechanical properties and SEM of the sintered samples, showed that the formulation, G4 - in which applied 10% of SBA replacing phyllite, sintering temperature 1210 ° C showed better performance as the previously mentioned properties due to the formation of mullite crystals, meeting the prerequisites of standards for enamelled porcelain tile, while reducing the environmental impact and the cost of production.

scholarly journals Development of a New KPI for the Economic Quantification of Six Big Losses and Its Implementation in a Cyber Physical System

2020 ◽  
Vol 10 (24) ◽  
pp. 9154
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Royo ◽  
Juan Carlos Sánchez ◽  
Jaime Latapia
Keyword(s):  
Real Time ◽  
Physical System ◽  
Performance Indicator ◽  
Cost Model ◽  
Relevant Information ◽  
Cyber Physical System ◽  
Time Data ◽  
Real Time Data ◽  
Different Dimensions ◽  
The Cost

The purpose of this work is to develop a new Key Performance Indicator (KPI) that can quantify the cost of Six Big Losses developed by Nakajima and implements it in a Cyber Physical System (CPS), achieving a real-time monitorization of the KPI. This paper follows the methodology explained below. A cost model has been used to accurately develop this indicator together with the Six Big Losses description. At the same time, the machine tool has been integrated into a CPS, enhancing the real-time data acquisition, using the Industry 4.0 technologies. Once the KPI has been defined, we have developed the software that can turn these real-time data into relevant information (using Python) through the calculation of our indicator. Finally, we have carried out a case of study showing our new KPI results and comparing them to other indicators related with the Six Big Losses but in different dimensions. As a result, our research quantifies economically the Six Big Losses, enhances the detection of the bigger ones to improve them, and enlightens the importance of paying attention to different dimensions, mainly, the productive, sustainable, and economic at the same time.

scholarly journals Model Integrasi Penjadwalan Batch dan Penjadwalan Preventive Maintenance dengan Kriteria Minimisasi Biaya Simpan, Biaya Setup, Biaya Pm, serta Biaya Rework pada Mesin Stabil

2014 ◽  
Vol 5 (1) ◽  
pp. 397
Author(s):  
Zahedi Zahedi
Keyword(s):  
Preventive Maintenance ◽  
Batch Scheduling ◽  
Problem Instance ◽  
Production Schedule ◽  
Batch Production ◽  
Setup Costs ◽  
The Cost ◽  
Single Batch

This study developed a model of batch scheduling involving the unavailability machine to minimize setup costs, cost of preventive maintenance and the cost of rework in a stable machine. This model is considered necessary in order to understand the effect of the unavailability machine for production runs and to understand the effect on the batch production schedule. The results of this study indicate that the first and last run will not give single batch. Given a hypothetical example of how the model and algorithm developed solve the problem instance. 

scholarly journals GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts

2020 ◽  
pp. 118-125 ◽  
Author(s):  
Elvira Albert ◽  
Jesús Correas ◽  
Pablo Gordillo ◽  
Guillermo Román-Díez ◽  
Albert Rubio
Keyword(s):  
Upper Bound ◽  
Cost Model ◽  
Gas Analysis ◽  
Smart Contracts ◽  
Cost Models ◽  
Automatic Optimization ◽  
Gas Consumption ◽  
The Cost ◽  
Storage Patterns ◽  
Program Line

Abstract We present the main concepts, components, and usage of Gasol, a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. Gasol offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum’s classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, Gasol returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, Gasol uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for which displays the gas and instructions bounds and, when applicable, the gas-optimized function.

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