THE EFFECTS OF INFLATION AND TIME VALUE OF MONEY ON A PRODUCTION MODEL WITH A RANDOM PRODUCT LIFE CYCLE

2010 ◽  
Vol 27 (04) ◽  
pp. 437-456 ◽  
Author(s):  
JONAS C. P. YU ◽  
HUI-MING WEE ◽  
GEDE A. WIDYADANA ◽  
JER-YUAN CHANG
Keyword(s):  
Life Cycle ◽  
Product Life Cycle ◽  
Planning Horizon ◽  
Production Model ◽  
Sensitivity Analyses ◽  
Product Life ◽  
Time Value Of Money ◽  
Time Value ◽  
Production Stage ◽  
Value Of Money

In Moon and Lee's model (2000), they developed a finite planning horizon economic order quantity (EOQ) model with time value of money and inflation. This paper extends Moon and Lee's model to examine a production system with a random life cycle. Two conditions are discussed: the first is when the product life cycle ends in the production stage and the second is when the product life cycle ends in the non-production stage. We develop an algorithm to derive the optimal period time and expected total cost. Numerical examples and sensitivity analyses are given to validate the results of the production model.

Agent Based Simulation Optimization of Waste Electrical and Electronics Equipment Recovery

2016 ◽  
Author(s):  
Ardeshir Raihanian Mashhadi ◽  
Sara Behdad ◽  
Jun Zhuang
Keyword(s):  
Life Cycle ◽  
Product Life Cycle ◽  
Electronic Waste ◽  
Recovery Process ◽  
Planning Horizon ◽  
Agent Based Simulation ◽  
Product Life ◽  
Agent Based ◽  
Take Back ◽  
Product Identity

The profitability of Electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in quantity, quality and timing of returns originating from consumers’ behavior. The cloud-based remanufacturing concept, data collection and information tracking technologies seems a promising solution toward proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an Agent Based Simulation (ABS) framework to model the overall product take-back and recovery system based on the product identity data available through cloud-based remanufacturing infrastructure. Socio-demographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process and product life cycle information have been considered to capture the optimum buyback price proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model.

scholarly journals Environmental Performance of Kettle Production: Product Life Cycle Assessment

2017 ◽  
Vol 25 (4) ◽  
pp. 255-261
Author(s):  
Andrzej Marcinkowski ◽  
Krzysztof Zych
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Product Life Cycle ◽  
Assessment Method ◽  
Sensitivity Analyses ◽  
Main Function ◽  
Product Life ◽  
Comparison Results ◽  
Lca Results

AbstractThe main objective of this paper is to compare the environmental impact caused by two different types of water boiling processes. The aim was achieved thanks to product life cycle assessment (LCA) conducted for stovetop and electric kettles. A literature review was carried out. A research model was worked out on the basis of data available in literature as well as additional experiments. In order to have a better opportunity to compare LCA results with reviewed literature, eco-indicator 99 assessment method was chosen. The functional unit included production, usage and waste disposal of each product (according to from cradle to grave approach) where the main function is boiling 3360 l of water during 4-year period of time. A very detailed life cycle inventory was carried out. The mass of components was determined with accuracy of three decimal places (0.001 g). The majority of environmental impact is caused by electricity or natural gas consumption during usage stage: 92% in case of the electric and kettle and 99% in case of stovetop one. Assembly stage contributed in 7% and 0.8% respectively. Uncertainty and sensitivity analyses took into consideration various waste scenario patterns as well as demand for transport. Environmental impact turned out to be strongly sensitive to a chosen pattern of energy delivery (electricity mix) which determined final comparison results. Basing on LCA results, some improvements of products were suggested. The boiling time optimization was pointed out for electric kettle's efficiency improvement. Obtained results can be used by manufacturers in order to improve their eco-effectiveness. Moreover, conclusions following the research part can influence the future choices of home appliances users.

scholarly journals A Partial Backlogging Inventory Model for Deteriorating Item under Fuzzy Inflation and Discounting over Random Planning Horizon: A Fuzzy Genetic Algorithm Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Dipak Kumar Jana ◽  
Barun Das ◽  
Tapan Kumar Roy
Keyword(s):  
Genetic Algorithm ◽  
Planning Horizon ◽  
Inventory Model ◽  
Expected Profit ◽  
Partial Backlogging ◽  
Inventory Models ◽  
Time Value Of Money ◽  
Time Value ◽  
Value Of Money ◽  
Random Planning Horizon

An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA) and fuzzy simulation-based genetic algorithm (FSGA) are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented.Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.

Agent Based Simulation Optimization of Waste Electrical and Electronics Equipment Recovery

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Ardeshir Raihanian Mashhadi ◽  
Sara Behdad ◽  
Jun Zhuang
Keyword(s):  
Life Cycle ◽  
Product Life Cycle ◽  
Electronic Waste ◽  
Recovery Process ◽  
Planning Horizon ◽  
Agent Based Simulation ◽  
Product Life ◽  
Agent Based ◽  
Take Back ◽  
Product Identity

The profitability of electronic waste (e-waste) recovery operations is quite challenging due to various sources of uncertainties in the quantity, quality, and timing of returns originating from consumers' behavior. The cloud-based remanufacturing concept, data collection, and information tracking technologies seem promising solutions toward the proper collection and recovery of product life cycle data under uncertainty. A comprehensive model that takes every aspect of recovery systems into account will help policy makers perform better decisions over a planning horizon. The objective of this study is to develop an agent based simulation (ABS) framework to model the overall product take-back and recovery system based on the product identity data available through cloud-based remanufacturing infrastructure. Sociodemographic properties of the consumers, attributes of the take-back programs, specific characteristics of the recovery process, and product life cycle information have all been considered to capture the optimum buy-back price (bbp) proposed for a product with the aim of controlling the timing and quality of incoming used products to collection sites for recovery. A numerical example of an electronic product take-back system and a simulation-based optimization are provided to illustrate the application of the model.

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