Managing Supply for Construction Project with Uncertain Starting Date

There is a growing interest in supply management systems ¬¬in today’s competitive business environment. Importance of implementing supply management systems especially in home construction industry is due to the fact that several risks arising from different sources can adversely affect the project financially or its timely completion. Some risks of construction projects are out of managers’ control while other risks such as supply related ones can usually be controlled and directed by effective managerial tactics. In this thesis, we address the supplier selection problem (SSP) in wood-base construction projects in the presence of project commencement uncertainties. The project could be delayed for any reason and thus materials required for the project may not be needed on the promised date, however, pursuing the supplier for new delivery date may not be easy and without risk. Accepting the delivery before the project commencement date will be again a costly option because of the high holding cost. In this thesis, we present two problem cases and present heuristic based solution approaches. In the first case we assume that price of the product increases with the delay. In the second case we assume that promised quantity at the agreed price reduces with the delay. The proposed approaches are tested on the randomly generated data set and compared with the optimal solutions. The problems considered in this research are novel and the proposed approaches deal with the important and common risks in construction industry in order to achieve a robust supply chain. The solution approaches presented in this thesis can be applied to different industries to improve the quality and efficiency of supplier-buyer collaborations

Forest management-consideration of multiple objectives

In Canada, as a major forested country, forest resources provide significant environmental, social, and economic values. Hence, consideration of multiple often-conflicting criteria in forest management planning has become a necessity rather than a special case. Since 2013, a new forest management regime came to effect in the province of Quebec, Canada where the Ministry of Forests, Fauna, and Parks (MFFP) became responsible for preparing and implementing integrated forest development plans. In order for the MFFP to take local needs and goals into account usually multiple objectives need to be targeted. So, the main objective of this thesis is to analyze and to propose new business models for forest management planning addressing several key factors. The first part of the thesis includes a review of a number of planning methods and decision support systems for tactical decisions in the forest-based value creation network. In the second part of the thesis, we have proposed a multi-objective optimization model for the problem of selection of harvest areas and allocation of timber to wood-processing mills over 5-year planning horizon. This model has been used to analyze a tactical forest management plan in Quebec. The forest management unit 07451 inside region 7, Outaouais in western Québec was considered as our case study. The solution of the proposed multi-objective model was compared with the traditional cost minimization strategy. Also, the impacts of logistics constraints were assessed. Finally, in the third part of the thesis we have proposed a planning support tool to group the harvest areas in a way that the spatial dispersion of the clusters is reduced, meaning the logistics of moving the machinery between areas in each cluster becomes more efficient. The results from the three parts of the thesis have demonstrated that simultaneous consideration of some important objectives in the tactical forest management could lead to a more balanced and economically sustainable plan, in addition systematical cluterization of harvest areas will reduce the spatial dispersion of the harvest areas that a typical harvesting team has to cut, which consequently reduce the time and cost of movement of harvesting machineries among the areas for the team. In general, the work in this thesis can support an efficient forest management plan considering multiple objectives and minimizing the spatial dispersion of harvest areas that a harvesting team would cut. The optimization models and approaches proposed in this thesis are novel and practical for the forest management planning problems

Energy Consumption and Modeling of output energy with Multilayer Feed-Forward Neural Network for Corn Silage in Iran

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the output energy for corn silage production in Esfahan province, Iran. For this purpose, the data on 65 corn silage production farms in the Esfahan province, were collected and analyzed. The results indicated that total energy input for corn silage production was about 83126 MJ ha–1; machinery (with 38.8 %) and chemical fertilizer (with 24.5 %) were amongst the highest energy inputs for corn silage production. The developed ANN was a multilayer perceptron (MLP) with eight neurons in the input layer (human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed, farm manure and pesticides ), one, two, three, four and five hidden layer(s) of various numbers of neurons and one neuron (output energy) in the output layer. The results of ANNs analyze showed that the (8-5-5-1)-MLP, namely, a network having five neurons in the first and second hidden layer was the best-suited model estimating the corn silage output energy. For this topology, MAB, MAE, RMSE and R2 were 0.109, 0.001, 0.0464 and 98%, respectively. The sensitivity analysis of input parameters on output showed that diesel fuel and seeds had the highest and lowest sensitivity on output energy with 0.0984 and 0.0386, respectively. The ANN approach appears to be a suitable method for modeling output energy, fuel consumption, CO2 emission, yield, and energy consumption based on social and technical parameters. This method would open new doors to advances in agriculture and modeling

Energy inputs – yield relationship and sensitivity analysis for tomato greenhouse production in Iran

This paper studies the energy balance between the input and the output energies per unit area for greenhouse tomato production.  For this purpose, the data on 30 tomato production greenhouses in Isfahan province, Iran were collected and analyzed.  The results indicated that a total specific input energy of 116,768.4 MJ ha-1 was consumed for tomato production.  Diesel fuel (with 40%) and chemical fertilizers and manure (with 30%) were amongst the highest input energies for tomato production.  The energy productivity was estimated to be 1.16 kg MJ-1.  The ratio of output energy to input energy was approximately 0.92. 19% and 81% of total energy input was in renewable and non-renewable forms, respectively.  The regression results revealed that the contribution of input energies on crop yield for human power, machinery, pesticides and electricity inputs was significant.  The human power energy had the highest impact (1.45) among the other inputs in greenhouse tomato production.  The marginal physical productivity of diesel fuel, seed and total chemical fertilizer with manure was negative.  It can be because of applying the inputs more than required or improperly applying.  The highest shares of expenses were found to be 34% and 21% for human power and total diesel fuel and machinery, respectively.  Cost analysis revealed that total cost of production for 1 ha greenhouse tomato production was around US$34939.  Accordingly, the benefit-cost ratio was estimated as 2.74.  Results of greenhouse gas emission indicated that tomato production is mostly depended on diesel fuel sources.  Diesel fuel had the highest share (2,719.98 kg CO2eq.ha-1) followed by electricity (729.6 kg CO2eq.ha-1) and nitrogen fertilizer (409.5 kg CO2eq.ha-1).   Keywords: tomato, greenhouse, energy productivity, economic analysis, Cobb-Douglas functio

Wood-based construction project supplier selection under uncertain starting date

There is a growing interest in supply management systems in today's competitive business environment. Importance of implementing supply management systems especially in home construction industry is due to the fact that several risks arising from different sources can adversely affect the project financially or its timely completion. Some risks of construction projects are out of managers' control while other risks such as supply related ones can usually be controlled and directed by effective managerial tactics. In this paper, we address the supplier selection problem (SSP) in wood-based construction industry (housing projects) in the presence of project commencement uncertainties. Based on the suppliers' (vendors') reaction towards these uncertainties in the delivery time, we explore two cases: (a) supplier selection with buyer penalty for a delay (SSPD) where the price of product increases with the delay; (b) supplier selection with quantity reduction for a buyer delay (SSQRD). Three heuristic-based supplier selection approaches are proposed and tested on randomly generated data sets. The proposed approaches show promising result

Investigation of energy inputs and CO2 emission for almond production using sensitivity analysis in Iran

The objective of this study is to examine input–output energy and CO2 emission of almond production in Shahrekord region, Iran. This article presents a comprehensive picture of the current status of energy consumption and some energy indices like energy use efficiency, energy productivity, specific energy and net energy gain. Sensitivity analysis of energy was carried out using the marginal physical productivity (MPP) technique. For this propose data were collected from 29almond farms using a face to face questionnaire. The results revealed that total energy input for almond production was found to be 106.61GJ/ha where the electricity was the major energy consumer (59.58%). The direct energy shared about (50.98%) whereas the indirect energy did (49.02%). Energy use efficiency, energy productivity, and net energy were 0.37, 0.016 kg/MJ, and -67350.16MJ/ha, respectively. The regression results revealed that the contribution of energy inputs on crop yield (except for farmyard manure and water energies) was insignificant. Water energy was the most significant input (0.674) which affects the output level. The results also showed that the impacts of direct, indirect and renewable energies on yield are significant. The GHG emissions were indicated a high CO2 output in diesel fuel consumption

Application of nonparametric method to improve energy productivity and CO2 emission for barley production in Iran

The nonparametric method of data envelopment analysis (DEA) was used to investigate the energy efficiency and CO2 emission of barley farm in Hamedan province of Iran.  The method was used based on eight energy inputs including human labor, machinery, diesel fuel, fertilizers, farmyard manure, biocide, electricity and seed energy and single output of barley yield and technical, pure technical, scale and cross efficiencies were calculated using CCR and BCC models.  The results showed that the average values of technical, pure technical and scale efficiency scores of farmers were 0.788, 0.941 and 0.833, respectively.  Also, energy saving target ratio for barley production was calculated as 11.45%, indicating that by following the recommendations of this study, about 2,865 MJ ha–1 of total input energy could be saved with the same constant level of barley yield.  Moreover the contribution of chemical fertilizer input from total saving energy was 34.88% which was the highest share followed by diesel fuel (25.88%) and electricity (20.89%) energy inputs.  On one hand, optimization of energy use improved the energy use efficiency, energy productivity and net energy by 12.94%, 15.55% and 6.16%, respectively.  On the other hand, total greenhouse gases (GHG) emission was 885.56 kg CO2eq ha–1, which indicated that, the total CO2 emissions can be reduced by 11.06%.    Keywords: data envelopment analysis, energy saving, barley, chemical fertilizer

Analysis of Energy Efficiency of Mechanized Cultivation in Potato Production Using a Data Envelopment Analysis Approach

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Immobilized nickel hexacyanoferrate nano particles on graphen for effective removal of Cs(I) ions from radionuclide wastes

In the current work synthesis and modification of graphene oxide with Nickel Hexa Ferrocyanide (NiHCF) nanoparticles has been reported. The Graphene oxide- Nickel Hexa Ferrocyanide (GO-NiHCF) was used as an adsorbent to remove Cesium (Cs) ions from a simulated solution. The obtained product was characterized with XRD, SEM, TGA, FTIR, and BET techniques. The SEM images and XRD pattern confirms the successful immobilization of Nickel Hexa Ferrocyanide on graphene oxide sheet. The cesium removal ability of GO-NiHCF was evaluated in batch mode. Effect of various parameters such as pH, initial concentration, contact time, and interferences ions were studied. The results cleared that the maximum adsorption for Cs removal was 240 mg g-1. Equilibrium modeling studies suggest that the data are reasonably and relatively fitted well to the Langmuir adsorption isotherm. Kinetic studies show that sorption process is fairly rapid and the kinetic data are fitted well to the pseudo-second order rate model. This composite offers strong potential in the field of elimination of Cs that requires rapid and complete decontamination