Abstract: Bayesian networks are now considered to be a promising tool in the field of energy with different applications. In this study, the aim was to indicate the states of a previous constructed Bayesian network related to the solar energy in Egypt and the factors affecting its market share, depending on the followed data distribution type for each factor, and using either the Z-distribution approach or the Chebyshev’s inequality theorem. Later on, the separate and the conditional probabilities of the states of each factor in the Bayesian network were derived, either from the collected and scrapped historical data or from estimations and past studies. Results showed that we could use the constructed model for scenario and decision analysis concerning forecasting the total percentage of the market share of the solar energy in Egypt by 2035 and using it as a stable renewable source for generating any type of energy needed. Also, it proved that whenever the use of the solar energy increases, the total costs decreases. Furthermore, we have identified different scenarios, such as the best, worst, 50/50, and most likely one, in terms of the expected changes in the percentage of the solar energy market share. The best scenario showed an 85% probability that the market share of the solar energy in Egypt will exceed 10% of the total energy market, while the worst scenario showed only a 24% probability that the market share of the solar energy in Egypt will exceed 10% of the total energy market. Furthermore, we applied policy analysis to check the effect of changing the controllable (decision) variable’s states acting as different scenarios, to show how it would affect the target nodes in the model. Additionally, the best environmental and economical scenarios were developed to show how other factors are expected to be, in order to affect the model positively. Additional evidence and derived probabilities were added for the weather dynamic nodes whose states depend on time, during the process of converting the Bayesian network into a dynamic Bayesian network.
Abstract: Clothing recycling bin is a traditional way to collect textile waste in many areas. In the clothing recycling business, the transportation cost normally takes over 50% of total costs. This case gives a good way to reduce transportation cost by reverse logistics system. In this reverse logistics system, there are offline strategic alliance partners, such as transport firms, convenience stores, laundries, and post office which are integrated onto the mobile APP. Offline strategic alliance partners provide the service of textile waste collection, and transportation by their vacant vehicles return journey from convenience stores, laundries and post offices to sorting centers. The results of the case study provide the strategic alliance with a valuable and light - asset business model by using the logistics of offline memberships. The company in this case just focuses on textile waste sorting, reuse, recycling etc. The research method of this paper is a case study of a clothing recycling company in Chengdu by field research and interview; the analysis is based on the theory of the reverse logistics system.
Abstract: Nowadays, in advanced countries, agriculture as one of the most significant sectors of the economy, plays an important role in its political and economic independence. Due to farmers' lack of information about products' demand and lack of proper planning for harvest time, annually the considerable amount of products is corrupted. Besides, in this paper, we attempt to improve these unfavorable conditions via designing an effective supply chain network that tries to minimize total costs of agricultural products along with minimizing shortage in demand points. To validate the proposed model, a stochastic optimization approach by using a branch and bound solver of the LINGO software is utilized. Furthermore, to accumulate the data of parameters, a case study in Mazandaran province placed in the north of Iran has been applied. Finally, using ɛ-constraint approach, a Pareto front is obtained and one of its Pareto solutions as best solution is selected. Then, related results of this solution are explained. Finally, conclusions and suggestions for the future research are presented.
Abstract: Optimal load factors (dead, live and seismic) used for the design of buildings may be different, depending of the seismic ground motion characteristics to which they are subjected, which are closely related to the type of soil conditions where the structures are located. The influence of the type of soil on those load factors, is analyzed in the present study. A methodology that is useful for establishing optimal load factors that minimize the cost over the life cycle of the structure is employed; and as a restriction, it is established that the probability of structural failure must be less than or equal to a prescribed value. The life-cycle cost model used here includes different types of costs. The optimization methodology is applied to two groups of reinforced concrete buildings. One set (consisting on 4-, 7-, and 10-story buildings) is located on firm ground (with a dominant period Ts=0.5 s) and the other (consisting on 6-, 12-, and 16-story buildings) on soft soil (Ts=1.5 s) of Mexico City. Each group of buildings is designed using different combinations of load factors. The statistics of the maximums inter-story drifts (associated with the structural capacity) are found by means of incremental dynamic analyses. The buildings located on firm zone are analyzed under the action of 10 strong seismic records, and those on soft zone, under 13 strong ground motions. All the motions correspond to seismic subduction events with magnitudes M=6.9. Then, the structural damage and the expected total costs, corresponding to each group of buildings, are estimated. It is concluded that the optimal load factors combination is different for the design of buildings located on firm ground than that for buildings located on soft soil.
Abstract: Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.
Abstract: Presently, it is necessary to ensure the sustainable
development of passenger and freight transport. Increasing
performance of road freight has had a negative impact to environment
and society. It is therefore necessary to increase the competitiveness
of intermodal transport, which is more environmentally friendly. The
study describes the effectiveness of logistical centers realization for
companies and society and research how the partial internalization of
external costs reflected in the efficient use of these centers and
increase the competitiveness of intermodal transport to road freight.
In our research, we use the method of comparative analysis and
market research to describe the advantages of logistic centers for their
users as well as for society as a whole. Method normal costing is used
for calculation infrastructure and total costs, method of conversion
costing for determine the external costs. We modelled total society
costs for road freight transport and inter modal transport chain (we
assumed that most of the traffic is carried by rail) with different
loading schemes for condition in the Slovak Republic. Our research
has shown that higher utilization of inter modal transport chain do
good not only for society, but for companies providing freight
services too. Increase in use of inter modal transport chain can bring
many benefits to society that do not bring direct immediate financial
return. They often bring the multiplier effects, such as greater use of
environmentally friendly transport mode and reduce the total society
costs.
Abstract: The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.
Abstract: Avoidable unscheduled maintenance events and unnecessary
spare parts deliveries are mostly caused by an incorrect choice
of the underlying maintenance strategy. For a faster and more efficient
supply of spare parts for aircrafts of an airline we examine options for
improving the underlying logistics network integrated in an existing
aviation industry network. This paper presents a dynamic prediction
model as decision support for maintenance method selection considering
requirements of an entire flight network. The objective is
to guarantee a high supply of spare parts by an optimal interaction
of various network levels and thus to reduce unscheduled maintenance
events and minimize total costs. By using a prognostics-based
preventive maintenance strategy unscheduled component failures are
avoided for an increase in availability and reliability of the entire
system. The model is intended for use in an aviation company that
utilizes a structured planning process based on collected failures data
of components.
Abstract: The article describes a case study on one of Czech
Republic-s manufacturing middle size enterprises (ME), where due to
the European financial crisis, production lines had to be redesigned
and optimized in order to minimize the total costs of the production
of goods. It is considered an optimization problem of minimizing the
total cost of the work load, according to the costs of the possible
locations of the workplaces, with an application of the Greedy
algorithm and a partial analogy to a Set Packing Problem. The
displacement of working tables in a company should be as a one-toone
monotone increasing function in order for the total costs of
production of the goods to be at minimum. We use a heuristic
approach with greedy algorithm for solving this linear optimization
problem, regardless the possible greediness which may appear and
we apply it in a Czech ME.