Industrial Management Studies

Abstract Supplier selection and quota allocation is an important decision in supply
chains. This decision can be considered as a complex multi-criteria group
decision making problem. This decision in many practical situations is very
difficult for vague and uncertain environment. This vagueness and uncertainty
can be handled by using fuzzy set theory. Therefore, this paper proposed a
fuzzy MCDM model to evaluate candidate suppliers and quota allocation. A
hybrid ANP-VIKOR method in fuzzy environment applied first with 16
criteria to evaluate suppliers. Then, a fuzzy multi-objective mathematical
model is used to quota allocation. Finally, the fuzzy model is solved by Tiwari
method. An illustration with a data set from a realistic situation is presented to
demonstrate the effectiveness of the proposed model.

Abstract During the recent years, extensive research has been done on the field of
project scheduling. There is always uncertainty in the area of project
scheduling that causes a deviation in the real plan from the scheduled plan.
One of the solutions to deal with this uncertainty is using the critical chain
method (CCM) in project scheduling. This method which is derived from the
theory of constraints (TOC) is a new method in project control which was first
proposed by Goldartt in 1997.In this research we attempt to use the principals
of critical chain in resource-constrained project scheduling problem. The main
innovation in this research is presentation of critical chain project scheduling
problem model with consideration of feeding buffer and using float as a
supplement for feeding buffer. For this matter, the project scheduling under
resources constraints with critical chain approach was first written and its
reliability was evaluated using the Lingo software. In the next step the
solution algorithm of this model was developed using the genetic algorithm
and finally different sample issues were investigated. The results of this
research show the efficiency of the presented genetic algorithm

Abstract In this paper, a mathematical model is proposed for project portfolio
selection and resource availability cost problem to scheduling activities in
order to maximize net present value of the selected projects preserving
precedence and resource constraints. Since the developed model belongs to
NP-hard problems list, so a genetic based meta-heuristic algorithm is
proposed to tackle the developed model. In the proposed algorithm beside
common operators of genetic algorithms such as crossover & mutation, some
intelligent operators are utilized for local search in computed resources and
shifting the activities with negative cash flows. The key parameters of the
algorithm are calibrated using Taguchi method to accelerate convergence of
the proposed algorithm. Then, the algorithm is used to solve 90 test
problems consisting 30 small-scale, 30 middle-scale and 30 large scale
problems to examine the algorithm’s performance. It is observed that, in
small problems, the obtained solutions from the proposed genetic algorithm
have been comparably better than the local optimum solutions stemmed
from Lingo software. On the other hand, for the middle and large size
problems which there is no local optimum available within the limited CPU
time, robustness of the proposed algorithm is appropriate

Abstract A redundancy allocation problem is a well-known NP-hard problem that
involves the selection of elements and redundancy levels to maximize the
system reliability under various system-level constraints. In many practical
design situations, reliability apportionment is complicated because of the
presence of several conflicting objectives that cannot be combined into a
single-objective function. A stele communications, manufacturing and power
systems are becoming more and more complex, while requiring short
developments schedules and very high reliability, it is becoming increasingly
important to develop efficient solutions to the RAP. In this paper, a new
hybrid multi-objective competition algorithm (HMOCA)based on
competitive algorithm (CA) and genetic algorithm (GA) is proposed for the
first time in multi-objective redundancy allocation problems. In the multiobjective
formulation, the system reliability is maximized while the cost and
volume of the system are minimized simultaneously. Additionally, ay RSM
is employed to tune the CA parameters. The proposed HMOCA is validated
by some examples with analytical solutions. It shows its superior
performance compared to a NSGA-II and PAES algorithms. Finally, the
conclusion is given

Abstract In this paper, a methodology is proposed to measure the efficiency of
national energy sector in IRAN. The technical and environmental
performance of the oil refineries in IRAN as a major producer of energy and
fuel are evaluated based on data from years 2010 to 2013. In this study, a
fuzzy multi-objective multi-period common weight network data
envelopment analysis approach is proposed and customized to evaluate the
performance of oil refineries. A certain scenario, called food-production in
which a refinery is assumed as a decision making unit (DMU) consuming
inputs to produce outputs, is considered to evaluate the technical and
environmental performance in presence of undesirable outputs. The main
contribution of this study are summarized as: (1) Proposing a multiobjective
common weight DEA model in order to determine the weights of
inputs and outputs in a single run; (2) Calculating the long term efficiency
scores during a multiple-periods of planning incorporating dynamic nature
of inputs and outputs; (3) Handling a compromise solution using fuzzy
mathematical programming to address multi-objective mathematical
programming; (4) Proposing a linear mathematical programming to achieve
the global optimum solutions; (5) Enhancing the discrimination power of the
DEA models; (6) Reducing the computational time of modeling and solution
procedure; (7) incorporating effective criteria in the modeling procedure.
The analysis of case study presents the efficacy and applicability of
proposed method in comparison with existing classic models.

Abstract Regarding to the increasing importance of supply chain risk in the last
decade in general and projects especially, in this research the dimensions and
elements of supply chain risk of projects have been determined. Therefore,
Fars Gas Transfer Pipeline projects were considered to discover codes and
themes according to the phenomena by using Grounded Theory (Corbin and
Strauss method). Based on final paradigm model, project supply chain risk
management issue was categorized in 6 main dimensions, 19 sub-dimensions
and 57 codes. The results show that supply chain risks as a core phenomenon,
consists of environmental risks, organizational risks and network risks. To
do this, we use theoretical sampling and interview with 10 experts from Gas
state company and some suppliers. These interview was deep one because
we employed open questionnaire. Then by using open encoding, axial
encoding and selective encoding, paradigm model was extracted. The main
phenomenon placed the center of model and other themes joined to that

Abstract Decision making (DM) is an important problem in most of the army
operations. One of the challenging issues in this area is uncertainty in wars
with uncertain information which causes many destructive effects on the
results of strategies in battlefields. In the Heravi et al. article’s, published in
the year 2013, utilizing a combination of Cognitive Agent (CA) and
Classification based on Fuzzy Association Rules (CFAR) as the most
effective and widely used methods, was able to relatively reduce this
problem and tried to reduce uncertainty. But still in critical condition, can’t
deny the need to act quickly and remove most invalid and inefficient rules
extracted in the effective decisions.
This paper aims to utilize the capabilities of Genetic Algorithm (GA) in a
more realistic selection rules as a meta-heuristic way to combine
complementary methods to minimize the uncertainty in DM. In comparison
with previous method, experimental results achieved, clearly show that this
combination in addition to the advantages of the previous method, due to the
further reduction of production rules for DM, are more understandable and
accurate and has more rational risk acceptance.