Industrial Management Studies

Abstract The Soft System Methodology (SSM), one of the OR techniques, is used to
solve complex real-world problems. Since to design of the after-sales service
models for the liquefied gas industry, various groups, such as refineries,
mopeds, silencers, taps, standardized organizations, and the consumer rights
protection organization should be considered, so decision making in this
situations are very complicated issue. In this study, using the above
approach, the problem of non-structured model design at the world-class
level is explained and then, by specifying its boundaries, the image of the
various actors of the system and their benefits are depicted. In the third step,
the CATWOE approach is used to explain the basic definition of the aftersales service model in this industry, and in the fourth stage, a conceptual
model of activities is presented using the root definition. This paper uses
integration of ISM-Fuzzy Delphi in the process of problem solving. In the
fifth step, the developed model is compared with the real world. In the sixth
stage, desirable and feasible changes were identified and explained by the
IPA method. Finally, using the results of the previous stages, and
suggestions for the development of the model to reach the world class level
are presented to the authorities and stakeholders.

Abstract With the tremendous progress in communications in the world, the transformation and
behavior of mobile operators and their digitalization, which in the past were only
service providers, as well as the creation of different experiences for customers, is
inevitable.The purpose of this study is to create a hybrid simulation of system
dynamics and agent based model in order to analyze the revenue of the first operator in
the country to enter the field of digital platform and development of native
applications. Using the model proposed, first operators need to enter the digital area
and produce native applications was expressed. Then, the factors that affect the mobile
ecosystem which, affect the production of applications and the development of
required platforms were described. By utilizing hybrid simulation of system dynamics
and agent based modeling, the income of mobile operator in entering and not entering
the digital arena and producing native applications were examined. The results show
that with the entry of the operator into the field of production of native applications
and the adoption of digital approach, consumers tended to use more data services, but
due to different tariffs for data and voice, the operator's income up to 2 Next years will
not change much.

Abstract Health care solid wastes include all types of waste that are produced as a result of
medical and therapeutic activities in hospitals and health centers. About 15% to
20% of these waste materials are infectious waste, which falls within the category
of hazardous materials. Infectious waste is the one that must be treated before
disposal or recycling. Hence, this paper seeks to develop a bi-objective mixed
integer programming model for the infectious waste management. In the proposed
model, in addition to minimizing the chain costs, the reduction of risks for the
population exposed to the spread of contamination resulting from infectious waste
is also considered. For this purpose, a multi-echelon chain is proposed by taking
into account the green location-routing problem, which involves the location of
recycling, disposal, and treatment centers through various treatment technologies
and routing of vehicles between treatment levels and the hospital. The routing
problem has been considered to be multi-depot wherein the criterion of reducing
the cost of fuel consumption of heterogeneous cars is used for green routing.
Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developed
and, following its validation, its function in solving large-scale problems has been
investigated. Results show that the proposed algorithm is effective and efficient.

Abstract Studies show that there are shortcomings in the deployment of the Internet of
Things (IoT) in the supply chain of agricultural products, especially in the
field of quality control in the logistics sector, and researchers can model the
existing theoretical gaps through modeling and optimization. Therefore, the
purpose of this paper is to identify the most important categories affecting
the deployment of the Internet of Things in the wheat supply chain storage
sector and explain and mapping the relationship between these categories.
For this purpose, the present article uses meta-synthesis method by searching
Web of Science and Scopus citation databases. Then, the grounded theory
coding procedures were used to determine categories and themes. Finally,
the results of meta-synthesis lead to the identification and extraction of 3
macro categories; IoT technology, the main category (IoT-based storage),
and the results and consequences of IoT deployment.

Abstract One of the most important approaches that can lead to the creation of various advantages
for enterprises is the districting regions into the service offering locations and the demand
units, which causes the increase in level of customers’ access to get the service. On the
other hand, if vehicle routing is carried out in districting regions in order to deliver products
to customers, the planning of customer service can be improved. However, in none of the
research conducted in the area of design supply chain, vehicle routing in districting regions
has been not investigated. Therefore, in the current study, a bi-objective mathematical
model is presented to simultaneously focus on districting regions, facility location–
allocation, service sharing, intra-district service transfer and vehicle routing. The first
objective function minimizes the total cost of designing the CLSC network, which includes
costs of opening facility and vehicle routing. The second objective function minimizes the
maximum volume of surplus demand from service providers in order to achieve an
appropriate balance in demand volume across all regions. Moreover, a robust optimization
approach is used to take into account uncertainty in some parameters of the proposed
model. In addition, the validity of the proposed mathematical model and the proposed
solution has been investigated on a real case in the oil and gas industry.

Abstract One Knowledge Management (KM) as one of the Supply Chain performance
improvement factors can be strengthened through frameworks like EFQM Excellence
Model in order to achieve competitive advantage. First, the KM enablers in SC are
classified based on EFQM enabler criteria. Then, the importance of each KM enabler is
evaluated by fuzzy DEMATEL-ANP. In addition, Analytical Hierarchy Process (AHP) is
applied to evaluate the importance of each KM enabler in knowledge sharing among
supply chain people. In the research, the multi-objective mixed integer programming is
used to optimize knowledge management and select KM strategy in each part of SC.
Likewise, it is approved to select suitable members of SC for Research and Development
(R&D) unit of SC. Results show that each part of SC should focus on developing some
KM enablers, and selection of a suitable strategy. These results also emphasis the
effectiveness of each KM enabler and their development in selecting of suitable members
for R&D unit of SC.

Abstract In the real world, firms with hybrid flow-shop manufacturing environment generally face
the human resource constraint, salary cost increasment and efforts to make better use of
labor, in addition to machine constraint. Given the limitations of these resources, product
delivery requierements to customers have made the job rejection essential in order to meet
distinct customer requirements. Therefore, this research has studied the dual resource
constrained hybrid flow-shop scheduling problem with job rejection in order to minimize
the total net cost (the sum of the total rejection cost and the total tardiness cost of jobs)
which is widely used in many industries. In this article, a mixed integer linear programming
model has developed for the research problem. In addition, an improved sooty tern
optimization algorithm (ISTOA) has proposed to solve the large-sized problems as well as
a decoding method due to the NP-hardness of the problem. In order to evaluate the
proposed optimization algorithm, five well-known algorithms in the literature including
(immunoglobulin-based artificial immune system (IAIS), genetic algorithm (GA), discrete
artificial bee colony (DABC), improved fruit fly optimization (IFFO), effective modified
migrating birds optimization (EMBO)) have adapted with the proposed problem. Finally,
the performance of the proposed optimization algorithm has investigated against the
adapted algorithms. Results and evaluations show the good performance of the improved
sooty tern optimization algorithm.