JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES
Year:2020 | Volume:18 | Issue:57|Papers Count:10

The turbulent and dynamic environment of today's business world has become increasingly challenging for organizations operating in different business areas. In such a situation, in order to get rid of these conditions, moving forward and towards the perspective of the new horizons of prosperity and survival is the dream of many of them. In the meantime, according to the specific circumstances and requirements that govern each institution or organization, and in order to achieve their desired productivity, they use certain strategies and programs that outsourcing is one of these strategies. Today, organizations are outsourcing to boost competitive ability and profit and focus on their competitive edge. In this research, a mathematical programming approach is proposed to optimize the issue of outsourcing in the supply chain. In this approach, at first the mathematical model of the problem showed and then in order to solve the problem of the theory of Markov chains described. The objective function of the problem involves minimizing the cost of purchasing, outsourcing and lost demand. In order to solve the problem, three genetic metamorphic algorithms, gray wolves and antlion have been used. After examining the numerical expressions, Gray wolf's algorithm has the highest level of performance. In order to expand the applied dimensions of research in real-world conditions, a company (MPEICO) that manufactures insulators is considered as the case study. .

The goal of the dynamic facility layout problem is to find the best layout for facilities at a multi period planning horizon so that the total cost of material handling and relocating the facilities is minimized. One of the most important aspects of this issue is to consider the transporters and minimizing material handling time. This paper developed a bi-objective mathematical model which is able to simultaneously minimize the material handling costs between facilities and the cost of rearrangement facilities and material handling time. Due to probabilistic characteristic of the transporters, such as the time of handling operation and existence of the failure, calculating the time required to carry the material using analytical relationships is impossible. Therefore, this paper uses the simulation approach and artificial neural networks. In this approach, a lot of scenarios are generated by combining of variables. Each scenario shows the location of the facilities and how to perform transportation operations in each period. Then each of these scenarios is implemented through computer simulation and simulation results are considered as the response variable. Finally, using input and response variable, an artificial neural network is trained so that it can accurately estimate the time of carrying out the transportation operations. Given that the above problem is a NP-hard; this paper proposes a new Multi-Objective meta heuristic algorithm to optimize the problem and compares the performance of the proposed algorithm with existing algorithms in literature.

This paper aims to propose a knowledge sharing optimization model based on the game theory that optimizes both employer and employee(s) decisions simultaneously. This model is a bi-level programming model. The upper-level problem includes employer decision about the reward, and the lower-level problem contains employee(s) decisions about time and effort allocation to knowledge sharing activity. Mathematical formulation of the model designed based on previous literature and in the framework of Motivation-Opportunity-Ability. The proposed bi-level programming model provides a foundation to investigate more different parameters comparing with previous models introduced in the literature. This model considers opportunity and ability factors in addition to the motivation. Also, payoff functions in this model are non-linear and therefore is more consistent with real cases relative to previous linear models. Additionally, this model analyzes the effects of available time as a key factor. The bi-level model coded in GAMS using EMP syntax and solved for a set of randomly generated data using BARON algorithm. Results indicated that the increase of applicability of codified knowledge and impact coefficient of social comparison could improve organizational performance and also save the cost of reward system. Therefore, neglecting these two parameters in designing a reward system could lead to under optimized decision making. This research provides a basis to consider more parameters simultaneously and help to improve organizational decisions. However, based on the results, BARON algorithm is not efficient to solve big problems, so developing a more efficient algorithm is needed.

Today, one of the main concerns of managers interested in novel paradigms of supply chain (SC) is identification of improvement priorities and determination of the focal points to promote the performance of SC. Studying the current performance situation of effective practices in the three tiers of Kalleh SC (first tier supplier, focal firm and first tier distributor), the present paper seeks to identify key components of LARG (Lean, Agile, Resilient and Green) SC and improve them in the company under study by developing a practical and strategic decision making model. The results indicate that the greatest gap in Pole Co. lies in just-in-time production measures and the smallest gap in the ability to reduce likely risks. In Kalleh Co., the greatest gap was found in close & long term relationship with suppliers and the least one was in the development of visibilities in SC. The greatest gap in Banchow was quick responsiveness to customers and the least one was reduction of likely risks.

Refineries, including the conversion industries, use oil and gas as row materials and their production processes could be so harmful to the environment if they could not manage the products detrimental effects. Nowadays these companies are working hard to minimize the environmental damages caused by their products, production processes, supply chain, distribution methods and etc. The first step in this way, is having access to the proper method to assess the products environmental impacts. In this paper a new method is presented to evaluate the refinery products degree of greenness. The developed model consists of influencing parameters on greenness degree, which are classified in a hierarchical structure. Due to the difficulties in defining an explicit function, fuzzy controllers are implemented to infer degree of greenness based on influencing parameters values. Effectiveness of the presented model is evaluated by assessing the greenness degree of Bandar Abbas refinery products and the results reflect good performance of the developed model.

Refineries, including the conversion industries, use oil and gas as row materials and their production processes could be so harmful to the environment if they could not manage the products detrimental effects. Nowadays these companies are working hard to minimize the environmental damages caused by their products, production processes, supply chain, distribution methods and etc. The first step in this way, is having access to the proper method to assess the products environmental impacts. In this paper a new method is presented to evaluate the refinery products degree of greenness. The developed model consists of influencing parameters on greenness degree, which are classified in a hierarchical structure. Due to the difficulties in defining an explicit function, fuzzy controllers are implemented to infer degree of greenness based on influencing parameters values. Effectiveness of the presented model is evaluated by assessing the greenness degree of Bandar Abbas refinery products and the results reflect good performance of the developed model.

This study is conducted to identify and analyze the relationship between the factors affecting the distribution network design (DND) in the food distribution industry in Iran. In this paper, for the first time, the distribution network design has been classified into two main areas of the strategic structure of the distribution network and the distribution management policies and the impact of the key variables on distribution network design has been investigated. This study is descriptive, exploratory and using quantitative research method. The statistical population of this research is all 54 food distribution companies in Iran. Using a census method, a questionnaire was sent to all general and marketing managers of these companies (108 people). 92 questionnaires were completed, according to the established criteria, only 81 questionnaires could be used, which also covers the standards of Cochran formula. The partial least squares (PLS) method is used to analyze the data and the results indicate that product characteristics, service requirements, demand characteristics, supply characteristics, and economic variables are the key variables that affect the distribution network design. Given the lack of research on the distribution network design and the study of the factors affecting the formation of strategic distribution structures and policies, it can be argued that this article provides valuable insights for academics and professionals, because in the very study the various components of distribution network design, the decisions related to the design of the distribution network and the analysis of the main factors affecting the distribution network structure are presented.

Today, the organization and industries are facing with global competition. It is requirement for organization to manufacture their products in a world class level to become successful in global competition to get it, they have to be more integrated at the level of organization partners as well as supply chain. In this article, World Class Manufacturing (WCM) factors have been identified by using fuzzy Delphi Method. Dimensions and components related to integrated supply chain have also been extracted from subject literatures. They include: internal integration, supplier integration and customer integration. Importance Performance Analysis (IPA) has been used to categorize and characterize the priorities related to improvement of supply chain integration (SCI) for achieving (WCM). We used opinions of 200 experts comprising managers and politicians of Iranian domestic appliance industry as well as research final model to identify optimal strategies for managing all factors. According to the outputs of IPA matrix, final ranking of all SCI factors were identified to help managers optimally allocate resources. Results show that having stable purchasing system with main suppliers, integrated system among internal departments, level of sharing information with suppliers and information sharing systems with customers are all considered as the most important factors to improve SCI for achieving WCM in the selected industry

One of the important techniques in the field of inventory management is inventory classification. The classification method based on ABC analysis is a well-known method which sets the items in a different class, according to their importance and values. In this paper, a three-level supply chain network for a bi-objective optimization model is proposed to improve the quality of inventory grouping based on the ABC technique. The first objective maximizes the net profit of the items in the central stock (level 2) and the second objective function maximizes the net profit of items in different wards (level 3) in uncertain conditions. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the assigned items to each group based on the set of constraints, inventory shortages, ordering cost, and purchasing cost. To solve this model, the heuristic and exact methods such as LP-metric and modified ε-constraint are applied. Then for comparing those methods, the statistical hypothesis test is used. In conjunction with, the AHP technique is exerted to choose the most efficient solving method. The results show that ε-constraint has the best performance. Ultimately, the proposed model has been implemented in one set of numerical examples to show its applicability in practice.

The development of a variety of public transportation systems that cover different areas, has made it difficult for passengers and users to choose the type of transportation system and appropriate route between two specified departures. In large cities such as Tehran, a network of public transportation systems, called multi-modal systems, consist of stations as nodes and public transport vehicles intermediate between the two consecutive stations as arcs, is formed. Travelers are looking continuously for a way to find the optimal route in complex multi-modal transportation networks to reach their desired destination with minimal cost and confusion. In this paper, two linear and nonlinear multi-objective programming models with three objective functions have been developed for routing in multi-modal transport systems. The objectives of the proposed model are to minimize the cost, travel time and the number of vehicle types. By examining the validation of models by test issues, two exact and meta-heuristic algorithms (ant colony algorithm) have been developed to solve the proposed model. The results of the evaluation of the performance of the solving methods indicate that problem solving by exact method for networks with more than 15 nodes are non-operating, while the meta-heuristic algorithm provides the same problems with same precision in the exact method but with logical time.