International Journal of Industrial Engineering: Theory, Applications and Practice
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A NON-LINEAR PROGRAMMING MODEL TO SOLVE MADM PROBLEMS WITH INTERVAL-VALUED INTUITIONISTIC FUZZY NUMBERS
No full textIt is believed that Multi Attribute Decision Making (MADM) problem is an ill-defined and unstructured problem. This difficulty intensifies while considering the uncertainty of decision-makers information about the problem. In recent years, interval-valued intuitionistic fuzzy sets (hereafter IVIFs), as a generalization of ordinal fuzzy sets, became a well-known and widely applied framework for dealing with the uncertainty of decision-making problems. However, the mathematical programming aspects of IVIFs, besides their applications in decision-making problems, were ignored. To reinforce the mathematical programming approach in the IVIF environment, an IVIF-MADM problem is formulated as a non-linear programming model. Using a variable transformation and the notion of aggregation operator, the proposed model is transformed into an equivalent non-linear programming model. Application of the proposed method is represented in a decision-making problem, and the results are compared with similar methods, proving the compatibility of the proposed method with previous ones. The solid and understandable logic with computational easiness are the main advantages of the proposed method
DEVELOPMENT OF SIMULATION-BASED SCHEDULER FOR BACK-END STAGES IN SEMICONDUCTOR FOUNDRY
No full textChangwon, Republic of Korea
As one of the fastest growing industries, the manufacturing processes of semiconductors have become more complex, given the need to flexibly and rapidly respond to various customer requirements. Due to the complex manufacturing processes, a customized scheduling system is required to efficiently and effectively sequence and assign the jobs to the proper machines in the front-end and back-end manufacturing processes. Compared with the technological advancements achieved in the front-end manufacturing process, the back-end manufacturing process has not been sufficiently developed technologically. In the back-end manufacturing foundry, the reduction of the turnaround time is viewed as an important step toward competing with global competitors in the current business environment. Thus, this study utilizes a simulation approach to develop a customized scheduler with customized dispatching rules, mainly focusing on a die attach process and a wire bond process, which are critical stages in the back-end manufacturing process. Two dispatching rules have been suggested with the main objective of turnaround time minimization. The first rule (Rule 1) relates to the wire bond process, while the second rule (Rule 2) relates to the die attach process. Finally, a combination of both rules is also suggested. The results of the simulation experiments demonstrate that the proposed combined rule outperforms the other popular dispatching rules in the back-end manufacturing process with regard to production rate, turnaround time, mean tardiness, and tardy rate. Furthermore, this study investigates the suitable production amount of the developed scheduler to satisfy manufacturing constraints
MODIFIED SWEEPING ALGORITHM FOR SOLVING CAPACITATED VEHICLE ROUTING PROBLEM WITH RADIAL CLUSTERED PATTERNS
Get PDFThis research proposes a modification to the Sweeping Algorithm (SWA) to solve the Capacitated Vehicle Routing Problem (CVRP) for real-world cases where locations are present on radial patterns. The SWA solves CVRP instances efficiently by performing angular sweeping to obtain clusters of locations. However, the SWA does not consider radial distances when clustering the locations, which may cause inefficient clustering when there is a presence of locations on radial patterns. Therefore, this research proposes a systematic approach to solve CVRP instances with apparent radial clusters by considering their angular and radial distances in the clustering phase. This method is configurable to locations’ geography and can handle different locations’ assignments. The experimental results indicate that the proposed heuristic outperforms SWA and its well-known variant, Sweep Nearest Neighbor (SNN), for the targeted instances designed with radial clusters. The results for CVRP benchmark instances show comparable performance when using the proposed heuristic
A TRIPARTITE EVOLUTIONARY GAME INVOLVING QUALITY REGULATION OF PREFABRICATED BUILDING PROJECTS CONSIDERING GOVERNMENT REWARDS AND PENALTIES
No full textIn recent years, there have been great efforts to develop prefabricated buildings and to promote the EPC mode. However, various types of construction quality accidents have frequently occurred, and the emergency management agency reported 272 prefabricated construction accidents in China between 2018 and 2022. To improve the quality regulation of prefabricated buildings, this paper constructs a tripartite evolutionary game model of the government, EPC general contractors, and supervision units. It analyzes the stability of the evolutionary strategies of all parties involved, and it tests the influence of the reward and punishment mechanism, rent-seeking costs, and other factors on the choice of tripartite strategies through simulation. The results show the following: When government regulators increase the punishment and set reasonable reward quotas, EPC general contractors choose to standardize construction, and supervision units supervise strictly. EPC general contractors and supervision units evolve in a positive direction if the sum of rewards and punishments is higher than the gains from speculation. Government regulation evolves to be stricter if higher authorities increase the punishment for lack of regulation
EFFICIENCY EVALUATIONS OF RAILWAYS USING RELATIONAL NETWORK DATA ENVELOPMENT ANALYSIS
No full textThe rate of increase in the world population has caused the rapid growth and differentiation of transportation systems. Therefore, it is becoming increasingly important for transportation systems to provide safe, faster, and less expensive transportation. The present study analyzed the efficiency of 14 European and Turkish railways selected from the International Union of Railways database using data from between 2000 and 2017. The system, production, and consumption process activities of these 14 railways were estimated using Relational Network Data Envelopment Analysis and interpreted with performance matrices. Significant variables affecting production, consumption, and system activities were identified by Tobit Regression Analysis. The most important variables affecting system efficiency were found to be passenger and freight train occupancy rates, gross national income per capita, and population density
DYNAMIC SIMULATION OF DUAL-CREDIT POLICY IN THE AUTOMOBILE INDUSTRY OF CHINA USING THE BASS MODEL
No full textAs a crucial emerging policy for the Chinese automotive industry to achieve energy saving and emission reduction as well as to promote the development of new energy vehicles (NEVs), the dual-credit policy has attracted considerable attention from scholars since its implementation. Nonetheless, the specific implementation outcomes have been less than satisfactory, necessitating further improvements. This paper establishes a prediction model based on the principles of the dual-credit policy and the core parameter of automobile sales using the Bass model as the system, thereby constructing a complex dynamic system composed of three subsystems to explore the effects of adjusting specific credit standards. The research findings are as follows: (1) In the initial years of policy implementation, the selection of different credit standards has little impact on the credit market. Only after more than 5 years, the differences become increasingly apparent. It is recommended that the government should persist in dynamically adjusting the credit policy. (2) Strict credit policies have a certain promoting effect on the proportion of new energy vehicles and their driving range capabilities. (3) This policy has limitations on the energy-saving and emission-reduction effects of traditional fuel vehicle enterprises
FORECASTING DEMAND LEVEL USING TIME SERIES AND MACHINE LEARNING UNDER UNCERTAINTY CONDITION
No full textDemand forecasting and studying customers’ (consumers) behaviors is of high importance. Without accurate planning for the future, organizations might risk their future. In other words, failure to have reliable planning for the future increases organizational expenses and in turn increases production costs, which is translated into a higher end-price of goods (services). Forecasting demand in the supply chain follows the same rule. In this paper, the level of customer demand in the chicken meat industry has been studied. Considering that the relationship between the decision variables is more linear or non-linear, artificial neural networks are used to analyze the data to overcome this challenge. The approach used in this study includes the combination of time series and artificial neural networks. After analyzing the data for a period of 120 days for 24 selected chicken meat stores located in the city of Arak (Iran), the future demand for each store has been determined. Forecasted demands are reported as triangular fuzzy numbers. Also, instead of point forecasting, interval forecasting has been provided and all data analysis has been done in MATLAB software
OPTIMIZING CARBON EMISSIONS AND COST IN VACCINE TRANSPORTATION: DESIGNING A TYPE-2 FUZZY ENVIRONMENT MODEL FOR EFFECTIVE VACCINE DISTRIBUTION
No full textVaccination represents one of the crowning achievements of world health. Contagious illness infestations can be controlled with the use of vaccines. During a virus outbreak, decision-making has high constraints on time and resources, and an efficacious mathematical model for vaccine transportation can assist healthcare planning organizations. The objective of this research work is to minimize the overall carbon emission, cost, and distance covered during transportation. Cost assessment and optimization are essential for the allocation of adequate monetary funds for vaccination plans. Due to non-uniform infrastructure and diverse topography, impreciseness arises thus to capture uncertainty in environment emission, cost, demand, and carbon cap parameters are regarded as gamma type-2 fuzzy variables. Chance constraint programming is employed using the critical value reduction method for the crisp conversion of fuzzy problems to crisp form. The proposed optimization-based framework is applied through a case study from the state of Uttarakhand in India
CAN MANUFACTURING OUTPUT SERVITIZATION REDUCE CARBON EMISSIONS?
Get PDFCarbon emissions from manufacturing have been a growing global concern in recent years. The growth in manufacturing firms’ service output and its carbon emission reduction effect have received less attention, though. Using data from 2008-2020 for listed companies in China, this study empirically analyzed the effects of manufacturing output servitization on carbon intensity. The results revealed a significant negative relationship between them. Heterogeneity analysis finds that the carbon emission reduction effect of manufacturing servitization is strongest in (i) private and relatively small-scale firms and (ii) developed regions and capital-intensive industries. The mediating effect study shows that green TFP and revenue growth rate are the transmission channels for the environmental impact of manufacturing servitization. This study verifies that servitization is a feasible path to coordinate high-quality economic development with resource and environmental constraints from different perspectives to provide a reference for the realistic development of diverse economies
AGENT-BASED MODELING OF URBAN RECOVERY: CONSTRUCTING AN UMBRELLA OF RECOVERY PLANS VIA VIRTUAL CITY TWIN AND ITERATIVE ALGORITHM APPROACH
No full textThis work presents a generalized iterative algorithm of short-term recovery management after large-scale disasters. The proposed method enables the coordination of reconstruction processes when the initial information is fluid and uncertain. The demonstrated algorithm supplies the recovery administration with a set of generated recovery plans, which may be compared from multiple perspectives. The city is modeled adopting the PCANS framework (Krackhardt et al., 1988), enabling to simulate of the complex interactions between individual recovery agents and recovery teams, recovery teams and individual infrastructure links, and between individual infrastructure links and the performance of a particular infrastructure or the whole city. The resulting model forming a virtual twin of an urban area is analyzed numerically with a set of different restoration strategies and comparing the efficiency of the resulting plans. As a result, the recovery coordinator is provided with a set of optional restoration strategies, which enables avoidance of the delay of recovery initiation caused by the ambiguity of the initial state of the system and the impossibility of defining an optimal recovery plan during the short-term recovery phase