International Journal of Industrial Engineering & Production Research

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     Fully fuzzy linear programming is applied to water resources management due to its close connection with human life, which is considered to be of great importance. This paper investigates the decision-making concerning water resources management under uncertainty based on two-stage stochastic fuzzy linear programming. A solution method for solving the problem with fuzziness in relations is suggested to prove its applicability. The purpose of the method is to generate a set of solutions for water resources planning that helps the decision-maker make a tradeoff between economic efficiency and risk violation of the constraints. Finally, a numerical example is given and is approached by the proposed method.
 

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 Sequence dependent set-up times scheduling problems (SDSTs), availability constraint and transportation times are interesting and important issues in production management, which are often addressed separately. In this paper, the SDSTs job shop scheduling problem with position-based learning effects, job-dependent transportation times and multiple preventive maintenance activities is studied. Due to learning effects, jobs processing times are not fixed during plan horizon and each machine has predetermined number of preventive maintenance activities. A novel mixed integer linear programming model is proposed to formulate the problem for minimizing Make Span. Owing to the high complexity of the problem; we applied Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) to find nearly optimal solutions for medium and large instances. Finally, the computational Results are provided for evaluating the performance and effectiveness of the proposed solution approaches.

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Crude oil production output forecast is very important in the formulation of genuine and suitable production policies; it is pivotal in planning and decision making. This paper explores the use of forecasting techniques to assist the oil field manager in decision making. In this analysis, statistical models of projected trends which involves graphical, least squares, simple moving average and exponential smoothing methods were compared. The least squares method was found to be most suitable to capture the recent random nature of crude oil production output in the oilfield of the Niger Delta region of Nigeria. In addition, a multiple linear regression model was developed for predicting daily, weekly, monthly or even yearly volume of crude oil production output in the oilfield facility.

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   This paper deals with a multi- objective linear fractional programming problem involving probabilistic parameters in the right- hand side of the constraints. These probabilistic parameters are randomly distributed with known means and variances through the use of Uniform and Exponential Distributions. After converting the probabilistic problem into an equivalent deterministic problem, a fuzzy programming approach is applied by defining a membership function. A linear membership function is being used for obtaining an optimal compromise solution. The stability set of the first kind without differentiability corresponding to the obtained optimal compromise solution is determined. A solution procedure for obtaining an optimal compromise solution and the stability set of the first kind is presented. Finally, a numerical example is given to clarify the practically and the efficiency of the study.
 

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   This paper aims to study multi- objective assignment (NMOAS) problem with imprecise costs instead of its prices information. The NMOAS problem is considered by incorporating single valued trapezoidal neutrosophic numbers in the elements of cost matrices. After converting the NMOAS problem into the corresponding crisp multiobjective assignment (MOAS) problem based on the score function, an approach to find the most preferred neutrosophic solution is discussed. The approach is used through a weighting Tchebycheff problem which is applied by defining relative weights and ideal targets.  The advantage of this approach is more flexible than the standard multi- objective assignment problem, where it allows the decision maker (DM) to choose the targets he is willing. Finally, a numerical example is given to illustrate the utility, effectiveness and applicability of the approach.
 
  
 

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The number of natural disasters and people affected by them has been increasing in recent years. The field of optimization is a significant element of a relief operation and has been extensively studied so far, especially during the last two decades. The design of a relief logistic network as a strategic decision and the relief distribution as an operational decision are the most important activities for disaster operation management before and after a disaster occurs. In the proposed mathematical model, pre-disaster decisions are determined according to the post-disaster decisions in a multi-stage stochastic problem. Then a well-known approach called branch and fixed coordination are applied to optimize the proposed model. The computational results confirm that the proposed approach has proper performance for disaster management in a multi-stage stochastic problem.

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A non-linear bi-level problem is suggested in this paper for wildfire self-evacuation planning, the upper problem of which includes binary variables and the lower problem includes continuous variables. In this model, the upper problem selects a number of links and adds them to the available evacuation network. It, moreover, predicts the traffic balance, and the time window of the links in the lower problem. A part of the objective function in the bi-level problem is non-linear which is linearized with a linear approximation method that does not require binary variables. Then the linear bi-level model is reformulated as a non- linear single level problem. This model is linearized and transferred into Mixed Integer Programing. The model is then used for the real case study of the Beechworth fire in 2009. The resulted outputs of the model are beneficial in planning design schemes for emergency evacuation to use the maximum potential of the available transportation network.

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So far, numerous studies have been developed to evaluate the performance of “Decision-Making Units (DMUs)” through “Data Envelopment Analysis (DEA)” and “Network Data Envelopment Analysis (NDEA)” models in different places, but most of these studies have measured the performance of DMUs by efficiency criteria. The productivity is considered as a key factor in the success and development of DMUs and its evaluation is more comprehensive than efficiency evaluation. Recently, studies have been developed to evaluate the productivity of DMUs through the mentioned models but firstly, the number of these studies especially in NDEA models is scarce, and secondly, productivity in these studies is often evaluated through the “productivity indexes”. These indexes require at least two time periods and also the two important elements of efficiency and effectiveness in these studies are not significantly evident. So, the purpose of this study is to develop a new approach in the NDEA models using “Multi-Objective Programming (MOP)” method in order to measure productivity of DMUs through efficiency and effectiveness “simultaneously, in one stage, in a period, and interdependently”. “Simultaneous and single-stage” study provides the advantage of sensitivity analysis in the model. One case study demonstrates application of the proposed approach in the branches of a Bank. Using proposed approach revealed that it is possible for a branch to be efficient by considering its subdivisions separately but not be efficient by considering the conjunction between its subdivisions. In addition, a branch may be efficient by considering the conjunction between its subdivisions but not be productive. Efficient branches are not necessarily productive, but productive branches are also efficient.
 

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One of the challenging issues in today’s competitive world for servicing companies is uncertainty in some factors or parameters that they often derive from fluctuations of market price and other reasons. With regard to this subject, it would be essential to provide robust solutions in uncertain situations. This paper addresses an open vehicle routing problem with demand uncertainty and cost of vehicle uncertainty. Bertsimas and Sim’s method has been applied to deal with uncertainty in this paper. In addition, a deterministic model of open vehicle routing problem is developed to present a robust counterpart model. The deterministic and the robust model is solved by GAMS software. Then, the mean and standard deviations of obtained solutions were compared in different uncertainty levels in numerous numerical examples to investigate the performance of the developed robust model and deterministic model. The computational results show that the robust model has a better performance than the solutions obtained by the deterministic model.
 

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Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the Malmquist productivity index. In this paper, we focus on the role of physical space facilities of bank branches as a factor affecting the decomposition of Malmquist productivity index. First, we propose a new model applying weight restrictions in basic DEA models for constant returns to scale technologies. The weight restrictions increase the discrimination power of basic DEA models. Then the new model is used to develop an extended Malmquist index, which gives a novel decomposition describing the roll of bank branch facilities on productivity growth or decline. The validity of proposed method is confirmed with a real data of 74 commercial bank branches in two time periods 2017 and 2018 and the results for both traditional and extended Malmquist index are analyzed.

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Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

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Operating rooms have become the most important areas in hospitals because of the scarcity and cost of resources. The present study investigates operating room scheduling and rescheduling considering the priority of surgical patients in a specialized hospital. The ultimate purpose of scheduling is to minimize patient waiting time, surgeon idle time between surgeries, and penalties for deviations from operating room preferences. A mathematical programming model is presented to solve the problem. Because the problem is strongly NP-hard, two heuristic algorithms are presented. A heuristic algorithm based on a mathematical programming model with local search obtains near-optimal solutions for all the samples. The average relative deviation of this algorithm is 0.02%. In continuous, heuristic algorithms performance have been investigated by increasing the number of patients and reduce the number of recovery beds. Next, a rescheduling heuristic algorithm is presented to deal with real-time situations. This algorithm presents fewer changes resulting from rescheduling in comparison with the scheduling problem.

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Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The parameters of both algorithms are calibrated using the Taguchi method. The performance of two proposed algorithms is evaluated by 10 real patients with liver cancer disease. The statistical analysis of results using paired samples t-test demonstrates the outperformance of the PSO algorithm compared to differential evolution, in terms of both the treatment plan quality and the computational time. Finally, a sensitivity analysis is performed to provide more insights about the performance of algorithms and the results revealed that increasing the number of beam angles and allowable apertures improve the treatment quality with a computational cost.
 

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Flood is a serious problem that can occur in many countries in the world. For tropical countries such as Indonesia, flooding is generally caused by rainfall that is high above normal. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. Considering that there are so many losses caused by flooding, it is necessary to have a disaster mitigation effort to minimize the possible risk of flooding. One of the risk mitigations due to natural disasters is to buy insurance products. However, not all people buy flood-impacted insurance products because of their economic and social factors. This research aims to create a model with Probit Regression Model to determine the factors that influence Indonesian's interest to buy flood-impacted insurance products. Furthermore, this study conducts a test. The results show that from the 19 factors used, eight factors significantly affect Indonesia's interest in purchasing flood-impacted insurance products. In the end, this research calculates the level of model accuracy and obtained 84.3%.
 

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The Coronavirus (Covid-19) has become a threat to the world. The government has implemented various policies to prevent its spread, such as self-isolation, social distancing, etc. The regulation turned out to pose a big threat to many companies, especially in the retail sector. To survive in a pandemic, the company needs to ensure brand loyalty as an important factor in maintaining company stability. This study aims to determine the effect of Corporate Social Responsibility, Service Quality, Customer Satisfaction on Brand Loyalty, and the effect of Service Quality on Customer Satisfaction in coffee shop brands from the US. The method used is descriptive quantitative with 100 respondents from Greater Jakarta. The findings show that Corporate Social Responsibility and Service Quality do not directly influence Brand Loyalty, while Customer Satisfaction has a positive and significant relationship with Brand Loyalty. Meanwhile, Service Quality affects Customer Satisfaction positively and significantly.

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The dynamic among farmer institutions has essential problems to be addressed, especially regarding the pattern and process of communication interactions developing farmer institutions. Therefore, an assembly of agribusiness information within the communication network of the farmer group is of primary interest for our study. This study aims to analyze the agribusiness network structure of beef cattle farmer groups in Subang Regency, West Java, Indonesia. The Social Network Analysis (SNA) used for discovering communication network structure. Data was collected through interviews using a questionnaire. The census method was used for the sampling technique and UCINET 6 used to analyze the data. The results of the study show: 1) The degree centrality and net draw illustrate the head of farmer groups still plays a role as a source of information for their members even if members can access 1-3 other sources, 2) The closeness centrality average is still high and approaching its maximum. The limitation of this study is that only in quantitative approach. Therefore, it is recommended to conduct further research in a qualitative approach to further analyze the roles play in the networks that can be considered in increasing group social capital.

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The main objective of this study is to determine factors influencing the adoption and impact of online social networks use in terms of performance among students within public universities in Abu Dhabi. Although various limitations exist, the findings have been encouraging, as it has managed to shed some lights on new variables affecting the use of online social networks. This study proposed an extended model of the Unified Theory of Acceptance & use of Technology (UTAUT) and found that five variables play an important role to determine the performance impact of online social networks namely performance expectancy, effort expectancy, social influence, facilitating conditions, and actual usage, in addition to the significant moderation role that service quality plays in the model which was significant on two relationships and insignificant in the remaining two. The findings of this study can provide policymakers with important insights on how to more successfully incorporate online social networks to improve students’ performance and public university services, and how to encourage the management to ensure that students are more likely to utilize new technologies and thereby enabling  better learning outcome, wider reach of services, gives students more control over their daily tasks and enhances their performance.

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The purpose behind this research work is to develop a GREEN performance metrics for wider firms. The metrics for wider firms is developed by using the nine independent and one dependent variable. The five independent variables include job position, recruitment, selection, training and development, performance assessment, rewards, team formation, organizational culture management and organizational learning management. The dependent variable is perceived performance. The instruments that are used for data collection include questionnaires and survey forms. The sample size is 200 out of which actual respondents are 150. The SPSS is used for analysis. Regression analysis, descriptive analysis and correlation are run to find the relation and impact of one variable over other and with perceived performance. Findings include the development of GREEN performance metrics for wider firms. The future studies may include the formation of green teams, employee motivation to be green, GHRM in services sector, GHRM and organizational culture management etc.

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This research aimed 1) to study the effects of the factors influencing the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand; 2) to study the development of a model for factors affecting the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand; and 3) to study the guideline and development of the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand. The study employed the quantitative research methodology and the statistical devices of percentage and Structural Equation Modeling (SEM). The population and sample group comprised executives in the ceramic industry in Thailand.
The findings revealed the following: 1) The factors concerning knowledge of information technology, warehouse management and digital system positively affected the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand at the statistically significant levels of β = 0.324, 0.163 and 0.271 respectively. The antecedent variables which had Direct Effect (DE) and Total Effect (TE) on the latent variable of the performance of warehouse management to increase the competitiveness were 1. knowledge of information technology (DE = 0.324, and TE = 0.324), 2. warehouse management (DE = 0.271, and TE = 0.271), and 3. digital system (DE = 0.163, and TE = 0.163) respectively; 2) The results of the study of the guideline and development of a model for factors affecting the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand revealed that the factors concerning knowledge of information technology, warehouse management and digital system were well-fitted with the empirical data at statistically significant levels; and 3) The factors concerning knowledge of information technology, warehouse management and digital system contributed to the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand in terms of speed, time and customer service.

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This research paper aims to examine the impact of the COVID-19 pandemic on consumer behaviour and the strategic adjustment implemented by small to medium-size businesses. Consumer behaviour has been altered. It has made organizations react to survive. To understand emerging consumer behaviour, and how organizations mitigate the changes in the environment, a qualitative study on small to medium size business owners was conducted in October-November 2020. An intensive 60-minute, semi-structured interview was conducted with 23 business owners in Jakarta and its surrounding cities. The findings revealed that there are positive and negative impacts of the COVID-19 pandemic on business depending on the industry type. The type of industry also influenced the scale of the effect. Regardless of the impact, most business owners were optimistic about their businesses surviving. Consumer behaviour changed to involving less human interaction, for example going online, and people became more cost-conscious. Business owners mitigated the change with a change in the type of products offered, offering promotions or price reductions and online access. From the business owners' perspective, some of the new behaviour will remain after the pandemic, whilst others will revert to the old behaviour. Those that offer convenience and simplicity will stay.