Journal of Applied Research on Industrial Engineering
Volume:10 Issue: 1, Winter 2023

Neutrosophic set theory plays an important role in dealing with the impreciseness and inconsistency in data encountered in solving real-life problems. The current paper focuses on the neutrosophic fuzzy multiobjective linear programming problem (NFMOLPP), where the coefficients of the objective functions, constraints, and right-hand side parameters are single-valued trapezoidal neutrosophic numbers (NNs). From the viewpoint of complexity of the problem, a ranking function of NNs is proposed to convert the problem into equivalent MOLPPs with crisp parameters. Then suitable membership functions for each objective are formulated using their lowest and highest value. With the aim of linear programming techniques, a compromise optimal solution of NFMOLPP is obtained. The main advantage of the proposed approach is that it obtains a compromise solution by optimizing truth-membership, indeterminacy-membership, and falsity-membership functions, simultaneously. Finally, a transportation problem is introduced as an application to illustrate the utility and practicality of the approach.

The aim of analyzing passengers' behavioral patterns is providing support for transportation management. In other words, to improve services like scheduling, evacuation policies, and marketing, it is essential to understand spatial and temporal patterns of passengers' trips. Smart Card Automated Fare Collection System (SCAFCS) makes it possible to utilize data mining tools for the purpose of passengers' behavioral pattern analysis. The specific goal of this research is to obtain functional information for passenger's behavioral pattern analysis in city express bus which is called BRT, and classification of passengers to improve performance of bus fast transportation system. Additionally, it is attempted to predict usage and traffic status in a line through predicting passenger's behavior in a bus line. In this paper, smart card data is applied to provide combinational algorithms for clustering and analysis based on data mining. To this end, we have used a combination of data mining methods and particle swarm optimisation algorithm and leveraged multivariate time series prediction to estimate behavioral patterns. Results show that price and compression ratio features are the most influencing features in the separability of transportation smart card data. According to obtained Pareto front, four features include a card identification number, bus identification number, bus line number, and charge times are influencing clustering criteria.

One of the most important decisions taken in a supply chain is the issue of aggregate production planning where a program-within a medium time-range-- is determined for optimum manufacturing of all products using shared equipment and resources. This research presents a multi-objective model that helps the decision makers to make such decisions. The proposed model comprises four main objectives, the first one of which considers minimizing costs (including costs of manufacturing product, supplying, maintenance, inventory stock shortage, and expenditures related to man power). The second objective is defined as maximizing customers’ satisfaction. Minimizing suppliers’ satisfaction makes up the third objective and maximizing the quality of the manufactured products constitutes the fourth objective. In this model, the demand parameter is investigated under uncertain conditions; hence, other parameters influenced by this parameter are also presented under uncertain conditions occurring within three differing scenarios. This model is solved through LP- metric and the LINGO v14.0.1.55 software. At first the model is solved by means of numerical example; then it is solved by the actual data that are related to a military industry. Finally, process, variables like inventory level, overtime work hours etc, are valued with the help of closed-loop supply chain of the proposed model.

Root cause analysis techniques are often applied to problems in the workplace; however, they may also prove very useful to home projects. This research explores the application of two root cause analysis techniques in home projects: (1) 5 Whys to determine the root cause of a home air conditioning unit that runs continuously but does not cool, and (2) an innovative Lean PFMEA to repair a John Deere riding mower that starts, then stops. Employing the 5 Whys technique led to the discovery of incorrect color-coded wiring from the original air conditioning unit to the thermostat. Lean PFMEA enabled a correct diagnosis and resolution of the mower start/stop issue via a Kaizen event, grass clippings in the fuel line, which was remedied by cleaning the fuel tank and replacing the fuel lines, fuel filter, and carburetor. These techniques provide Lean methodological approaches to problem-solving, which often leads to reduced homeowner aggravation, repair time, and repair expense.

This paper provides an integrated model for a two-stage assembly flow shop scheduling problem and distribution through vehicle routing in a soft time window. So, a mixed-integer linear programming (MILP) model has been proposed with the objective of minimizing the total cost of distribution, holding of products, and penalties of violating delivery time windows. To solve this problem, an improved meta-heuristic algorithm based on whale optimization algorithm (WOA) has been developed. A comparison of the integrated and non-integrated model in a case study of industrial gearboxes production shows that the integrated model compared to the non-integrated model has saved 15.6% and 13.6% in terms of delay time and total costs, respectively. Computational experiments also indicate the efficiency of improved WOA in converging to optimal solution and achieve better solution in comparison to the genetic algorithm (GA).

In traditional cost-efficiency models, inputs and outputs, as well as input prices were known as constant values for each decision-making unit In our daily applications, however, market entry prices vary at different times. In other words, input prices for decision-making units (DMUs) are time dependent. Traditional methods cannot calculate the cost efficiency of DMUs with time-dependent prices. This paper proposes a new method to calculate the cost efficiency of DMUs in the presence of time-dependent prices. The proposed model is a parametric programming problem model depending on time. In the presented model, the inputs and outputs are functions in terms of time, which is not present in the models introduced by other researchers. New definitions for time-dependent cost efficiency have also been introduced. The cost efficiency of DMUs is measured over a given time and the units are ranked according to the time obtained. Finally, a numerical example has been presented to illustrate the proposed method.

One of the most complex and costly systems in the industry is the Gas turbine (GT). Because of the complexity of these assets, various indicators have been used to monitor the health condition of different parts of the gas turbine. Turbine exit temperature (TET) spread is one of the significant indicators that help monitor and detect faults such as overall engine deterioration and burner fault. The goal of this article is to use data-driven approaches to monitor TET data to detect faults early, as fault detection can have a significant impact on gas turbine reliability and availability. In this study, the TET data of v94.2 GT is measured by six temperature transmitters to show a detailed profile. According to the statistical tests, TET data are high dimensional and time-dependent in the real world industry. Hence, three distinctive methods in the field of the gas turbine are proposed in this study for early fault detection. Conventional principal component analysis (PCA), moving window PCA (MWPCA), and incremental PCA (IPCA) were implemented on TET data. According to the results, the conventional PCA model is a non-adaptive method, and the false alarm rate is high due to the incompatibility of this approach and the process. The MWPCA based on V-step-ahead and IPCA approaches overcame the non-stationary problem and reduced the false alarm rate. In fact, these approaches can distinguish between the normal time-varying and slow ramp fault processes. The results showed that IPCA could detect fault situations faster than MWPCA based on V-step-ahead in this study.

Economic or local disruptions that affect organizations' production activities often result in unexpected losses. An excellent example is the recent COVID-19 pandemic disruption which affected many economies globally. This study presents a deterministic model and uses simple regression analysis to estimate the average condition for production losses. Its corresponding components' input resources impact the overall estimates for selected organizations in Nigeria. It is anticipated that variability in economic activities is always accompanied by unconventional stock returns whose behaviour indicates prevailing economic trends. Here we have looked at two organizations in the manufacturing sector as a case study; Nigerian Breweries and Nestle Nigeria, whose stock prices[X] upon analysis reveal that at[X]≤N30 and [X]≤N821 are estimated conditions for zero net profit for both organizations respectively. Therefore, for Nigerian Breweries, during the four quarters of the 2020 fiscal year, the following were assessed production losses,3.47 billion naira(Q1), 4.17 billion naira(Q2), 3.72 billion naira(Q3) and 0.68 billion naira(Q4) with a total of 12.04 billion naira annual estimated losses; with COGS,OPEX and SAEX having 39.6%,44.5% and 15.9% impact on the estimates. Nestle Nigeria records estimated production losses of 5.8 billion naira (Q1), 6.4 billion naira(Q2),4.2 billion naira(Q3), and -0.8 billion naira(Q4) (gain), resulting in a total 15.6 billion naira annual estimated loss; and COGS,OPEX, and SAEX having 45.9%, 48.2% and 5.9% impact on the estimates respectively. This implies, Selling and Advertising Expenses (SAEX) had the most negligible percentage impact on overall estimated production losses for both organizations compared to Costs of Goods Sold (COGS) and Operating Expenses (OPEX).This study, therefore, reiterates the position of other economic reports describing the adverse effects of the pandemic in Nigeria; while also serving as an investment analysis guide to potential investors..

The aim of this study is providing a developed model for SMEs' in open innovation activities. In this regard, an appropriate model was defined by studying the literature. Then, after selecting a sample of 60 small and medium enterprises the data were collected by a questionnaire and were analyzed with the Smart PLS software. In the third stage, the relative importance of factors was tested from the perspective of 10 experts in the field of open innovation along with experienced managers of the small and medium enterprises with more than 15 years of work experience with the help of ANP and PROMETHEE methods. The results showed that these factors include the parameters: Product Characteristics, Inter-organizational Factors, and Environmental Factors. In addition, the most important factors include Product Characteristics. Finally, several implications were made such as changing the degree of SMEs' participation in open innovation activities over time according to continuous monitoring of these moderators.

In this study, we model a stochastic scheduling problem for a robotic cell with two unreliable machines susceptible to breakdowns and subject to the probability of machine failure and machine repair. A single gripper robot facilitates the loading/unloading of parts and cell-internal movement. Since it is more complicated than the other cycles, the focus has been on the S_2 cycle as the most frequently employed robot movement cycle. Therefore, a multi-objective mathematical formulation is proposed to minimize cycle time and operational costs. The -constraint method is used to solve small-sized problems. Non-dominated sorting genetic algorithm II (NSGA-II), is used to solve large-sized instances based on a set of randomly generated test problems. The results of several Test problems were compared with those of the GAMS software to evaluate the algorithm's performance. The computational results indicate that the proposed algorithm performs well. Compared to GAMS software, the average results for maximum spread (D) and non-dominated solutions (NDS) are 0.02 and 0.04, respectively.