JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2018 | Volume:14 | Issue:3|Papers Count:15

In this manuscript, the reliability of a roboticsystem has been analyzed using the available data (containingvagueness, uncertainty, etc). Quantification ofinvolved uncertainties is done through data fuzzificationusing triangular fuzzy numbers with known spreads assuggested by system experts. With fuzzified data, if theexisting fuzzy lambda– tau (FLT) technique is employed, then the computed reliability parameters have wide rangeof predictions. Therefore, decision-maker cannot suggestany specific and influential managerial strategy to preventunexpected failures and consequently to improve complexsystem performance. To overcome this problem, the presentstudy utilizes a hybridized technique. With this technique, fuzzy set theory is utilized to quantify uncertainties, fault tree is utilized for the system modeling, lambda– taumethod is utilized to formulate mathematical expressionsfor failure/repair rates of the system, and genetic algorithmis utilized to solve established nonlinear programmingproblem. Different reliability parameters of a robotic systemare computed and the results are compared with theexisting technique. The components of the robotic systemfollow exponential distribution, i. e., constant. Sensitivityanalysis is also performed and impact on system mean timebetween failures (MTBF) is addressed by varying otherreliability parameters. Based on analysis some influentialsuggestions are given to improve the system performance.

Industrial forecasting is a top-echelon researchdomain, which has over the past several years experiencedhighly provocative research discussions. The scope of thisresearch domain continues to expand due to the continuousknowledge ignition motivated by scholars in the area. So, more intelligent and intellectual contributions on currentresearch issues in the accident domain will potentially sparkmore lively academic, value-added discussions that will be ofpractical significance to members of the safety community. Inthis communication, a new grey– fuzzy– Markov time seriesmodel, developed from nondifferential grey interval analyticalframework has been presented for the first time. Thisinstrument forecasts future accident occurrences under timeinvarianceassumption. The actual contribution made in thearticle is to recognise accident occurrence patterns anddecompose theminto grey state principal pattern components. The architectural framework of the developed grey– fuzzy– Markov pattern recognition (GFMAPR) model has fourstages: fuzzification, smoothening, defuzzification andwhitenisation. The results of application of the developednovel model signify that forecasting could be effectivelycarried out under uncertain conditions and hence, positions themodel as a distinctly superior tool for accident forecastinginvestigations. The novelty of thework lies in the capability ofthe model inmaking highly accurate predictions and forecastsbased on the availability of small or incomplete accident data.

The primary objective of this research is toobtain an accurate forecasting model for the US presidentialelection. To identify a reliable model, artificial neuralnetworks (ANN) and support vector regression (SVR)models are compared based on some specified performancemeasures. Moreover, six independent variables such asGDP, unemployment rate, the president’ s approval rate, and others are considered in a stepwise regression toidentify significant variables. The president’ s approval rateis identified as the most significant variable, based onwhich eight other variables are identified and considered inthe model development. Preprocessing methods are appliedto prepare the data for the learning algorithms. The proposedprocedure significantly increases the accuracy of themodel by 50%. The learning algorithms (ANN and SVR)proved to be superior to linear regression based on eachmethod’ s calculated performance measures. The SVRmodel is identified as the most accurate model among theother models as this model successfully predicted theoutcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantlyincreases the accuracy of the forecast.

DEA models help a DMU to detect its (in-)efficiencyand to improve activities, if necessary. Efficiencyis only one economic aim for a decision-maker; however, up-or downsizing might be a second one. Improvingefficiency is the main topic in DEA; the long-term strategytowards the right production size should attract our attentionas well. Not always the management of a DMU primarilyfocuses on technical efficiency but rather isinterested in gaining scale effects. In this paper, a formulafor returns to scale (RTS) is developed, and this formula iseven applicable for interior points of technology. Particularly, technical and scale inefficient DMUs need sophisticatedinstruments to improve their situation. ConsideringRTS as well as efficiency, in this paper, we give an advicefor each DMU to find an economically reliable path fromits actual situation to better activities and finally to mostproductive scale size (mpss), perhaps. For realizing thispath, we propose an interactive algorithm, thus harmonizingthe scientific findings and the interests of the management. Small numerical examples illustrate such pathsfor selected DMUs; an empirical application in theatremanagement completes the contribution.

Globalization, advancement of technologies, andincrement in the demand of the customer change the way ofdoing business in the companies. To overcome these barriers, the six-sigma define– measure– analyze– improve– control (DMAIC) method is most popular and useful. Thismethod helps to trim down the wastes and generating thepotential ways of improvement in the process as well asservice industries. In the current research, the DMAICmethod was used for decreasing the process variations ofbead splice causing wastage of material. This six-sigmaDMAIC research was initiated by problem identificationthrough voice of customer in the define step. The subsequentstep constitutes of gathering the specification data ofexisting tire bead. This step was followed by the analysisand improvement steps, where the six-sigma quality toolssuch as cause– effect diagram, statistical process control, and substantial analysis of existing system were implementedfor root cause identification and reduction in processvariation. The process control charts were used forsystematic observation and control the process. UtilizingDMAIC methodology, the standard deviation wasdecreased from 2. 17 to 1. 69. The process capability index(Cp) value was enhanced from 1. 65 to 2. 95 and the processperformance capability index (Cpk) value was enhancedfrom 0. 94 to 2. 66. A DMAIC methodology was establishedthat can play a key role for reducing defects in the tiremanufacturingprocess in India.

The reconfigurable vibrating screen (RVS)machine is an innovative beneficiation machine designedfor screening different mineral particles of varying sizesand volumes required by the customers’ through the geometrictransformation of its screen structure. The successfulRVS machine upkeep requires its continuous, availability, reliability and maintainability. The RVSmachine downtime, which could erupt from its breakdownand repair, must also be reduced to the barest minimum. This means, there is a need to design and develop amaintenance system model that could be used to effectivelymaintain the RVS machine when utilized in surface andunderground mines. In view of this, this paper aims todevelop a maintenance system model that could be used toeffectively maintain the RVS machine when used in surfaceand underground mines. The maintenance systemmodel unfolds the predictive (i. e. diagnosis and prognosis)algorithms, the e-maintenance strategic tools as well as thedynamic maintenance strategic algorithms required toeffectively maintain the RVS machine. Four different casestudies were presented in this paper to illustrate theapplicability of this maintenance system model in maintainingand managing the RVS machine when utilized inthe mining industries.

This paper studies a location– routing– inventoryproblem in a multi-period closed-loop supply chain withmultiple suppliers, producers, distribution centers, customers, collection centers, recovery, and recycling centers. In thissupply chain, centers are multiple levels, a price increasefactor is considered for operational costs at centers, inventoryand shortage (including lost sales and backlog) are allowed atproduction centers, arrival time of vehicles of each plant to itsdedicated distribution centers and also departure from themare considered, in such a way that the sum of system costs andthe sum of maximum time at each level should be minimized. The aforementioned problem is formulated in the form of abi-objective nonlinear integer programming model. Due tothe NP-hard nature of the problem, two meta-heuristics, namely, non-dominated sorting genetic algorithm (NSGA-II)and multi-objective particle swarm optimization (MOPSO), are used in large sizes. In addition, a Taguchi method is usedto set the parameters of these algorithms to enhance theirperformance. To evaluate the efficiency of the proposedalgorithms, the results for small-sized problems are comparedwith the results of the e-constraint method. Finally, fourmeasuring metrics, namely, the number of Pareto solutions, mean ideal distance, spacing metric, and quality metric, areused to compare NSGA-II and MOPSO.

Flexible flow shop (or a hybrid flow shop)scheduling problem is an extension of classical flow shopscheduling problem. In a simple flow shop configuration, ajob having ‘ g’ operations is performed on ‘ g’ operationcentres (stages) with each stage having only one machine. If any stage contains more than one machine for providingalternate processing facility, then the problem becomes aflexible flow shop problem (FFSP). FFSP which containsall the complexities involved in a simple flow shop andparallel machine scheduling problems is a well-known NPhard(Non-deterministic polynomial time) problem. Owingto high computational complexity involved in solving theseproblems, it is not always possible to obtain an optimalsolution in a reasonable computation time. To obtain nearoptimalsolutions in a reasonable computation time, a largevariety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solvingFFSP is rather tricky and time consuming. To address thislimitation, teaching– learning-based optimization (TLBO)and JAYA algorithm are chosen for the study because theseare not only recent meta-heuristics but they do not requiretuning of algorithm-specific parameters. Although thesealgorithms seem to be elegant, they lose solution diversityafter few iterations and get trapped at the local optima. Toalleviate such drawback, a new local search procedure isproposed in this paper to improve the solution quality. Further, mutation strategy (inspired from geneticalgorithm) is incorporated in the basic algorithm to maintainsolution diversity in the population. Computationalexperiments have been conducted on standard benchmarkproblems to calculate makespan and computational time. Itis found that the rate of convergence of TLBO is superiorto JAYA. From the results, it is found that TLBO andJAYA outperform many algorithms reported in the literatureand can be treated as efficient methods for solving theFFSP.

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested onthe best portfolio of projects in each period such that the net profit is maximized. We also consider more realisticassumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model ispresented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each timeperiod. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedureis presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solvethis type of problems. The GA is enhanced by a new solution representation and well selected operators. It also ishybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposedalgorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization(PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation resultsshow the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, theproposed algorithm is wisely combined with PSO to improve the computing time considerably.

Facility location problem (FLP) is a mathematicalway to optimally locate facilities within a set of candidatesto satisfy the requirements of a given set of clients. This study addressed the uncapacitated FLP as it assuresthat the capacity of every selected facility is finite. Thus, even if the demand is not known, which often is the case, inreality, organizations may still be able to take strategicdecisions such as locating the facilities. There are differentapproaches relevant to the uncapacitated FLP. Here, thecuckoo search via Le´ vy flight (CS-LF) was used to solvethe problem. Though hybrid methods produce betterresults, this study employed CS-LF to determine first itspotential in finding solutions for the problem, particularlywhen applied to a real-world problem. The method wasapplied to the data set obtained from a department store inDavao City, Philippines. Results showed that applying CSLFyielded better facility locations compared to particleswarm optimization and other existing algorithms. Although these results showed that CS-LF is a promisingmethod to solve this particular problem, further studies onother FLP are recommended to establish a strong foundationof the capability of CS-LF in solving FLP.

This paper presents a dual-objective facilityprogramming model for a green supply chain network. Themain objectives of the presented model are minimizingoverall expenditure and negative environmental impacts ofthe supply chain. This study contributes to the existingliterature by incorporating uncertainty in customer demand, suppliers, production, and casting capacity. An industrialcase study is also analyzed to reveal the feasibility of theproposed model and its application. A fuzzy approachwhich is known as TH is used to solve the suggested dualobjectivemodel. TH approach is integration of a max– minmethod (LH) and modified version of Werners’ approach(MW). The outcome of this study reveals that the presentedmodel can support green supply chain network in differentlevels of uncertainty. In presented model, cost and negativeenvironmental impacts derived from the supply chain networkwill increase of higher levels of uncertainty.

In the current global market, organizations use many promotional tools to increase their sales. One such tool is sales teams’ initiatives or promotional policies, i. e., free gifts, discounts, packaging, etc. This phenomenon motivates the retailer/orbuyer to order a large inventory lot so as to take full benefit of promotional policies. In view of this the present paperconsiders a two-warehouse (owned and rented) inventory problem for a non-instantaneous deteriorating item with inflationand time value of money over a finite planning horizon. Here, demand depends on the sales team’ s initiatives and shortagesare partially backlogged at a rate dependent on the duration of waiting time up to the arrival of next lot. We design analgorithm to obtain the optimal replenishment strategies. Numerical analysis is also given to show the applicability of theproposed model in real-world two-warehouse inventory problems.

Nowadays, with respect to knowledge growthabout enterprise sustainability, sustainable supplier selectionis considered a vital factor in sustainable supply chainmanagement. On the other hand, usually in real problems, the data are imprecise. One method that is helpful for theevaluation and selection of the sustainable supplier and hasthe ability to use a variety of data types is data envelopmentanalysis (DEA). In the present article, first, the supplierefficiency is measured with respect to all economic, social and environmental dimensions using DEA andapplying imprecise data. Then, to have a general evaluationof the suppliers, the DEA model is developed usingimprecise data based on goal programming (GP). Integratingthe set of criteria changes the new model into acoherent framework for sustainable supplier selection. Moreover, employing this model in a multilateral sustainablesupplier selection can be an incentive for the suppliersto move towards environmental, social and economicactivities. Improving environmental, economic and socialperformance will mean improving the supply chain performance. Finally, the application of the proposedapproach is presented with a real dataset.

The cell formation (CF) is one of the mostimportant steps in the design of a cellular manufacturingsystem (CMS), which it includes machines’ grouping incells and part grouping as separate families, so that thecosts are minimized. The various aspects of the problemshould be considered in a CF. The machine reliability andthe tool assigned to them are the most important problemswhich have to be modeled correctly. Another importantaspect in CMS is material handling costs that they consistof inter-cell and intra-cell movement costs. Moreover, setup and tool replacement costs can be effective in CFdecision making. It is obvious that CF cannot be completedwithout considering the number of demand. With consideringof all of the above aspects, an extended linear integerprogramming is represented for solving the cell formationproblem (CFP) in this study. The objective is to minimizethe sum of inter-cell movement, intra-cell movement, toolreplacement, machine breakdown, and setup costs. In theother terms, for states that cost of movement is higher thantool-changing cost, although a part can have the inter-and/or intra-cell movements, the model tries to find a solutionwhich part is allocated to one cell and with changing thetools, processes of that part is completed. In addition, tovalidate the model and show its efficiency and performance, several examples are solved by branch and bound(B&B) method.

The purpose of this research was to study therecognition, application and quantification of the risksassociated in managing projects. In this research, themanagement of risks in an oil and gas project is studied andimplemented within a case company in Oman. In thisstudy, at first, the qualitative data related to risks in theproject were identified through field visits and extensiveinterviews. These data were then translated into numericalvalues based on the expert’ s opinion. Further, the numericaldata were used as an input to Monte Carlo simulation. RiskyProject ProfessionalTM software was used to simulatethe system based on the identified risks. The simulationresult predicted a delay of about 2 years as a worse casewith no chance of meeting the project’ s on stream date. Also, it has predicted 8% chance of exceeding the totalestimated budget. The result of numerical analysis from theproposed model is validated by comparing it with the resultof qualitative analysis, which was obtained through discussionwith various project managers of company.