Journal of Optimization in Industrial Engineering
Volume:13 Issue: 28, Summer and Autumn 2020

Multitasking is an important part of today’s manufacturing plants. Multitask machine tools are capable of processing multiple operations at the same time by applying a different set of part and tool holding devices. Mill-turns are multitask machines with the ability to perform a variety of operations with considerable accuracy and agility. One critical factor in simultaneous machining is to create a schedule for different operations to be completed in minimum make-span. A Mixed Integer Linear Programming (MILP) model is developed to address the machine scheduling problem. The adopted assumptions are more realistic when compared with the previous models. The model allows for processing multiple operations simultaneously on a single part; parts are being processed on the same setup and multiple turrets can process a single operation of a single job simultaneously performing multiple depths of cut. A Simulated Annealing algorithm with a novel initial solution and assignment approach is developed to solve large instances of the problem.

Proper planning of assembly lines is one of the production managers’ concerns at the tactical level so that it would be possible to use the machine capacity, reduce operating costs and deliver customer orders on time. The lack of an efficient method in balancing assembly line can create threatening problems for manufacturing organizations. The use of assembly line balancing methods cannot balance load distribution on the assembly line which can cause higher depreciation of machinery heir related problems in the assembly line. In the present paper Robin Hood and Johnson algorithm combined approach is used for the problem of load distribution and optimized online load balance, based on the optimized order of works on the assembly line for polymer cover print in Iran Plot Company the results of which are analyzed in terms of creating load balance in the assembly line. The results that we have obtained in the present study include the uniform load distribution of orders to the machines and the presence of reservations and backup machine that causes the production process to continue with a proper sequence and the priority without any interruption.

The selection and implantation of sufficient and appropriate continuous improvement strategy are the key success factors for improving firm performance and enhancement of competitive advantage on manufacturing industries. As a result special role are given to Continuous improvement programs such as Supply Chain Management (SCM), Six-Sigma, Total Quality Management (TQM), Kaizen, Just-in-Time (JIT) and Total Productive Maintenance (TPM) in a production system, which attain sustainable business environment and competitive advantage. Thus, the aim of the study is to study the challenges and trends of global competitiveness of basic metal industries, investigates the way of integrating continuous improvement tools and then developed an integrated continuous improvement model from TQM, JIT, and SCM to improve the global competitiveness of steel and metal industries. From numerous contentious improvement programs, we have to emphasize integrated SCM, JIT and TQM implementation on basic metal industries to improve competitive performance. Mainly the study was conducted through field observation, questioner, company reports used as primary data and literature review of research articles, books, manuals, magazines, and electronic-sources, which used as secondary data. The survey data is analyzed using descriptive analysis, SPCT (fishbone diagram).The result found that poor supply chain system, workforce, raw material uncertainty, energy fluctuation, outdated technology, manufacturing systems, financial and logistics problems were identified as the obstacles and influence the Ethiopian basic metal industry performance and global competitiveness. Thus, to tackle the problems an integrated continuous improving model was developed to improve the performance and global competitiveness of basic metal industries.

Energy is one of the primary inputs in textile processing industries that have a significant impact on the cost of a cotton product. The energy cost-share is reported between 5-10% of the total production cost of woven cotton fabric in textile factories of developed countries. However, it is far higher in developing countries. This study aims to contribute to the understanding of energy use and energy efficiency in Ethiopian cotton textile industries through multi-level comparisons and analyses. Determination of specific energy consumption, specific cost-share and specific energy consumption and cost estimation model is developed in cotton textile processing industries of Ethiopia to show the level of energy utilization practice effectiveness and to point out specific energy conservation measure. Actual and designed energy consumption data has been gathered from machine nameplate and factory design documents. Then important performance indicating data is collected through on-site measurement. This research pointed out that, the actual energy consumption per unit textile product is higher than the estimated consumption of each involved textile processing stage. The cost-share of energy in Ethiopian cotton textile industries accounts for an average of 16.01% of the total production cost of a cotton product and it is the second-highest cost of a product next to cotton. This indicates the existing of poor energy management practices in the textile industries. As a result, they face high production costs, poor product quality, and non-conducive working environment. This study shows that any productivity improvement measure in the textile industries of Ethiopia has to give more emphasis on the reduction of energy cost than any other production inputs.

The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projects’ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.The earned value technique is a crucial and important technique in analysis and control the performance and progress of mega projects by integrating three elements of them, i.e., time, cost and scope. This paper proposes a new version of earned value analysis (EVA) to handle uncertainty in mega projects under interval-valued fuzzy (IVF)-environment. Considering that uncertainty is very common in mega projects’ activities, the proposed IVF-EVA model is very useful and applicable in evaluating the progress of projects. In this paper, analyzing earned value indices and calculating them with linguistic terms have been discussed. They are then converted into interval-valued fuzzy numbers (IVFNs) for the evaluations. Finally, an application example from the recent literature is presented and steps of the proposed IVF-EVA are elaborated.

The hub location problem is employed for many real applications, including delivery, airline and telecommunication systems and so on. This work investigates on hierarchical hub network in which a three-level network is developed. The central hubs are considered at the first level, at the second level, hubs are assumed which are allocated to central hubs and the remaining nodes are at the third level. In this research, a novel multi-product multi-objective model for capacitated hierarchical hub location problem with maximal covering under fuzzy condition first is suggested. Cost, time, hub and central hub capacities are considered as fuzzy parameters, whereas manyparameters are uncertainty and indeterministic in the real world. To solve the proposed fuzzy possibilistic multi-objective model, first, the model is converted to the equivalent auxiliary crisp model by hybrid method and then is solved by two meta-heuristic algorithms such as Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) using MATLAB software The statistical results report that there is no significant difference between means of two algorithms exception CPU time criteria. In general, in order to show efficiency of two algorithms, we used Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the resultsclearly show that the efficiency of NRGA is better than NSGA-II and finally, figures are achieved  by MATLAB software that analyze the conflicting between two objectives.

One of the solutions for organizations to succeed in highly competitive markets is to move toward emerging sciences. These areas provide many opportunities, but, if organizations do not meet requirements of emerging sciences, they may fail and eventually, may enter a crisis. In this matter, one of the important requirements is to develop suitable roadmaps in variety fields such as strategic, capability, product, technology and etc. In this subject, it should be mentioned that there are connections among types of roadmaps, therefore, organizations need to understand these relations. According to this, we propose a model which describes relations among the roadmaps based on braid theory and appropriate methods for developing those roadmaps. The validity of developed model was conducted through the Delphi method. Additionally, we illustrate a to design a roadmap and implement it in quantum computing as an emerging science.

Based on the findings of Massachusetts Institute of Technology, organizations’ data double every five years. However, the rate of using data is 0.3. Nowadays, data mining tools have greatly facilitated the process of knowledge extraction from a welter of data. This paper presents a hybrid model using data gathered from an ATM manufacturing company. The steps of the research are based on CRISP-DM. Therefore, based on the first step, business understanding, the company and its different units were studied. After business understanding, the data collected from sale's unit were prepared for preprocess. While preprocessing, data from some columns of dataset, based on their types and purpose of the research, were either categorized or coded. Then, the data have been inserted into Clementine software, which resulted in modeling and pattern discovery. The results clearly state that, the same Machines’ Code and the same customers in different provinces are struggling with significantly different Problems’ Code, that could be due to weather condition, culture of using ATMs, and likewise. Moreover, the same Machines’ Code and the same Problems’ Code, as well as differences in Technicians' expertise, seems to be some causes to significantly different Repair Time. This could be due to Technicians' training background level of their expertise and such. At last, the company can benefit from the outputs of this model in terms of its strategic decision-making.

Due to the variety of products, simultaneous production of different models has an important role in production systems. Moreover, considering the realistic constraints in designing production lines attracted a lot of attentions in recent researches. Since the assembly line balancing problem is NP-hard, efficient methods are needed to solve this kind of problems. In this study, a new hybrid method based on unconscious search algorithm (USGA) is proposed to solve mixed-model assembly line balancing problem considering some realistic conditions such as parallel workstation, zoning constraints, sequence dependent setup times and learning effect. This method is a modified version of the unconscious search algorithm which applies the operators of genetic algorithm as the local search step. Performance of the proposed algorithm is tested on a set of test problems and compared with GA and ACOGA. The experimental results indicate that USGA outperforms GA and ACOGA.

Recently, public bicycle sharing system (PBSS) has become one of the most favorite urban transportation systems that can help governments to decrease environmental problems such as pollution and traffic. This paper studies a sharing system that includes two types of stations. The first category contains stations that users can rent or return back bicycles and each bicycle can be rented by any new user who arrives to the stations. The second group is the stations which are near shopping centers, historical and other places that users and tourists can stop and visit them. These stations are used only for parking the rented bicycles for a period of time and after that, the users must ride their bicycles and turn them back to their destination stations. After discussing the network of the model under the closed Jackson network, the Mean Value Analysis (MVA) method will be used to calculate the mean queue of each station and analyzing the proposed model.

The purpose of this study is to identify the effective factors of optimal use of social media in the brand equity of startup companies. To this end, the researcher collected the required data using snowball sampling, protocol design, and exploratory interviews with sixteen university experts, the industry in this field, and senior managers of accelerator companies and startups. Maxqda 18 software was used to analyze the interviews. In the open coding process, the researcher first identified 170 codes and categorized them into 8 categories. Then, based on the theoretical literature and using the opinion of academic experts in axial coding based on Strauss and Corbin paradigm model, the relevant model was extracted and finally the selective coding was performed. To evaluate the validity of coding, Peer check method, the member check method and reliability by Field note were conducted. The results indicate that the main phenomena is using social media in startups and the communicational dimension as causal factors, cognitive dimension, and attitude in the position of strategies, brand characteristics as background factors, relational dimension, and rules and regulations as intervening factors and finally behavioral dimension were obtained as the consequences of the model.

This paper formulates a mathematical model for the Green Vehicle Routing Problem (GVRP), incorporating bi-fuel (natural gas and gasoline) pickup trucks in a mixed vehicle fleet. The objective is to minimize overall costs relating to service (earliness and tardiness), transportation (fixed, variable and fuel), and carbon emissions. To reflect a real-world situation, the study considers: (1) a comprehensive fuel consumption function with a soft time window, and (2) an en-route fuel refueling option to eliminate the constraint of driving range. A linear set of valid inequalities for computing fuel consumption were introduced. In order to validate the presented model, first, the model is solved for an illustrative example. Then each component of cost objective function is considered separately so as to investigate the effects of each part on the obtained solutions and the importance of vehicles speed on transportation strategies. Computational analysis shows that, despite the limitation of an appropriate service infrastructure, the proposed model demonstrated an average reduction of 44%, 6% and 5% in carbon emission costs, total distribution costs, and transportation costs respectively. Moreover, the study found paradoxical effects of average speed, suggesting the need to manage trade-offs: while higher speeds reduced service costs, they increased carbon emission costs. In the next stage, some experiments modified from the literature are solved. According to these experiments, in all instances greater objective function values for Gasoline vehicles are gained. The difference in the carbon emission objective is also significant, with an average of 44.23% increase. Finally, managerial and institutional implications are discussed.

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.

This paper considers a different version of the parallel machines scheduling problem in which the parallel jobs simultaneously requirea pre-specifiedjob-dependent number of machines when being processed.This relaxation departs from one of the classic scheduling assumptions. While the analytical conditions can be easily statedfor some simple models, a graph model approach is required when conflicts of processor usage are present. The main decisions and solving steps are as follows, respectively. (i)        Converting the scheduling problem to graph model; (ii)      Dividing jobs into independent sets: in this phase, we propose a semi-definite relaxation algorithm in which we use graph coloring concept; (iii)    Sequencing the independent sets as a single-machine scheduling in which jobs in such a system arejob sets formed by using a semi-definite relaxation solution and determining the problem as a schedule that minimizes the sum of the tardiness of jobs. In this regard, after grouping the jobs by a semi-definite programming relaxation algorithm, we used the rounding algorithm for graph coloring. We also proposed a variable neighborhood search algorithm for sequencing the obtained job sets in order to minimize the sum of the tardiness. Experimental results show that this methodology is interesting by obtaining good results.

Owing to rising intentional events, supply chain disruptions have been considered by setting up a game between two players, namely, a designer and an interdictor contesting on minimizing and maximizing total cost, respectively. The previous studies have found the equilibrium solution by taking transportation, penalty and restoration cost into account. To contribute further, we examine how incorporation of inventory cost influences the players’ strategies. Assuming risk-averse feature of the designer and fully optimizing property of the interdictor with limited budget, the conditional-value-at-risk is employed to be involved in total cost. Using special order sets of type two and duality role, the linearized tri-level problem is solved by column-and-constraint generation and benders decomposition algorithms in terms of small-sized instances. In terms of larger-sized instances, we also contribute to prior studies by hybridizing corresponding algorithms with bio-geography based optimization method. Another non-trivial extension of our work is to define adapted stochastic measures based on the proposed mean-risk tri-level formulation. Borrowing instances from prior papers, the computational results indicate the managerial insights on players’ decisions, the model’s efficiency and performance of the algorithms.

The technique for order of preference by similarity to ideal solution (TOPSIS) is a method based on the ideal solutions in which the most desirable alternative should have the shortest distance from positive ideal solution and the longest distance from negative ideal solution. Depending on type of evaluations or method of ranking, different approaches have been proposing to calculate distances in the TOPSIS method. In a recent paper, Dymova et al. (2015) extended the TOPSIS approach using interval type 2 fuzzy sets (IT2FSs) in which distances were calculated using alpha cuts. When investigating their paper, we found out that the extended method has some drawbacks such that it leads to the incorrect calculations and results when solving an IT2FSs-based multi-criteria decision making (MCDM) problem. In this note, the corrected version of extended TOPSIS method is being presented to eliminate its limitations. In order to show effectiveness and possibility of the proposed approach, it is also implemented in two illustrative examples and one case study. The results have showed that the optimal alternative obtained by the corrected TOPSIS approach has the similar rank to the others, whereas it is different from the results of existing TOPSIS approach.

Technology is changing very quickly at very cheap prices. Along with this, it becomes easier to buy any electronic product right now as compared to previous years. There are many channels (Online & Offline) available in the market to buy your desired products. Nowadays it becomes a trend: to buy new electronic products to use the latest available technology. People prefer to buy new product instead of upgrading the old product. Mobiles and laptops are the most common products which are bought frequently instead of to upgrade. Expanding usage of the mobiles, computers/laptops, and LCD/LED in all the sectors inflicting dramatically increase in generation of e-waste. E-waste referred to all kind of electric and electronic appliances that is thrown by their end user. The electronics equipments are very complex assembly structure. It contains toxic gases, toxic metals plastics, circuits, some precious metals like gold in very small quantity. The toxic substances have very adverse effect on the health of human being. These are also very dangerous for our environment also. . The objective of the study is to find how the individual consumers behave towards the e-waste. Awareness level of the individual consumers also checked. Northern Indian states are selected to conduct a study to check the awareness level of individual consumers. In this paper consumer recycling behavior was analyzed with the help of questionnaire. Figure 1 Graphical Abstract The figure shows that in all the factors, the value of mean lies between 1 and 2. It shows that most of the consumers either have no idea of hazardous materials in e-waste, concept of e-waste management and electronic waste management policies implemented in India or they have very little knowledge.

Probabilistic safety assessment (PSA) which plays a crucial role in risk evaluation is a quantitative approach intended to demonstrate how a nuclear reactor meets the safety margins as part of the licensing process. Despite PSA merits, some shortcomings associated with the final results exist. Conventional PSA uses crisp values to represent the failure probabilities of basic events. This causes a high level of uncertainty due to the inherent imprecision and vagueness of failure input data. In this paper, to tackle this imperfection, a fuzzy approach is employed with fault tree analysis and event tree analysis. Thus, instead of using the crisp values, a set of fuzzy numbers is applied as failure probabilities of basic events. Hence, in the fault tree and event tree analysis, the top events and the end-states frequencies are treated as fuzzy numbers. By introducing some fuzzy importance measures the critical components which contribute maximum to the system failure and total uncertainty are identified. As a practical example, under redesign Iranian heavy water research reactor loss of coolant accident is studied. The results show that the reactor protection system has the largest index in sequences lead to a core meltdown. In addition, the emergency core cooling system has a main role in preventing abnormal conditions.

Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.

Abstract Vendor Selection Problem (VSP) has been considered in this paper as an integrated method of Fuzzy Analytic Hierarchy Process (FAHP) and Quality Function Deployment (QFD) in the pharmaceutical company. In QFD method, determining the importance of the "weights" for the customer requirements is an essential and crucial issue. FAHP has been used to determine the importance of the "weights" for Product Designing which incorporates the four important attributes in a pharmaceutical company namely Cost, Standing Supplier, Delivery Time and Quality. The new approach can improve the imprecise ranking of customer requirements and provides a decision tool that facilitates the vendor selection. The most significant advantage of this integrated method is using it as a self-evaluation tool in the organization to determine weaknesses and strengths, so it can help researchers to solve this specific subject for supplier’s selection.