فهرست مطالب

  • Volume:12 Issue:26, 2019
  • تاریخ انتشار: 1398/04/10
  • تعداد عناوین: 15
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  • Masoud Rabbani *, Leyla Aliabadi, Hamed Farrokhi, Asl Pages 1-20
    Mixed model two-sided assembly lines (MM2SAL) are applied to assemble large product models, which is produced in high-volume. So, the sequence planning of products to reduce cost and increase productivity in this kind of lines is imperative. The presented problem is tackled in two steps. In step 1, a framework is developed to select and prioritize customer orders under the finite capacity of the proposed production system. So, an Analytic Network Process (ANP) procedure is applied to sort customers’ order based on 11 assessment criteria. In step 2, a mathematical model is formulated to determine the best sequence of products to minimize the total utility work cost, total idle cost, tardiness/earliness cost, and total operator error cost. After validation of the presented model using GAMS software, according to the NP-hard nature of this problem, a genetic algorithm (GA) and particle swarm optimization (PSO) are used. The performance of these algorithms are evaluated using some different test problems. The results show that the GA algorithm is better than PSO algorithm. Finally, a sign test for the two metaheuristics and GAMS is designed to display the main statistical differences among them. The results of the sign test reveal GAMS is an appropriate software for solving small-sized problems. Also, GA is better than PSO algorithm for large sized problems in terms of objective function and run time.
    Keywords: Mixed model assembly line, Two-sided assembly line, Sequencing problem, Make to order
  • Alie Wube Dametew *, Danile Ketaw, Ebinger Frank Pages 21-32
    This study is conducted to developed innovative production planning and control strategies to manufacturing industries so as to improve production performance and competitiveness of basic metal sectors Though the study was conducted through field observation and questioner used as primary data and literature review on research articles, books, and electronic-sources which used as secondary data. While the questioner and filed observation data collection were done from two selected Ethiopian basic metal industries. Since the collected data were employed by both using descriptive and empirical analysis. Waste in the production process, poor plant layout systems, defective products, improper material requirement planning, deficiency on control and monitoring systems, insufficient inventory control, poor workflow strategies, null warehouse management systems, problems in information systems and information management strategies were investigated as the main challenges of developing the nation basic metal industries. As a result of these challenges, the performance and global competitiveness of local basic metal industries are poor and weak. As well the literature’ finding endorse that production planning and controls have gradual advancement in developed manufacturing industries but it is found to be at its infant stage in developing manufacturing industries. Due to these challenges and weak performances on the developing firms, the entire production process on the industries was declining, and then they approach to die. Though the new product planning and controlling strategies can bridge the gap and birth will begin within proper implementations of the model to basic metal industries.
    Keywords: Production Planning, Control, Death, Birth, manufacturing industries, Basic Metal Industry, Implementation, Innovative Conceptual Model
  • Shivaji G. Chavan *, Anil B. Shinde Pages 33-44
    Presents article is deal with optimization of the fixture for end milling process, the most important objective being the minimization of work piece deformation by changing the layout of fixture elements and the clamping forces. The main objective of this work has been the optimization of the fixtures Work piece deformation subjected to clamping forces for End milling operation. The present analysis is  used in hollow rectangular  isotropic material work piece for FEA analysis and its optimization A linear programing (L.P) simplex model optimization activity has been performed both on fixture-work piece systems modeled with FEM and on fixture-work piece systems modeled with 3-D solid elements. The optimization constraints is selected as W/P deformation in x,y,z direction for various clamping forces, in order to provide a new design of fixture.  The MATLAB code has been developed for L.P. model optimization purpose. Present MATLAB code is validated by using available literature. This paper deals with application of the L.P. model for w/p deformation optimization for a accommodating work piece. A simplex iterative algorithm that minimizes the work piece elastic deformation for the entire clamping force is proposed. It is shown via an example of milling fixture design that this algorithm yields a design that is superior to the result obtained from either fixture layout or w/p deformation optimization alone.
    Keywords: 3-2-1 Location Principle, FEM, Linear Programing simplex Algorithom, Hollow Rectangular Work Piece Fixtures
  • Dessie Tarekegn Bantelay * Pages 45-54
    Water is an essential element of life. The government of Ethiopia in collaboration with development allies’ attempts to increase pure water supply. Even though the coverage boosted dramatically still there is critical challenges in maximizing equipment’s reliability, improving service quality, maximizing capacity utilization, minimizing life cycle costs of water production machinery and reducing water waste. The objective of this study was to identify installation, operation, maintenance and related challenges, to evaluate the performance of pump station and to investigate the root causes so as improvements can be made deliberately. In this regard 20 town water supply stations were selected in Amhara region of Ethiopia and initially on site visit carried out and various existing situational surveys regarding the existing installation, operation and maintenance practice have been conducted. Next information collected using questionnaires, interview and focus group discussions. Then imperative performance indicating measurements taken and the data organized and important performance indicating parameters were analyzed using quantitative techniques. The study proved that all the pump stations run under the minimum performance requirements and the problems are deep enough to challenge the service quality and service cost of the pump stations. Unless the problems will be solved soon systematically the problems may be even mature to the nastiest situation that cannot handled.
    Keywords: Submersible Pump, Electro-Mechanical Equipment, Pump Installation, Operation, Maintenance
  • Mohammad Velayati *, Mohammadreza Shahriari, Farhad Hosseinzadeh Lotfi Pages 55-63
    Effective knowledge and awareness of customers require the market segmentation, through which the customers who have the same needs and purchasing patterns as well as the same response to marketing plans are identified. The selection of a proper variable is a requirement, among other, for a successful market segmentation. In today' world, on one hand, the consumers are bombarded with new goods and new services, and on the other hand, they face the varying qualities of the goods and services. Consequently, such uncertainties will lead to more vague decisions and cumulative data. The timely and accurate analysis of these cumulative data can bring about competitive advantages to the enterprises. Furthermore, thanks to new technology and global competition, the majority of organizations have focused on Customer Relationship Management (CRM), with the goal of better serving the customers. The customer relationship planning entails the facilitation and creation of interfaces related to market segmentation, which is considered as a requirement for predicting behavior of the prospective customers in the future. Market segmentation refers to the process of dividing the customers into some segments based on their common characteristics while different groups have the least similarity to each other. This is followed by the formulation of plans for new product production, advertisement and marketing in accordance with the characteristics of each group of customers. Current study aims at identifying the profitable customers of a telecom System, based on their first transaction, using binary tree. The customers of System 780 participated in this case study.  The dependent variable and independent variable of the study were identified through mining the data of customers, registered in the databases of System 780. The results showed the acceptable calculation error in distinguishing the profitable customers from other customers.
    Keywords: Segmentation, Telecom, Decision tree, Customer Relationship Management (CRM)
  • Abdollah Nazari *, Mohammadreza Mehregan, Reza Tehrani Pages 65-78
    In any country, commercial banks lay the groundwork for economic growth by collecting national resources and capitals and allocating them to different economic sectors. Optimal allocation of resources is especially important in achieving this goal. Banks with an effective and dynamic system of customer assessment can efficiently allocate their resources to customers regardless of their geographic area. Following[M1]  a linear programming optimization approach, this research employs the UTilités Additives DIScriminantes (UTADIS) model for credit scoring of bank customers. The advantages of the proposed technique are high flexibility, mutual interaction with decision makers, and the ability to update under various macroeconomic conditions. The chosen environment is a branch of Bank Refah Kargaran, one of the popular banks in Iran. According to the experimental results, the proposed technique demonstrates high effectiveness. Also, the results indicate that the initial credit score and age of the applicants are the most influential factors for credit scoring of customers.
    Keywords: Credit Scoring, Clustering, Data Mining, UTADIS
  • Javad Rezaeian *, Sadegh Hosseini, Kia, Iraj Mahdavi Pages 79-92
    Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs.  In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined .The proposed model is solved by three meta-heuristic algorithms: genetic algorithm (GA), imperialist competitive algorithm (ICA) and hybrid of GA and ICA. To evaluate the performance of the proposed algorithms many test problems have been designed. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding thebest solution in reasonable computational time.
    Keywords: JIT scheduling, Flow shop, Preemption, Idle time
  • Seyed Meysam Mousavi, Hossein Gitinavard, Behnam Vahdani *, Nazanin Foroozesh Pages 93-105
    Proposing a hierarchical group compromise method can be regarded as a one of major multi-attributes decision-making tool that can be introduced to rank the possible alternatives among conflict criteria. Decision makers’ (DMs’) judgments are considered as imprecise or fuzzy in complex and hesitant situations. In the group decision making, an aggregation of DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classical compromise ranking. This research extends a new hierarchical group compromise ranking methodology under a hesitant fuzzy (HF)environment to handle uncertainty, in which for the margin of error, the DMs could assign the opinions in several membership degrees for an element. The hesitant fuzzy set (HFS)is taken into account for the process of the proposed hierarchical group compromise ranking methodology, namely HFHG-CR, and for avoiding the data loss, the DMs’ opinions with risk preferences are considered for each step separately. Also, the Euclidean–Hausdorff distance measure is utilized in a new proposed index for calculating the average group score, worst group score and compromise measure regarding each DM. A new ranking index is presented for final compromise solution for the evaluation. Proposed HFHG-CR methodology is applied to a practical example for a facility location selection problem, i.e. cross-dock location problem, to show the validation and application.
    Keywords: Compromise ranking, Group decision-making, Last aggregation, Euclidean–Hausdorff distance measure, Hesitant fuzzy sets, Facility location selection problem
  • Javad Behnamian * Pages 107-119
    The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society whose members behave anarchically to improve their situations. Such anarchy lets the algorithm explore the solution space perfectly and prevent falling in the local optimum traps. Besides, for the first time, for the hybrid flowshop, we proposed eight different local search algorithms and incorporate them into the algorithm in order to improve it with the help of systematic changes of the neighborhood structure within a search for minimizing the makespan. The proposed algorithm was tested and the numerical results showe that the proposed algorithm significantly outperforms other effective heuristics recently developed.
    Keywords: particle Swarm optimization, Scheduling, Sequence-dependent, Hybrid flowshop
  • Saeed Dehnavi, Arani, Ali Sabaghian *, Mehdi Fazli Pages 121-129
    When the Automated Guided Vehicles (AGVs) are transferring the parts from one machine to another in a job shop environment, it is possible that AGVsstopon their guidepaths since their batteries are discharged.Consequently, it is essential to establish at least one Battery Charging Storage (BCS) to replace full batteries with empty batteries for the stopped AGVs. Due to non-predictable routes for AGVs in the manufacturing systems, to find the best place toestablish the BCS can impact performance of the system. In this paper, anintegrated mathematical modelof job shop and AGV schedulingwith respect tothe location of a BCS is proposed. The proposed nonlinear model is transformed into a linear form to beefficiently solvedin GAMS software. Finally, several numerical examplesare presented to test the validity of the proposed mathematical model.The results show that the optimal cost and location of BCS can be obtained with respect to the number of AGVs, machines, parts, and other problem parameters. In addition, it is concluded that the increasing number of AGVs in a manufacturing systemcannot be always a suitable policy for reducing the cost because in such conditions.Further to that, the conflict of AGVs may increase leading tothe increase of the makespan. In other words, following the optimal point, increasing AGVs leads to the increase in costs.
    Keywords: job shop scheduling, Battery Charging Storage (BCS), AGV, GAMS software
  • Sadigh Raissi *, Ramtin Rooeinfar, Vahid Reza Ghezavati Pages 131-147
    Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fixed interval preventive maintenance (PM) and budget constraint are considered.PM activity is a crucial task to reduce the production efficiency. In the current research we focused on a scheduling problem which a job is processed at the upstream stage and all the downstream machines get busy or alternatively PM cost is significant, consequently the job waits inside the buffers and increases the associated holding cost. This paper proposes a new more realistic mathematical model which considers both the PM and holding cost of jobs inside the buffers in the stochastic flexible flow shop scheduling problem. The holding cost is controlled in the model via the budget constraint. In order to solve the proposedmodel, three hybrid metaheuristic algorithms are introduced. They include a couple of well-known metaheuristic algorithms which have efficient quality solutions in the literature. The two algorithms of them constructed byincorporationof the particle swarm optimization algorithm (PSO) and parallel simulated annealing (PSA) methods under different random generation policies. The third one enriched based on genetic algorithm (GA) with PSA. To evaluate the performance of the proposed algorithms, different numerical examples are presented. Computational experiments revealed that the proposed algorithms embedboth desirable accuracy and CPU time. Among them, the PSO-PSAП outperforms than other algorithms in terms of makespan and CPU time especially for large size problems.
    Keywords: Stochastic flexible flow shop, Budget constraint, Preventive maintenance, genetic algorithm, Simulated annealing, particle Swarm optimization
  • Parham Azimi *, Abulfazl Asadollahi Pages 149-154

    In this research, a hierarchical location-allocation problem is modeled in a queue framework. The queue model is considered as M/M/1/k, in which system capacity is finite, equals to k. This is the main contribution of the current research. Customer's enters to the system in order to find the service according to a Poisson. In this problem, the hierarchical location-allocation model is considered in two levels. Also, the model has two objective functions: maximizing the total number of demand coverage and minimizing the waiting time of customers in queues to receive services. After modeling and verifying the validity of the presented model, it is solved using NSGA II and MOPSO meta-heuristics.

    Keywords: Location-Allocation Problems, Hierarchical Models, Multi-objective programming, Taguchi method, NSGA-II Algorithm, M-M-m Queuing Model
  • Amir Hossein Hosseinian, Vahid Baradaran * Pages 155-178
    This paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. Hence, in this paper, a mixed-integer formulation called the MMSRCPSP is proposed to minimize the completion time of project. Since the MMSRCPSP is strongly NP-hard, a new genetic algorithm is developed to find optimal or near-optimal solutions in a reasonable computation time. The proposed genetic algorithm (PGA) employs two new strategies to explore the solution space in order to find diverse and high-quality individuals. Furthermore, the PGA uses a hybrid multi-attribute decision making (MADM) approach consisting of the Shannon’s entropy method and the VIKOR method to select the candidate individuals for reproduction. The effectiveness of the PGA is evaluated by conducting numerical experiments on several test instances. The outputs of the proposed algorithm is compared to the results obtained by the classical genetic algorithm, harmony search algorithm, and Neurogenetic algorithm. The results show the superiority of the PGA over the other three methods. To test the efficiency of the PGA in finding optimal solutions, the make-span of small size benchmark problems are compared to the optimal solutions obtained by the GAMS software. The outputs show that the proposed genetic algorithm has obtained optimal solutions for 70% of test problems.
    Keywords: RCPSP, Multi-skilled resources, Optimization, Meta-heuristics, MADM
  • Mohammad Mehdi Movahedi *, Seyedreza Seyedghasemi Pages 179-188
    Tolerancing is one of the most important tools for planning, controlling, and improving quality in the industry. Tolerancing conducted by design engineers to meet customers’ needs is a prerequisite for producing high-quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not a new concept, engineers often use known distributions, including the normal distribution. However, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. Therefore, in this study we want to offer a proper statistical method for determining tolerance. The use of statistical methods to design tolerance is not a new concept; however, flexible use of statistical distributions can enhance its performance. In this regard, Weibull distribution is proposed. To illustrate the proposed method first technical characteristics of production parts were selected randomly, and then manufacturing parameters were determined using maximum likelihood method.  Finally, the Goodness of Fit test was used to ensure the accuracy of the obtained results.
    Keywords: Tolerancing, Weibull distribution, Statistical quality control
  • Amir Hossein Yazdanpanah, Ali Akbar Akbari *, Marzieh Mozafari Pages 189-197
    In this research, firms aim at maximizing two purposes of social welfare (environment) and profitability in the supply chain system. It is assumed that there are two supply chains, a green and an ordinary, each consists of a manufacturer and a supplier; in which the manufacturer generates profit through franchises. The green and the ordinary manufacturers form a cartel on the market of a certain product with the goal of increasing their mutual profits and maintaining a certain level of social welfare, while the government, as a leader, intervene financially using tax rates and incentives. We formulate the problem as a Stackelberg game model seeking the equilibrium solutions. A numerical example is presented and a sensitivity analysis is carried out. The results show that the investment’s encouraging tax rate in green technology has no impact on the optimal production of the green and ordinary manufacturers. Therefore, it is not an affective variable on the product market, but it is an important variable for the state utility function. Another highlight is that if tax rates are not equal for green and ordinary goods, then either the green or the ordinary producer will be withdrawn from the market. The most important result of this study is that if the government wants to maximize its utility function when the final product’s market is facing with a cartel and the price collusion between the green and ordinary producer, it should realize the equality between the ordinary and green tax rate and there is no difference between these two parameters of the government's decision. If the government is willing to keep the green producer in the market, the optimal and absolute tax rate of green chain is obtained by assuming zero profit of the green manufacturer.
    Keywords: Green Supply Chain Management, Stackelberg game, Social responsibility, Tax rate