Journal of Optimization in Industrial EngineeringJournal of Optimization in Industrial Engineering
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Feed provided by Journal of Optimization in Industrial Engineering. Click to visit.Integrated Due Date Setting and Scheduling on a Single Machine Considering an Unexpected ...
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In this paper, an integrated machine scheduling withits due date setting problem has been considered. It is assumed that the machine is subject to some kind of random unavailability. Due dates should be set in an attractive and reliable manner, implying that they should be short and possible to be met. To this end, first, long due dates are penalized in the objective function. Then, for each customer order, the probability of meeting his/her promised due dateis forced to be at least as large as his/her required service level. To handle this integrated problem, first, the optimal due date formulafor any arbitrary sequence is derived. By using this formula, the mathematical programming formulation of the problem,including a nonlinear non-convex expression, is developed. By defining a piecewise linear under-estimator, the solutions of the resultantmixed integer linear programming formulation have become the lower bounds of the problem. Dynasearch is a very efficient heuristic utilizing the dynamic programming approach to search exponential neighborhoods in the polynomial time. Aniterated dynasearch heuristic is developed for the sequencing part of the problem. Each generated sequence is evaluated by computing its optimal due datesusing the above-mentioned formula. Numerical results confirmed the high quality of the solutions found by this algorithm, as compared with the lower bound.Thu, 28 Feb 2019 20:30:00 +0100A Two-dimensional Warranty Model with Consideration of Customer and Manufacturer Objectives ...
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Warranty is a powerful implement for marketing strategy that is used by manufacturersand creates satisfaction for consumers by ensuring to compensate for incorrect operation of the product. Warranty serviceresults ina cost named warranty cost for a manufacturer.This cost is a function of warranty policy, regions, and product failures pattern. Since this service coversthe cost of uncertain failure of the product, it makes some utility for customers. In this paper, we developed a novel customer utility function that is used as a customer objective to be maximized. In addition to the manufacturer objective, minimizing the warranty costisconsidered simultaneously. There are four restrictions on warranty parameters such as time, usage, unit product price and the R&D expenditure to be considered. Finally, we will propose a novel bi-objective model that maximizesthe utility function for customers and minimizesthe warranty cost for the manufacturer. This model will be solved with an evolutionary algorithmcalled Non-Dominated Sorting Genetic Algorithm (NSGA-II) and non-dominated Pareto solutionswill be gained from this method.To give a numerical instance, for a certain usage rate’s range of costumers, different warranties are provided and compared. It is believed that the computational results can help manufacturers to determine optimal solutions for the objective functions and consequentlywarranty parameters.Thu, 28 Feb 2019 20:30:00 +0100Application of Failure Mode Effect Analysis (FMEA) for Efficient and Cost-effective ...
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The aim of this paper is to show the application of Failure Mode Effect Analysis (FMEA) for efficient and cost-effective manufacturing. Companies need better economic gains from enhanced production, but downtime affects this paradigm. Bair Dar Textile Share Company (BDTSC) is no exception. The looming section of the case company faces on average 38.69% of downtime from the total production time which highly affects its production performance, and thus profitability. The research tries to show the economic gain from the reduced high downtime in the case company by taking the advantages of Failure Mode Effect Analysis (FMEA). As a result FMEA, failure modes, cause, and their effects on the specific section of the company were identified and prioritized using their Risk Priority Numbers (RPN). By taking the FMEA on the looming process machines and focusing on the vital few 20% causes of the identified failure modes, the findings of the research show that the company can decrease the total downtime from its 178 loom machines by 299.04hrs/day. As a result, the company can save downtime that can produce 1,672.82 meters of fabric/cloth and enhance its performance by 4.18%. This downtime reduction in turn results in a daily profit of 38,220.56 ETB (Ethiopian Birr) or 11,466,168.00 ETB annually.Thu, 28 Feb 2019 20:30:00 +0100An Aggregated Supplier Selection Method Based on QFD and TOPSIS (Case Study: A Financial ...
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With daily development of information technology supply chain of service-based organizations like financial institutions and the increased value of outsourced activities, also the importance of customer satisfaction, outsourced affairs must have done by the suppliers who have the ability of accomplishing the organizational demands. To mitigate the risk of invalid supplier selections, verification and selection of the suppliers should be performed with an optimized and systematic solution. In order to help the selection of suppliers in the IT department of financial organizations, a different model by using a hybrid QFD-TOPSIS solution in MCDM methods is suggested, in this study. First goal of the provided model is finding the most related criteria and the second one is offering an optimized solution to the supplier selection problem. To begin the QFD part in the mentioned method, two categories of criteria are needed. Then, after the formation of the House of Quality, in a real case study that was performed in a private bank in Iran, the suppliers are ranked by using the proposed method. The greatest efficiencies of this method are not only finding the best supplier by measuring the nearest distance to the ideal and the farthest one to the negative-ideal solution but also closing the opinions of employers to the technical requirements (sub-criteria) of information technology supplier qualifications. Finally, a model reliability part is designed to indicate the validation of the proposed method and a sensitivity analysis is implemented to find the most sensitive sub-criteria. That is the results of ranking alter if sensitive sub-criteria change .Thu, 28 Feb 2019 20:30:00 +0100Optimization of Multi-period Three-echelon CitrusSupply Chain Problem
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In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model.Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.Thu, 28 Feb 2019 20:30:00 +0100Solving the Fixed Charge Transportation Problem by New Heuristic Approach
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The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this paper, we propose a new heuristic along with well-known metaheuristic like Geneticalgorithm (GA), simulated annealing (SA) and recently developed one, Keshtel algorithm (KA) to solve the FCTP. Contrary to previous works, we develop a simple and strong heuristic according to the nature of the problem and compare the result with metaheuristics. In addition, since the researchers recently used the priority-based representation to encode the transportation graphs and achieved very good results, we consider this representation in metaheuristics and compare the results with the proposed heuristic. Furthermore, we apply the Taguchi experimental design method to set the proper values of algorithms in order to improve their performances. Finally, computational results of heuristic and metaheuristics with different encoding approaches, both in terms of the solution quality and computation time, are studied in different problem sizes.Thu, 28 Feb 2019 20:30:00 +0100Classification of Streaming Fuzzy DEA Using Self-Organizing Map
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The classification of fuzzy data is considered as the most challenging areas of data analysis and the complexity of the procedures has been obstacle to the development of new methods for fuzzy data analysis. However, there are significant advances in modeling systems in which fuzzy data are available in the field of mathematical programming. In order to exploit the results of the researches on fuzzy mathematical programming, in this study, a new fuzzy data classification method based on data envelopment analysis (DEA) is provided when fuzzy data are imported as a stream. The proposed method can classify data that changes are created in their behavioral pattern over time using updating the criteria of predicting fuzzy data class. To reduce computational time, fuzzy self-organizing map (SOM) is used to compress incoming data. The new method was tested by simulated data and the results indicated the feasibility of this technique in the face of uncertain and variable conditions.Thu, 28 Feb 2019 20:30:00 +0100An Efficient Hybrid Algorithm for Dynamic Facility Layout Problem using Simulation Technique and PSO
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One of the most important and effective issues of nowadays manufacturing companies, is how to arrange their facilities in the most economic manner. Different methods and approaches have been examined by researchers to address the so-called facility layout problem (FLP). As a combinatorial problem FLP is a NP-Hard one, and so classic and exact optimization methods just can be useful in small scale problems. Therefore, developing of meta-heuristic algorithms to solve and analysis of such problems is used by many researchers. In this research, a new hybrid heuristic method is developed by combination of discrete Particle Swarm Optimization (PSO) algorithm and simulation technique to address Dynamic Facility Layout Problem (DFLP) which is the main contribution of the current study. To show the efficiency of the proposed algorithm, several test problems which were taken from the literature have been examined and the results were compared to other algorithms. As the computational results show, the quality of solutions and the algorithm speed are suitable enough to be used in real world problems.Mon, 05 Feb 2018 20:30:00 +0100Modelling and solving the job shop scheduling Problem followed by an assembly stage considering ...
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This paper considers job shop scheduling problem followed by an assembly stage and Lot Streaming (LS). It is supposed here that a number of products have been ordered to be produced. Each product is assembled with a set of several parts. The production system includes two stages. The first stage is a job shop to produce parts. Each machine can process only one part at the same time. The second stage is an assembly shop that contains several parallel machines. Maintenance operations and access restrictions to machines in the first stage are also considered. The objective function is to minimize the completion time of all products (makespan). At first, this problem is described and modelled as a mixed integer linear programming, and GAMS software is applied to solve small-sized problems. Since this problem has been proved to be strongly NP-hard, two new algorithms based on GA and SA are developed to solve the medium- and large-sized problems. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor and well-known criterion. IMP. Various problems are solved by the proposed algorithms. Computational results reveal that both of the two proposed algorithms have good performance. However, the method based on the genetic algorithm performs better than the other proposed algorithm with respect to the objective functiThu, 28 Feb 2019 20:30:00 +0100Participative Biogeography-Based Optimization
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Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.Thu, 28 Feb 2019 20:30:00 +0100A Regret Minimization Approach in Product Portfolio Management with respect to Customers’ ...
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In an uncertain and competitive environment, product portfolio management (PPM) becomes more challenging for manufacturers to decide what to make and establish the most beneficial product portfolio. In this paper, a novel approach in PPM is proposed in which the environment uncertainty, competitors’ behavior and customer’s satisfaction are simultaneously considered as the most important criteria in achieving a successful business plan. In terms of uncertainty, the competitors’ product portfolios are assumed as different scenarios with discrete occurrence probabilities. In order to consider various customer preferences, three different market segments are assumed in which the sensitivity of customers towards the products price are considered as high, medium and low and modeled by means of a modified utility functions. The best product portfolio with minimum risk of loss and maximum customer satisfaction is then established by means of a novel regret minimization index. The proposed index aims at finding the best product portfolio which minimizes the total possible loss and regret of the manufacturer, with respect to the other competitors of the market. To better illustrate the practicality of the approach, a numerical example is presented. The results show that the selected products in the suggested portfolio have the highest utility value in all market segments and also they are expected to achieve the highest expected payoff in each possible marketing scenario.Thu, 28 Feb 2019 20:30:00 +0100Presenting a joint replenishment-location model under all-units quantity discount and solving ...
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In this paper a model is proposed for distribution centers location and joint replenishment of a distribution system that is responsible for orders and product delivery to distribution centers. This distribution centers are under limitedwarehouse space and this can determine amount of requirement product by considering proposed discount.The proposed model is develop to minimize total costs consists of location, ordering, purchaseunder All-units quantity discount condition and items maintenance by adjustment Frequency of replenishment in each distribution center. To solve this model, first we solve the model with genetic algorithm by confining the time between too replenishments then by use of the Quantity Discount RAND algorithm method the upper and lower limits of the time between two replenishments will be determined. After obtaining the optimal upper and lower limits, the model will be resolved by harmony search and genetic algorithms. The results show that the presented chromosome structure is so efficient so that the statistical experiments result indicates there isn’t much difference between solution means after finding the optimal upper and lower limits. We used response surface methodology for tune proposed algorithms parameters. Efficiency of proposed algorithms is examined by diverse examples in different dimensions. Results of these experiments are compared by using of ANOVA and TOPSIS with indexes of objective function value and algorithms runtime. In both comparisons harmony search algorithm has more efficiency than genetic algorithm.Sun, 04 Feb 2018 20:30:00 +0100Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops
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This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed.Thu, 28 Feb 2019 20:30:00 +0100Healthcare Districting Optimization Using Gray Wolf Optimizer and Ant Lion Optimizer Algorithms ...
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In this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solutions in order to implement in the system. Therefore in this study two meta-heuristic algorithms, Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO), have been applied to solve the problem in the dimensions of the real world. The objective function of the problem is to maximize the population balance in each district. Problem constraints include unique assignment as well as non-existent allocation of abnormalities. Abnormal allocation means compactness, lack of contiguous, and absence of holes in the districts. According to the obtained results, GWO has a higher level of performance than the ALO. The results of this problem can be applied as a useful scientific tool for districting in other organizations and fields of application.Thu, 28 Feb 2019 20:30:00 +0100The Preemptive Just-in-time Scheduling Problem in a Flow Shop Scheduling System
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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.Sun, 04 Feb 2018 20:30:00 +0100Multi-response Optimization of Grooved Circular Tubes Filled with Polyurethane Foam as Energy ...
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The main objective of this research is to improvethe design and performance of the polyurethane foam-filled thin-walled aluminum grooved circular tubes using multi-response optimization (MRO) technique. The tubes are shaped with the inner and the outer circular grooves at different positions along the axis. For this aim, several numerical simulations using ABAQUS finite element explicit code are performed to study the energy absorption of these structures. The effects of the grooves distance, tube diameter, grooves depth, foam density, and tube thickness are investigated onthecrashworthiness parameters of grooved circular tubes. Finite-element analysis is performed along the lines defined by design of experiments (DOE) technique at different combinations of the design parameters. The MRO is carried out using the mathematical models obtained from response surface methodology (RSM) for two crashworthiness parameters termed as the specific energy absorption (SEA) and the crushing force efficiency (CFE). Finally, by analyzing all the design criteria including theabsorbed energy of tube, themass of tube, the mean crushing load, and the maximum crushing load, the optimal density of polyurethane foam and geometric parameters were obtained through both multi-objective optimization process and Pareto diagram. A comparison of the obtained results indicates the significance of grooves distance and the inner diameter of thetube as the most influential parameters.Thu, 28 Feb 2019 20:30:00 +0100An Integrated Approach for Facility Location and Supply Vessel Planning with Time Windows
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This paper presents a new model of two-echelon periodic supply vessel planning problem with time windows mix of facility location (PSVPTWMFL-2E) in an offshore oil and gas industry. The new mixed-integer nonlinear programming (MINLP) modelconsists ofa fleet composition problem and a location-routing problem (LRP). The aim of the model is to determine the size and type of large vessels in the first echelon and supply vessels in the second echelon.Additionally,the location of warehouse(s),optimal voyages and related schedules in both echelons are purposed.The total cost should be kept at a minimum and the need of operation regions and offshore installationsshould be fulfilled.A two-stage exact solution method, which is common for maritime transportation problems, is presented for small and medium-sized problems. In the first stage, all voyages are generated and in the second stage, optimal fleet composition, voyages and schedules are determined. Furthermore, optimal onshore base(s) to install central warehouse(s)and optimal operation region(s) to send offshore installation’s needs are decided in the second stage.Thu, 28 Feb 2019 20:30:00 +0100A New Version of Earned Value Analysis for Mega Projects Under Interval-valued Fuzzy Environment
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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 projectsâ€™ 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 projectsâ€™ 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.Sun, 25 Dec 2016 20:30:00 +0100An Algorithm Based on Theory of Constraints and Branch and Bound for Solving Integrated ...
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One of the most important decision making problems in many production systems is identification and determination of products and their quantities according to available resources. This problem is called product-mix. However, in the real-world situations, for existing constrained resources, many companies try to provide some products from external resources to achieve more profits. In this paper, an integrated product-mix-outsourcing problem (IPMO) is considered to answer how many products should be produced inside of the system or purchased from external resources. For this purpose, an algorithm based on Theory of Constraints (TOC) and Branch and Bound (B&B) algorithm is proposed. For investigation of the proposed algorithm, a numerical example is presented. The obtained results show the optimal result by the new algorithm is as same as the results of integer linear programming.Thu, 28 Feb 2019 20:30:00 +0100Hierarchical Group Compromise Ranking Methodology Based on Euclidean–Hausdorff Distance ...
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Proposinga hierarchical group compromise method can be regarded as one of major multi-attributedecision-making tools that can be introduced to rankthe possible alternatives among conflict criteria. In complex and hesitantsituations, crisp data are inadequate to model thedecision-making problems. In this respect, decision makers’ (DMs) judgmentsare considered as imprecise or fuzzy. In the group decision making,an aggregation of the DMs’ judgments and fuzzy group compromise ranking is more capable and powerful than the classicalcompromise ranking. In this respect, the main purpose of this paper is to extend a new hierarchical group compromise rankingmethodology under a hesitant fuzzy environment to handle uncertainty. In the hesitant fuzzy environment for the margin of error, the DMs could assign the opinions toseveral membership degrees for an element. For this reason, the hesitant fuzzy set (HFS) is a very useful tool to deal with the hesitant/vague situations. The HFSis taken into accountforthe process of the proposed hierarchical groupcompromise rankingmethodology, namely HFHG-CR, and for avoiding the data loss,the DMs’ opinions with risk preferences are considered for each step separately. Hence, the DMs’ judgments are aggregated at the end of the proposed methodology. Also, the Euclidean–Hausdorff distance measure is utilizedin a new proposed index for calculating the average group score, worst group score and compromise measureregarding each DM. In addition, a new ranking index is introduced to obtain the final compromise solutiontothe evaluation.The proposed HFHG-CR methodologyis applied to a practical example for afacility location selection problem, i.e.,cross-dock location problem,to show the validation and application. Finally, the ranking of the proposed methodology is compared with the recent method from the literature.Sun, 25 Dec 2016 20:30:00 +0100A Multi-Objective Fuzzy Approach to Closed-Loop Supply Chain Network Design with Regard to ...
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During the last decade, reverse logistics networks received a considerable attention due to economic importance and environmental regulations and customer awareness. Integration of leading and reverse logistics networks during logistical network design is one of the most important factors in supply chain. In this research, an Integer Linear Programming model is presented to design a multi-layer reverse-leading, multi-product, and multi-period integrated logistics network by considering multi-capacity level for facilities under uncertainty condition. This model included three objectives: maximizing profit, minimizing delay of goods delivering to customer, and minimizing returned raw material from suppliers. This research gives financial incentives to encourage customers in order to return their used product. Considering that the remaining value of used products is the main incentive of a company to buy second-handed goods, a dynamic pricing approach is determined to define purchase price for these types of products, and based on that, the percentage of returned products were collected by customers. In addition, in this study, parameters have uncertainty features and are vague; therefore, at first, they are converted into exact parameters and, then, because model is multi-objective, the fuzzy mathematical programming approach is used to convert multi-objective model into a single objective; finally, the model by version 8 of Lingo is run. In order to solve a large-sized model, a non-dominated sorting genetic algorithm II (NSGA-II) was applied. Computational results indicate the effect of the proposed purchase price on encourage customers to return the used products.Thu, 28 Feb 2019 20:30:00 +0100Parallel Jobs Scheduling with a Specific Due Date: Asemi-definite Relaxation-based Algorithm
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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.Wed, 31 Jan 2018 20:30:00 +0100Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling
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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.Tue, 13 Feb 2018 20:30:00 +0100A Job Shop Scheduling and Location of Battery Charging Storage for the Automated Guided ...
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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 incosts.Sun, 25 Feb 2018 20:30:00 +0100A Mathematical Model for Multiple-Load AGVs in Tandem Layout
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Reducing cost of material handling has been a big challenge for companies. Flexible manufacturing system employed automated guided vehicles (AGV) to maintain efficiency and flexibility of manufacturing system. This paper presents a new non-linear integer mathematical programming model to group n machines into N loops, to make an efficient configuration for AGV system in Tandem layout. The model minimize both inter-loop, intra-loop flow and use balanced-loops strategy to balance workload in system simultaneously. This paper significantly considers multiple-load AGVs, which has ability to reduce fleet size and waiting time of works. A modified variable neighborhood search method is applied for large size problems, which has good accuracy for small and medium size problems. The results indicate that using multiple load AGV instead of single load AGV will reduce system penalty cost up to 44%.Thu, 28 Feb 2019 20:30:00 +0100Design and Analysis of Assembled components Tolerance Using Weibull Distribution
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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.Wed, 17 Oct 2018 20:30:00 +0100A Game Theoretical Approach to Optimize Policies of Government Under the Cartel of Two Green ...
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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.Wed, 17 Oct 2018 20:30:00 +0100Bottleneck Identification Using Time Study And Simulation Modeling Of Apparel Industries
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This study deals with bottleneck detection with simulation techniques and time study. Modeling and simulation are potential tools for analyzing assembly lines such as clothing in a garment. Thus, the researchers attempted to experiment 160 numbers of replication in the arena tool.
The experiment monitors the assembly process resources without altering the actual production scheme. In this study, the arena software and POM dedicated to modeling and measuring the performance of the existing Ronny t-shirt sewing line. The t-shirt is formed and has 12 main parts are assembled. For each activity, the researchers took 15 sampling observations using stopwatch. All the collected data are statistically analyzed using the input analyzer in arena for statistical significance and the expression resolution to be used for the simulation model. The result shows the line is operating with a line balance efficiency of 72.56%. In the course of action using Opt Quest different types of scenarios had been developed and line balance efficiency increase to 75.3% and the company saves 518,400 birr per year that spent for salary and in addition to it the line can be produced additional 60 t-shirts from the 20 lines of the production. This arena simulation model has considered the production resources, processing time of each activity and testing different scenarios.
Sat, 20 Oct 2018 20:30:00 +0100Three Hybrid Metaheuristic Algorithms for Stochastic Flexible Flow Shop Scheduling Problem with ...
http://www.qjie.ir/article_543744_0.html
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.Sat, 20 Oct 2018 20:30:00 +0100Developing a new bi-objective functions model for a hierarchical location-allocation problem ...
http://www.qjie.ir/article_543781_0.html
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: Hierarchical-allocation problem, Queue Theory, Meta-heuristic Algorithm.Sat, 20 Oct 2018 20:30:00 +0100Bi-objective Optimization of a Multi-product multi-period Fuzzy Possibilistic Capacitated Hub ...
http://www.qjie.ir/article_543806_0.html
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.Sat, 20 Oct 2018 20:30:00 +0100A Bi-objective Model to Optimize Reliability and Cost of System for the Aggregate Production ...
http://www.qjie.ir/article_545392_0.html
In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a ε-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.Sun, 30 Dec 2018 20:30:00 +0100Production Planning and Control Strategies Used as A Gear Train for The Death and Birth of ...
http://www.qjie.ir/article_545430_0.html
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.Wed, 02 Jan 2019 20:30:00 +0100The Regional Performance Impacts in the Supply Chain integration: Evidence from East Africa ...
http://www.qjie.ir/article_545822_0.html
The objective of this study was to investigate the relationship between regional firm performance and supply chain integration practices. In addition to literature survey, the primary data collection instrument used was a questionnaire which was administrated to a total sample of 200 industry experts, administrative officers, functional department heads and directors, the industry managing directors and senior staff from 21 manufacturing firms in Ethiopian were incorporated. The data were studied using the AHP, fuzzy TOPSIS, the mind map, and descriptive analysis. Further, the challenges, practices, performance, the practices of East African basic metal industry supply chain integration influences the dynamics contribution of the regional economy was fragmental. According to this study, due to weak, fragmental and non-integrated supply chain systems, the regional economic dynamic contribution of the sector is weak and poor in quality, productivity, flexibility, and global competitiveness were investigated. Moreover, these investigations and analysis of this paper have both practical and scholarly implications. The result is provided to contribute to the future supply chain integration model development and make an action to supply chain integrations for process improvements. Finally, the implications for the design on the dynamic capability of supply chain strategies are showing, and the future research agenda was presentedMon, 21 Jan 2019 20:30:00 +0100A Multi-objective Mixed Model Two-sided Assembly Line Sequencing Problem in a Make –To- Order ...
http://www.qjie.ir/article_545824_0.html
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.Mon, 21 Jan 2019 20:30:00 +0100Multi-Objective Optimization for Multi-Product Multi-Period Four Echelon Supply Chain Problems ...
http://www.qjie.ir/article_545827_0.html
The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are determined the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty.Mon, 21 Jan 2019 20:30:00 +0100An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the ...
http://www.qjie.ir/article_545828_0.html
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.Mon, 21 Jan 2019 20:30:00 +0100An Optimization Model for Heterogeneous Vehicle Routing and Scheduling Problem with Fixed Cost ...
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Vehicle routing problem aims to find the optimal routes that must be traveled by a fleet of vehicles to satisfy the demand of the customers. In this research, the vehicle routing and scheduling problem is developed for a heterogeneous fleet with the fixed cost of applying vehicles and earliness and tardiness costs in a green reverse logistics network. Since the complexity order of these problems is higher than that of the polynomial ones, this problem is known as NP-hard. As the problem dimensions increase, the exact solving time of the problem increases considerably. Thus, metaheuristic methods are proposed to approximately solve these problems. After developing mixed integer nonlinear model, the Genetic Algorithm (GA) is used to find the near-optimal solutions for the large-scale cases. Finally, the performance of the GA is investigated for several examples by comparing its computation time and solution quality with the computation time and exact solution of the LINGO software. According to the results, the developed GA has an acceptable performance in providing solutions with minimum error in a rational time.Mon, 21 Jan 2019 20:30:00 +0100Investigating Water Supply System Electro-Mechanical Equipments Problems: A Case Study of Ethiopia
http://www.qjie.ir/article_545832_0.html
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 equipments 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. Mon, 21 Jan 2019 20:30:00 +0100Fixture Design and Work Piece Deformation Optimization Using the Iterative Simplex Algorithm
http://www.qjie.ir/article_545833_0.html
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.Mon, 21 Jan 2019 20:30:00 +0100Classifying the Customers of Telecommunication Company in order to Identify Profitable ...
http://www.qjie.ir/article_545834_0.html
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.Mon, 21 Jan 2019 20:30:00 +0100