Computers and Industrial Engineering (03608352)193
Designing an efficient production planning system will reduce production costs, increase customer satisfaction, and ensure sustainability in the global competitive market. This paper focuses on multi-objective optimization in production planning through the utilization of the Demand-Driven material requirement planning (DDMRP) approach. We employ a simulation–optimization method that uses a novel hybrid genetic algorithm and particle swarm optimization, to simultaneously optimize conflicting inventory cost and stockout objectives. To the best of our knowledge, there are rarely studies in the literature that optimized and reviewed the suitable value of lead time and variability factors in this approach. For the first time, this study considers the integration of the strategic level (strategic buffer positioning phase) and operational level (planning phase) of the DDMRP approach due to the interaction of these two issues. This study focuses on the subjective to prove the performance of the DDMRP approach by eliminating its effect. The results demonstrate that an integrated review of both the strategic and operational levels in determining the lead time and variability factors results in lower costs. The average inventory cost, stockout, and total cost are reduced by 21%, 68%, and 78% respectively, compared with the results of the state-of-the-art methods. © 2024
International Journal of Environmental Science and Technology (17351472)21(2)pp. 1619-1636
The waste collection problem is one of the critical problems in today’s world, and ignoring this issue or the existence of a fault in this system can cause huge costs and damages. The advanced countries in the world are trying to improve the efficiency of their waste collection system with modern methods to solve the challenges of this system. The application of Internet of Things (IoT) and RFID tags is an interesting field of research in urban waste management systems. This study develops three models for urban waste collecting. The ST model is a traditional and static method currently used in many cities. The DSA is a semi-modern model based on greedy algorithms in which RFID tags are installed on garbage bins. The DAIoT model is a modern system working with IoT equipment installed on trucks and waste bins. This model uses a combination of greedy algorithm and harmony search metaheuristics. The main purpose of this study is to schedule the waste collection system and vehicle routing to reduce trucks' gas emissions and empty garbage bins on time. The results on Isfahan city show that compared to the traditional ST model, the DSA model causes a 2% reduction in gas emissions and a 6.7% reduction in the number of required trucks and improves system performance in critical situations. The DAIoT model, as the best model, causes a 33.9% reduction in greenhouse gas emissions and a 60% reduction in the number of trucks compared to the traditional ST model. © 2023, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.
Annals of Operations Research (02545330)328(1)pp. 727-754
This study considers a combination of strategic and tactical levels of operating room planning where two types of full-day and half-day blocks are considered. Constraints on available beds in hospital wards and different durations of stay for elective patients in the ward are the main assumptions of the problem. The aim is to minimize overtime and idleness of operating rooms, maximize surgeons' satisfaction and minimize the number of unscheduled surgeries in the master surgery schedule. We propose a mixed integer programming model as well as a novel heuristic algorithm by combining simulated annealing meta-heuristic and linear programming models. Real data from a teaching-educational hospital with 20 operating rooms and 47 surgeons’ groups as well as some random problem instances are used in the experiments. The results indicate high performance of the proposed heuristic algorithm in generating near-optimal Pareto solutions compared with the mathematical model and a local search algorithm from the literature. Sensitivity analysis is done on some parameters of the problem like overtime cost of operating rooms, maximum allowable overtime, available beds in the ward, and the number of attendance days preferred by each surgeon. The results from our case study show that 6% of operating room costs are related to the fact that some surgeons are unwilling to perform surgeries on some days of a week. Also, adding 20% to the capacity of ward beds results in 3% and 10% decrease in unscheduled surgeries and operating rooms idleness, respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
International Journal of Industrial Engineering : Theory Applications and Practice (1943670X)29(1)pp. 45-63
This paper investigates the integrated master surgical scheduling and case-mix planning problem with the objective of a weighted sum of minimizing the costs of overtime and idle time, increasing surgeons' preferences, and reducing uncovered demands. Considering the uncertainty in surgery demands, a robust optimization model is proposed. A two-stage method is designed for creating and updating this scheduling with respect to downstream resources. The first stage allocates the time blocks to each surgeon to determine the mix of surgery types assigned to each block. In the second stage, having the weekly waiting list of patients, the schedule is updated on the weekly horizon to cope with demand fluctuations and to maximize the use of operating rooms capacity. The proposed method is validated using actual data from a teaching hospital in Iran. The results of the proposed models significantly outperformed the actual plan of a hospital, which indicated the efficiency of the designed models. Comparing the deterministic and robust models shows that robust models lead to better results in over 70% of the test instances. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
Applied Soft Computing (15684946)119
With the increase in the volume of greenhouse gases and pollutants, supply chain managers have sought to design and set up networks that pay special attention to environmental factors besides the economic aspects. In this research, a three-level supply chain network is considered including one manufacturer, several retailers and several customers. Greenhouse gas emissions as an environmental issue, the dependence of demand on the selling price of products and cooperative advertising have been examined to design this network. Also, several transportation systems and production methods with different environmental effects and different costs have been considered. The problem has been modeled by both the general and advertising cost classification approaches. Each model has been linearized by McCormick and sequential linear programming methods, and in each approach, Stackelberg, Nash and cooperative games have been used to determine the relationship between members of the supply chain. Finally, an algorithm has been proposed from a combination of variable neighborhood search algorithm and mathematical modeling and after adjusting its parameters by Taguchi statistical method, Stackelberg, Nash and cooperative games have been implemented on it. The results of linearization methods and the meta-heuristic algorithm show that the profit of the manufacturers, retailers and the whole supply chain depends on the type of the game selected. The profit of the whole supply chain is greater in cooperative conditions than in non-cooperative conditions, and in non-cooperative games, the final profit of the manufacturer will be greater in Stackelberg game. © 2022 Elsevier B.V.
Journal Of Grid Computing (15707873)20(3)
In this research, the grid scheduling problem has been investigated in order to maximize profit considering the dynamic voltage and frequency scaling technique, customer-centric quality of service and time-dependent energy pricing. Mixed-integer linear programming, constraint programming, a greedy heuristic algorithm along with a hybrid method of genetic algorithm and constraint programming are developed. Some techniques are proposed to improve the efficiency of the presented constraint programming model, and their effectiveness is investigated using a full factorial experiment. Parameters of the proposed hybrid algorithm have been set by Taguchi test. The hybrid meta-heuristic algorithm, with a short execution time, generates solutions of about 18% and 88% better than the best solution of the constraint programming model for large-scale problem instances. The results show that the final profit will be reduced by about 22% if the electricity prices are wrongly considered with a flat rate during the scheduling process. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Applied Mathematical Modelling (0307904X)105pp. 438-453
The planning of operating rooms under block strategy is addressed in this study. The decisions are about opening the operating rooms and assigning specialties and surgeons to blocks at the tactical level, and sequencing the surgeries at the operational level. This problem aims to minimize the costs of opening the operating rooms and their overtime, the waiting costs of patients, and the surgeons' idle costs. We propose two mixed-integer linear programming models, a constraint programming (CP) model and a constraint programming-based column generation (CPCG) method for handling the problem. The performance of the models is evaluated by random test instances. The results indicate that CP and CPCG models are more efficient than the linear programming models in terms of computational time, and the number of variables and constraints. The proposed method CPCG generates optimal solutions for problem instances of up to 30 surgeries in less than 4 min. The CP model finds the optimal solutions in about one minute but proving the optimality of the found solutions is time-consuming in some instances. The maximum optimality gap for the proposed two-step linear programming model is 2%, while its run time is less than 20 s. A sensitivity analysis is done on the main parameters of the problem like objectives' weights, opening cost of ORs, unit waiting cost of patients, and the maximum available time in surgery blocks. Among the three objectives, the unit waiting cost of patients has the most sensitivity to variations of the objective function weights. © 2022
Thermal Science and Engineering Progress (24519049)22
As one of the thermal management elements, heat pipes are frequently used in cooling or heat recovery systems. They are devices that can quickly transfer large amounts of heat with a slight temperature difference between hot and cold sources. The present study aims to evaluate and measure the lifetime of heat pipes in space applications such as spacecraft and satellites. In order to investigate the lifetime of heat pipes in different operating temperatures, three prediction methods of particle filtering, Arrhenius, and FIDES are applied. Innovative approaches are adopted to combine the Arrhenius and FIDES methods besides adjusting the FIDES method for mechanical devices. The failure times at the accelerated temperatures are estimated through the particle filtering method as well as linear and exponential regressions, and failure times at the operating temperatures are estimated by the Arrhenius model. The FIDES method adds more aspects of reliability to the prediction procedure, such as the quality of the used materials, technical conditions of the manufacturing process and the stress cycles applied to the heat pipes. A sensitivity analysis is also performed on the failure criteria. The results show that the failure time for the heat pipes is between 12 and 60 years, which entirely fits the related standards. © 2021 Elsevier Ltd
Computers and Industrial Engineering (03608352)159
Cellular manufacturing system (CMS) is a novel production system adaptable to the make-to-order production. The present study focuses on scheduling CMS aimed at maximizing total profits as a function of the revenues earned from sales as well as energy consumption cost and order tardiness penalties. The components to be considered in the problem in hand include the time-dependency of energy price, price elasticity of demand, and speed-based power consumption of machines. Two linearization approaches are used to determine order quantities. The first chooses lot sizes from a continuous range while the second chooses them among prespecified discrete levels. Especially developed mathematical models are used to solve the problem in either approach. For the second linearization approach, a constraint programming model, and a hybrid algorithm based on the fix-and-optimize and variable neighborhood search metaheuristic (FOVNS, for short) are additionally developed. Changing the branching procedure as a technique and three dominance rules are also proposed to improve the performance of the CP and FOVNS models while their effectiveness is examined using the full factorial design of experiments. Also, the parameters of the FOVNS are tuned using the Taguchi method. Exact methods are found capable of optimizing medium-size problems in less than an hour while FOVNS is able to optimize large-size ones in 822 seconds on average with a deviation of 1.8% from the optimal solution. Statistical analysis show that considering a time-dependent energy price in the scheduling decreases the energy cost by about 40%. © 2021 Elsevier Ltd
International Journal of Services and Operations Management (17442370)40(3)pp. 305-323
The aim of the study is to find how a framework could be developed for selecting the relevant strategy and policy of maintenance by TOPSIS and weighted mean approaches. For this purpose, first maintenance criteria and KPIs have been weighted; and then, maintenance strategy and policy have been selected. Esfahan Steel Company (ESCO) has been selected as the case study where in, the proposed approach has been examined. Statistical population included maintenance experts and managers of the company. TOPSIS and weighted mean approaches have been used for analysis. Findings imply that the strategy of preventive maintenance and policy of proactive maintenance have been selected and suggested to ESCO for decision making with the weights of 144.86 and 162.84, respectively. Copyright © 2021 Inderscience Enterprises Ltd.
International Journal of Services and Operations Management (17442370)36(4)pp. 531-557
Computational grids consist of the innovative technologies of the new era, which seek to accelerate performance through distributing tasks on computing resources. A grid system makes it feasible to run great computing operations through the connected processors. In this article, a bi-objective problem of grid scheduling based on quality of service concept is discussed. The first objective is to increase the profit earned from customers and the second, to increase the utilisation of computational resources. A mathematical programming model is proposed for the problem and a meta-heuristic NSGA-II algorithm is designed and customised for the problem. In the numerical analysis, by drawing Pareto diagrams and analysing the sensitivity thereof, the efficiency of the proposed methods and the effect of different parameters of the problem on both the methods are assessed. According to the results, the proposed NSGA-II algorithm is highly efficient in terms of solution quality and run time. © 2020 Inderscience Enterprises Ltd.
Annals of Operations Research (02545330)292(1)pp. 191-214
Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Computers and Industrial Engineering (03608352)135pp. 265-274
A three-level supply chain, including manufacturers, distribution centers and customer zones is assessed in this article. The objective is to maximize the gross profit without violating the operational constraints and capacities of production and inventory. Gross profit is achieved by subtracting costs from the final revenue. Different sources of cost related to raw materials, transportation, production and advertising are addressed. The final demand for a product depends on its price and advertising expenditures. Three solution methods of a mathematical model, a harmony search algorithm and a new combined algorithm of both are proposed for the problem. The mathematical model is of type mixed integer quadratically constrained quadratic programming and its characteristics are analyzed. The experimental results reveal that, statistically speaking, the proposed heuristic algorithms converge into optimal solution with gaps of less than 2 percent. A comprehensive sensitivity analysis is run on price and advertising elasticity coefficients, manufacturers and distribution centers' capacity, base demand and unit transportation cost. © 2019 Elsevier Ltd
Operations Research for Health Care (22116923)23
Operating Room (OR) Scheduling is one of the most critical problems at the operational level for hospital managers. A useful strategy for OR scheduling, especially in large hospitals is the block strategy. In this strategy, a specific time is blocked for each surgeon or surgical team. This strategy usually leads to unused operating rooms’ capacity. To overcome this problem, in this article, a novel modified block strategy is presented for the daily scheduling of elective patients. This study aims to find the optimal sequence and schedule of patients by minimizing the cost of overtime, makespan and completion time of surgeons’ operations by considering the resource constraints. Considering the limitations and real conditions of Al-Zahra Hospital, the largest educational hospital in Isfahan, Iran, is also an aspect of this study. The problem is modeled by mixed integer programming and Constraint Programming (CP). The performance of the models is verified by several random test instances. The results indicate that CP is more efficient than mathematical modeling in terms of the computational time for solving the considered problems, especially for large-size instances. The average percent of improvement in computational time is about 53% using the CP model. The proposed CP model is also used to solve real problem instances from Al-Zahra hospital. The results show that by using the CP model, the completion time of surgeons’ operations is shortened by 9% and ORs’ overtime and makespan objectives are reduced by 55% and 20% respectively. © 2019
Journal of Manufacturing Technology Management (1741038X)29(8)pp. 1296-1315
Purpose: The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators. Design/methodology/approach: First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined. Findings: The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively. Research limitations/implications: While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve. Originality/value: While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies. © 2018, Emerald Publishing Limited.
Computational and Applied Mathematics (18070302)37(2)pp. 867-895
This paper addresses minimizing Tardy/Lost penalties with common due dates on a single machine. According to this penalty criterion, if tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. The problem is formulated as an integer programming model, and a heuristic algorithm is constructed. Then, using the proposed dominance rules and lower bounds, we develop two dynamic programming algorithms as well as a branch and bound. Experimental results show that the heuristic algorithm has an average optimality gap less than 2 % in all problem sizes. Instances up to 250 jobs with low variety of process times are optimally solved and for high process time varieties, the algorithms solved all instances up to 75 jobs. © 2016, SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional.
Journal of Supercomputing (15730484)71(3)pp. 1143-1162
Scheduling dynamically arriving parallel jobs on a grid system is one of the most challenging problems in supercomputer centers. Response time guarantee is one aspect of providing quality of service (QoS) in grids. Jobs are differently charged depending on the response time demanded by the user and the system must provide completion time guarantees. To tackle these challenges, we propose a new type of utility function for defining QoS in user-centric systems. The proposed utility function is a general form of functions in the literature. This function provides customers and system managers with more options to design SLA contracts. Also, its two due dates can make customers more confident and produce more profit for system providers. This paper develops a novel simulated annealing algorithm combined with geometric sampling (GSSA) for scheduling parallel jobs on a grid system. The proposed algorithm is compared with two other methods from the literature using three metrics of total utility, system utilization and the percentage of accepted jobs. The results show that the proposed GSSA algorithm is able to improve the metrics via better use of resources and also through proper acceptance or rejection decisions made on newly arriving jobs. © 2014, Springer Science+Business Media New York.
Journal Of Applied Mathematics (16870042)2014
This paper addresses a new performance measure for scheduling problems, entitled "biased tardiness penalty." We study the approximability of minimum biased tardiness on a single machine, provided that all the due dates are equal. Two heuristic algorithms are developed for this problem, and it is shown that one of them has a worst-case ratio bound of 2. Then, we propose a dynamic programming algorithm and use it to design an FPTAS. The FPTAS is generated by cleaning up some states in the dynamic programming algorithm, and it requires O n 3 / ε time. © 2014 G. Moslehi and K. Kianfar.
Discrete Applied Mathematics (0166218X)161(13-14)pp. 2205-2206
In this note, through a counter-example we show that the results in a recent paper (Kacem, 2010) [1] are incorrect. Since the proposed dynamic programming in Kacem (2010) [1] fails to optimally solve the problem in some instances, the designed FPTAS with O(n2/ε) complexity is wrong. Then, we provide a modified FPTAS of O(n3/ε) time complexity for the considered problem. © 2013 Elsevier B.V. All rights reserved.
Computers and Operations Research (03050548)39(12)pp. 2978-2990
This paper considers the problem of scheduling a single machine, in which the objective function is to minimize the weighted quadratic earliness and tardiness penalties and no machine idle time is allowed. We develop a branch and bound algorithm involving the implementation of lower and upper bounding procedures as well as some dominance rules. The lower bound is designed based on a lagrangian relaxation method and the upper bound includes two phases, one for constructing initial schedules and the other for improving them. Computational experiments on a set of randomly generated instances show that one of the proposed heuristics, used as an upper bound, has an average gap less than 1.3% for instances optimally solved. The results indicate that both the lower and upper bounds are very tight and the branch-and-bound algorithm is the first algorithm that is able to optimally solve problems with up to 30 jobs in a reasonable amount of time. © 2012 Elsevier Ltd. All rights reserved.
Kianfar, K.,
Fatemi ghomi s.m.t., ,
Oroojlooy jadid a., Engineering Applications of Artificial Intelligence (09521976)25(3)pp. 494-506
A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules. © 2011 Published by Elsevier Ltd. All rights reserved.
Zadeh, A.H.,
Maleki, H.,
Kianfar, K.,
Fathi, M.,
Zaeri, M.S. Advances in Intelligent and Soft Computing (18675670)73pp. 161-169
This work is concerned with the fuzzy clustering problem of different products in j variant catalogs, each of size i products that maximize customer satisfaction level in customer relationship management. The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example. © Springer-Verlag Berlin Heidelberg 2010.
International Journal of Advanced Manufacturing Technology (02683768)47(1-4)pp. 269-281
Reverse logistics is becoming more important in overall industry area because of the environmental and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and a nice social picture for companies. But, most of logistics networks are not equipped to handle the return products in reverse channels. This paper proposes a mixed integer linear programming model to minimize the transportation and fixed opening costs in a multistage reverse logistics network. Since such network design problems belong to the class of NP-hard problems, we apply a simulated annealing (SA) algorithm with special neighborhood search mechanisms to find the near optimal solution. We also compare the associated numerical results through exact solutions in a set of problems to present the high-quality performance of the applied SA algorithm. © 2009 Springer-Verlag London Limited.
In this paper, a metaheuristic approach for the two-machine flow-shop problem with a common due date and the weighted late work performance measure (F2|dj=d|Yw) are presented. The late work criterion estimates the quality of a solution with regard to the duration of the late parts of jobs, not taking into account the quantity of the delay for the fully late activities. Since the problem mentioned is known to be NP-hard, a trajectory methods, namely GRASP is proposed based on the special features of the case under consideration. Then, the results of computational experiments are reported, in which the metaheuristic solution is compared with exact approach and three other heuristic methods' results. ©2009 IEEE.
This work is concerned with customer-oriented catalog segmentation that each catalog consists of specific number of products. In this problem, requirements of a specific ratio of customers should be satisfied. According to the definition, when a customer is satisfied that at least t required products exist in his/her catalog. The objective of this problem is to minimize the number of catalogs, regarding to minimum number of customers constraint that was comply. In this paper, we present a mixed-integer programming model for this clustering problem. This problem is NP-Hard in large scales and the optimum solution is almost impossible to reach. Hence, a solution procedure is developed based on genetic algorithm. Then, the results of computational experiments are reported, in which the GA solution is compared with exact solution of mixed-integer programming model. ©2009 IEEE.
This work is concerned with the fuzzy clustering problem of different products in k variant catalogs, each of size r products that maximize customer satisfaction level in customer relationship management (CRM). The satisfaction degree of each customer is defined as a function of his/her needed product number that exists in catalog and also his/her priority. To determine the priority level of each customer, firstly customers are divided to three clusters with high, medium and low importance based on his/her needed products list. Then, all customers have been ranked based on their membership level in each of the above three clusters. In this paper in order to cluster customers, fuzzy c-means algorithm is applied. The proposed problem is firstly modeled as a bi-objective mathematical programming model. The objective functions of the model are to maximize the number of covered customers and overall satisfaction level results of delivering service. Then, this model is changed to a single integer linear programming model by applying fuzzy theory concepts. Finally, the efficiency of the proposed solution procedure is verified by using a numerical example. ©2009 IEEE.
International Journal of Advanced Manufacturing Technology (02683768)45(7-8)pp. 759-771
This paper studies a flexible flow shop system considering dynamic arrival of jobs and the ability of acceptance and rejection of new jobs. The problem objective is to determine a schedule that minimizes sum of the tardiness and rejection costs of jobs. A 0-1 mixed integer model of the problem is formulated. Since this problem class is NP-hard, four dispatching rules have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the flexible flow shop system is developed for the purpose of experimentation. Four dispatching rules from the literature and four new dispatching rules proposed in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization level, due date tightness and number of stages in flexible flow shop. The results indicate that proposed dispatching rules provide better performance under problem assumptions. © 2009 Springer-Verlag London Limited.
