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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: chance constraint,Mixed-Model assembly line sequencing,Stochastic operation time
- چکیده:
- چکیده انگلیسی: In today’s competitive market, those producers who can quickly adapt themselves todiverse demands of customers are successful. Therefore, in order to satisfy these demands of market, Mixed-model assembly line (MMAL) has an increasing growth in industry. A mixed-model assembly line (MMAL) is a type of production line in which varieties of products with common base characteristics are assembled on. This paper focuses on this type of production line in a stochastic environment with three objective functions: 1) total utility work cost, 2) total idle cost, and 3) total production rate variation cost that are simultaneously considered.
In real life, especially in manual assembly lines, because of some inevitable human mistakes, breakdown of machines, lack of motivation in workers and the things alike, events are notdeterministic, sowe consideroperation time as a stochastic variable independently distributed with normal distributions; for dealing with it, chance constraint optimization is used to model the problem. At first, because of NP-hard nature of the problem, multi-objective harmony search (MOHS) algorithm is proposed to solve it. Then, for evaluating the performance of the proposed algorithm, it is compared with NSGA-II that is a powerful and famous algorithm in this area. At last, numerical examples for comparing these two algorithms with some comparing metrics are presented. The results have shown that MOHS algorithm has a good performance in our proposed model.- انتشار مقاله: 30-09-1394
- نویسندگان: Parviz Fattahi,Arezoo Askari
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: particle swarm optimization,Simulated Annealing,Multi-objective optimization,Mixed-model assembly line balancing,different skilled workers
- چکیده:
- چکیده انگلیسی: This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm. This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minimization of the total human cost for a given cycle time. In addition, the performance of proposed algorithm is evaluated against a set of test problems with different sizes. Also, its efficiency is compared with a Simulated Annealing algorithm (SA) in terms of the quality of objective functions. Results show the proposed algorithm performs well, and it can be used as an efficient algorithm
- انتشار مقاله: 11-06-1393
- نویسندگان: Parviz Fattahi,Parvaneh Samouei
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: particle swarm optimization,Queuing theory,Branch and bound,Cell formation
- چکیده:
- چکیده انگلیسی: This paper presents a new nonlinear mathematical model to solve a cell formation problem which assumes that processing time and inter-arrival time of parts are random variables. In this research, cells are defined as a queue system which will be optimized via queuing theory. In this queue system, each machine is assumed as a server and each part as a customer. The grouping of machines and parts are optimized based on the mean waiting time. For solving exactly, the proposed model is linearized. Since the cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Also, full factorial and Taguchi methods are used to set the crucial parameters in the solutions procedures. Numerical experiments are used to evaluate the performance of the proposed algorithms. The results of the study show that the proposed algorithms are capable of generating better quality solutions in much less time. Finally, a statistical method is used which confirmed that the MPSO algorithm generates higher quality solutions in comparison with the genetic algorithm (GA).
- انتشار مقاله: 01-11-1392
- نویسندگان: Parviz Fattahi,Bahman Esmailnezhad,Amir Saman Kheirkhah
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Inventory Control,Preventive maintenance,Joint Optimization,stochastic demand
- چکیده:
- چکیده انگلیسی: Joint optimal inventory control and preventive maintenance is a rich area of academic research that is still in its infancy and has the potential to affect manufacturing systems' performance. Also, due to uncertainties in demand, maintenance and inventory loss are virtually unavoidable. Therefore, determining the optimal amount of inventory storage, the time to create an additional inventory for storage, and the time of maintenance operations is a concern of many manufacturers. In this paper, a joint optimization model has been developed. In which, for the proximity of reality, demand is considered as an uncertain parameter. The strategy is such that the production component is placed under maintenance as soon as it reaches the level or in the event of a malfunction earlier than , stopped system and placed under maintenance and repairs. Inventory of period with level is created, which during maintenance operations, stochastic demand will be provided. Finally, a model for joint optimization of maintenance and inventory control with random failure is used that minimize the cost and create the maximum level of accessibility. A numerical study is conducted to show the effectiveness and applicability of the proposed integrated model. An accurate algorithm is provided to solve the model. The results show that the model is generally sensitive to the cost.
- انتشار مقاله: 14-01-1399
- نویسندگان: Parviz Fattahi,Samane Babaeimorad,Fateme Karimi,Elham SabetiSaleh
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Mixed-model,Theory of Constraints,Simulated Annealing Algorithm (SA),Two-sided assembly line balancing problem (TSALBP),worker assignment,particle swarm optimization algorithm (PSO)
- چکیده:
- چکیده انگلیسی: This paper addresses a multi-objective mixed-model two-sided assembly line balancing and worker assignment with bottleneck analysis when the task times are dependent on the worker’s skill. This problem is known as NP-hard class, thus, a hybrid cyclic-hierarchical algorithm is presented for solving it. The algorithm is based on Particle Swarm Optimization (PSO) and Theory of Constraints (TOC) and consists of two stages. In stage one, simultaneous balancing and worker assignment are studied. In stage two, bottleneck analysis and product-mix determination are carried out. In addition, a bi-level mathematical model is presented to describe the problem.
The following objective functions are verified in this paper: (1) minimizing the number of mated-stations (2), minimizing the number of stations (3) minimizing the human costs (4) minimizing the weighted smoothness index and (5) maximizing the total profit. In addition to the proposed algorithm, another algorithm, which is based on the simulated annealing and the theory of constraints, is developed to compare the performance of the proposed algorithm in terms of the running time and the solution quality over the different benchmarked test problems. Moreover, several lower bounds are developed for the number of the stations and the number of the mated-stations. The results show and support the efficiency of the proposed approaches.- انتشار مقاله: 11-06-1396
- نویسندگان: Parvaneh Samouei,Parviz Fattahi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Lot streaming,Job shop,Parallel Assembly,HGAPSA,HGAPVNS
- چکیده:
- چکیده انگلیسی: In this paper, a Job shop scheduling problem with a parallel assembly stage and Lot Streaming (LS) is considered for the first time in both machining and assembly stages. Lot Streaming technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. Hence, to solve job shop scheduling problem with a parallel assembly stage and lot streaming, decision makers not only need to determine the processing sequences on machines in first stage, but also need to assign each product to a machine and determine the assembly sequences of the products in second stage and the sub-lot sizes of all jobs and products to minimize the makespan. At first, this problem is modeled as a mixed integer linear programming and GAMS software is applied to solve small problems. Since this problem is classified as NP-hard, four hybrid algorithms based on iterative procedures are suggested to solve the problem in medium and large dimensions. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor. Computational results revealed that the hybrid genetic and parallel simulated annealing algorithm (HGAPSA) and the hybrid genetic and parallel variable neighborhood search algorithm (HGAPVNS) perform better than the other proposed algorithms with respect to the objective function. Also, considering lot streaming for both stages instead of applying it only to the first stage leads to achieve better solutions. Finally, the HGAPSA algorithm is compared with a hybrid genetic algorithm (HGA). Experimental results showed that the HGAPSA outperforms the HGA in terms of solution quality.
- انتشار مقاله: 03-11-1395
- نویسندگان: Parviz Fattahi,Fatemeh Daneshamooz
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Assembly line rebalancing,problem, worker assignment,heuristic-simulation algorithm,task time dependent on the worker’s skill
- چکیده:
- چکیده انگلیسی: In this paper, an analytical approach is used for assembly line rebalancing and worker assignment for single and mixed-model assembly lines based on a heuristic-simulation algorithm. This approach helps to managers to select a better marketing strategy when different combinations of demands are suitable.Furthermore, they can use it as a guideline to know which worker assignment is better for each combination. We show the efficiency of the proposed approach for single and mixed-model assembly lines using different benchmarked standard test problems with different number of tasks, stations, skilled workers and demands. Comparisons show the heuristic-simulation algorithm is faster than the GAMS software, and its results are optimum or very close to the optimum values.
- انتشار مقاله: 09-06-1394
- نویسندگان: Parviz Fattahi,Parvaneh Samouei
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Ant Colony Algorithm,Production Planning,Capacitated lot-sizing,Shifting technique
- چکیده:
- چکیده انگلیسی: The economical determination of lot size with capacity constraints is a frequently complex, problem in the real world. In this paper, a multi-level problem of lotsizing with capacity constraints in a finite planning horizon is investigated. A combination of ant colony algorithm and a heuristic method called shifting technique is proposed for solving the problem. The parameters, including the costs, demands and capacity of resources vary during the time. The goal is to determine the economical lot size value of each product in each period, so that besides fulfilling all the needs of customers, the total cost of the system is minimized. To evaluate the performance of the proposed algorithm, an example is used and the results are compared other algorithms such as: Tabu search (TS), simulated annealing (SA), and genetic algorithm (GA). The results are also compared with the exact solution obtained from the Lagrangian relaxation method. The computational results indicate that the efficiency of the proposed method in comparison to other meta-heuristics.
- انتشار مقاله: 17-12-1392
- نویسندگان: Vahid Hajipour,Parviz Fattahi,Arash Nobari
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Engineering, Transactions A: Basics
- نوع مقاله: Journal Article
- کلمات کلیدی: non,Programming,Production inventory routing problem,IRP,mixed integer,perishable,dominant sorting genetic algorithm
- چکیده:
- چکیده انگلیسی: In this study, a two echelons supply chain system in which a supplier is producing perishable product and distribute it to multiple customers is considered. By allowing lateral transshipment mechanism, it is also possible to deliver products to some customers in some periods in bulk, then customers using their own vehicle to transship goods between each other seeking further reduction in the overall cost. The aim here is minimizing the production, inventory carrying cost, and distribution as the first objective, and transshipment cost as the second objective, which is contrary objectives, without facing any shortage anywhere in the chain during the planning horizon. This problem is formulated as a bi-objectives mixed integer programming (BOMIP), and then a proper Pareto front as a set of multiple decision alternatives is provided using NSGAII and NRGA approach. Novelty of this research is providing a bi-objectives mathematical modeling of perishable product inventory routing with production and transshipment (BO-P-PIRPT) that help the decision maker to choose the best mixture of routing and transshipment.
- انتشار مقاله: 11-10-1348
- نویسندگان: Parviz Fattahi,Mahdi Bashiri,Mehdi Tanhatalab
- مشاهده
- جایگاه : پژوهشی
- مجله: Advances in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: reliability,Cell formation problem,Queuing theory,Metaheurstic algorithm
- چکیده:
- چکیده انگلیسی: In this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. Machine as server and part as customer are assumed where servers should service to customers. Since, the cell formation problem is NP-Hard, therefore, deterministic methods need a long time to solve this model. In this study, genetic algorithm and modified particle swarm optimization algorithm are presented to solve problems. Because the metaheurstic algorithms quality depends strongly on selected operators and parameters, design of experiment is done for set parameters. The deterministic method of branch and bound algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm.Evaluates indicate better performance of the proposed algorithms in quality the metaheurstic algorithms final solution and solving time in comparing with the method of Lingo software’s branch and bound. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.
- انتشار مقاله: 03-12-1392
- نویسندگان: Parviz Fattahi,Amir Saman Kheirkhah,Bahman Esmailnezhad
- مشاهده