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Articles by M. Zandieh
Total Records ( 9 ) for M. Zandieh
  M. Zandieh and S. Molla- Alizadeh-Zavardehi
  In this study, for coordination of production and distribution scheduling in the implementation of a supply chain solution, we studied the problem of synchronized scheduling of single machine and air transportation in supply chain management. The overall problem is decomposed into two sub-problems, which consists of air transportation allocation problem and a single machine scheduling problem which they are considered together. We have taken into consideration different constraints and assumptions in our modeling such as due window, delivery tardiness and no delivery tardiness. For these purposes, mathematical models have been proposed to minimize supply chain total cost which encompasses transportation, makespan, delivery earliness tardiness and departure time earliness tardiness costs.
  B. Vahdani , M. Zandieh and A. Alem-Tabriz
  In this study, Multiple Criteria Decision Model (MCDM) is used for optimization of several conflicting criteria dependent systems. A MCDM approach taking into account the performance defining attributes such as profitability of supplier, relationship closeness, technological capability, conformance quality and conflict resolution, was adopted to determine the performance ranking of suppliers. It is a three-stepped procedure to derive an overall complete final order of the suppliers. The out-ranking matrix is derived, indicating the frequency of the relative superiority of suppliers with respect to each other based on each criterion. The out-ranking matrix is triangularised to obtain an implicit ordering or provisional order of suppliers, based on sequential application of a balancing principle supported by the pair wise comparison of the suppliers with the help of advantages-disadvantages table.
  M. Amiri , M. Zandieh , B. Vahdani , M. Yazdani and R. Soltani
  In this study, we present a hybrid Multi-Criteria Decision Making (MCDM) model to solve convention site selection. In the proposed model, interval comparison matrix which has been inspired by Analytical Hierarchy Process (AHP) is employed to compare the criteria against each other. Furthermore, to calculate the interval weights of criteria, we make use of Goal Programming (GP). Moreover, interval data is utilized to evaluate the alternatives with respect to the criteria. In order to rank the alternatives with respect to criteria, technique for order preference by similarity to an ideal solution (TOPSIS) with interval data and weights is used. In the conditions where there exist uncertainties for both the comparison of criteria against each other and alternatives evaluation with respect to influential criteria in the process of decision making, using this model facilitates the decision making process and causes the quality of decision will be enhance.
  M. Zandieh , I. Mahdavi and A. Bagheri
  A meta-heuristic approach for solving the flexible job-shop scheduling problem (FJSP) is presented in this study. This problem consists of two sub-problems, the routing problem and the sequencing problem and is among the hardest combinatorial optimization problems. We propose a Genetic Algorithm (GA) for the FJSP. Our algorithm uses several different rules for generating the initial population and several strategies for producing new population for next generation. Proposed GA is tested on benchmark problems and with due attention to the results of other meta-heuristics in this field, the results of GA show that our algorithm is effective and comparable to the other algorithms.
  M. Zandieh , A. Azadeh , B. Hadadi and M. Saberi
  This study presents an integrated Artificial Neural Networks (ANN) to estimate and predict airline number of passenger in Iran. All type of ANN-Multi Layer Perceptron (MLP) is examined to this estimation. The ANN models are implemented on MATLAB software. Auto-Correlation Function (ACF) is utilized to define input variables. Finally, the best type of ANN-MLP is determined with Data Envelopment Analysis (DEA). Kruskal-Wallis test is used for asses the impact of raw data, preprocessed data and post process method on ANN performance. Monthly airline number of passenger of Iran airline from 1993 to 2005 is considered as the case of this study.
  M.B. Abiri , M. Zandieh and A. Alem-Tabriz
  This study describes a Tabu Search (TS) algorithm approach to the scheduling of a sequence-dependent setup times hybrid flow shop. The details of a TS approach are described and implemented. The results obtained are compared with those computed by Random Key Genetic Algorithm (RKGA) presented earlier. From the results, it was established that TS outperformed RKGA.
  M. Zandieh , F. Jolai , S.M.T. Fatemi Ghomi and M. Mirabi
  This study investigate the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimization criteria considered is the minimization of the makespan or Cmax. Many heuristics and meta-heuristics have been successfully applied to this kind of problem before like genetic algorithm, tabu search and greedy algorithm and the objective of this study is to assess their effectiveness in a more realistic and complex environment. We present a hybrid electromagnetism-like (HEM) algorithm for the permutation flowshop scheduling with sequence dependent setup times that have shown superior performance against other meta-heuristics when applied to proposed problem. The proposed HEM algorithm benefits of a new concept named priority assigning to calculate electrostatic force and also it implements a new formulation for solution charge. Using a good approach for acquiring the initial solutions and also some effective local searches to finding neighborhood solutions are other novelties of the HEM. For evaluating the proposed algorithm we have coded several well-known algorithms for SDST flowshop. All methods including HEM are tested on the randomly instances and results indicate that HEM is very competitive with the existing best-performing algorithms.
  M. Yazdani , M. Gholami , M. Zandieh and M. Mousakhani
  This study addresses the flexible job-shop scheduling problem to minimize makespan. In fact, the FJSP mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines and the second one is to sequence the assigned operations on all machines. Hence, to solve this NP-hard problem, a simulated annealing algorithm is proposed. The meta-heuristic algorithm explores the solution space using a stochastic local search while trying to avoid local optima through accepting probabilistic moves to the worse solutions. The neighborhood search structures of assignment and sequencing are used for generating neighboring solutions to search the solution space. To evaluate the performance of the algorithm, twenty benchmark problems adopted from the literature are used. Consequently, the computational results validate the quality of present approach.
  A. Alem Tabriz , M. Zandieh and Z. Vaziri
  This study deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board and automobile manufacture. This study describes a simulated annealing algorithm to the scheduling of a hybrid flow shop with sequence-dependent setup times. The obtained results are compared with those computed by RKGA presented previously. The superiority and effectiveness of our novel simulated annealing algorithm is inferred from all the results obtained in various situations.
 
 
 
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