Scientia Iranica
Volume:24 Issue: 3, 2017

Consider a supply chain including a refinery producing evaporating chemical product, an exporter and one or some engine oil producers outside of exporter’s country. Exporter to decrease his inventory cost uses vendor managed inventory system implemented between refinery and exporter. This paper develops two models with partial backordering for evaporating chemical productdeveloped in a two-layer chain including single refinery and single exporter with one product before and after utilizing vendor managed inventory policy respectively. Demand and partial backordering rates are deterministic and constant. A numerical example is provided to illustrate the applicability of the proposed model and solution method.

Mixed-model assembly line (MMAL) is a type of production line where a variety of products’ models similar to products’ characteristics is assembled in the same line. Many manufacturers tend to use mixed-model assembly line in their production lines, since this policy make possible to assemble various products with the Make to order (MTO) environment. In this research, the sequence of U-type mixed model assembly line is achieved through considering downstream help and kits’ storage as effective help policies for reducing total line stoppages and tardiness of products’ delivering time to customers. Since this problem, is NP-hard, hybrid GA-Beam search algorithm is developed to solve the problem. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. To the best of our knowledge, this is the first study that considers getting help from downstream worker or using kits’ storage, which has ready to assemble parts in the conditions that workers cannot complete the remained task in the work horizon.

Group decision making (GDM) is usually used for solving the complex decision problems, which is an important part of modern decision science. This paper investigates a GDM method based on projection measurement in an intuitionistic fuzzy environment. First, this article introduces an ideal decision of all individual decisions, and the weights of decision makers are determined by using the projection measurement. Then the individual decisions are aggregated into a collective decision by using the weighted averaging operator. Finally, the preference order of alternatives is ranked based on the collective decision by using the score and accuracy function of the intuitionistic fuzzy number. In addition, a comparison with another GDM method is also done. The feasibility and practicability of the developed method are illustrated by an experimental analysis. The experimental result shows that the projection-based method is a high-resolution decision method.

Globalization, increased governmental regulations, and customer demands regarding environmental issues has led organizations to review measures necessary for implementation of supply chain management, in order to improve environmental and economic performance. In this study, a competitive market is considered consisting of product producers and raw material suppliers with a focus on automotive industry. This research also utilizes the rules of Oligopoly and Cournot games to compete with each other and to achieve greater profits. In other words, price of a product is a function of market demand. In this regard, a nonlinear bi-level model is proposed at its first level of which, the government controls environmental pollution to maximize its net income. In the second level, the main objective is to maximize the profit of each Green Supply Chain member’s. The bi-level model is converted to a single level model by replacing the second level with its Karush Kuhn Tucker conditions and linearization techniques. Subsequently, a Genetic Algorithm is utilized to solve the single level model using MATLAB software. Afterwards, the obtained results are compared with optimal solutions acquired by Enumerative method (EM) to evaluate validity and feasibility of the proposed Genetic Algorithm. A sensitivity analysis of this model indicates that fiscal policy of the government heavily impacts reduction of environmental pollution costs caused by industrial activities such as automobile production in a competitive market. Therefore, the amount of taxes for non-green products is directly related to reduction of the environmental pollution.

This paper presents a multi-product, multi-period inventory problem in an uncertain environment where the main suppliers are prone to yield uncertainty. In order to overcome the arisen uncertainties, two basic approaches including emergency ordering and product substitutability is taken into consideration. In the proposed emergency ordering scheme, two sets of suppliers including cheap unreliable and expensive reliable (emergency) suppliers are taken into consideration and a tradeoff between the cheap price of the main suppliers and reliability of emergency supplier is attained. In product substitution scheme, the demand of each product is fulfilled directly by the related product or other substitute products. A risk-averse decision maker is taken into consideration in which the risk-averseness level of decision maker is controlled by the portion of demand which should be definitely satisfied and not backordered or lost. A robust optimization approach with two variability measures is proposed to minimize the variability of the model. The results reveal the value of emergency ordering and product substitution. In addition, the results suggest which measure should be selected according to the decision maker’s attitude toward the desired profit, variability and service level.

In this article, a new model and a novel solving method are provided to address the non-exponential redundancy allocation problem in series-parallel k-out-of-n systems with repairable components based on Optimization Via Simulation (OVS) technique. Despite the previous studies, in this model, the failure and repair times of each component were considered to have non-negative exponential distributions. This assumption makes the model closer to the reality where the majority of used components have greater chance to face a breakdown in comparison to new ones. The main objective of this research is the optimization of Mean Time to the First Failure (MTTFF) of the system via allocating the best redundant components to each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique was applied to model the problem, and di erent experimental designs were produced using DOE methods. To solve the problem, some meta-Heuristic Algorithms were integrated with the simulation method. Several experiments were carried out to test the proposed approach; as a result, the proposed approach is much more real than previous models, and the near optimum solutions are also promising.

The main goal of this work is to find a better solution for a kind of multi-objective optimization problem subject to a system of fuzzy relational inequalities with max-arithmetic mean composition. First, this problem is solved and in the case that the decision maker is not satisfied with any of its solutions then, by assigning linear membership functions to the inequalties in the constraints and objective functions and using Bellman-Zadeh decision, a new solution is found. This new solution does not belong to the feasible domain but is considered acceptable based on the decision maker’s view. In order to find this solution easier, some simplification processes are given. After that, an algorithm is presented to generate the new solution. Finally, an example is given to illustrate the steps of the algorithm.

In this paper, a proactive-reactive approach has been considered for achieving stable and robust schedules despite uncertain processing times and unexpected machine failures in a two-machine flow shop system. In the literature, Surrogate Measures (SMs) have been developed for achieving stable and robust solutions against the occurrence of stochastic disruptions. These measures proactively provide an approximation of the real conditions of the system in the event of a disruption. Because of the discrepancies of these measures with their real values, a different approach is developed in this paper in a two-step structure. First, an initial robust schedule is produced, and then, based on a multi-component measure, an appropriate reaction is adopted against unexpected machine failures. Computational results indicate that this method produces better solutions compared to the other two classical scheduling approaches considering their effectiveness and performance.

Redundancy allocation problem (RAP) is one way to increase system reliability. In most of the models developed so far for the RAP, system components are considered to have a binary state consisting of «working perfect» or «completely failed.» However, to suit real-world applications, this assumption has been relaxed in this paper such that components can have three states. Moreover, a bi-objective RAP (BORAP) is modeled for a system with serial subsystems, in which non-repairable tri-state components of each subsystem are configured in parallel and the subsystem works under the k-out-of-n policy. Furthermore, to enhance system reliability, technical and organizational activities that can affect failure rates of the components and hence can improve the system performance are also taken into account. The aim is to find the optimum number of redundant components in each subsystem such that the system reliability is maximized while the cost is minimized within some real-world constraints. In order to solve the complicated NP-hard problem at hand, the multi-objective strength Pareto evolutionary algorithm (SPEA-II) is employed. As there is no benchmark available, the non-dominated sorting genetic algorithm (NSGA-II) is used to validate the results obtained. Finally, the performances of the algorithms are analyzed using 20 test problems.

Control charts act as the most important tool for monitoring process parameters. The assumption of independence that underpins the implementation of the charts is violated when process observations are correlated.The effect of this can lead to the malfunctioning of the usual control charts by causing a large number of false alarms or slowing the detection ability of the chart to unstable situations. In this paper, we investigated the performance of the Mixed EWMA-CUSUM and Mixed CUSUM-EWMA charts for the efficient monitoring of autocorrelated data. The charts are applied on the residuals obtained from fitting an autoregressive (AR) model to the autocorrelated observations. The performance of these charts is compared with residual Shewhart, EWMA, CUSUM, combined Shewhart-CUSUM and combined Shewhart-EWMA charts. Performance criteria such as average run length (ARL) and extra quadratic loss (EQL) are used for the evaluation and comparison of the charts. Illustrative examples are presented to demonstrate the application of the charts on serially correlated observations.