Scientia Iranica
Volume:24 Issue: 1, 2017

Given the initial layout of a container terminal with a bay, the container retrieval problem aims at obtaining a movement sequence for the crane to retrieve all the containers arranged in a pre-defined order. In this study, we develop a computationally efficient heuristic, called constant summation (CSUM), to minimize the total crane''s working time required to retrieve all the containers from a given bay. Numerical results show that CSUM is very promising in dealing with the container retrieval problems and outperforms the best known approaches recently presented in the literature in terms of the crane''s working time.

In this paper, for a given infeasible network, we change the lower and upper bounds such that the sum of the violations costs from the lower and upper bounds is minimum. We call this problem the adjusting problem and show it is transformed to a minimum cost flow problem on a parallel network. Thus, the adjusting problem can be solved using any minimum cost flow algorithm on a parallel network. Solving a minimum cost flow problem with parallel arcs, in practice, is complicated and needs more time in comparison with a minimum cost flow problem without parallel arcs. If the parallel arcs are eliminated then we achieve substantial saving in the storage requirements, which translates into enhanced speed of algorithms. One of the best algorithms to solve the minimum cost flow problem is the cost scaling algorithm of Goldberg and Tajan(1990). In this paper, we present two modified versions of their algorithm to solve the adjusting problem. In the first modification, in order to achieve an enhanced speed of algorithm, the parallel arcs are eliminated using an especial residual network. In the second modification, the adjusting problem is transformed to a convex cost flow problem and the cost scaling algorithm is modified in away which performs fewer operations than our first implementation.

Nowadays, continuous improvement can be regarded as the essence of survival and growth in order to not only increase the competition in global market, but also requirement for ever-decreasing defect levels in processes. Therefore, new statistical analysis techniques and decision-making procedures have been continuously evolved, both to handle high quality processes and to look for process improvement opportunities. CCC-r chart, or extended approach of CCC charts, is generally a technique for high quality processes, when nonconforming items are rarely observed. This study develops a mathematical model based on the average number of inspected items for the economic design of CCC-r chart, so that the average cost per item is minimized. The optimal designed parameters for di erent nonconforming fractions and di erent parameters in each iteration are calculated. In addition, with respect to Type I error ( ) and Type II error ( ) in the process, sensitivity analysis of the model is carried out.

In this paper a two-stage supply chain including a wholesaler and a retailer is investigated. In which retailer acts as a vendor and sells the products to the final customers. This chain only produces a single deteriorating product with constant deteriorating rate. Demand in this chain is deterministic and lead time for replenishment and replacement of retailer’s stock is considered to be zero. The objective of this study is to maximize total chain’s profit by determining the optimal values of retailer’s selling price () and order cycle length (). Since due to deterioration, a part of the initial stock is deteriorated, so the retailer residuals (recycles) the deteriorated part and replace healthy product as needed instead. Two scenarios with shortage and without shortage assumption are developed. Finally numerical examples are presented to show the applicability of the proposed models and sensitivity analyze on some parameter’s values is conducted.

In this study, a multi-state degraded system is studied, where status of system is degrading over time continuously. As time progresses, system may either deteriorate gradually and go to lower performance state or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections revealed that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to find the optimal PM level so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent different states of system. An integrated optimization approach is also suggested based on the desirability function statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision maker’s acceptable region. In order to show the efficiency of the proposed approach, a numerical example is presented and analyzed.

A real manufacturing system faces lots of real world situations such as stochastic behaviors which lack of this issue is noticeable in previous researches. The aim of this paper is to find the optimum layout and the most appropriate handling transporters for the problem by a novel solving algorithm. The new model contains two objective functions including the material handling costs (MHC) and the complication time of jobs (make span). Real world situations such as stochastic processing times, random breakdowns and cross traffics among transporters are considered in this paper. Several experiment designs have been produced using DOE technique in simulation software and an artificial neural network (ANN) as a meta-model was used to estimate the objective functions in the meta-heuristic algorithms. A hybrid non-dominated sorting genetic algorithm (H-NSGA-II), is applied for optimization task. The proposed methodology is evaluated through a real case study. First, simulation model is validated by comparing with real data set. Then, the prediction performance of ANN is investigated. Finally, the ability of H-NSGA-II, in searching the solution space, is compared to the traditional NSGA-II. The results show that the proposed approach, combing simulation, ANN and H-NSGA-II, provides promising solutionsfor practical applications.

The exponentially weighted moving average (EWMA) control chart is an effective tool for the detection of small shifts in the process variability. This research studied the properties of EWMA charts based on unbiased sample variance for monitoring of changes in the process dispersion. However, since an increase in process variance could lead to an increased number of defective products, we consider only upward shifts in the process variance. The proposed schemes are based on simple random sampling and extreme variations of ranked set sampling technique for efficient monitoring. Using Monte Carlo simulations, we compare the relative performance of EWMA charts based on unbiased sample variance and its logarithmic transformation as well as some existing schemes for monitoring increases in variability of a normal process. It is found that the proposed schemes significantly outperform several other procedures for detecting increases in the process dispersion. Numerical example is given to illustrate the practical application of the proposed schemes using real industrial data.

Regularly, manufacturing systems produce perfect and imperfect quality items. The perfect items start deteriorating as soon as these enter to the inventory. On the other hand, the suppliers provide a delay in payment in order to motivate their buyers to purchase more products. This paper develops a two-warehouse inventory model that considers jointly the imperfect quality items, deterioration and one level of trade credit. The proposed inventory model optimizes the order quantity to maximize the total profit per unit time. Finally, the proposed inventory model and its solution procedure are validated with numerical examples and a sensitivity analysis is done to show how inventory model reacts to changes in parameters.

In this paper, the multi-objective multi-echelon supply chain network design problem is investigated. The resulted problem considers uncertainty of single product demand and the downstream risk attitude simultaneously and integrate it into the greenness concept. That is, makes the model more realistic in comparison with the others. According to the related literature study, there is no specific study to design the green supply chain network based on the risk attitude of the retailer. This paper aims to deal with this research gap by formulating robust counterpart of the main proposed uncertain problem. To do this, the conditional value at risk (CVaR) method through the data-driven approach is applied to deal with demand uncertainty to makes the model to be convex and sensitive to the risk averseness level in a robust manner. In addition, uncertainty sets approach is employed to deal with the uncertainty of other three parameters according to CO2 emission. The augmented e-constraint method is used to transform the resulted multi-objective mathematical programming problem into the single objective one, to obtain the global optimal solution through the exact mathematical solution method. Finally, the model is successfully simulated and its results are illustrated through a numerical example.

Control charts are popular tounderols for process monitoring and are mainly classi ed into memory and memory-less structures. Exponentially Weighted Moving Average control charts (EWMA) are widely used for the detection of small shifts in process parameters. The traditional design structures of these types of charts rely on the stringent assumption of normality. In practice, quality characteristics generally do not follow normality; hence, alternative charting structures are needed. Nonparametric control chart is one such alternative that do not depend on the mentioned distributional assumption(s). This study deals with nonparametric EWMA charts for an ecient detection of smaller shifts in location. We have investigated asymptotic, time-varying, and Fast Initial Response (FIR) based time varying control limits for nonparametric EWMA charts. We have considered two variants of FIR with time varying limits for nonparametric EWMA structure. A variety of run length properties have been investigated for performance evaluations. It is observed that FIR-based nonparametric EWMA charts with time-varying limits are quite sensitive to detecting smaller shifts, and they o er attractive run length properties. A real-life example is also provided to illustrate the application of the time-varying and FIR-based nonparametric EWMA control charts.