The terminal condition of inventory level to be zero at the end of the cycle time adopted by Soni and Shah (2008, 2009) is not viable when demand is stock-dependent. To rectify this assumption, we extend their model for (1) an ending – inventory to be non-zero; (2) limited floor space; (3) a profit maximization model; (4) selling price to be a decision variable, and (5) units in inventory deteriorate at a constant rate. The algorithm is developed to search for the optimal decision policy. The working of the proposed model is supported with a numerical example. Sensitivity analysis is carried out to investigate critical parameters.

In this paper, a new formulation model for cellular manufacturing system (CMS) design problem is proposed. The proposed model of this paper considers assembly operations and product structure so that it includes the scheduling problem with the formation of manufacturing cells, simultaneously. Since the proposed model is nonlinear, a linearization method is applied to gain optimal solution when the model is solved using direct implementation of mixed integer programming. A new genetic algorithm (GA) is also proposed to solve the resulted model for large-scale problems. We examine the performance of the proposed method using the direct implementation and the proposed GA method. The results indicate that the proposed GA approach could provide efficient assembly and product structure for real-world size problems.

This paper deals with an inventory model, which considers the impact of marketing strategies such as pricing and advertising as well as the displayed inventory level on the demand rate of the system. In addition, the demand rate during the stock-out period differs from that during the stock-in period by a function varied on the waiting time up to the beginning of the next cycle. Shortage are allowed and partially backlogged. Here, the deterioration rate is assumed to follow the Weibull distribution. Considering all these factors with others, different scenarios of the system are investigated. To obtain the solutions of these cases and to illustrate the model, an example is considered. Finally, to study the effects of changes of different parameters of the system, sensitivity analyses have been carried out with respect to the different parameters of the system.

This research defines a new application of mathematical modeling to design a cellular manufacturing system integrated with group scheduling and layout aspects in an uncertain decision space under a supply chain characteristics. The aim is to present a mixed integer programming (MIP) which optimizes cell formation, scheduling and layout decisions, concurrently where the suppliers are required to operate exceptional products. For this purpose, the time in which parts need to be operated on machines and also products & apos; demand are uncertain and explained by set of scenarios. This model tries to optimize expected holding cost and the costs regarded to the suppliers network in a supply chain in order to outsource exceptional operations. Scheduling decisions in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part group are operated in the same cell and no inter-cellular transfer is required. An efficient hybrid method made of genetic algorithm (GA) and simulated annealing (SA) will be proposed to solve such a complex problem under an optimization rule as a sub-ordinate section. This integrative combination algorithm is compared with global solutions and also, a benchmark heuristic algorithm introduced in the literature. Finally, performance of the algorithm will be verified through some test problems.

Many real-world processes generate autocorrelated and/or Weibull data. In such cases, the independence and/or normality assumptions underlying the Shewhart and EWMA control charts are invalid. Although data transformations exist, such tools would not normally be understood or employed by naive practitioners. Thus, the question arises, “What are the effects on robustness whenever these charts are used in such applications?” Consequently, this paper examines and compares the performance of these two control charts when the problem (the model) is subjected to autocorrelated and/or Weibull data. A variety of conditions are investigated related to the magnitudes of various parameters related to the process shift, the autocorrelation coefficient and the Weibull shape parameter. Results indicate that the EWMA chart outperforms the Shewhart in 62% of the cases, particularly those cases with low to moderate autocorrelation effects. The Shewhart chart outperforms the EWMA chart in 35% of the cases, particularly those cases with high autocorrelation and zero or high process shift effects.

This paper introduces the effect of task deterioration in simple assembly line balancing problem. In many realistic assembly lines, a deterioration task is considered when a task is started earlier than the assigned time since the station time is constant and the earliness of the task does not reduce the cycle time. This phenomenon is known as deteriorating tasks. Therefore, we seek an optimal assignment and schedule of tasks in workstations, in order to minimize the number of stations for a given cycle time, which is known as SALBP-1. For this purpose, a mathematical model is proposed. Since the pure SALBP-1 is proved to be NP-hard and considering task deterioration complicates problem further, we propose a genetic algorithm for solving such problem. Several well-known test problems are solved to study the performance of the proposed approach.

This paper focuses on routing flexibility, which is the ability to manufacture a part type via several routes and/or to perform different operations on more than one machine. Specifically, the paper presents a comprehensive method for the measurement of routing flexibility, in a generic manufacturing system. The problem is approached in a modular way, starting from a basic set of flexibility indexes. These are progressively extended to include more comprehensive and complex routing attributes, such as: the average efficiency, the range and the homogeneous distribution of the alternative routes. Two procedures are finally proposed to compare manufacturing systems in terms of routing flexibility. The first one uses a vectorial representation of the previously defined indexes and the second one is based on data envelopment analysis, a multi-criteria decision making approach. The paper concludes with a numerical example, supported by discrete event simulation, which validates the proposed approach.

Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.

The selection of appropriate alternative in the industrial environment is an important but, at the same time, a complex and difficult problem because of the availability of a wide range of alternatives and similarity among them. Therefore, there is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations. In this paper, a hybrid decision making method of graph theory and matrix approach (GTMA) and analytical hierarchy process (AHP) is proposed. Three examples are presented to illustrate the potential of the proposed GTMA-AHP method and the results are compared with the results obtained using other decision making methods.

Random variates play key role in any simulation system and there are different algorithms to generate random variates. One of the best algorithms for generating random variates is uniform fractional part algorithm. The algorithm has high performance in terms of efficiency, speed and simplicity. Although the algorithm has useful results, it is an approximate algorithm. In this article, the approximate form of the algorithm has been studied, and some suggestions have also been presented. Through acceptance-rejection approach and hat and squeeze function, the approximate algorithm is transformed to near exact algorithm. The proposed model of this paper has been examined and compared with the traditional one and the preliminary results indicate that it performs better than the other existing algorithms.