Journal of Optimization in Industrial Engineering
Volume:1 Issue: 2, Summer and Autumn 2008

In this paper, an efficient extension of network simplex algorithm is presented. In static scheduling problem, where there is no change in situation, the challenge is that the large problems can be solved in a short time. In this paper, the Static Scheduling problem of Automated Guided Vehicles in container terminal is solved by Network Simplex Algorithm (NSA) and NSA+, which extended the standard NSA. The algorithms are based on graph model and their performances are at least 100 times faster than traditional simplex algorithm for Linear Programs. Many random data are generated and fed to the model for 50 vehicles. We compared results of NSA and NSA+ for the static automated vehicle scheduling problem. The results show that NSA+ is significantly more efficient than NSA. It is found that, in practice, NSA and NSA+ take polynomial time to solve problems in this application.

Batch sizing in different planning period is categorized as a classical problem in production planning, that so many exact & heuristic methods have been proposed to solve this problem, each of which considering various aspects of the original problem. The solution obtained from majority – e.g. MRP – is in this format that there may be some periods of idleness or each period should produce as needed in different adjacent periods. If there are more the one final independent product to be produced in a factory, this makes the production planning experience strong variations in batch sizes for different periods, which production managers are opposed to these proposed production plans. In this paper, some of the models are proposed to solve this shortcoming of the production plan to smooth the variation of batch sizes and consequently to meet the managers ideal. Finally all of the proposed models are used in a real case problem and the best model is introduced in that case.

One of the most important issues in the plant layout design especially in mass production organizations with high inter-plant logistics is‘material flow and inter-plant traffic analysis and its effects on the production capabilities or pauses in production lines. In this paper the inter-plant traffic analysis issue on the basis of single channel queue model (M/M/1) is analyzed in a carmaker company (IKCO). Through the analysis, the production stop rate and relevant costs are estimated.

When a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. Upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. However, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always lead to successful identification of assignable cause(s). If process engineers could identify the change point, i.e. the time when the disturbance first manifested itself into the process, then corrective actions could be directed towards effective elimination of the source of disturbance. In this paper we develop a maximum likelihood estimator (MLE) for process change point designed to detect changes in process variance of a normal quality characteristic when the change follows a linear trend. We describe how this estimator can be used to identify the change point when a Shewhart S-control chart signals a change in the process variance. Numerical results reveal that the proposed estimator outperforms the MLE designed for step change when a linear trend disturbance is present.

The objective of this article is to develop an empirically based framework for formulating and selecting a vendor in supply chain. This study applies the fuzzy set theory to evaluate the vendor selection decision. Applying Analytic Hierarchy Process (AHP) in obtaining criteria weights and applied Technique for Order Performance by Similarity to Idea Solution (TOPSIS) for obtaining final ranking of vendors. The usefulness of this model is explained through an empirical study for vendor selection.

Solvable Graphs (also known as Reachable Graphs) are types of graphs that any arrangement of a specified number of agents located on the graph’s vertices can be reached from any initial arrangement through agents’ moves along the graph’s edges, while avoiding deadlocks (interceptions). In this paper, the properties of Solvable Graphs are investigated, and a new concept in multi agent motion planning, called Minimal Solvable Graphs is introduced. Minimal Solvable Graphs are the smallest graphs among Solvable Graphs in terms of the number of vertices. Also, for the first time, the problem of deciding whether a graph is Solvable for m agents is answered, and a new algorithm is presented for making an existing graph solvable and lean for a given number of agents. Finally, through an industrial example, it is demonstrated that how the findings of this paper can be used in designing and reshaping transportation networks (e.g. railways, traffic roads, AGV routs, robotic workspaces, etc.) for multiple moving agents such as trains, vehicles, and robots.

Estimating the final price of products is of great importance. For manufacturing companies proposing a final price is only possible after the design process over. These companies propose an approximate initial price of the required products to the customers for which some of time and money is required. Here using the existing data of already designed transformers and utilizing the bayesian analysis of generalize poisson models and artificial neural networks, a shortcut method for estimating the material and final price of transformers is established. The proposed method being quite precise and fast, without any cost.

In this paper, we develop a capacitated location-covering model considering interval values for demand and service parameters. We also consider flexibility on distance standard for covering demand nodes by the servers. We use the satisfaction degree to represent the constraint of service capacity. The proposed model belongs to the class of mixed integer programming models. Our model can be reduced to the p-median problem in polynomial time so it is NP-Hard. A genetic algorithm is proposed to solve the developed model and experimental results of solving the model are presented.