An integrated decision support system based on simulation and mathematical programming of Petroleum transportation logistics

Discrete Event simulation (DES), mathematical programming (MP) and analysis of variance (ANOVA) are among the popular tools in operational research (OR) used in dynamic industry like petroleum industry. The integration of these methods even becomes more significant to managerial application in the industry. The objective of this thesis is to present an integrated decision support system by which a decision maker should be able to choose the optimal number of tanks, tank size and truck arrival rate to maximize average total profit and minimize the total transportation cost for an oil refinery terminal operations. The petroleum transportation management system (PTMS) is developed as a DSS using a discrete-event simulation program with ARENA software, mathematical linear programming (LP) with I-Log software and analysis of variance (ANOVA) with SPSS software, and these models are combined in complex program developed using visual basic software (VB). The simulation model represents the logistics operations from oil arriving to the refinery terminal to the supply points. The model process used as a decision support tool to help in evaluating and improving the comprehensive oil terminal operations. And also understanding and assessing of the different steps in a simulation process. An optimization model was formulated with the objective to minimize the total transportation cost. In the model formulation, hard constraints were considered and the linear programming (LP) technique was used. Result obtained suggests the use of certain types of trucks can reduce the operation costs, if compared to that of the current situation. The reduction of costs is due to the reduction of travelling trips as based on the problem constraints. Overall, output of this study has given positive impacts on the transportation operations. The effect of the changes can help the management of the transportation company to make efficient decisions. Multifactor ANOVA is used to determine whether different levels of the three-factors and their interactions significantly impact the oil refinery terminal's profit. ANOVA is also used to determine the flow rate of oil into the tanks station; tank and truck fill rate and a cost and revenue structure. The final step is to expand the model to cover the whole models (DES, LP and ANOVA) and create the integrated user interface. To sum up the combination of these techniques which allows evaluating the actual feasibility of supply planning considering all operations restrictions and variability of the supply logistics and the total transportation cost. In another words, a DSS have been developed to support a decision maker, who is planning to build a new facility or expand an existing oil refinery terminal, should be able to choose the optimal value for all important factors. The PTMS is able to predict with 99% confidence a set of factor levels that yields the highest average total profit

Simulation

Welcome to this graduate course on Discrete-Event Simulation, a hybrid discipline that combines knowledge and techniques from Operations Research (OR) and Computer Science (CS) (Figure 1). Due to the fast and continuous improvements in computer hardware and software, Simulation has become an emergent research area with practical industrial and services applications. Today, most real-world systems are too complex to be modeled and studied by using analytical methods. Instead, numerical methods such as simulation must be employed in order to study the performance of those systems, to gain insight into their internal behavior and to consider alternative (“what-if”) scenarios. Applications of Simulations are widely spread among different knowledge areas, including the performance analysis of computer and telecommunication systems or the optimization of manufacturing and logistics processes. This course introduces concepts and methods for designing, performing and analyzing experiments conducted using a Simulation approach. Among other concepts, this course discusses the proper collection and modeling of input data and system randomness, the generation of random variables to emulate the behavior of the real system, the verification and validation of models, and the analysis of the experimental outputs. FigurePeer ReviewedPostprint (published version

Simulation Modeling: Obstacles Faced by Small and Medium Manufacturing Enterprises

Abstract Simulation modeling has been acknowledged as one of the key engineering tools of the 21 st century and many large enterprises, such as automotive and aerospace, have taken advantage of its benefits. Unfortunately, these benefits have not generally been available to Small and Medium Enterprises (SMEs) due to various barriers. An example of one of these key barriers is the time and level of expertise it takes to build a valid simulation model of even a relatively simple manufacturing operation. This is particularly true for SMEs which are often engaged in "low volume, high variety" manufacturing environments, which are complex to model adequately. This paper provides an overview of the importance of SMEs to the nation's manufacturing base and develops a prioritized list of obstacles that SMEs face in terms of tapping into the power of simulation modeling. In addition, recommendations are developed regarding how these obstacles can best be overcome and a research agenda is defined that targets both the issue of preparing a SME"s maturity level and readiness for the technology, as well as technical simulation modeling issues that need to be resolved in order to make the technology more accessible

Analysis and improvement of a bottling line using a simulation modelling approach

This project thesis is focused on the study of a bottling production line using a modelling simulation method, through which we analyse the inefficiencies and then improve their performance. Moreover, the line is also analysed thorugh an analytic approach applying a formula to optimize the buffer sizing. The two approachs are compared to highlight the differences

A simulation modelling approach to improve the OEE of a bottling line

This dissertation presents a simulation approach to improve the efficiency performance, in terms of OEE, of an automated bottling line. A simulation model of the system is created by means of the software AnyLogic; it is used to solve the case. The problems faced are a sequencing problem related to the order the formats of bottles are processed and the buffer sizing problem. Either theoretical aspects on OEE, job sequencing and simulation and practical aspects are presented