A simulation study of logistics activities in a chemical plant

Many companies utilize a number of complex product distribution networks to transport various types of goods. Most of their distribution networks consist of transporting goods from their suppliers to their distribution centers (DCs). In the process of transporting goods from suppliers to DCs, cross docking facilities are utilized to consolidate loads in order to minimize costs. A simulation model of a generic cross-docking facility (CF) was developed to examine the operational risks associated with individual CFs within a company's distribution network under a dynamic environment. The model was tested and validated on a large cross-docking facility. In addition, it was used to examine the effect of increasing demand through the cross-docking facility. The model is generic and can easily be expanded to model other cross-docking facilities.

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Modeling and analysis of a generic cross-docking facility

This study focuses on the personnel planning of materials handling at a real cross-docking center under a retail distribution environment. Materials handling at a cross-docking center is complicated and labour intensive. Managers are usually under enormous pressure to optimize personnel planning to deal with an increasing variety of items handled by different processes. In this study, a flexible and stepwise procedure of personnel planning is proposed to minimize the total personnel expenses at a cross-docking center, especially taking merchandise mixing and operator skills into consideration. The procedure includes adopting simulation together with integer programming. In addition, the procedure is applied to an actual cross-docking center in order to confirm its effectiveness. Furthermore, the proposed method is found to be both practical and powerful to assist logistics managers in their personnel planning efforts.

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Simulation-based personnel planning for materials handling at a cross-docking center under retail distribution environment

The paper presents a new framework for carrying out simulations of continuoustime stochastic processes by exploiting a discrete event approach. The application scope of this work mainly refers to industrial production processes executed on a continuous flow of material (e.g. food and beverage industry) as well as production processes working on discrete units but characterized by a high speed flow (e.g. automated packaging lines). The proposed model, developed adopting the DEVS formalism, defines a single generalized base unit able to represent, by means of an event scheme generated by state changes, the base behaviors needed for the modeling of a generic manufacturing unit, that is, (i) breakdowns and repairs, (ii) speed and accumulation, and (iii) throughput time. Moreover, the possibility to keep trace of additional measures of parameters related to the process and the flowing material (i.e. temperature, concentration of pollutant, and so on) is also considered. Since these parameters can change over time in a continuous manner, a specific discretization aprroach has been introduced to avoid the need to integrate parameter variation functions over time.

Simulating Continuous Time Production Flows in Food Industry by Means of Discrete Event Simulation

The discrete event simulation is one of the most powerful tools available for modeling and problem analysis. Frequently it is used for planning, designing and controlling complex systems in several segments. The logistic system area has also applied it extensively to analyse the logistic capacities, evaluate the impacts of changes in the productive processes, test different configurations in the allocation of limited resources, determine operation parameters, validate new projects, among other applications. Thus, the growth of the simulation awoke the interest of the companies specialized in software development. This fact resulted in several options with different characteristics and costs. Because of this, it was identified the necessity of a selection method for discrete event simulation software dedicated to the resolution of logistic problems. The available works in Literature only approach the choice of softwares for generic purposes. To fill in this gap, a set of criteria that address the specific necessities of the development of models dedicated to the analysis of logistic systems was proposed. These criteria formed the base of the proposed method. It has incorporated the best characteristics of the works identified in Literature. Its first phase generated a list with softwares with widely recognized efficiency and simplicity that supported the essential criteria through the application of a scoring model. Its second phase used the Analytic Hierarchy Process (AHP) which is recommended for the decision taking based on multiple criteria because it allows the identification and correction of the inconsistencies generated by the human judgment. Two test problems have also been proposed for the development of applications in logistic systems and performance of experiments during the second phase. Arena, ProModel and @Risk, extensively used for education and research in the Polytechnical School of the University of São Paulo, were evaluated during the practical application of the proposed method. The results confirmed the perception acquired during the second phase of the method that Arena and ProModel present similar characteristics. They presented a discrete difference in their total score. The several scenes developed in the sensitivity analysis also ratified this trend.

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Método para avaliação e seleção de softwares de simulação de eventos discretos aplicados à análise de sistemas logísticos.

The paper describes the application of discrete everit simulation to study continuous material flow. Logistics is an integrated part of most manufacturing companies. The purpose of the study is to determine the required logistics operations to allow continuous operations of a chemical manufacturing plant. The application has been used to provide critical decision support.

S.S. Kuo

Papers

Modeling continuous flow with discrete-event simulation