Integrating Simulation-Based Optimization for Lean Logistics: A Case Study

Abstract

The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization.

Continuous Genetic Algorithms in the Optimization of Logistic Networks: Applicability Assessment and Tuning

“…In the first step, the vector of initial stock levels is calculated according to (15), i.e., assuming the worst-case conditions of a persistent maximum uncertainty (demand). The simulation of network performance using this vector allows one to determine the largest holding cost, i.e., HC max , and establish the initial population.…”

“…The corresponding domain for the exhaustive search encompassed 10 33 combinations, thus being infeasible for the common computing platforms. The comparison of the initial RSLs calculated for the controlled nodes using (15) and the optimal vector determined using CGA is visualized in Figure 10. Figure 11 depicts the performance measures obtained for the considered network topology and Table 9 groups the numerical data.…”

“…These methods, depending on their specifics and implementation choices, can solve a given optimization problem by self-adjusting to the changes in the system variables and constraints. Among other promising approaches one can also point out machine learning, which finds relatively widespread acceptance in the industrial framework [14], and simulation-based optimization [15,16]. The computational intelligence approach is recommended for complex systems in which the optimal solution cannot be formulated in an analytical way, e.g., owing to model non-linearities, and the evaluation of the fitness function requires substantial resources and is time-consuming [17].…”

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

“…Researchers used an integrated data-driven stochastic degradation model to find the optimal maintenance strategy in chemical and manufacturing processes, where unit failures are caused due to equipment degradation [46]. Different types of simulation methods and tools can be used to optimize in-plant and external manufacturing related logistics processes, like discrete event simulation [47], timed Petri net simulation [48], and hybrid simulation integrating discrete and continuous time event simulation [49]. The Petri net modelling, the timed, colored, and fuzzy Petri net approaches are widely spread in the field of simulation of manufacturing related logistics systems [50,51].…”

Agent-Based Simulation of Value Flow in an Industrial Production Process

“…In the context of increased demand for a higher degree of product customization and personalization, simulation leads to resource economy [28][29][30]. Manufacturing processes simulation, as an intermediate stage in implementing digital manufacturing asks for the use of specific technologies and methods [31][32][33][34][35][36][37][38][39].…”