A Flexsim-based Optimization for the Operation Process of Cold-Chain Logistics Distribution Centre

Logistics Distribution Route Optimization Based on Genetic Algorithm

Aiming at the problem of logistic division based on genetic algorithm, it is planned to study the improvement of logistic distribution methods. We first meet the requirements of the genetic algorithm of logistic development, use the division method to divide the delivery area of the gene, and formulate a functional delivery plan, which generally includes weight measurement, measurement time, customer value measurement, instrument measurement time, and the whole process index. We set weight goals and find the best way to improve genetic algorithm delivery. The experimental comparison results show that the optimal method takes less than 2 minutes to find the optimal method, while the normal process takes 4 minutes to find the optimal method, and the longest can reach 5 minutes. The comparison shows that the traditional algorithm takes longer to find the correct way than the algorithm developed this time. Finally, the simple logistic distribution optimization method model and the soft time-limited logistic distribution processing optimization model are calculated and simulated by the genetic testing algorithm and genetic algorithm development. The effectiveness of the improved genetic algorithm in local research and the effectiveness of the logistic transportation allocation solution are determined.

Coupling relationship between cold chain logistics and economic development: A investigation from China

This paper builds an evaluation index system, uses the entropy weight method (EWM) to decide the weights and, based on the coupling coordination degree model (CCDM), it systematically studies the coupling relationship between Chinese cold chain logistics and the Chinese economy from 2010 to 2019. It performs a grey relational analysis (GRE) to explore the main factors influencing the coordinated development of the two. The results show that the coupling coordination degree between the two presents a steady upward trend, and their coupling relationship has been upgraded from ‘coordination’ to ‘good coordination’. They also indicate that the added value in the tertiary industry, the per capita gross domestic product (GDP), and household consumption levels are the main factors affecting the development of cold chain logistics, while the per capita cold storage capacity, the turnover of road cold chain freight, and the volume of human-power employed in cold chain logistics are the main factors affecting economic development. This study makes suggestions to support the coordinated development of cold chain logistics and economy, and provides a scientific basis for further research.

Workforce Planning Framework for a Mobile Call Center Considering a Special Event

Workforce planning is essential in today’s business management. If an organization can find and keep enough staff who have the right values, then they can provide high-quality service. This paper presents a workforce planning framework for selecting the best forecasting model in order to provide minimum wage and computer electricity costs for a mobile call center during the Songkran festival event, and to optimize workforce planning. The framework is constructed with four main steps: a study of a separate period; the separation of models with different data types; the simulation of models under different service levels to determine the number of customers waiting in a call center; and the evaluation of the models. The results from the proposed framework presented the best forecasting method and the optimal workforce plan. It is clear that this approach can assist in systematically selecting the best forecasting model. In addition, a workforce planner can use this framework to support workforce planning and cost evaluation in other event periods.

Application of Lean Six Sigma framework for improving manufacturing efficiency: a case study in Indian context

Purpose

This study aims to utilize DMAIC methodology along with value stream mapping and other Lean Six Sigma tools in a major automobile light manufacturing industry to reduce defect rates and increase production capacity in their manufacturing line. The study also proposes a modified framework based on lean principles and FlexSim to identify and reduce waste in the selected industry.

Design/methodology/approach

A Lean Six Sigma modified framework has been deployed with DMAIC to reduce the defect rate and increase the production rate. Various tools like value stream mapping, brainstorming, Pareto charts, 5S, kanban, etc. have been used at different phases of DMAIC targeting wastes and inventory in the production line. Also, a simulation model has been utilized for the automobile light manufacturing industry to improve the machine utilization time with varying batch sizes.

Findings

The results of the study indicated a 53% reduction in defect rates. Thus, there would be an expected improvement in sigma value from 3.78 to 3.89 and a reduction in defects per million opportunities (DPMO) from 11,244 to 8,493. Additionally, simulation model using FlexSim was developed, and the optimum ordering batch size of raw material was obtained. It was also analyzed that idle time for various stations could be reduced by up to 30%.

Practical implications

The utilized framework helps identify defects for managers to increase production efficiency. The workers, operators and supervisors on the production line also need to be trained regularly for identifying the areas of improvement.

Originality/value

The modified Lean Six Sigma framework used in this study includes FlexSim simulation to make the framework robust, which has not been used with LSS tools in the literature studied. Also, the LSS finds very less application in the manufacturing domain, considering which this study tends to add value in existing literature taking a case of an automobile light manufacturing industry.

A non-invasive method for quantitative monitoring of quality changes and water migration in bigeye tuna (Thunnus obesus) during simulated cold chain logistics using low-field nuclear magnetic resonance coupled with PCA

Bigeye tuna (Thunnus obesus) is an important fish species worldwide due to its desirable flavour and rich nutrition. Cold chain logistics is one of the most common methods to store and sell bigeye tuna (Thunnus obesus). The purpose of this study was to investigate how bigeye tuna (T. obesus) deteriorate during simulated cold chain logistics with fluctuating temperatures by monitoring the quality changes and water distribution. The physical and chemical results showed that the sensory score, texture profile analysis (TPA), and water holding capacity (WHC) decreased, while total volatile basic nitrogen (TVB-N), K value, aerobic plate counts (APC), and psychrotrophic bacterial counts increased with fluctuating temperatures during 132 h. Low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) detection showed that the T₂₁ (trapped water) and proton density decreased, while T₂₂ (free water) decreased initially and then increased, indicating continuous water loss and water migration during storage. Principal component analysis (PCA) revealed that springiness, hardness, chewiness, T₂₁, and sensory score were correlated with each other (p < 0.05) and the linear combination of quality indicators and T₂transverse relaxation time was established. Besides, temperature fluctuation (batch 3) accelerated the deterioration of bigeye tuna according to the comprehensive score of PCA. Results were based on LF-NMR which can be used to monitor quality changes in a fast non-invasive manner.

Hierarchical network modeling with multidimensional information for aquatic safety management in the cold chain

The cold-chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain. Each of the single hierarchy levels constitutes a sequence of node information in a network, which is applied as internal information analysis. The cross-layer information structure is defined as "bridge" information which is able to record the information transmissions among every hierarchy from the point of view of the whole chain. The study has established a novel structured modeling to describe the cold chain of aquatic products based on a network-hierarchy framework. An organized and sustainable transmission process can be built and recorded by the multidimensional attributes for the whole course of cold chain of aquatic products. In addition, seamless connections among every hierarchy are attainable by the environmental information records continuously to monitor the quality of aquatic products. The quality assessments and shelf life predictions are estimated properly as the risk control in order to monitor and trace the safety of aquatic products under the supply chain perspective.