scholarly journals Optimization Of Raw Material Inventory Costs In The Food Supply Chain Using Differential Evolution Algorithm

2018 ◽  
Vol 73 ◽  
pp. 13016
Author(s):  
Mara Huriga Priymasiwi ◽  
Mustafid
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Food Industry ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Raw Material ◽  
Inventory Cost ◽  
De Algorithm ◽  
Material Requirement ◽  
Total Inventory

The management of raw material inventory is used to overcome the problems occuring especially in the food industry to achieve effectiveness, timeliness, and high service levels which are contrary to the problem of effectiveness and cost efficiency. The inventory control system is built to achieve the optimization of raw material inventory cost in the supply chain in food industry. This research represents Differential Evolution (DE) algorithm as optimization method by minimizing total inventory based on amount of raw material requirement, purchasing cost, saefty stock and reorder time. With the population size, the parameters of mutation control, crossover parameters and the number of iterations respectively 80, 0.8, 0.5, 200. With the amount of safety stock at the company 7213.95 obtained a total inventory cost decrease of 39.95%. Result indicate that the use of DE algorithm help providein efficient amount, time and cost.

scholarly journals An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

2018 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Ayda Emdadian ◽  
S. G. Ponnambalam ◽  
G. Kanagaraj
Keyword(s):  
Supply Chain ◽  
Differential Evolution ◽  
Network Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Value Added ◽  
Raw Material ◽  
Supply Chain Network ◽  
De Algorithm ◽  
Chain Problem

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.

Optimization of FLADE Variable Cycle Engine Performance Based on Improved Differential Evolution Algorithm

2017 ◽  
Author(s):  
Zhang Xiao-bo ◽  
Wang Zhan-xue
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Engine Performance ◽  
Optimization Method ◽  
Performance Simulation ◽  
De Algorithm ◽  
Civil Aircraft ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Integrated Performance

In this paper, a double bypass variable cycle engine with FLADE (Fan on Blade) is considered. The FLADE VCE is one of the research hotspots for future military and civil aircraft power device, which shows outstanding performance advantages. Compared to the mixed-flow turbofan, FLADE VCE is more complex than conventional aero-engine for its multi-components and multi-variable parts, which make it difficult to modeling and optimization. For getting the performance of FLADE VCE, the model for engine performance simulation is researched. The method for FLADE performance simulation and the steady-state performance simulation model for FLADE VCE are developed. And a component-based engine performance simulation system is established based on object-oriented modeling method. For obtaining the optimal integrated performance of FLADE VCE, suitable optimization method is required. Unfortunately, the optimization of FLADE VCE is a non-linear non-differentiable problem, which makes it difficult to solve by conventional deterministic optimization method. In order to solve this problem, the differential evolution (DE) algorithm is considered. To overcome the limitations of original DE algorithm, an improved DE algorithm with modifying mutation operator is proposed by this paper. The FLADE VCE optimization problem is solved by employing the improved DE algorithm.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

scholarly journals Analisis Pengendalian Persediaan Bahan Baku dengan Menerapkan Metode EOQ (Economic Order Quantity) pada PT. Jatisari Furniture Work

2021 ◽  
Vol 5 (1) ◽  
pp. 215
Author(s):  
Arga Sutrisna ◽  
Rizki Ginanjar ◽  
Suci Putri Lestari
Keyword(s):  
Production Costs ◽  
Raw Material ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Analysis Tool ◽  
Order Quantity ◽  
Inventory Cost ◽  
Total Cost ◽  
Inventory Costs ◽  
Total Inventory

This research objectives aims to determine and analyze raw material inventory control, the ideal raw material inventory that the company must provide and the efficiency of production costs carried out by Jatisri Furniture Work in Tasikmalaya for the period 2018.11 – 2020.02. The data collection method in this study is by direct observation at Jatisari Furniture Work in Tasikmalaya. Using techniques such us interviews, observation, and documentation. These observations were made in production reports for the years 2018 – 2020. The analysis tool was carried out using the Economic Order Quantity (EOQ) method such us safety stock, reorder point, and total inventory cost. The result of the Economic Order Quantity (EOQ) analysis show that the total cost of raw the material inventory that must be incurred by the company is greater than the total cost of inventories calculated according to the EOQ method. Companies should follow the calculations from the EOQ method so that they can save on raw material inventory costs, so that production costs are more efficient.

scholarly journals PENGENDALIAN PERSEDIAAN BAHAN BAKU DALAM PROSES PRODUKSI DENGAN MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY (EOQ)

2019 ◽  
Vol 1 (2) ◽  
pp. 415-423
Author(s):  
Elia Rahayu R ◽  
Nor Norisanti ◽  
Acep Samsudin
Keyword(s):  
Raw Materials ◽  
Raw Material ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Analysis Method ◽  
Inventory Cost ◽  
Total Inventory ◽  
The Difference ◽  
Quantitative Descriptive

The purpose of this study is to control the supply of raw materials using the Economic Order Quantity (EOQ) method in Tahu Nugraha Jaya Sukabumi UKM. The data analysis method used is quantitative descriptive to describe and describe the data to be examined and then processed using EOQ. This study uses the EOQ method to determine the total inventory cost. The data needed in this study are the number of purchases of raw materials, the amount of use of raw materials, storage costs, and ordering costs. The results of this study indicate that by applying the EOQ method can further optimize the supply of raw materials by minimizing raw materials with increased inventory. With the application of the Economic Order Quantity (EOQ) method it shows more efficient than conventional methods of the company. Conclusions, seen from the difference in the TIC of the two methods, the more efficient method is the Economic Order Quantity (EOQ) method that is equal to 244,392.94 while the calculation used by the company is 374,325. so that it can be obtained that there is a difference between the Company TIC and the EIC method TIC. Keywords: Raw Material Inventory, Production Process

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

scholarly journals ANALISIS KINERJA MANAJEMEN PERSEDIAAN DI BARJAZ COMPANY MENGGUNAKAN PENDEKATAN EOQ

2018 ◽  
Vol 8 (1) ◽  
pp. 203
Author(s):  
I Made Antony Dwi Putra ◽  
Agoes Ganesha Rahyuda
Keyword(s):  
Control System ◽  
Inventory Control ◽  
Study Data ◽  
Inventory System ◽  
Raw Material ◽  
Inventory Cost ◽  
Safety Stock ◽  
Analysis Techniques ◽  
Inventory Turnover ◽  
Total Inventory

A large amount of inventory in the company makes high inventory cost, while low inventory will risk the occurrence of shortage of inventory. The research was conducted at Barjaz Company, to find out how the raw material inventory system applied by the company, and whether the system is efficient or not. Methods of data collection is done by conducting interviews to parties related to inventory and observation on the object under study. Data analysis techniques used are; EOQ analysis, determining safety stock, determining reorder point, determining maximum inventory, calculating inventory turnover and calculating total inventory cost. The results show that the inventory system applied today is still not effective. Companies should conduct inventory control system using EOQ method. With the implementation of EOQ, the company's inventory turnover value increased and the company issued a total inventory cost of Rp 1,099,982, lower than the company's inventory control system at Rp 1,671,100.

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