A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain

2008 ◽  
Vol 111 (2) ◽  
pp. 229-243 ◽  
Author(s):  
Reza Zanjirani Farahani ◽  
Mahsa Elahipanah
2016 ◽  
Vol 28 (11) ◽  
pp. 3413-3427 ◽  
Author(s):  
Ashkan Memari ◽  
Abd. Rahman Abdul Rahim ◽  
Adnan Hassan ◽  
Robiah Ahmad

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Yixin Zhou ◽  
Zhen Guo

With the advent of the era of big data (BD), people’’s living standards and lifestyle have been greatly changed, and people’s requirements for the service level of the service industry are becoming higher and higher. The personalized needs of customers and private customization have become the hot issues of current research. The service industry is the core enterprise of the service industry. Optimizing the service industry supply network and reasonably allocating the tasks are the focus of the research at home and abroad. Under the background of BD, this paper takes the optimization of service industry supply network as the research object and studies the task allocation optimization of service industry supply network based on the analysis of customers’ personalized demand and user behavior. This paper optimizes the supply chain network of service industry based on genetic algorithm (GA), designs genetic operator, effectively avoids the premature of the algorithm, and improves the operation efficiency of the algorithm. The experimental results show that when m = 8 and n = 40, the average running time of the improved GA is 54.1 s. The network optimization running time of the algorithm used in this paper is very fast, and the stability is also higher.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Petra Vrbová ◽  
Václav Cempírek

Abstract Managing inventory is considered as one of the most challenging tasks facing supply chain managers and specialists. Decisions related to inventory locations along with level of inventory kept throughout the supply chain have a fundamental impact on the response time, service level, delivery lead-time and the total cost of the supply chain. The main objective of this paper is to identify and analyse the share of a particular logistic model adopted in the Czech Republic (Consignment stock, Buffer stock, Safety stock) and also compare their usage and adoption according to different industries. This paper also aims to specify possible reasons of particular logistic model preferences in comparison to the others. The analysis is based on quantitative survey held in the Czech Republic.


2015 ◽  
Vol 115 (6) ◽  
pp. 1086-1112 ◽  
Author(s):  
Amir Hossein Niknamfar

Purpose – The production-distribution (P-D) problems are two critical problems in many industries, in particular, in manufacturing systems and the supply chain management. In previous researches on P-D planning, the demands of the retailers and their inventory levels have less been controlled. This may lead into huge challenges for a P-D plan such as the bullwhip effects. Therefore, to remove this challenge, the purpose of this paper is to integrate a P-D planning and the vendor-managed inventory (VMI) as a strong strategy to manage the bullwhip effects in supply chains. The proposed P-D-VMI aims to minimize the total cost of the manufacturer, the total cost of the retailers, and the total distribution time simultaneously. Design/methodology/approach – This paper presents a multi-objective non-linear model for a P-D planning in a three-level supply chain including several external suppliers at the first level, a single manufacturer at the second level, and multi-retailer at the third level. A non-dominated sorting genetic algorithm and a non-dominated ranking genetic algorithm are designed and tuned to solve the proposed problem. Then, their performances are statistically analyzed and ranked by the TOPSIS method. Findings – The applicability of the proposed model and solution methodologies are demonstrated under several problems. A sensitivity analysis indicates the market scale and demand elasticity have a substantial impact on the total cost of the manufacturer in the proposed P-D-VMI. Originality/value – Although the P-D planning is a popular approach, there has been little discussion about the P-D planning based on VMI so far. The novelty comes from developing a practical and new approach that integrates the P-D planning and VMI.


2013 ◽  
Vol 321-324 ◽  
pp. 2137-2140 ◽  
Author(s):  
Bing Chang Ouyang

Considering discrete demand and time-vary unit production cost under a foreseeable time horizon, this study presents an adaptive genetic algorithm to determine the production policy for one manufacturer supplying single item to multiple warehouses in a supply chain environment. Based on Distribution Requirement Planning (DRP) and Just in Time (JIT) delivery policy, we assume each gene in chromosome represents a period. Standard GA operators are used to generate new populations. These populations are evaluated by a fitness function using the total cost of production scheme. An explicit procedure for obtaining the local optimal solution is provided.


Author(s):  
Masoud Rabbani ◽  
Soroush Aghamohamadi Bosjin ◽  
Neda Manavizadeh

In the contemporary world, combining the concept of agile and lean manufacturing (LM) is one of the most strategic and appealing concerns in the industrial environments. In this paper, a new Leagile structure is proposed for a supply chain. This research covers long term and mid-term horizon by designing a supply chain network up to the order penetration point (OPP) and final assembly and sale planning respectively. The problem is programmed in two phases. First, a bi-objective optimization is developed to minimize the total cost related with LM. In the second phase, the total cost and the customer service level (CSL) are considered as the agile manufacturing (AM) architecture. In the proposed model, a utility function is applied to set balance between the price and customer satisfaction. In addition, a robust credibility-based fuzzy programming (RCFP) is developed to handle uncertainty of the first phase. The proposed model and the solution method are implemented for a real industrial case study to show the applicability and usefulness of this study. According to the results, improving the customer service level can enhance the total cost of the second phase meaning that customer responsiveness price is too high for the proposed system.


2019 ◽  
Vol 11 (22) ◽  
pp. 6464 ◽  
Author(s):  
Tseng ◽  
Wee ◽  
Reong ◽  
Wu

The purpose of this study was to achieve supply chain sustainability by considering Just in Time (JIT) in return vehicle usage. In response to a general increase in modern environmental awareness, consumer and government attention towards product and service compliance with environmental protection standards has increased. Consequently, manufacturers and stakeholders are pressured to use eco-friendly supply chains. In this paper, we analyzed the JIT model, a transportation network that ensure agile responses and delivery of goods in a supply chain, which reduces inventory costs. We then compared two return vehicle transportation scenarios. In the first, goods were transported from the central warehouse to the distribution base, and the return vehicle delivered recyclable packaging materials back to the central distribution warehouse. In the second scenario, goods were transported from the manufacturer to the distribution center (warehouse) more frequently, leading to reduced inventory. We then utilized the aforementioned JIT system with ILOG CPLEX12.4 to ascertain which scenario would produce the lowest carbon emissions for the lowest total cost.


2021 ◽  
Vol 6 (4) ◽  
pp. 10-19
Author(s):  
Huda Zuhrah Ab Halim ◽  
Nureffa Natasha Mohd Azliana ◽  
Nuridawati Baharom ◽  
Nur Fatihah Fauzi ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  

Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.


2012 ◽  
Vol 59 (2) ◽  
Author(s):  
Salah Alden Ghasimi ◽  
Rizauddin Ramli ◽  
Nizaroyani Saibani

Supply chain management (SCM) is a field of study which covers a wide range of research issues involving strategic to operational models. In the past two decades, SCM has drawn much attention from manufacturers and organizations for optimizing their operations. In this paper, a mathematical model to optimize costs of the supply chain for defective and repairable goods through the application of just-in-time (JIT) logistics is proposed. The hypothesis is that the defective goods are repairable and some of them are considered as scraps. The aim of this model is to minimize the total cost of production, maintenance, freight, reworking, scrap goods and shortage to retailers. The proposed model is novel and LINGO has been used to solve it. The validity of the model was proven by testing 12 sample problems and the results showed correctness and fine function of the model. Based on the data parameters, this model can also determine which manufacturer or distributor in the particular period of the production needs to be active. The model is applicable for all producers that are encountering with problem of producing of defective goods.


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