scholarly journals A New Extended MILP MRP Approach to Production Planning and Its Application in the Jewelry Industry

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Erhan Yazıcı ◽  
Gülçin Büyüközkan ◽  
Murat Baskak
Keyword(s):  
Production Planning ◽  
Fuzzy Model ◽  
Mixed Integer ◽  
Planning Problem ◽  
Production Environment ◽  
Material Flows ◽  
Proposed Model ◽  
Material Requirement ◽  
Production Planning Problem ◽  
Jewelry Industry

It is important to manage reverse material flows such as recycling, reusing, and remanufacturing in a production environment. This paper addresses a production planning problem which involves reusing of scrap and recycling of waste that occur in the various stages of the production process and remanufacturing/recycling of returns in a closed-loop supply chain environment. An extended material requirement planning (MRP) is proposed as a mixed integer linear programming (MILP) model which includes—beside forward—these reverse material flows. The proposed model is developed for the jewelry industry in Turkey, which uses gold as the primary resource of production. The aim is to manage these reverse material flows as a part of production planning to utilize resources. Considering the mostly unpredictable nature of reverse material flows, the proposed model is likewise transformed into a fuzzy model to provide a better review of production plan for the decision maker. The suggested model is examined through a case study to test the applicability and efficiency.

Optimization Methods for Lot-Sizing Problem in an Automated Foundry

2013 ◽  
Vol 58 (3) ◽  
pp. 863-866 ◽  
Author(s):  
J. Duda ◽  
A. Stawowy
Keyword(s):  
Production Planning ◽  
Lot Sizing ◽  
Planning Horizon ◽  
Optimization Methods ◽  
Mixed Integer ◽  
Planning Problem ◽  
Lot Size ◽  
Mip Model ◽  
Production Planning Problem ◽  
Problem Instances

Abstract In the paper we studied a production planning problem in a mid-size foundry that provides tailor-made cast products in small lots for a large number of clients. Assuming that a production bottleneck is the furnace, a mixed-integer programming (MIP) model is proposed to determine the lot size of the items and the required alloys to be produced during each period of the finite planning horizon that is subdivided into smaller periods. As using an advanced commercial MIP solvers may be impractical for more complex and large problem instances, we proposed and compared a few computational intelligence heuristics i.e. tabu search, genetic algorithm and differential evolution. The examination showed that heuristic approaches can provide a good compromise between speed and quality of solutions and can be used in real-world production planning.

scholarly journals Production Planning Problem of a Two-Level Supply Chain with Production-Time-Dependent Products

2021 ◽  
Vol 11 (20) ◽  
pp. 9687
Author(s):  
Jun-Hee Han ◽  
Ju-Yong Lee ◽  
Bongjoo Jeong
Keyword(s):  
Supply Chain ◽  
Production Planning ◽  
Programming Model ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Planning Problem ◽  
Manufacturing Plant ◽  
Production Lines ◽  
Multiple Production ◽  
Production Planning Problem

This study considers a production planning problem with a two-level supply chain consisting of multiple suppliers and a manufacturing plant. Each supplier that consists of multiple production lines can produce several types of semi-finished products, and the manufacturing plant produces the finished products using the semi-finished products from the suppliers to meet dynamic demands. In the suppliers, different types of semi-finished products can be produced in the same batch, and products in the same batch can only be started simultaneously (at the same time) even if they complete at different times. The purpose of this study is to determine the selection of suppliers and their production lines for the production of semi-finished products for each period of a given planning horizon, and the objective is to minimize total costs associated with the supply chain during the whole planning horizon. To solve this problem, we suggest a mixed integer programming model and a heuristic algorithm. To verify performance of the algorithm, a series of tests are conducted on a number of instances, and the results are presented.

A Differential Evolution Based Optimization Algorithm for Production Planning Model in Defense Industry

2013 ◽  
Vol 709 ◽  
pp. 249-252
Author(s):  
Yu Zhou ◽  
Jiang Jiang
Keyword(s):  
Differential Evolution ◽  
Production Planning ◽  
Optimization Algorithm ◽  
Programming Model ◽  
Solution Space ◽  
Defense Industry ◽  
Nonlinear Integer Programming ◽  
Planning Problem ◽  
Proposed Model ◽  
Production Planning Problem

The production planning problem in defense industry has the non-regular nonlinearity of the objective function and the NP-hard nature of the solution space. A nonlinear integer programming model was proposed for this problem. A differential evolution based optimization algorithm was developed to solve the proposed model. A case study validated the effectiveness of the developed algorithm, and proved that it is superior to the standard genetic algorithm on the global searching capability. The algorithm can support the weapons production planning, and also can be applied to other industries.

scholarly journals Stochastic Chebyshev Goal Programming Mixed Integer Linear Model for Sustainable Global Production Planning

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 483
Author(s):  
Chia-Nan Wang ◽  
Nhat-Luong Nhieu ◽  
Trang Thi Thu Tran
Keyword(s):  
Sustainable Development ◽  
Production Planning ◽  
Goal Programming ◽  
Production System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Planning Problem ◽  
Objective Functions ◽  
Proposed Model ◽  
Random Distributions

Production planning is a necessary process that directly affects the efficiency of production systems in most industries. The complexity of the current production planning problem depends on increased options in production, uncertainties in demand and production resources. In this study, a stochastic multi-objective mixed-integer optimization model is developed to ensure production efficiency in uncertainty conditions and satisfy the requirements of sustainable development. The efficiency of the production system is ensured through objective functions that optimize backorder quantity, machine uptime and customer satisfaction. The other three objective functions of the proposed model are related to optimization of profits, emissions, and employment changing. The objective functions respectively represent the three elements of sustainable development: economy, environment, and sociality. The proposed model also assures the production manager’s discretion over whether or not to adopt production options such as backorder, overtime, and employment of temporary workers. At the same time, the resource limits of the above options can also be adjusted according to the situation of each production facility via the model’s parameters. The solutions that compromise the above objective functions are determined with the Chebyshev goal programming approach together with the weights of the goals. The model is applied to the multinational production system of a Southeast Asian supplier in the textile industry. The goal programming solution of the model shows an improvement in many aspects compared to this supplier’s manufacturing practices under the same production conditions. Last but not least, the study develops different scenarios based on different random distributions of uncertainty demand and different weights between the objective functions. The analysis and evaluation of these scenarios provide a reference basis for managers to adjust the production system in different situations. Analysis of uncertain demand with more complex random distributions as well as making predictions about the effectiveness of scenarios through the advantages of machine learning can be considered in future studies.

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