scholarly journals Multi-Objective Master Production Schedule for Balanced Production of Manufacturers

2020 ◽  
Vol 19 (4) ◽  
pp. 678-688
Author(s):  
C. Wang ◽  
B. Yang ◽  
H. Q. Wang
Keyword(s):  
Fitness Function ◽  
Production Capacity ◽  
Nonlinear Integer Programming ◽  
Production Schedule ◽  
Multi Objective ◽  
Logic Control ◽  
Multi Stage ◽  
Order Preference ◽  
Master Production Schedule ◽  
Global And Local

Focusing on the balanced use of production capacity in the formulation of master production schedule (MPS), this paper sets up a single-product, multi-stage, multi-objective MPS model based on balanced production. Whereas the model aims to achieve multiple objectives through nonlinear integer programming, a genetic algorithm based on automatic transformation (AT-GA) was designed to solve the model. Specifically, the chromosomes were encoded as integers to satisfy the model constraints; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to handle the four nonlinear objectives of the model, thereby obtaining the fitness function; the fuzzy logic control (FLC) was introduced to automatically adjust the crossover and mutation parameters, and balance the global and local search abilities of the GA, enhancing the computing power of the algorithm. The experimental results show that the AT-GA can effectively solve the multi-objective MPS optimization problem under balanced production.

scholarly journals Solving multi-objective master production schedule problem using memetic algorithm

2020 ◽  
Vol 18 (2) ◽  
pp. 938
Author(s):  
Shireen S. Sadiq ◽  
Adnan Mohsin Abdulazeez ◽  
Habibollah Haron
Keyword(s):  
Simulated Annealing ◽  
Memetic Algorithm ◽  
Lot Sizing ◽  
Production Schedule ◽  
Service Levels ◽  
Non Linear Programming ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Dynamic Lot Sizing ◽  
Master Production Schedule

<span>A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. </span>

scholarly journals Perencanaan dan Penentuan Jadwal Induk Produksi di PT. Arwina Triguna Sejahtera

2017 ◽  
Vol 1 ◽  
pp. 11
Author(s):  
Akhmad Sutoni ◽  
Muhammad Nasir Siddiq
Keyword(s):  
Capacity Planning ◽  
Production Capacity ◽  
Production Schedule ◽  
Master Schedule ◽  
Proportion Method ◽  
Master Production Schedule ◽  
The Cost ◽  
Production Plans ◽  
A Company ◽  
Planning Production

P.T. Arwina Triguna Sejahtera is a company engaged in the production of molding. Common issues in each company include P.T. Arwina Triguna Sejahtera, is experiencing constraints in planning the production amount according to production capacity. So planned to be created master production schedule so that the cost is not too high. Steps that are done is to start by doing data processing of the past by using demand aggregation, then do the forecasting in accordance with the characteristics of demand. Aggregate procurement planning is carried out for the next 1 (years) period using a permanent employment strategy. Then proceed at the disaggregation stage by using the proportion method, calculating the Master Production Schedule to find out how much quantity of product to produce, and when to start the production. Finally perform the calculation of Rought Cut Capacity Planning to know the balance between the available capacity and the required capacity. Production master schedule obtained, production will be smooth and able to meet the actual demand of 20,000 pcs by adding overtime hours and produce end items as much as 2.368 pcs for deabetamil scoop products, while for featherlock brown plastic products actual demand can be fulfilled. The feasibility of the master production schedule is calculated based on the suitability between Regular Man Hour on production plans, with the amount of time available in the calculated capacity capacity of 17,1360 hours to meet the demand for this year. Keywords—  Production Controlling and Planning; Master Production Schedule; Agregate Production Planning; Rought Cut Capacity Planning;  Production System.

Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Ya Ge ◽  
Feng Shan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Genetic Algorithm ◽  
Heat Sink ◽  
Volumetric Flow Rate ◽  
Pareto Solutions ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Conflicting Objectives ◽  
Order Preference ◽  
General Method

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

scholarly journals ANALISIS PERENCANAAN BAHAN BAKU DI UD. AA DENGAN MENERAPKAN METODE MATERIAL REQUIREMENT PLANNING (MRP)

2020 ◽  
Vol 5 (1) ◽  
pp. 1-12
Author(s):  
Rudi Abdika Saputra ◽  
Inna Kholidasari ◽  
Susanti Sundari ◽  
Lestari Setiawati
Keyword(s):  
Lot Sizing ◽  
Raw Materials ◽  
Raw Material ◽  
Production Schedule ◽  
Material Requirements Planning ◽  
Forecasting Method ◽  
Material Requirement Planning ◽  
Material Requirement ◽  
Material Procurement ◽  
Master Production Schedule

This study discusses the application of the material requirements planning (MRP) method in the planning of raw materials in a furniture company. The purpose of this research is to know the planning of raw materials for furniture products in UD. AA, determine the most suitable inventory model to be applied to material inventory planning and analyze the role of the MRP system in raw material procurement planning. The forecasting method used is the quantitative method of time series analysis, determining the master production schedule, calculating lot sizing (LFL, EOQ, POQ methods). From determining the Master Production Schedule, it is found that the cabinet production plan for the next three months is 4 units per period or week, and based on the calculation of Material Requirement Planning (MRP) it can be seen what components are needed for the manufacture of cabinets, how many and when each component is required. Therefore it is obtained that the total raw material requirement for wood for the next three months is 11.34 m³.

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