scholarly journals Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xinbo Zhang ◽  
Feng Zhang ◽  
Xiaohong Chen ◽  
Zhong Wan
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Raw Material ◽  
Interval Numbers ◽  
Robust Solution ◽  
Deterministic Equivalent ◽  
Uncertain Optimization ◽  
Planning Problems ◽  
Fuzzy Interval

A polymorphic uncertain linear programming (PULP) model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems.

scholarly journals PERENCANAAN BAHAN BAKU DAN HASIL PRODUKSI MENGGUNAKAN METODE LINIER PROGRAMMING SIMPLEK

2017 ◽  
Vol 12 (2) ◽  
Author(s):  
Monika Handayani ◽  
Eka Kusuma Dewi
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Simplex Method ◽  
Raw Materials ◽  
Raw Material ◽  
Planning System ◽  
Testing System ◽  
Basic Solution ◽  
Linear Programming Method

<p>CV. Baja Utama Landasan Ulin is a business entity that manufactures various products using the basic ingredients of iron. In the management of raw materials for the production of common regulatory process raw materials into sections for further processing. This setting is often done manually without doing careful planning, so that at the end of each production process there are many remaining pieces of the raw materials that should be used in production. In addition to the determination of the production is necessary to reference how the product should be made for each type of existing products. This is often an important factor that pushed for the optimization of production planning in determining the number of products for each type of product and raw material consumption.Linear Programming is one of the methods used in production planning to regulate the use of raw materials is limited. Simplex method is part of the linear programming method that can be used in the production planning system implementation. Simplex method identifies an initial basic solution and then move systematically to other basic solution that has the potential to improve the value of the objective function.The calculation result of production planning using the simplex method can be used as a reference in the decision making production planning. By building an application using the simplex method can assist in the calculation of production peencanaan more efficiently and effectively. Accuracy testing system constructed show significant results with great value reached 94% level of accuracy.<br />Keywords: simplex, production planning, the maximum gain, linear programming</p>

Production Planning Optimization in F Company: A Scheduling Theory Case Study

2013 ◽  
Vol 655-657 ◽  
pp. 1646-1649 ◽  
Author(s):  
Chi Zhang ◽  
Zhen He ◽  
Yuan Peng Ruan
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Optimization Problem ◽  
Assembly Line ◽  
Scheduling Theory ◽  
Scheduling Model ◽  
Planning Optimization ◽  
Optimization Methodology ◽  
Optimum Production

Scheduling is an effective optimization methodology which has been widely used for production planning. This paper presents a scheduling model to optimize the output of an assembly line in F Semiconductor Company in Tianjin, China. The authors formulate the optimization problem as linear programming. The model and its implementation are described in detail in this article. The optimum production allocations have been founded by the scheduling model and the output has been increased.

scholarly journals Integer linear programming outperforms simulated annealing for solving conservation planning problems

2019 ◽  
Author(s):  
Richard Schuster ◽  
Jeffrey O. Hanson ◽  
Matt Strimas-Mackey ◽  
Joseph R. Bennett
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Integer Linear Programming ◽  
Conservation Planning ◽  
Practical Advantage ◽  
Processing Times ◽  
Conservation Plans ◽  
The Cost ◽  
Planning Problems

AbstractThe resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and Integer linear programming (ILP). Using a case study in British Columbia, Canada, we compare the cost-effectiveness and processing times of SA versus ILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on ILP algorithms were 12 to 30% cheaper than plans using SA. The best ILP solver we examined was on average 1071 times faster than the SA algorithm tested. The performance advantages of ILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using ILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of ILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process.

Integrating a Weighted Additive Multiple Objective Linear Model with Possibilistic Linear Programming for Fuzzy Aggregate Production Planning Problems

2020 ◽  
Vol 9 (2) ◽  
pp. 1-30
Author(s):  
Navee Chiadamrong ◽  
Noppasorn Sutthibutr
Keyword(s):  
Linear Programming ◽  
Linear Model ◽  
Production Planning ◽  
Hybrid Approach ◽  
Optimization Method ◽  
Multiple Objective ◽  
Aggregate Production Planning ◽  
Aggregate Production ◽  
Planning Problems ◽  
Possibilistic Linear Programming

This study uses an integrated optimization method by applying a weighted additive multiple objective linear model with Possibilistic Linear Programming (PLP) to fuzzy Aggregate Production Planning (APP) problems under an uncertain environment. The uncertainty conditions include uncertainties of operating times and costs, customer demand, labor level, as well as machine capacity. The aim of this study is to minimize total costs of the plan that consist of the production cost and costs of changes in labor level. The proposed hybrid approach minimizes the most possible value of the imprecise total costs, maximizes the possibility of obtaining lower total costs, and minimizes the risk of obtaining higher total costs from PLP as multiple objectives for the fuzzy multiple objective linear model optimization. The outcome of the proposed approach shows that the solution is closer to the ideal solution obtained from Linear Programming than a typical solution obtained from PLP. There is also a higher overall satisfaction value.

scholarly journals Optimization of the Supply Chain in the Production of Ethanol from Agricultural Biomass Using Mixed-Integer Linear Programming (MILP): A Case Study

2020 ◽  
Vol 2020 ◽  
pp. 1-25 ◽  
Author(s):  
Edgar León-Olivares ◽  
Hertwin Minor-Popocatl ◽  
Omar Aguilar-Mejía ◽  
Diana Sánchez-Partida
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Raw Material ◽  
Mixed Integer ◽  
Study Region ◽  
Total Cost ◽  
Agricultural Biomass

The production of biofuels from agricultural biomass has attracted much attention from researchers in recent years. Biomass residues generated from agricultural production of corn and barley represent an essential source of raw material for the production of biofuels, and a mathematical programming-based approach can be used to establish an efficient supply chain. This paper proposes a model of mixed-integer linear programming (MILP) that seeks to minimize the total cost of the bioethanol supply chain. The proposal allows determining the optimal number and location of storage centers, biorefineries, and mixing plants, as well as the flow of biomass and bioethanol between the facilities. To show the proposed approach, we present a case study developed in the region of Tulancingo, Hidalgo, in Mexico (case study), considering the potential of biomass (corn and barley residues) in the region. The results show the costs for the production of bioethanol, transportation, and refining and total cost of the bioethanol supply chain, besides a sensitivity analysis on the costs of the bioethanol supply chain which is presented by mixing different percentages of bioethanol with fossil fuel to satisfy the demand. We conclude that the proposed approach is viable in the process of configuring the supply chain within the proposed study region.

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