scholarly journals Mixed integer linear programming approaches for solving the raw material allocation, routing and scheduling problems in the forest industry

2020 ◽  
pp. 525-548 ◽  
Author(s):  
Maximiliano R. Bordón ◽  
Jorge M. Montagna ◽  
Gabriela Corsano
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Raw Material ◽  
Mixed Integer ◽  
Forest Industry ◽  
Scheduling Problems ◽  
Routing And Scheduling

scholarly journals A mixed-integer linear programming model for harvesting, loading and transporting sugarcane. A case study in Peru

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 173-179 ◽  
Author(s):  
Marcela María Morales-Chávez ◽  
José A. Soto-Mejía ◽  
William Ariel Sarache
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Raw Material ◽  
Linear Programming Model ◽  
Mixed Integer ◽  
Production Plant ◽  
Integer Linear Programming Model ◽  
The Cost

Due to opportunities for economic and social development in the biofuels market, improvement to the supply chain has become a relevant matter. In agro-industrial supply chains, procurement costs are highly relevant. Since sugarcane is a high performance raw material for ethanol production, this paper proposes a Mixed-Integer Linear Programming Model for cost optimization for harvesting, loading and transportation operations. The model determines the quantity of machines and workers to meet the biofuel plant requirements. Costs of resources for harvesting and loading as well as transportation costs from the land parcel to the production plant are minimized. Also, the model calculates the cost of penalties for shortages (unmet demand) and the cost of equipment idle time. The implementation of the model in a Peruvian biofuels company, showed a cost reduction of around 11 % when compared to the current costs.

scholarly journals Testing the Robustness of Linear Programming* Using a Diet Problem on a Multi-Shop System

2019 ◽  
pp. 1-12
Author(s):  
Sampson Takyi Appiah ◽  
Bernard Atta Adjei ◽  
Dominic Otoo ◽  
Eric Okyere
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Programming Models ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Paper Test ◽  
Optimal Cost ◽  
Decision Variables ◽  
Diet Problem

Time, raw materials and labour are some of the nite resources in the world. Due to this, Linear Programming* (LP) is adopted by key decision-markers as an innovative tool to wisely consume these resources. This paper test the strength of linear programming models and presents an optimal solution to a diet problem on a multi-shop system formulated as linear, integer linear and mixed-integer linear programming models. All three models gave different least optimal values, that is, in linear programming, the optimal cost was GHS15.26 with decision variables being continuous (R+) and discrete (Z+). The cost increased to GHS17.50 when the models were formulated as mixed-integer linear programming with decision variables also being continuous (R+) and discrete (Z+) and lastly GHS17.70 for integer linear programming with discrete (Z+) decision variables. The difference in optimal cost for the same problem under different search spaces sufficiently establish that, in programming, the search space undoubtedly affect the optimal value. Applications to most problems like the diet and scheduling problems periodically require both discrete and continuous decision variables. This makes integer and mixed-integer linear programming models also an effective way of solving most problems. Therefore, Linear Programming* is applicable to numerous problems due to its ability to provide different required solutions.

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.

scholarly journals Optimal Airport Surface Traffic Planning Using Mixed-Integer Linear Programming

2008 ◽  
Vol 2008 ◽  
pp. 1-11 ◽  
Author(s):  
P. C. Roling ◽  
H. G. Visser
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Planning Process ◽  
Research Effort ◽  
Mixed Integer ◽  
Automation System ◽  
Ongoing Research ◽  
Support Tool ◽  
Routing And Scheduling

We describe an ongoing research effort pertaining to the development of a surface traffic automation system that will help controllers to better coordinate surface traffic movements related to arrival and departure traffic. More specifically, we describe the concept for a taxi-planning support tool that aims to optimize the routing and scheduling of airport surface traffic in such a way as to deconflict the taxi plans while optimizing delay, total taxi-time, or some other airport efficiency metric. Certain input parameters related to resource demand, such as the expected landing times and the expected pushback times, are rather difficult to predict accurately. Due to uncertainty in the input data driving the taxi-planning process, the taxi-planning tool is designed such that it produces solutions that are robust to uncertainty. The taxi-planning concept presented herein, which is based on mixed-integer linear programming, is designed such that it is able to adapt to perturbations in these input conditions, as well as to account for failure in the actual execution of surface trajectories. The capabilities of the tool are illustrated in a simple hypothetical airport.

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