Solving Scheduling Problems in Distribution Centers by Mixed Integer Linear Programming Formulations

2011 ◽  
Vol 44 (1) ◽  
pp. 8205-8210
Author(s):  
Maria Pia Fanti ◽  
Gabriella Stecco ◽  
Walter Ukovich
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Distribution Centers

scholarly journals Transshipment model considering environmental cost using mixed integer linear programming: Beef distribution problem

2020 ◽  
Vol 11 (2) ◽  
pp. 155-164
Author(s):  
Januardi Januardi ◽  
Zakia Puspa Ramdhani ◽  
Rizky Novera Harnaningrum
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Operational Research ◽  
Carbon Emission ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Optimum Result ◽  
Destination Point

The operational research paper in the transportation model nowadays is heading to the environmental issue. One of the famous operational research models is transshipment. Transshipment is an expanded model of transportation, in each distribution center between the start to the destination point. In this research, the transshipment model is integrated into an environmental function. The challenge is to find the right shipment of each route from the start, distribution, and destination point considering the transportation cost and carbon emission. This research proposed a transshipment model by minimizing transportation and carbon emission cost using mixed--integer linear programming for model formulation. The solution searching used branch and bound method. This research analyzed the environmental objective function and constrain effect in the transshipment model. The model was tested in a beef distribution case study in Bogor, Indonesia that has eight source points, three distribution centers, and six destination points. The model was experimented using carbon emission limitation scenarios. The optimum result in source allocation, distribution and destination were different between the two scenarios. The carbon emission limitation affected carbon emission production and total cost.

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.

Green Transhipment Model Considering Carbon Emission Cost Using Mixed Integer Linear Programming (Case Study: Beef Distribution in Bogor, Indonesia)

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Januardi , Zakia Puspa Ramdhani, Rizky Novera Harnaningrum
Keyword(s):  
Linear Programming ◽  
Branch And Bound ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Operational Research ◽  
Carbon Emission ◽  
Mixed Integer ◽  
Distribution Centers ◽  
Destination Point

The operational research paper in the transportation model nowadays is heading to the environmental issue. One of the famous operational research models is transshipment. Transshipment is an expanded model of transportation, whether each distribution center between the start to the destination point. In this research, the transshipment model is integrated into an environmental function, the challenge is to find the right shipment of each route from the start, distribution, and destination point considering the transportation cost and carbon emission. This research proposed a transshipment model with minimizing transportation and carbon emission cost using mixed-integer linear programming for model formulation. The solution searching used branch and bound method. This research analyzed the environmental objective function and constrain effect in the transshipment model. The model is tested in a beef distribution case study in Bogor, Indonesia that has eight source points, three distribution centers, and six destination points. The model is experimented by carbon emission limitation scenarios. The optimum result in source allocation, distribution and destination are different between the two scenarios. The carbon emission limitation affects carbon emission production and total cost.   Keywords: Branch and Bound, Environmental Cost, Green Transhipment, Mixed Integer Linear Programming preferably 2-scenarios are mentioned

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