Multi-objective Integer Linear Programming

2008 ◽  
pp. 2448-2454
Author(s):  
Jacques Teghem
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Multi Objective

scholarly journals A multi-objective approach under uncertainty for designing a green meat supply chain network

2021 ◽  
Author(s):  
Fatemeh Mohebalizadehgashti
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Mixed Integer ◽  
Environmental Concerns ◽  
Supply Chain Network ◽  
Food Supply Chain ◽  
Multi Objective ◽  
Proposed Model

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature. Keywords: Meat supply chain; Decision tree; Multi-objective programming; Mixed-integer linear programming; Augmented εε-constraint.

scholarly journals A fuzzy approach to multi-objective mixed integer linear programming model for multi-echelon closed-loop supply chain with multi-product multi-time-period

2020 ◽  
Vol 30 (1) ◽  
Author(s):  
Sema Akin Bas ◽  
Beyza Ahlatcioglu Ozkok
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Closed Loop ◽  
Mixed Integer ◽  
Fuzzy Approach ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Time Period

By the green point of view, supply chain management (SCM), which contains supplier and location selection, production, distribution, and inventory decisions, is an important subject being examined in recent years by both practitioners and academicians. In this paper, the closed-loop supply chain (CLSC) network that can be mutually agreed by meeting at the level of common satisfaction of conflicting objectives is designed. We construct a multi-objective mixed-integer linear programming (MOMILP) model that allows decision-makers to more effectively manage firms’ closed-loop green supply chain (SC). An ecological perspective is brought by carrying out the recycling, remanufacturing and destruction to SCM in our proposed model. Maximize the rating of the regions in which they are located, minimize total cost and carbon footprint are considered as the objectives of the model. By constructing our model, the focus of customer satisfaction is met, as well as the production, location of facilities and order allocation are decided, and we also carry out the inventory control of warehouses. In our multi-product multi-component multi-time-period model, the solution is obtained with a fuzzy approach by using the min operator of Zimmermann. To illustrate the model, we provide a practical case study, and an optimal result containing a preferable level of satisfaction to the decision-maker is obtained.

scholarly journals Decision space robustness for multi-objective integer linear programming

2021 ◽  
Author(s):  
Michael Stiglmayr ◽  
José Rui Figueira ◽  
Kathrin Klamroth ◽  
Luís Paquete ◽  
Britta Schulze
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Efficient Solutions ◽  
Second Phase ◽  
Decision Space ◽  
Two Phase ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Linear Programming Problems ◽  
Consistency Properties

AbstractIn this article we introduce robustness measures in the context of multi-objective integer linear programming problems. The proposed measures are in line with the concept of decision robustness, which considers the uncertainty with respect to the implementation of a specific solution. An efficient solution is considered to be decision robust if many solutions in its neighborhood are efficient as well. This rather new area of research differs from robustness concepts dealing with imperfect knowledge of data parameters. Our approach implies a two-phase procedure, where in the first phase the set of all efficient solutions is computed, and in the second phase the neighborhood of each one of the solutions is determined. The indicators we propose are based on the knowledge of these neighborhoods. We discuss consistency properties for the indicators, present some numerical evaluations for specific problem classes and show potential fields of application.

Analysis of network design for a circular production system using multi-objective mixed integer linear programming model

2019 ◽  
Vol 30 (3) ◽  
pp. 628-646 ◽  
Author(s):  
K.E.K. Vimal ◽  
Sonu Rajak ◽  
Jayakrishna Kandasamy
Keyword(s):  
Mathematical Model ◽  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Economic Benefits ◽  
Mixed Integer ◽  
Data Set ◽  
Content Type ◽  
Multi Objective ◽  
Case Organization

PurposeThe purpose of this paper is to propose a mathematical model for the design of a circular production system (CPS) for an Indian manufacturing organization participating in a symbiotic network.Design/methodology/approachA multi-objective mixed integer linear programming is used to model the network for quantifying the economic benefits. The data set collected from the case organization is used. The GAMS optimization package is used to simulate the model.FindingsThe model is able to compute the economic benefit achieved through circular operations in the case organization. The flow of different items through the network is also obtained.Research limitations/implicationsThe data set of the single organization in the symbiotic network is used to validate the proposed mathematical model. Further research can be done considering the all the organizations in the considered symbiotic network.Practical implicationsThis paper will help the authors to better understand the role of sustainable supply chains in a circular economy model especially in energy and materials intensive industries.Originality/valueThis study has uniquely utilized a multi-objective mixed-integer linear programming approach for the analysis of variables in CPSs and the corresponding economic benefits.

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