A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts

Rahimi, Erfan; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid; Arabsheybani, Amir

doi:10.1080/21681015.2018.1441195

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…-A multi-commodity multi-period optimization model to analyse market demand disruption risk in agribusiness supply chains [35] -Two-Stage Stochastic Network-Based Model [35] -A new robust multi-objective multiperiod model for supply chain planning under uncertainty considering quantity discounts [36] -Single-Objective Mixed-Integer Programming Model [36] -Optimization the decisions of facility location, emergency inventory pre-positioning, and relief delivery operations within a single-commodity disaster relief network [37] -Min-Max Robust Optimization [37] -The intermodal freight network expansion problem consisting of multiple periods [38] -Hybrid Simulated Annealing (SA) Algorithm [38] Project management -The robust resource-constrained project scheduling problem [39] -Mixed-Integer Linear Programming (MILP) [39] -The robust resource-constrained project scheduling problem (RCPSP) with discrete time/resource trade-offs [40] -Monte Carlo Simulation [40] , 0…”

Section: Discussionmentioning

confidence: 99%

Robust optimization in production engineering – methods and application

et al. 2019

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Methods that use robust optimization are aimed at finding robustness to decision uncertainty. Uncertainty may affect the input parameters (problem) and the final solution. Robust optimization is applicable in many areas, such as: operational research, IT, energy, production engineering and others. The aim of the work was to indicate the main methods and examples of applications of robust optimization in the area of production engineering. Documents (articles and proceedings paper) indexed in the Web of Science - Core Collection database (WoSCC) from 2014-2018 were used for analysis. The search has been limited to the WoS-CC category: Engineering Industrial and Engineering Manufacturing. The main areas of application were: the scheduling of projects and tasks, production planning, and risk management. The most common methods were: linear programming, evolutionary algorithms, mixed integer programming, dynamic programming and many others.

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Section: Discussionmentioning

confidence: 99%

Robust optimization in production engineering – methods and application

et al. 2019

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“…Hence, the food supply chain needs to be restructured to handle the demands effectively. Supply chain management techniques are effective tools for researchers and manufacturers to acquire and analyze the results (De Laporte et al, 2016;Rahimi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Design and optimization of a soybean supply chain network under uncertainty

Shekarian¹

2021

Preprint

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Demands of foods have been increased in recent years for human and animal nutrition. Food supply chain management has been required to administer series of products and services in efficient ways for agriculture and food production to achieve customer satisfaction at the lowest cost. Agricultural systems have been changed during recent years, and have caused improvements in consumption and production patterns. However, there is not much research on supply chains of seeds (e.g., soybean) which have been produced in Canada. In this research, we propose a mixed-integer linear optimization formulation for a soybean supply chain network. The profit is maximized in the objective function. The mathematical formulation consists of multiple products, growers, potential farm company facilities, potential locations of distributers, and customers. Then, the mathematical model is extended by possibilistic approach to include uncertain parameters. In addition, the results are discussed and analyzed for the soybean supply chain network.

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“…Mohammed et al (2017) formulated a multi-objective model to optimize four conflicting objectives: the minimization of the total cost, maximization of the integrity of Halal meat products, maximization of the ROI, and maximization of capacity utilization for Halal meat logistics under uncertainty. They employed the modified weighted-sum and ε-constraint techniques to solve the proposed model Rahimi et al (2018). made a trade-off between the present and future profits by introducing a bi-objective and multi-period mixed-integer programming model under uncertainty of demand, selling price, and purchasing cost.…”

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confidence: 99%

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

Mohebalizadehgashti¹

2021

Preprint

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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.

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A robust optimization model for multi-objective multi-period supply chain planning under uncertainty considering quantity discounts

Cited by 8 publications

References 36 publications

Robust optimization in production engineering – methods and application

Robust optimization in production engineering – methods and application

Design and optimization of a soybean supply chain network under uncertainty

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

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