Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition

Keyvanshokooh, Esmaeil; Ryan, Sarah M.; Kabir, Elnaz

doi:10.1016/j.ejor.2015.08.028

Cited by 226 publications

(56 citation statements)

References 54 publications

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“…Despite popularity of the above three leading paradigms for optimization under uncertainty, these approaches have their own limitations and specific application scopes. To this end, research efforts have been made on "hybrid" methods that leverage the synergy of different optimization approaches to inherit their corresponding strengths and complement respective weaknesses [107][108][109][110][111][112]. For instance, stochastic programming was integrated with robust optimization for supply chain design and operation under multi-scale uncertainties [50].…”

Section: Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

262

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This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities. A brief review of classical mathematical programming techniques for hedging against uncertainty is first presented, along with their wide spectrum of applications in Process Systems Engineering. A comprehensive review and classification of the relevant publications on data-driven distributionally robust optimization, data-driven chance constrained program, data-driven robust optimization, and data-driven scenario-based optimization is then presented. This paper also identifies fertile avenues for future research that focuses on a closed-loop data-driven optimization framework, which allows the feedback from mathematical programming to machine learning, as well as scenario-based optimization leveraging the power of deep learning techniques. Perspectives on online learningbased data-driven multistage optimization with a learning-while-optimizing scheme is presented.

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Section: Robust Optimizationmentioning

confidence: 99%

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Ning

You

2019

Computers & Chemical Engineering

262

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“…The optimization part selects a suitable solution based on the output of the evaluation part and generates a new candidate solution. The optimization part is usually according to the solution techniques, which include the branch-and-cut [24,25], branches-and-bound [25], and Benders' decomposition [14,26] in the standard mathematical optimization techniques of MILP and simulated annealing (SA) algorithm [20,27], genetic algorithm [18,28], and swarm optimization algorithm [29] from the heuristic methods. As the RLN design problem belongs to a class of combinatorial optimization problems that require computational resources at an exponentially growing rate when the number of decision variables increases, hence, heuristic methods are usually adopted when the number of decision variables is large [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Improved Simulated Annealing Based Network Model for E-Recycling Reverse Logistics Decisions under Uncertainty

Wang

Goh

Ding

et al. 2018

Mathematical Problems in Engineering

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Electronic waste recycle (e-recycling) is gaining increasing importance due to greater environmental concerns, legislation, and corporate social responsibility. A novel approach is explored for designing the e-recycling reverse logistics network (RLN) under uncertainty. The goal is to obtain a solution, i.e., increasing the storage capacity of the logistics node, to achieve optimal or nearoptimal profit under the collection requirement set by the government and the investment from the enterprise. The approach comprises two parts: a matrix-based simulation model of RLN formed for the uncertainty of demand and reverse logistics collection which calculates the profit under a given candidate solution and simulated annealing (SA) algorithm that is tailored to generating solution using the output of RLN model. To increase the efficiency of the SA algorithm, network static analysis is proposed for getting the quantitative importance of each node in RLN, including the static network generation process and index design. Accordingly, the quantitative importance is applied to increase the likelihood of generating a better candidate solution in the neighborhood search of SA. Numerical experimentation is conducted to validate the RLN model as well as the efficiency of the improved SA.

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“…Wang et al [22] used prediction sets to solve an expansion planning problem for waste-to-energy (WtE) systems facing future waste supply uncertainty. Keyvanshokooh et al [23] proposed a novel hybrid robust-stochastic programing (HRSP) approach to simultaneously model two different types of uncertainties by using stochastic scenarios for the transportation costs and polyhedral uncertainty sets for the demand and returns. However, the DC and the CC were separate and the collection disposal rate was a certain variable.…”

Section: Introductionmentioning

confidence: 99%

“…Uncertainties exist in both forward and reverse supply chains. However, the uncertainties in the reverse flow are higher than in the forward supply chain [7,24,25] as returned product quantity is generally seen as uncertain [23,26]. Subjective uncertainties such as the decision maker's choices and environmental coefficients can be dealt with using fuzziness, while objective uncertainties such as unit transportation costs, product prices, and the quantity of unusable products can be dealt with using randomness.…”

Section: Introductionmentioning

confidence: 99%

A Collection‐Distribution Center Location and Allocation Optimization Model in Closed‐Loop Supply Chain for Chinese Beer Industry

Kang

Wang

2017

Mathematical Problems in Engineering

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Recycling waste products is an environmental-friendly activity that can result in manufacturing cost saving and economic efficiency improving. In the beer industry, recycling bottles can reduce manufacturing cost and the industry’s carbon footprint. This paper presents a model for a collection-distribution center location and allocation problem in a closed-loop supply chain for the beer industry under a fuzzy random environment, in which the objectives are to minimize total costs and transportation pollution. Both random and fuzzy uncertainties, for which return rate and disposal rate are considered fuzzy random variables, are jointly handled in this paper to ensure a more practical problem solution. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A beer company case study is given to illustrate the application of the proposed model and to demonstrate the priority-based global-local-neighbor particle swarm optimization.

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Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition

Cited by 226 publications

References 54 publications

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming

Improved Simulated Annealing Based Network Model for E-Recycling Reverse Logistics Decisions under Uncertainty

A Collection‐Distribution Center Location and Allocation Optimization Model in Closed‐Loop Supply Chain for Chinese Beer Industry

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