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Effect of carbon tax on reverse logistics network design

COMPUTERS & INDUSTRIAL ENGINEERING 2020年 139卷

作者： Reddy, K. Nageswara Kumar, Akhilesh Sarkis, Joseph Tiwari, Manoj Kumar Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Worcester Polytech Inst Foisie Sch Business Worcester MA 01609 USA

Reverse logistics network design (RLND) is getting momentum as more organizations realize the benefits of recycling or remanufacturing of their end-of-life products. Similarly, there is an impetus for organizations to become more environmentally conscious or green. This environmental context has driven many organizations to invest in green technologies, with a recent emphasis on reducing greenhouse gas emissions. This environmental investment situation and decision can be addressed through the integration of facility location, operational planning, and vehicle type selection, while simultaneously accounting for carbon emissions from vehicles, inspection centers, and remanufacturing centers in a reverse logistics (RL) context. In the current study, we present a mixed-integer linear programming (MILP) model to solve a multi-tier multi-period green RL network, including vehicle type selection. This research integrates facility locations, vehicle type selection with emissions producing from transportation and operations at various processing centers. Prior research does not account for carbon emissions for this design problem type. Valuable managerial insights are obtained when incorporating carbon emissions cost.

关键词： Reverse logistics Remanufacturing Network design mixed integer linear programming Carbon footprint

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Scheduling directed acyclic graphs with optimal duplication strategy on homogeneous multiprocessor systems

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2020年 138卷 115-127页

作者： Tang, Qi Zhu, Li-Hua Zhou, Li Xiong, Jun Wei, Ji-Bo Natl Univ Def Technol Dept Elect Sci & Engn Changsha Peoples R China Hunan Univ Changsha Peoples R China

Modern applications generally need a large volume of computation and communication to fulfill the goal. These applications are often implemented on multiprocessor systems to meet the requirements in computing capacity and communication bandwidth, whereas, how to obtain a good or even the optimal performance on such systems remains a challenge. When tasks of the application are mapped onto different processors for execution, inter-processor communications become inevitable, which delays some tasks' execution and deteriorates the schedule performance. To mitigate the overhead incurred by inter-processor communications and improve the schedule performance, task duplication strategy has been employed in the schedule. Most available techniques for the duplication-based scheduling problem utilize heuristic strategies to produce sub-optimal solutions, however, how to find the optimal duplication-based solution with the minimal schedule makespan remains an unsolved issue. To fill in this gap, this paper proposes a novel mixed integer linear programming (MILP) formulation for this problem, together with a set of key theorems which enable and simplify the MILP formulation. The proposed MILP formulation can optimize the duplication strategy, serialize the execution of task instances on each processor and determine data precedences among different task instances, thus producing the optimal solution. The proposed method is tested on a set of synthesized applications and platforms and compared with the well-known algorithm. The experimental results demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Task duplication Multiprocessor Schedule mixed integer linear programming Makespan

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A mathematical programming approach for optimizing on-specs production for industrial processes under input uncertainty

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IFAC-PapersOnLine 2022年第10期55卷 2822-2827页

作者： Pavlos Eirinakis Gregory Koronakos

Starting from a critical problem in oil refineries, namely on-specs LPG production, we propose a generic mathematical programming approach that incorporates flow and blending constraints for process industries in which impurities must adhere to certain specifications. Moreover, we extend our approach to accommodate the uncertainty that may arise from the level of impurities in the input feed.

关键词： LPG production quality specifications on-specs recovery mixed integer linear programming Chance Constrained programming process flow network blending

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ALGORITHMS 2022年第11期15卷

作者： Olteanu, Alexandru-Liviu Sevaux, Marc Ziaee, Mohsen Univ Bretagne Sud Lab STICC UMR 6285 CNRS F-56100 Lorient France Univ Bojnord Dept Ind Engn Bojnord Iran

In this paper, an unrelated parallel machine scheduling problem with job (product) and machine acceptance and renewable resource constraints was considered. The main idea of this research was to establish a production facility without (or with minimum) investment in machinery, equipment, and location. This problem can be applied to many real problems. The objective was to maximize the net profit;that is, the total revenue minus the total cost, including fixed costs of jobs, job transportation costs, renting costs of machines, renting cost of resources, and transportation costs of resources. A mixed-integer linear programming (MILP) model and several heuristics (greedy, GRASP, and simulated annealing) are presented to solve the problem.

关键词： scheduling unrelated parallel machines job acceptance machine acceptance mixed integer linear programming heuristics

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A simple layout optimization formulation for load-carrying tensegrity structures

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第6期62卷 2935-2949页

作者： Nanayakkara, K. I. U. He, Linwei Fairclough, Helen E. Gilbert, Matthew Univ Sheffield Dept Civil & Struct Engn Mappin St Sheffield S1 3JD S Yorkshire England

Traditional tensegrity structures comprise isolated compression members lying inside a continuous network of tension members. In this contribution, a simple numerical layout optimization formulation is presented and used to identify the topologies of minimum volume tensegrity structures designed to carry external applied loads. Binary variables and associated constraints are used to limit (usually to one) the number of compressive elements connecting a node. A computationally efficient two-stage procedure employing mixed integer linear programming (MILP) is used to identify structures capable of carrying both externally applied loads and the self-stresses present when these loads are removed. Although tensegrity structures are often regarded as inherently 'optimal', the presence of additional constraints in the optimization formulation means that they can never be more optimal than traditional, non-tensegrity, structures. The proposed procedure is programmed in a MATLAB script (available for download) and a range of examples are used to demonstrate the efficacy of the approach presented.

关键词： Layout optimization Tensegrity structures mixed integer linear programming

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Robust bi-level risk-based optimal scheduling of microgrid operation against uncertainty

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RAIRO-OPERATIONS RESEARCH 2020年第4期54卷 993-1012页

作者： Golpira, Heris Bahramara, Salah Khan, Syed Abdul Rehman Zhang, Yu Islamic Azad Univ Sanandaj Branch Dept Ind Engn Sanandaj Iran Islamic Azad Univ Sanandaj Branch Dept Elect Engn Sanandaj Iran Tsinghua Univ Sch Econ & Management Beijing Peoples R China Changan Univ Sch Econ & Management Xian 710064 Shaanxi Peoples R China

The model introduced in this paper is the first to propose a decentralized robust optimal scheduling of MG operation under uncertainty and risk. The power trading of the MG with the main grid is the first stage variable and power generation of DGs and power charging/discharging of the battery are the second stage variables. The uncertain term of the initial objective function is transformed into a constraint using robust optimization approach. Addressing the Decision Maker's (DMs) risk aversion level through Conditional Value at Risk (CVaR) leads to a bi-level programming problem using a data-driven approach. The model is then transformed into a robust single-level using Karush-Kahn-Tucker (KKT) conditions. To investigate the effectiveness of the model and its solution methodology, it is applied on a MG. The results clearly demonstrate the robustness of the model and indicate a strong almost linear relationship between cost and the DMs various levels of risk aversion. The analysis also outlines original characterization of the cost and the MGs behavior using three well-known goodness-of-fit tests on various Probability Distribution Functions (PDFs), Beta, Gumbel Max, Normal, Weibull, and Cauchy. The Gumbel Max and Normal PDFs, respectively, exhibit the most promising goodness-of-fit for the cost, while the power purchased from the grid are well fitted by Weibull, Beta, and Normal PDFs, respectively. At the same time, the power sold to the grid is well fitted by the Cauchy PDF.

关键词： Microgrid mixed integer linear programming robust optimization conditional value at risk goodness of fit test

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Data-driven optimization model customization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第2期287卷 438-451页

作者： Hewitt, Mike Frejinger, Emma Loyola Univ Quinlan Sch Business Chicago IL 60611 USA Univ Montreal CIRRELT Montreal PQ Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada

When embedded in software-based decision support systems, optimization models can greatly improve organizational planning. In many industries, there are classical models that capture the fundamentals of general planning decisions (e.g., designing a delivery route). However, these models are generic and often require customization to truly reflect the realities of specific operational settings. Yet, such customization can be an expensive and time-consuming process. At the same time, popular cloud computing software platforms such as Software as a Service (SaaS) are not amenable to customized software applications. We present a framework that has the potential to autonomously customize optimization models by learning mathematical representations of customer-specific business rules from historical data derived from model solutions and implemented plans. Because of the wide-spread use in practice of mixed integer linear programs (MILP) and the power of MILP solvers, the framework is designed for MILP models. It uses a common mathematical representation for different optimization models and business rules, which it encodes in a standard data structure. As a result, a software provider employing this framework can develop and maintain a single code-base while meeting the needs of different customers. We assess the effectiveness of this framework on multiple classical MILPs used in the planning of logistics and supply chain operations and with different business rules that must be observed by implementable plans. Computational experiments based on synthetic data indicate that solutions to the customized optimization models produced by the framework are regularly of high-quality. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Decision support systems Statistical learning mixed integer linear programming Optimization modeling

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A MILP Optimization Method for Building Seasonal Energy Storage: A Case Study for a Reversible Solid Oxide Cell and Hydrogen Storage System

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BUILDINGS 2020年第7期10卷 1-27页

作者： Lindholm, Oscar Weiss, Robert Hasan, Ala Pettersson, Frank Shemeikka, Jari VTT Tech Res Ctr Finland FI-02044 Espoo Finland Abo Akad Univ Fac Sci & Engn Lab Proc & Syst Technol FI-20500 Turku Finland

A new method for the optimization of seasonal energy storage is presented and applied in a case study. The optimization method uses an interval halving approach to solve computationally demanding mixed integer linear programming (MILP) problems with both integer and non-integer operation variables (variables that vary from time step to time step in during energy storage system operation). The seasonal energy storage in the case study uses a reversible solid oxide cell (RSOC) to convert electricity generated by solar photovoltaic (PV) panels into hydrogen gas and to convert hydrogen gas back to electricity while also generating some heat. Both the case study results and the optimization method accuracy are examined and discussed in the paper. In the case study, the operation of the RSOC and hydrogen storage system is compared with the operation of a reference system without energy storage. The results of the study show that installing an RSOC and hydrogen storage system could increase the utilization of onsite renewable energy generation significantly. Overall, the optimization method presents a relatively accurate solution to the case study optimization problem and a sensibility analysis shows a clear and logical pattern.

关键词： energy storage hydrogen power-to-gas reversible solid oxide cell optimization mixed integer linear programming MILP

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A column generation-based approach for proportionate flexible two-stage no-wait job shop scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第2期58卷 487-508页

作者： Pei, Zhi Zhang, Xuefang Zheng, Li Wan, Mingzhong Zhejiang Univ Technol Dept Ind Engn Hangzhou 310023 Zhejiang Peoples R China Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China

Job shop scheduling, as one of the classical scheduling problems, has been widely studied in literatures, and proved to be mostly NP-hard. Although it is extremely difficult to solve job shop scheduling with no-wait constraint to optimality, the two-machine no-wait job shop scheduling to minimise makespan could be solvable in polynomial time when each job has exactly two equal length operations (proportionate job shop). In the present paper, an extension is attempted by considering a proportionate flexible two-stage no-wait job shop scheduling problem with minimum makespan, and a set-covering formulation is put forward which contains a master problem and a pricing problem. To solve this problem, a column generation (CG)-based approach is implemented. In comparison, a mixed integer programming model is constructed and optimised by Cplex. A series of randomly generated numerical instances are calculated. And the testing result shows that the mixed integer model handled by Cplex can only solve small scale cases, while the proposed CG-based method can conquer larger size problems in acceptable time.

关键词： Flexible job shop job shop scheduling branch and bound mixed integer linear programming column generation

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Efficient optimization of a large-scale biorefinery system using a novel decomposition based approach

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2020年 160卷 175-189页

作者： Punnathanam, Varun Shastri, Yogendra Indian Inst Technol Dept Chem Engn Mumbai 400076 Maharashtra India

Biorefinery design problems that consider processing and supply chain together are generally large-scale mixed integer linear programs (MILP) that are computationally difficult to solve. This work considers one such large-scale problem and proposes a solution method to solve it efficiently. The proposed method utilizes the Dantzig-Wolfe decomposition framework and a novel heuristic to simplify the original problem. This simplification is done using the sub-problem solutions obtained within the Dantzig-Wolfe decomposition iterations. The resulting simplified problem can be easily solved using standard procedures to obtain the final solution for the original problem. The proposed method is observed to be up to 92% faster than the standard CPLEX (R) MILP solver and can solve large-scale problems which are not solvable using standard approaches. A large-scale biorefinery design problem for the production of ethanol from lignocellulosic biomass in Maharashtra, India is solved. For an ethanol demand of 20 million kg/month, the minimum ethanol cost was determined to be INR 47.4 per litre. This method enables the formulation of more comprehensive biorefinery design models and can be employed on similarly structured large MILP problems in other fields. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： OR in energy Large scale optimization Integrated biorefinery optimization mixed integer linear programming

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