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An Innovative Algorithm-Driven Optimization Framework for Landfill Mining: Aiming Sustainable Profitability Expeditiously

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IEEE ACCESS 2023年 11卷 122792-122820页

作者： Bhakuni, Pooja Das, Amrit Vellore Inst Technol Sch Adv Sci Dept Math Vellore 632014 India

Landfill mining (LFM) offers a potential solution to the environmental issues associated with landfilling. The current work aims to develop an efficient optimization framework for LFM that is sustainable, profit-yielding, and time minimizing at the same instance. The proposed framework involves a multi-objective multi-level solid transportation model (MOMLSTM). This model can be adapted by the organization across various geographies as it incorporates uncertainty of all the parameters of time, cost, and emission through pentagonal fuzzy numbers (PFN). The other crucial contribution of this work is the development of a genetic algorithm for offspring refinement (GAOR) that contributes in optimizing multi-objective optimization (MOO) problems. The GAOR's performance has been verified using the Congress on evolutionary computation (CEC) 2020 multi-objective benchmark test functions. GAOR is assessed against six robust MOO algorithms, including the multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA), enhanced multi-objective particle swarm optimization (EMOPSO), multi-objective gorilla troops optimizer (MOGTO), adaptive crossover strategy enhanced NSGA-II (ASDMSGA-II), multi-objective slime mould algorithm (MOSMA), and multi-objective equilibrium optimizer algorithm (MEOA). GAOR delivered outstanding results across three crucial performance indicators. To rank these algorithms, a Friedman test was conducted, and GAOR achieved the highest ranking among the tested MOO algorithms. A case study is considered for real-life application of the model and solution technique GAOR. The outcomes of MOMLSTM from GAOR are compared to the epsilon-constraint method. The comparison revealed noteworthy improvements: a 0.14% increase in profits, a 1.29% reduction in carbon emissions, and a 3.81% decrease in the time required.

关键词： epsilon-constraint method genetic algorithm multi-objective solid transportation problem pentagonal fuzzy number sustainable landfill mining

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Bi-objective cyclic scheduling in a robotic cell with processing time windows and non-Euclidean travel times

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第9期52卷 2505-2518页

作者： Feng, Jianguang Che, Ada Wang, Nengmin Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China

This paper addresses bi-objective cyclic scheduling in a robotic cell with processing time windows. In particular, we consider a more general non-Euclidean travel time metric where robot's travel times are not required to satisfy the well-known triangular inequality. We develop a tight bi-objective mixed integer programming (MIP) model with valid inequalities for the cyclic robotic cell scheduling problem with processing time windows and non-Euclidean travel times. The objective is to minimise the cycle time and the total robot travel distance simultaneously. We propose an iterative epsilon-constraint method to solve the bi-objective MIP model, which can find the complete Pareto front. Computational results both on benchmark instances and randomly generated instances indicate that the proposed approach is efficient in solving the cyclic robotic cell scheduling problems.

关键词： bi-objective cyclic scheduling robotic cell mixed integer programming epsilon-constraint method

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Effect of mitigation technologies on the total cost and carbon dioxide emissions of a cement plant under multi-objective mixed linear programming optimisation

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2022年 186卷 326-349页

作者： Ishak, Siti Aktar Hashim, Haslenda Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Dept Chem Engn Johor Baharu Johor Malaysia Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Green Energy & Environm Planning Res Grp Johor Baharu Johor Malaysia

With the increase of industrial activities in past decades, climate change has been a challenge worldwide. The cement industry is considered one of the industries that generate high carbon dioxide (CO2) emissions. With an imminent increase in CO2 emissions due to increased cement demand, this study proposed a systematic model for a cost-optimal cement plant that fulfils carbon limitations without compromising product specifications. This study considered three mitigation methods for cement manufacturing: co-processing, kiln system improvements, and carbon capture. The mixed integer linear programming model was executed in GAMS. From the optimisation model, it was found that a cement plant with co-processing, kiln improvements, and carbon capture managed to reduce more than 90 % CO2 emissions with USD 135.96/tonne clinker. (C) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Mixed integer linear programming Cement plant Carbon dioxide mitigation Optimisation Mathematical programming Multi-objective epsilon-constraint method

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Location and hierarchical allocation disaster with combined ε-constraint and simulation-based optimization approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2022年第5期98卷 407-432页

作者： Mousavi, Sogol Sajadi, Seyed Mojtaba AlemTabriz, Akbar Najafi, Seyyed Esmaeil Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran Iran Univ Tehran Fac Entrepreneurship New Business Dept Farshi Moghadam 16 StNorth Kargar Ave Tehran *** Iran Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management Tehran Iran

The increasing frequency of natural disasters and the necessity of proper planning to minimize the impact and casualties of such crises have always been matters of great concern to human societies. In this study, a hybrid mathematical-simulative location-allocation model is proposed to carry out disaster management (DM) efforts with maximum coverage in the immediate aftermath of an earthquake. The proposed model consists of two phases: determining the optimal location of the temporary emergency stations (TECs), followed by optimal and hierarchical allocation of casualties to said temporary medical centers (TMCs). Given the contradictory nature of the model's two objectives, that is, minimizing the cost of setting up TMCs and the time taken to transfer casualties to TMC. In the second phase, a simulation-based optimization approach is employed to simulate casualties' behavior at the onset of the disaster and to determine the optimal capacity of the medical centers. The findings indicate that the costs and distance traveled by casualties during the earthquake have been reduced by 15%.

关键词： Simulation-based optimization hierarchical mathematical model disaster management temporary medical centers epsilon-constraint method

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Energy-efficient bi-objective single-machine scheduling with power-down mechanism

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COMPUTERS & OPERATIONS RESEARCH 2017年第Sep.期85卷 172-183页

作者： Che, Ada Wu, Xueqi Peng, Jing Yan, Pengyu Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Univ Sci & Technol China Sch Management Hefei 230026 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China

This paper considers a single-machine scheduling, problem with power-down mechanism to minimize both total energy consumption and maximum tardiness. The aim is to find an optimal processing sequence of jobs and determine if the machine should be executed a power-down operation between two consecutive jobs. To formulate the problem, a mixed-integer linear programming (MILP) model is developed. Then a basic epsilon - constraint method is proposed to obtain the complete Pareto front of the problem. Considering the particularity of the problem, we also develop local search, preprocessing technique and valid inequalities to strengthen the basic epsilon - constraint method. Finally, to obtain approximate Pareto fronts for large-size problems, we utilize the method of cluster analysis to divide the jobs into several sorted clusters according to their release times and due dates. Any job in a preceding cluster must be processed before all jobs in a subsequent cluster. Thus, the solution space is reduced significantly. Computational experiments on benchmark and randomly generated instances demonstrate the effectiveness of the proposed exact and approximation approaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Energy-efficient scheduling Single machine Bi-objective scheduling Power-down mechanism epsilon-constraint method

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Markovian queue optimisation analysis with an unreliable server subject to working breakdowns and impatient customers

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2015年第12期46卷 2165-2182页

作者： Liou, Cheng-Dar Natl Formosa Univ Dept Business Adm Huwei 63201 Yunlin Taiwan

This study investigates an infinite capacity Markovian queue with a single unreliable service station, in which the customers may balk (do not enter) and renege (leave the queue after entering). The unreliable service station can be working breakdowns even if no customers are in the system. The matrix-analytic method is used to compute the steady-state probabilities for the number of customers, rate matrix and stability condition in the system. The single-objective model for cost and bi-objective model for cost and expected waiting time are derived in the system to fit in with practical applications. The particle swarm optimisation algorithm is implemented to find the optimal combinations of parameters in the pursuit of minimum cost. Two different approaches are used to identify the Pareto optimal set and compared: the epsilon-constraint method and non-dominate sorting genetic algorithm. Compared results allow using the traditional optimisation approach epsilon-constraint method, which is computationally faster and permits a direct sensitivity analysis of the solution under constraint or parameter perturbation. The Pareto front and non-dominated solutions set are obtained and illustrated. The decision makers can use these to improve their decision-making quality.

关键词： particle swarm optimisation Pareto front impatient customers working breakdown epsilon-constraint method

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A stochastic multi-objective location-allocation-routing problem for tire supply chain considering sustainability aspects and quantity discounts

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JOURNAL OF CLEANER PRODUCTION 2018年 198卷 704-720页

作者： Ebrahimi, Seyed Babak KN Toosi Univ Technol Dept Ind Engn Pardis St Molasadra AveVanak Sq Tehran 193951999 Iran

Sustainable supply chain is widely studied and investigated in various papers due to the presence of increasing environmental pollution and social risks caused by manufactories, transportation vehicles, and unused and defective products spilled into the nature;e.g. tires. This paper designs a closed loop supply chain network taking into consideration sustainability aspects and quantity discounts under uncertainty. To this end, a stochastic multi-objective optimization for supplier selection and location allocation -routing problems is formulated. The first and second objectives of the model attempt to minimize total costs and environmental emission effects, respectively. The third objective seeks to maximize the responsiveness of the integrated network. Also, c-constraint method is applied to solve the proposed framework. To verify the model, a real-world case study placed in Iran is provided and a sensitivity analysis is performed to measure the efficiency of the proposed framework. Results confirm the efficiency of the proposed approach, showing the practicability of the proposed approach for managers. Finally, future research directions are discussed. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Stochastic programming Multi-objective optimization Closed loop sustainable supply chain Tire industry epsilon-constraint method

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Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 129卷 192-209页

作者： Moheb-Alizadeh, Hadi Handfield, Robert North Carolina State Univ Grad Program Operat Res Raleigh NC 27695 USA North Carolina State Univ Dept Business Management Coll Management Raleigh NC 27695 USA

Sustainable supplier selection is the process of identifying the appropriate supply partners of an organization with the most beneficial monetary value, while diminishing the various effects of its operations on society and environment. Therefore, it plays a significant role in moving an organization toward sustainable development. Alongside sustainable supplier selection, assigning proper quantity of orders to suppliers satisfying sustainability criteria is another activity that should be simultaneously carried out in order to develop a comprehensive tool in sustainable supplier management. Meanwhile, the issue of sustainable supplier selection and order allocation has attracted limited attention in the literature. The present paper aims at developing an inclusive multi-objective mixed integer linear programming model, which accounts for multiple periods, multiple products and multimodal transportation, to evaluate suppliers and allocate order quantities. Furthermore, the developed model that includes all sustainability aspects also takes both shortage and discount conditions into account. A hybrid three-step solution methodology is then presented, using which the original multi-objective problem is firstly converted to a single objective model by epsilon-constraint method. In the second step, the current single objective programming model is solved using the Benders decomposition algorithm that in turn is accelerated by a variety of algorithmic enhancements. Finally, among all the Pareto optimal solutions of the original multi-objective programming problem, the preferable solution is intelligently selected based on the DEA super efficiency score of all purchasing firms as a decision support tool. The applicability of the proposed approach is illustrated by a real-world case study in automotive industry.

关键词： Supplier selection Order allocation Sustainability epsilon-constraint method Benders decomposition algorithm Data envelopment analysis

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Optimization of supply chain based on macro ergonomics criteria: A case study in gas transmission unit

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2016年第0期43卷 332-351页

作者： Azadeh, A. Shabanpour, N. Gharibdousti, M. Soltanpour Nasirian, B. Univ Tehran Coll Engn Sch Ind Engn Tehran 14174 Iran Milan Polytech Univ Dept Mech Engn Milan Italy

Natural gas supply chain is potentially susceptible to high costs caused by horrible outcomes such as explosion, injuries and mortality of workers, greenhouse gas emissions and production process halt. Macro-ergonomics analysis and design method is an efficient methodology which can lead to an overall gas supply chain optimization. This study presents a novel concept of macro-ergonomics analysis and design method, in form of a multi-objective mathematical model. Problem objectives include reducing operating and transportation costs, environmental costs, and delay costs besides increasing in value of purchasing from importers. Results of the mathematical model in form of a multi-objective optimization, were obtained through augmented e-constraint method. The model have been applied this in the supply chain of Iran gas transmission which is one of the country's most sensitive and vital industries. Results of the model were analyzed after implementing the suggestions of macro-ergonomics approach to evaluate the considered supply chain performance, and the result shown 10% reduction in operating costs per unit. This study can be entitled the first research which evaluates the macro-ergonomics analysis and design method trough mathematical model supply chains. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Macro-ergonomics Natural gas supply chain Multi-objective optimization epsilon-constraint method

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Voltage stability constrained multi-objective optimisation model for long-term expansion planning of large-scale wind farms

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 548-555页

作者： Nikkhah, Saman Rabiee, Abbas Univ Zanajn Dept Elect Engn Zanajn Iran

This study proposes a new model for long-term planning of large-scale wind farms (WFs) considering voltage stability constraints. The proposed model is a multi-objective optimisation problem which aims to maximise the profit of WF investor from wind energy procurement and to minimise the overall power generation cost. By performing modal analysis on the reduced power flow Jacobian matrix, proper locations for installation of WFs are determined. Moreover, the impact of voltage stability constraints on the capacity of WFs is investigated. In comparison with other studies, the main contributions of this study are: (i) to propose a new methodology for determination of the best locations for WFs, (ii) to study the impact of voltage stability as an important security constraint in long-term planning of wind energy. The proposed voltage stability constrained multi-objective wind power planning (VSC-MOWPP) model is solved via -constraint method in General Algebraic Modelling System optimisation package environment. The compromise solution of Pareto optimal set is selected using fuzzy satisfying approach. The proposed VSC-MOWPP model is examined on the IEEE 39-bus standard test system. The numerical investigations substantiate the suitability of the proposed model for multi-year planning of WFs at the presence of long-term voltage stability constraints.

关键词： power system stability power generation planning wind power plants profitability power generation economics procurement minimisation modal analysis load flow Jacobian matrices power system security Pareto optimisation fuzzy set theory voltage stability constrained multiobjective optimisation model long-term expansion planning large-scale wind farm WF voltage stability constraint profit maximisation WF investor profit maximisation wind energy procurement power generation cost minimisation modal analysis power flow Jacobian matrix security constraint VSC-MOWPP model voltage stability constrained multiobjective wind power planning model epsilon-constraint method general algebraic modelling system optimisation package environment Pareto optimal set fuzzy satisfying approach IEEE 39-bus standard test system multi-year planning

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