New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especia...
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New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjectiveimperialistcompetitivealgorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.
In this article, a novel bi-objective integer model is presented to integrate reliability and intra-cell layout in designing a cellular manufacturing system (CMS). Minimising the total costs (e.g. inter and intra-cell...
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In this article, a novel bi-objective integer model is presented to integrate reliability and intra-cell layout in designing a cellular manufacturing system (CMS). Minimising the total costs (e.g. inter and intra-cell material handling, machine overhead and operation, and setting up routes) is the first objective with considering operation time, operation sequence, intra-cell layout, alternative process routing, routes selection, machines capacity, parts demand and parts movements in batches. Maximising the processing routes reliability is the second objective. The presented model is capable of modelling different failure characteristics including a decreasing, increasing, or constant value for machine failure rate. An illustrative example is solved to represent the capability of the presented model using the e-constraint method in order to demonstrate the conflict between the maximum value of the system reliability and the total costs of the system. Next, a multi-objective imperialist competitive algorithm (MOICA) is employed to find near-optimal solutions for medium-and large-sized test problems. Also, the efficiency of the proposed MOICA is revealed by comparison with the performance of a non-dominated sorting genetic algorithm (NSGA-II). The computational results demonstrate that the performance of the proposed MOICA is superior to the NSGA-II. Furthermore, a real-world case study is conducted to validate the proposed model.
A novel multi-objective evolutionary algorithm (MOEA) is developed based on imperialistcompetitivealgorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are ...
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A novel multi-objective evolutionary algorithm (MOEA) is developed based on imperialistcompetitivealgorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The numerical results indicate that MOICA shows significantly higher efficiency in terms of accuracy and maintaining a diverse population of solutions when compared to the existing salient MOEAs, namely fast elitism non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Considering the computational time, the proposed algorithm is slightly faster than MOPSO and significantly outperforms NSGA-II. (C) 2013 Elsevier Inc. All rights reserved.
This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the...
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This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted.
Purpose The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today's glob...
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Purpose The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today's global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model. Design/methodology/approach As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm II (NSGAII) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances. Findings The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions. Originality/value The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by
Wind energy is attracting increasing attention with its sustainability and cleanliness. However, owing to the volatility and intermittency of wind speed, it is challenging to establish a scientific and reliable foreca...
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Wind energy is attracting increasing attention with its sustainability and cleanliness. However, owing to the volatility and intermittency of wind speed, it is challenging to establish a scientific and reliable forecasting system. Most research has mainly been based on simple data preprocessing, single objective optimization, and point prediction, which may lead to poor forecasting performance. Hence, in this study, an innovative wind speed forecasting system is developed that incorporates effective data preprocessing and a novel algorithm. In order to alleviate the complexity and chaos of a wind speed series, a fuzzy data pre-processing scheme is designed based on "decomposition and ensemble" and a fuzzy time series. Following this, a multi-objective imperialist competitive algorithm (MOICA) is proposed and applied for optimizing an extreme learning machine (ELM), and a corresponding hybrid predictor MOICA-ELM is conducted for wind speed forecasting. For further investigation the uncertainty of wind speed, both point and interval forecasting are employed in this system. Simulation results on four wind speed datasets collected from two wind farms in China are in good accordance with the empirical data with multiple criterion and scientific evaluation;these results and show a good performance of the proposed system in terms of accuracy and stability. (C) 2019 Elsevier Ltd. All rights reserved.
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