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multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm

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CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS 2018年第4期26卷 367-377页

作者： Jiao, Yu-ling Xing, Xiao-cui Zhang, Peng Xu, Liang-cheng Liu, Xin-Ran Jilin Univ Coll Transportat Changchun 130022 Jilin Peoples R China Jilin Univ Architecture Coll Grad Changchun Jilin Peoples R China

Aiming at the requirement of working efficiency and security of automated warehouse and taking the operation time of outbound-inbound, the equivalent center of gravity of overall shelf and the degree of relative accumulation of related products as the multi-objective functions, the mathematical model is constructed for multi-objective storage location allocation optimization. According to the simple weighted genetic algorithm, it is easily prone to the problem of immature convergence when solving multi-objective programming problems. So, the multi-population genetic algorithm is proposed to solve the mathematical model of storage location allocation optimization. Combining with the experiment data of toy car assembly and automated warehouse, the results of the automated warehouse storage location allocation are obtained. FlexSim dynamic simulation model is established based on the storage location allocation solution, the physical parameters of automated warehouse and the experimental requirements plan of vehicle model assembly. The operation effect of the model and the utilization rate of the equipment are analyzed. The result of multi-population genetic algorithm is more reasonable and effective. It is proved that the result of multi-population genetic algorithm is superior to the result of simple weighted genetic algorithm, which provides an effective method for storage location allocation optimization and outbound-inbound dynamic simulation.

关键词： storage location allocation multi-population genetic algorithm multi-objective programming automated warehouse FlexSim simulation

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Fuzzy multi-period portfolio selection model with discounted transaction costs

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SOFT COMPUTING 2018年第1期22卷 177-193页

作者： Liu, Yong-Jun Zhang, Wei-Guo Zhao, Xue-Jin South China Univ Technol Sch Business Adm Guangzhou 510641 Guangdong Peoples R China

In this paper, we discuss a multi-period portfolio selection with discounted transaction costs in a fuzzy uncertain investment environment, which has not been given much attention before. We assume that an investor's motivation is to find the portfolio with maximizing terminal wealth and the cumulative skewness on portfolios, and minimizing the cumulative risk on portfolios. We consider the major criteria including wealth, risk, skewness, transaction costs, proportion entropy, transaction lots, the maximum holding number of assets in the portfolio and budge constraint. We propose a possbilistic mean-semivariance-skewness model with discounted transaction costs for multi-period fuzzy portfolio selection. To solve the multi-objective portfolio selection model, we first introduce a weighted max-min fuzzy goal programming approach to take investor's different investment preferences into account and transform it into a single-objective programming problem and then design a dynamic differential evolution algorithm for solution. Finally, we provide an empirical study with the sample data from Chinese stock market to analyze the application of the model and the performance of the solution algorithm.

关键词： Fuzzy portfolio selection Mean-semivariance-skewness model multi-objective programming Differential evolution algorithm

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Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 104卷 347-369页

作者： Zhong, Shuya Pantelous, Athanasios A. Beer, Michael Zhou, Jian Natl Univ Singapore Logist Inst Asia Pacific Singapore Singapore Monash Univ Dept Econometr & Business Stat Clayton Vic Australia Univ Liverpool Inst Risk & Uncertainty Liverpool Merseyside England Univ Liverpool Dept Math Sci Liverpool Merseyside England Leibniz Univ Hannover Inst Risk & Reliabil Hannover Germany Tongji Univ Sch Civil Engn Shanghai Peoples R China Tongji Univ Shanghai Inst Disaster Prevent & Relief Shanghai Peoples R China Shanghai Univ Dept Management Sci & Engn Shanghai Peoples R China

Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Reliability Maintenance Scheduling Cost parameters Offshore wind farms multi-objective programming

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Building organizational resilience in the face of multiple disruptions

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2018年 197卷 63-83页

作者： Sahebjamnia, Navid Torabi, S. Ali Mansouri, S. Afshin Univ Sci & Technol Mazandaran Dept Ind Engn Behshahr Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Brunel Univ London Brunel Business Sch Uxbridge Middx England

The increasing number of natural and man-made hazards is forcing organizations to build resilience against numerous types of disruptions that threaten continuity of their business processes. This paper presents an integrated business continuity and disaster recovery planning (IBCDRP) model to build organizational resilience that can respond to multiple disruptive incidents, which may occur simultaneously or sequentially. This problem involves multiple objectives and accounts for inherent epistemic uncertainty in input data. A multi-objective mixed-integer robust possibilistic programming model is formulated, which accounts for sensitivity and feasibility robustness. The model aims to plan both internal and external resources with minimal resumption time, restoration time, and loss in the operating level of critical functions by making tradeoffs between required resources for continuity plans, recovery time, and the recovery point. A real case study in a furniture manufacturing company is conducted to demonstrate the performance and applicability of the proposed IBCDRP model. The results confirm the capability of the proposed model to improve organizational resilience. In addition, the proposed model demonstrates the interaction between the organizational resilience and required resources, particularly in respect to the total budget and external resources, which is necessary for developing continuity and recovery strategies.

关键词： Organizational resilience Business continuity management Disaster operations management multi-objective programming Robust possibilistic programming

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Picking efficiency and stock safety: A bi-objective storage assignment policy for temperature-sensitive products

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 115卷 240-252页

作者： Accorsi, R. Baruffaldi, G. Manzini, R. Univ Bologna Alma Mater Studiorum Dept Ind Engn Viale Risorgimento 2 I-40136 Bologna Italy Univ Padua Dept Management & Engn Stradella S Nicola 3 I-36100 Vicenza Italy

The increasing attention of consumers to product quality and safety raises new challenges for logistics. Enhancing the operation efficiency and mutually ensuring the safety and quality of the handled products are key levers for logistics providers and other operators in temperature-sensitive supply chain. Handling the temperature conditions experienced by the inventory is valuable in warehouses and any other points along the supply chain where products pause for long periods. This paper proposes an original adaptive storage assignment policy for temperature-sensitive products, which enables to mutually manage both efficiency and stock safety goals. This policy is based on a bi-objective integer programming model and an original solving algorithm. We intend our policy for warehouses that handle temperature-sensitive products in presence of high demand and weather seasonality and strong inventory mix turn-over. To the best of authors' knowledge, this is the first attempt to integrate into a storage assignment policy the issue of stock quality conservation, the optimization of the picking activities, and the management of weather and demand seasonality at the warehouse. A multi scenario what-if analysis was applied to a 3PL warehouse of biomedical products to validate the policy and explore its insight in a real-world application. This policy autonomously balances the management of the inventory between the efficiency and stock safety levers and records savings of 12% of the picking travel time and up to 20% of the inventory safety. In conclusion, this policy assesses how the warehouse infrastructure can respond to the demand and weather seasonality in accordance with the efficiency and safety requirements.

关键词： Storage assignment Perishable products Warehouse Temperature multi-objective programming

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multi-objective optimum charging management of electric vehicles through battery swapping stations

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ENERGY 2018年第PartB期165卷 549-562页

作者： Amiri, Saeed Salimi Jadid, Shahram Saboori, Hedayat Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

The rapid growth of electric vehicle (EV) penetration is promoted by fossil fuels depletion, environmental concerns, and energy efficiency initiatives. Battery charging time duration is of the main obstacles to large-scale deployment of this technology. Battery swapping station (BSS) is a new concept to handle this issue in which depleted EV batteries are replaced with a previously full-charged one at a significantly less time duration. To this end, the optimum location of the EV charging among BSS5 in the network in addition to the priority charging of the depleted batteries in each BSS should be determined. In this context, the present paper is to perform these tasks optimally and simultaneously. The problem is formulated as a multi-objective programming model in which three non-homogenous objectives are taken into account and solved using the NSGA II algorithm. Two cost-based objectives including minimizing EV batteries charging and power loss cost along with two technical based objectives, comprising voltage profile flattening and network capacity releasing, are considered. Additionally, besides dynamic pricing scheme, a time window method to prevent interruptions in the battery charging is developed. The proposed model is implemented on 33-bus IEEE test system where the results demonstrate its functionality. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Battery swapping station Electric vehicle charge timing multi-objective programming Dynamic pricing NSGA-II

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Pathways to a low-carbon economy: Estimations on macroeconomic costs and potential of carbon emission abatement in Beijing

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JOURNAL OF CLEANER PRODUCTION 2018年 199卷 603-615页

作者： Li, Yan Wei, Yigang Shan, Siqing Tao, Yuan Shandong Univ Weihai Business Sch Shandong Peoples R China Beihang Univ Sch Econ & Management Beijing Peoples R China Beijing Key Lab Emergency Support Simulat Technol Beijing Peoples R China Univ Cambridge Dept Engn Cambridge England

Developing reliable frameworks to estimate the potential of CO2 emission abatement and its associated costs merits urgent attention. This study aims to estimate the potential as well as macroeconomic costs of CO2 emission abatement in Beijing. Using the industrial economy as the basis, multiobjective programming and genetic algorithm are integrated into the input output analysis. Several important findings have been gathered in the study: (1) The abatement cost varies between $259.97 and $535.46 per ton at different rates of economic growth. (2) A reinforced emission abatement target can induce higher macroeconomic costs. (3) An inverse U-shaped relationship between CO2 reduction cost and GDP growth rate was found. When GDP growth rate is above the threshold value of 7%, marginal abatement costs decrease rapidly in parallel with GDP growth. When the GDP growth rate is below 7%, the marginal abatement cost increases significantly with economic growth. (4) Industries that have the highest and least potential of abatement have been identified. For effectively reducing CO2 emissions, Beijing needs to promote economic restructuring to maximize the potential of emission abatement and also fulfil the emission abatement target in a cost-effective way. This paper provides a quantitative approach to rationalize the policy design process of enacting emission targets and recommends cost-effective abatement strategies. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： CO2 emission abatement Macro-economic costs Potential multi-objective programming Input-output analysis

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An evolutionary computation approach to solving repairable multi-state multi-objective redundancy allocation problems

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 127-139页

作者： Tavana, Madjid Khalili-Damghani, Kaveh Di Caprio, Debora Oveisi, Zeynab La Salle Univ Business Syst & Analyt Dept Distinguished Chair Business Syst & Analyt Philadelphia PA 19141 USA Univ Paderborn Business Informat Syst Dept Fac Business Adm & Econ D-33098 Paderborn Germany Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran York Univ Dept Math & Stat Toronto ON Canada Polo Tecnol IISS G Galilei Bolzano Italy

The redundancy allocation problem (RAP) is an optimization problem for maximizing system reliability at a predetermined time. Among the several extensions of RAPs, those considering multi-state and repairable components are the closest ones to real-life availability engineering problems. However, despite their practical implications, this class of problems has not received much attention in the RAP literature. In this paper, we propose a multi-objective nonlinear mixed-integer mathematical programming to model repairable multi-state multi-objective RAPs (RMMRAPs) where a series of parallel systems experiencing repairs, partial failures, and component degrading through time is considered. The performance of a component depends on its state and may decrease/increase due to minor and major failures/repairs which are modeled by a Markov process. The proposed RMMRAP allows for configuring multiple components and redundancy levels in each sub-system while evaluating multiple objectives (i.e., availability and cost). A customized version of the non-dominated sorting genetic algorithm (NSGA-II), where constraints are handled using a combination of penalty functions and modification strategies, is introduced to solve the proposed RMMRAP. The performance of the proposed NSGA-II and that of an exact multi-objective mathematical solution procedure, known as the epsilon-constraint method, are compared on several benchmark RMMRAP instances. The results obtained show the relative dominance of the proposed customized NSGA-II over the epsilon-constraint method.

关键词： Redundancy allocation problem Availability computation Repairable component multi-state component Markov process multi-objective programming NSGA-II

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A Cost-Efficiency Equilibrium Problem of Regional Single Emergency Resource Guarantee with multi-objective programming

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Systems Engineering Procedia 2012年 5卷 29-36页

作者： Tie Liu Ruguo Bu Jun Huang College of Engineering Graduate University of Chinese Academy of Sciences Beijing 100049 China

In the background of regional emergency resource guarantee engineering to respond to earthquake disasters, a multi-objective model of cost-efficiency equilibrium problem is built to guarantee the supply of single emergency resource in an area, combined with qualitative analysis of key factors affecting the resource layout. The model quantifies constitutional indexes about emergency resource guarantee cost and rescue efficiency. With robust optimization ideas, the model is transformed to single-objective programming model according to three decision criteria, and solved with branch-and-bound algorithm by Lingo software. Finally, a numerical example is illustrated to verify the model and decision criteria.

关键词： Cost-efficiency equilibrium multi-objective programming Regional resource guarantee engineering Emergency resource reserve

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Bi-level multi-objective traffic network optimisation with sustainability perspective

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 104卷 294-306页

作者： Kolak, Orhan Ilker Feyzioglu, Orhan Noyan, Nilay Galatasaray Univ TR-34349 Istanbul Turkey Sabanci Univ TR-34956 Istanbul Turkey

Recent technological advancements provide a level of mobility never seen before to modern societies. Sustaining today's economy and societies depend on maintaining this mobility. However, this mobility also causes undesirable effects, especially in high urban population areas. Even though, higher priority is given to public transport in metropolitan areas, road network is still an important part of everyday commute and should be planned and managed with great care. In this study, we propose an optimisation methodology that would help the traffic authorities to better predict the results of strategic management decisions in a realistic traffic model. We formulate a bi-level multi-objective traffic optimisation model with a sustainability perspective. The upper level of the proposed model considers the traffic authority's management strategies while the lower level considers the traffic users' decisions. The lower level is modelled using the Stochastic User Equilibrium since it allows more realistic results than the deterministic one. A case study is provided to illustrate the proposed model. The proposed methodology provides an avenue for understanding the trade-offs among conflicting objectives and for designing an environmentally and socially sustainable transportation system. More importantly, it builds the foundation for an intelligent traffic management decision support system. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Traffic network multi-objective programming Bi-level optimisation Sustainability Stochastic User Equilibrium

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