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Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human-robot collaboration

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APPLIED SOFT COMPUTING 2020年 93卷 106394-106394页

作者： Cil, Zeynel Abidin Li, Zixiang Mete, Suleyman Ozceylan, Eren Izmir Democracy Univ Dept Ind Engn Izmir Turkey Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Gaziantep Univ Dept Ind Engn Gaziantep Turkey

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： mixed-model assembly line Human-robot collaboration Bee algorithm Artificial bee colony algorithm mixed-integer linear programming

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A novel unit-specific event-based formulation for short-term scheduling of multitasking processes in scientific service facilities

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COMPUTERS & CHEMICAL ENGINEERING 2020年 133卷

作者： Rakovitis, Nikolaos Zhang, Nan Li, Jie Univ Manchester Ctr Proc Integrat Dept Chem Engn & Analyt Sci Manchester M13 9PL Lancs England

Scientific service facilities examine a number of samples from different customers for several physical and chemical properties using processing units with large capacities. A processing unit can process a great number of samples simultaneously. The process in such scientific service facility can be treated as a multi-tasking multipurpose batch process. Despite the great interest in developing models for scheduling of process industry during the past three decades, scheduling of multi-tasking multipurpose batch processes in a scientific service facility has not been considered adequately. In this work, we develop three novel mathematical models using the well-established unit-specific event-based modelling approach. The computational results demonstrate that the proposed mathematical models are able to reduce the number of event points required, which leads to a significant reduction in the model size and computational time. One of the proposed models in which the timing variables are defined based on processing units is the most efficient in most cases especially when minimization of makespan is used as the objective, where at least one order of magnitude less computational time than all other models is required to generate the optimum solution compared to other existing models. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Scheduling Multi-tasking Batch process mixed-integer linear programming Unit-specific event-based approach

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An efficient method for Contingency-Constrained Transmission Expansion Planning

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ELECTRIC POWER SYSTEMS RESEARCH 2020年第0期182卷 106208-000页

作者： Gutierrez-Alcaraz, Guillermo Gonzalez-Cabrera, Nestor Gil, Esteban Tecnol Nacl Mexico IT Morelia Dept Elect Engn Morelia Michoacan Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Cdmx Mexico Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile

This paper proposes an efficient method based on a mixed-integer linear programming (MILP) model to solve the multistage Contingency-Constrained Transmission Expansion Planning (CCTEP) problem. To account for security, an iterative algorithm based on Line Outage Distribution Factors (LODF) screens the worst-case contingency (either in existing or candidate lines) and dynamically adds constraints to the Transmission Expansion Planning (TEP) formulation to enforce the security criterion, reducing both the number of decision variables and simulation times with respect to alternative approaches. Transmission losses are included through the use of piecewise linear expressions. Furthermore, operational flexibility of generation resources is considered through the use of representative daily load curves (RDLC) to allow modeling of generator ramping constraints. The method is initially illustrated and validated through numerical simulations of the classic Garver's system. Then, the IEEE 118-bus and 300-bus systems are used to test its performance. The proposed approach can avoid under-investment in network capacity caused by neglecting transmission losses, security, and flexibility constraints.

关键词： Optimization Transmission Expansion Planning mixed-integer linear programming Power system security Risk management

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Decision support method for early-phase design of offshore hydrocarbon fields using model-based optimization

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2020年第4期10卷 1473-1495页

作者： Gonzalez, Diana Stanko, Milan Hoffmann, Arnaud NTNU Trondheim Norway

This paper presents the development of a method to provide decision support in the feasibility studies and concept planning phases of oil and gas field development. The objective in developing the methodology was to provide an easy-to-use facility to integrate the production-governing elements of oil and gas fields that capture the integrated production and economic performance of the system. This in a modular and scalable manner includes numerical optimization and uncertainty analyses needed to support engineering decisions. The method follows a series of steps that allow determining the optimal field production profile, drilling schedule, type of offshore structure, pressure support method and selection of artificial lift. The first step consists of creating efficient (low running time) proxy models of the production performance of the field and the costs figures associated with the project. The proxy model of the production performance is based on curves of maximum production rates versus cumulative production and contains all relevant field design features and computation of the most relevant performance indicators to consider in the evaluation. The proxy model to estimate the costs associated with the project is based on linear equations function of production rates and number of wells. The second step is to perform numerical optimization to find optimal production profile and drilling schedule that maximize the net present value of the specific development strategies considered. For the last step, an evaluation of the effect of uncertainties on the results of the numerical optimization using probabilistic methods is performed. The method was applied in a synthetic production system based on public data of Wisting field (currently under development). The field is a remote low-energy oil reservoir located in the Barents Sea. Nine strategies, obtained from the combination of three recovery support methods and three processing facilities, were compared using the n

关键词： Field development planning Optimization Proxy model Uncertainty analysis mixed-integer linear programming

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A linearisation approach to the stochastic dynamic capacitated lotsizing problem with sequence-dependent changeovers

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第16期58卷 4980-5005页

作者： De Smet, Niels Minner, Stefan Aghezzaf, El-Houssaine Desmet, Bram Univ Ghent Dept Ind Syst Engn & Prod Design ISyEP EA18 Fac Engn & Architecture Zwijnaarde Belgium Solventure NV Ghent Belgium Tech Univ Munich TUM Sch Management Munich Germany Flanders Make Lommel Belgium

We present a mixed-integer linear programming formulation that simultaneously optimises lot sizes and production sequences on a capacity constrained machine with sequence-dependent changeovers subject to stochastic dynamic demand while at the same time satisfying a fill rate constraint. To tackle the non-linearity of the exact formulation, we introduce a piecewise linearisation technique both for the expected inventory on hand and for the backorder functions that uses the target service level and the parameters of the demand distribution to assign breakpoints to the most promising intervals of the linearisation domain. We show that our strategy leads to lower cost and to more conservative production plans, in comparison to techniques recommended by earlier research. In addition, we discuss why any breakpoint selection strategy that does not exclude the concave region for , is prone to be outperformed by the approach we present. Finally, we propose a Relax-and-Fix with Fix-and-Optimize heuristic, and show based on the broad set of instances from Haase, Knut, and Alf Kimms [2000. "Lot sizing and scheduling with sequence-dependent setup costs and times and efficient rescheduling opportunities." International Journal of Production Economics 66 (2): 159-169], that it is more effective than a state-of-the-art solver in terms of run time and solution quality.

关键词： production planning lotsizing and scheduling random demand mixed-integer linear programming sequence-dependent changeovers

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Allocating Small Transporters to Large Jobs

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ALGORITHMS 2022年第2期15卷 60页

作者： Jami, Neil Leithaeuser, Neele Weiss, Christian Fraunhofer Inst Ind Math Fraunhofer Pl 1 D-67663 Kaiserslautern Germany

We optimize the assignment of transporters to several jobs. Each job consists of processing a large, decomposable volume. A fleet of transporters is given, each of which can only process a limited volume at a time. After processing its share, a transporter must rest for a short time before being able to process another part. This time is only dependent on the assigned job, not on the transporter. Other transporters can take over the processing while a transporter rests. Transporters assigned to the same job wait for their turn in a queue. A transporter can only be assigned to one job. Our goal is to simultaneously minimize the maximum job completion time and the number of assigned transporters by computing the frontier of Pareto optimal solutions. In general, we show that it is NP-hard in the strong sense to compute even a single point on the Pareto frontier. We provide exact methods and heuristics to compute the Pareto frontier for the general problem and compare them computationally.

关键词： transporter assignment bi-objective optimization heuristics mixed-integer linear programming logistics heterogeneous fleet

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Multi-objective optimization on total cost and carbon dioxide emission of coal supply for coal-fired power plants in Indonesia

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SOCIO-ECONOMIC PLANNING SCIENCES 2022年 81卷

作者： Baskoro, Firly Rachmaditya Takahashi, Katsuhiko Morikawa, Katsumi Nagasawa, Keisuke Hiroshima Univ 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan Inst Teknol Bandung Jl Ganesha 10 Bandung 40132 Indonesia

Indonesian coal is distributed in several islands, such as Sumatra, Kalimantan, Sulawesi, and Papua. Most of them are classified as low and medium rank coal, which is very suitable for coal-fired power plants. In the future, domestic coal demand will increase, driven by the government plans to increase the electricity generation capacity and primary energy demand. In the existing scheme, only a coal mining company whose coal quality is an exact match with the coal-fired power plant specification can be selected as a supplier, without considering a blending mechanism. This condition may have some issues for long-term supply, as the coal will be exhausted in time and tends to come at a high cost. To improve the decision making for securing the long-term coal demand for electricity generation, optimization with coal blending should be considered. This research includes the consideration of the coal quality parameters;the power plant's requirement criteria;the location of the coal-blending facility;and ship types. A multi-objective optimization using mixed-integer linear programming, consisting of linear inequalities in binary and continuous variables as the constraints, is introduced to find the optimal solution, with cost and carbon dioxide emission as the objective functions. Several scenarios, which are the baseline condition, chartered ship, and environmental consciousness, will be analyzed. The results obtained by using the epsilon-constraint method show the benefits of the proposed schemes and scenarios, which are able to secure long-term demand with a more flexible solution, and reduce the total cost and carbon dioxide emission. Furthermore, considering the parameter setting and modeling, the optimization can be considered as applicable for solving similar problems related to the transportation selection and supply chain for similar commodities in the greater area.

关键词： Carbon dioxide emission Coal blending Coal-fired power plant mixed-integer linear programming Multi-objective optimization

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A Rolling-Horizon Approach for a Surgery Case Scheduling Problem with Sterilizing Constraints

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IFAC-PapersOnLine 2022年第10期55卷 1625-1630页

作者： H. Al Hasan C. Guéret D. Lemoine D. Rivreau UCO LARIS 44 Rue Rabelais Angers 49000 France Univ Angers LARIS SFR MATHSTIC Angers 49000 France IMT Atlantique LS2N 4 Rue Alfred Kastler Nantes

In this paper, we study a real scheduling problem which consists in scheduling a set of elective surgical cases that arrive over the time taking into account the uncertainties on their duration, and the delay to sterilize surgical instruments. The objectives are to schedule as many surgeries as possible, and to minimize the overtime of the surgical block staff and the number of instruments processed in emergency in the sterilizing unit. This research was performed in collaboration with the University Hospital of Angers in France, which has also provided historical data for the experiments. We propose a rolling horizon approach based on the iterative resolution of a robust mixed integer linear programming model, in which the objective functions are taken into account in a lexicographic order. The experiments on real data show a reduction of more than 50% of overtime, and around 87% less stress situations in the sterilizing unit.

关键词： Operations Research Elective surgeries scheduling mixed-integer linear programming Rolling horizon approach Robust scheduling

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Optimal design of supply chain network with carbon dioxide injection for enhanced shale gas recovery

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APPLIED ENERGY 2020年 274卷 115334-115334页

作者： Ahn, Yuchan Kim, Junghwan Kwon, Joseph Sang-Il Korea Inst Ind Technol Green Mat & Proc R&D Grp Ulsan South Korea Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA

To optimize the configuration of a supply chain network for shale gas production (SGSCN), we develop a novel optimization model that considers 'enhanced gas recovery by carbon dioxide (CO2) injection' (EGR-CO2) technology, which simultaneously achieves decrease in net CO2 emissions. Then, the developed framework is used to identify the optimal SGSCN configuration in a mixed-integer linear programming problem that maximizes the overall profit of shale gas production. The optimal framework of the proposed SGSCN model is compared to the case (Case 1) when the improvement technology for the shale gas production rate like EGR-CO2 is not used, to demonstrate its superiority over existing approaches. The simulation results that consider application on the Marcellus shale play indicate that the overall profit of SGSCN that uses EGR-CO2 technology and purchases the CO2 on the market (Case 2) achieves 2.56% higher profit than the SGSCN without an injection strategy (Case 1) and 10.00% higher profit than the SGSCN that uses CO2 that is recovered from the flue gases generated during combustion of shale gas to produce electricity (Case 3). The profitability of Case 3 is reduced by the cost of constructing and operating a CO2-capture facility. For Case 3 to achieve the same profitability as Case 2, the CO2 purchase must be more expensive than 5 US$ per MCF CO2 (0.18 US$ per m(3)).

关键词： Enhanced gas recovery mixed-integer linear programming Optimization CO2 capture CO2 purchase Marcellus shale

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Transitioning of localized renewable energy system towards sustainable hydrogen development planning: P-graph approach

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APPLIED ENERGY 2020年第0期263卷 114635-000页

作者： Lim, Juin Yau How, Bing Shen Rhee, Gahee Hwangbo, Soonho Yoo, Chang Kyoo Kyung Hee Univ Coll Engn Ctr Environm Studies Dept Environm Sci & Engn Seocheon Dong 1 Yongin 446701 Gyeonggi Do South Korea Swinburne Univ Technol Fac Engn Comp & Sci Chem Engn Dept Jalan Simpang Tiga Sarawak 93350 Malaysia Tech Univ Denmark Proc & Syst Engn Ctr PROSYS Dept Chem & Biochem Engn Soltofts Plads 229 DK-2800 Lyngby Denmark

This study aimed to transition localized hybrid renewable energy microgrid system in multiple regions by integrating excess renewable electricity and carbon dioxide generated with biogas produced in wastewater treatment plant. The integrated optimization network in this contribution addresses the following aspects: (1) biogas supply-demand distribution network, (2) hydrogen production technology allocation involving steam methane reforming and electrolysis, (3) carbon dioxide allocation, and (4) utilization of external resources. The proposed framework is mathematically expressed as a mixed-integer linear programming problem to minimize total annual cost which will be solved by graphical optimization approach, P-graph. A case study of the existing petrochemical industry complex in South Korea has been applied to the developed model. Feasible configuration for meeting base hydrogen demand (7200 tons H-2/year) has been proposed with total annual cost of 75,772,460 US $/year. Apart from base hydrogen demand, the developed model is verified with the increase of hydrogen demand for 10%, 20%, 30%, and 40%. Nonetheless, scenario of tight environmental regulations enforcement has been established among all cases where carbon dioxide emitted within the system will be captured and sequestrated. Pareto Frontier Analysis and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) have been implemented to enhance the overall sustainability by re-ranking the pool of near-optimal solutions to propose new sustainable configuration with the consideration of both economic and environmental aspects (i.e., 40% increase of hydrogen demand;Before: 142,579,000 US $/year, 109,933.00 tons CO2/year;After: 142,651,000 US $/year, 109,845.00 tons CO2/year).

关键词： Sustainability Integrated optimization network mixed-integer linear programming P-graph Pareto Frontier Analysis TOPSIS

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