| 1. |
- Kans, Mirka, 1971-, et al.
(författare)
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Data Driven Maintenance : A Promising Way of Action for Future Industrial Services Management
- 2022
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Ingår i: International Congress and Workshop on Industrial AI 2021. IAI 2021. - Cham : Springer. - 9783030936389 - 9783030936396 ; , s. 212-223, s. 212-223
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Konferensbidrag (refereegranskat)abstract
- Maintenance and services of products as well as processes are pivotal for achieving high availability and avoiding catastrophic and costly failures. At the same time, maintenance is routinely performed more frequently than necessary, replacing possibly functional components, which has negative economic impact on the maintenance. New processes and products need to fulfil increased environmental demands, while customers put increasing demands on customization and coordination. Hence, improved maintenance processes possess very high potentials, economically as well as environmentally. The shifting demands on product development and production processes have led to the emergency of new digital solutions as well as new business models, such as integrated product-service offerings. Still, the general maintenance problem of how to perform the right service at the right time, taking available information and given limitations is valid.The project Future Industrial Services Management (FUSE) project was a step in a long-term effort for catalysing the evolution of maintenance and production in the current digital era. In this paper, several aspects of the general maintenance problem are discussed from a data driven perspective, spanning from technology solutions and organizational requirements to new business opportunities and how to create optimal maintenance plans. One of the main results of the project, in the form of a simulation tool for strategy selection, is also described.
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| 2. |
- Laksman, Efraim, 1983, et al.
(författare)
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The stochastic opportunistic replacement problem, part III: improved bounding procedures
- 2020
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Ingår i: Annals of Operations Research. - : Springer Science and Business Media LLC. - 1572-9338 .- 0254-5330. ; 292:2, s. 711-733
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51–75, 2015), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy.
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| 3. |
- Rezaei, Mahdieh, et al.
(författare)
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A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes
- 2020
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Ingår i: Computers and Industrial Engineering. - : Elsevier BV. - 0360-8352. ; 147
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Tidskriftsartikel (refereegranskat)abstract
- Earthquakes are the most sudden and unpredictable natural disaster which can cause serious damages in terms of deaths, injuries, and property loss. When an earthquake occurs, it is very important to respond immediately to peoples' emergency needs through proper distribution of critical resources such as medical care, water, food, shelters, etc. Fuel is also one of the most critical needs which must be provided without delay to the population affected by the earthquake, especially the vulnerable children and elderly people. This paper develops a nonlinear bi-objective optimization framework for operating an efficient and effective fuel supply chain network in earthquake-hit areas. The objective functions include minimizing the penalties due to unsatisfied and/or lost fuel demands and minimizing the difference between the satisfied demands in different damaged areas. Some assumptions and constraints, such as the existence of multiple central depots, limited vehicle capacities, time available to respond to the incident, are also considered in the modeling. Two multi-objective evolutionary algorithms (MOEAs), including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization (MOPSO), are proposed to solve the optimization problem. Since the performance of these algorithms is significantly dependent on their parameters, a Taguchi method is used to tune the algorithms' parameters. In addition, four performance metrics are defined to evaluate and compare the performance of the algorithms. A hypothetical earthquake with actual dimensions and realistic data in Yazd province of Iran is presented as a case study, and finally, helpful managerial insights are provided through conducting a sensitivity analysis.
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| 4. |
- Strömberg, Ann-Brith, 1961, et al.
(författare)
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Mixed-Integer Linear Optimization: Primal–Dual Relations and Dual Subgradient and Cutting-Plane Methods
- 2020
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Ingår i: Numerical Nonsmooth Optimization: State of the Art Algorithms. - Cham : Springer International Publishing. ; , s. 499-547, s. 499-547
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents several solution methodologies for mixed-integer linear optimization, stated as mixed-binary optimization problems, by means of Lagrangian duals, subgradient optimization, cutting-planes, and recovery of primal solutions. It covers Lagrangian duality theory for mixed-binary linear optimization, a problem framework for which ultimate success—in most cases—is hard to accomplish, since strong duality cannot be inferred. First, a simple conditional subgradient optimization method for solving the dual problem is presented. Then, we show how ergodic sequences of Lagrangian subproblem solutions can be computed and used to recover mixed-binary primal solutions. We establish that the ergodic sequences accumulate at solutions to a convexified version of the original mixed-binary optimization problem. We also present a cutting-plane approach to the Lagrangian dual, which amounts to solving the convexified problem by Dantzig–Wolfe decomposition, as well as a two-phase method that benefits from the advantages of both subgradient optimization and Dantzig–Wolfe decomposition. Finally, we describe how the Lagrangian dual approach can be used to find near optimal solutions to mixed-binary optimization problems by utilizing the ergodic sequences in a Lagrangian heuristic, to construct a core problem, as well as to guide the branching in a branch-and-bound method. The chapter is concluded with a section comprising notes, references, historical downturns, and reading tips.
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| 5. |
- Yu, Quanjiang, 1990, et al.
(författare)
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Optimal scheduling of the next preventive maintenance activity for a wind farm
- 2021
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Ingår i: Wind Energy Science. - : Copernicus GmbH. - 2366-7451 .- 2366-7443. ; 6:3, s. 949-959
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Tidskriftsartikel (refereegranskat)abstract
- A large part of the operational cost for a wind power farm is due to the cost of equipment maintenance, especially for offshore wind farms. How to reduce the maintenance cost, and hence increase profitability, is this article’s focus. It presents a binary linear optimization model whose solution may suggest the wind turbine owners which components, and when, should undergo the next preventive maintenance (PM) replacements. The suggested short-term scheduling strategy takes into account eventual failure events of the multi-component system, in that after the failed system is repaired, the previously scheduled PM plan should be updated, assuming that the restored components are as good as new. The optimization algorithm of this paper, NextPM, is tested through numerical case studies applied to a four component model of a wind turbine. The first study addresses the important case of a single component system, used for parameter calibration purposes. The second study analyses the case of seasonal variations of mobilization costs, as compared to the constant mobilization cost setting. Among other things, this analysis reveals a 35% cost reduction achieved by the NextPM model, as compared to the pure corrective maintenance (CM) strategy. The third case study compares the NextPM model with another optimization model - the preventive maintenance scheduling problem with interval costs (PMSPIC), which was the major source of inspiration for this article. This comparison demonstrates that the NextPM model is accurate and much faster in terms of computational time.
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| 6. |
- Yu, Quanjiang, et al.
(författare)
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Optimal scheduling of the next preventive maintenance activity for a wind farm
- 2021
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Ingår i: Wind Energy Science. - : Copernicus GmbH. - 2366-7443 .- 2366-7451. ; 6:3, s. 949-959
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Tidskriftsartikel (refereegranskat)abstract
- A large part of the operational cost for a wind farm is due to the cost of equipment maintenance, especially for offshore wind farms. How to reduce the maintenance cost, and hence increase profitability, is this article's focus. It presents a binary linear optimization model whose solution may inform the wind turbine owners about which components, and when, should undergo the next preventive maintenance (PM) replacements. The suggested short-term scheduling strategy takes into account eventual failure events of the multi-component system - in that after the failed system is repaired, the previously scheduled PM plan should be updated, assuming that the restored components are as good as new. The optimization algorithm of this paper, NextPM, is tested through numerical case studies applied to a four-component model of a wind turbine. The first study addresses the important case of a single component system, used for parameter calibration purposes. The second study analyses the case of seasonal variations of mobilization costs, as compared to the constant mobilization cost setting. Among other things, this analysis reveals a 35% cost reduction achieved by the NextPM model, as compared to the pure corrective maintenance (CM) strategy. The third case study compares the NextPM model with another optimization model - the preventive maintenance scheduling problem with interval costs (PMSPIC), which was the major source of inspiration for this article. This comparison demonstrates that the NextPM model is accurate and much faster in terms of computational time.
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