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Optimization Strategies for Resource-Constrained Project Scheduling Problems in Underground Mining

INFORMS JOURNAL ON COMPUTING 2022年第6期34卷 3042-3058页

作者： Hill, Alessandro Brickey, Andrea J. Cipriano, Italo Goycoolea, Marcos Newman, Alexandra Calif Polytech State Univ San Luis Obispo Dept Ind & Mfg Engn San Luis Obispo CA 93407 USA South Dakota Sch Mines & Technol Min Engn & Management Rapid City SD 57701 USA Univ Adolfo Ibanez Alicanto Labs Santiago 7941169 Chile Univ Adolfo Ibanez Sch Business Santiago 7941169 Chile Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

Effective computational methods are important for practitioners and researchers working in strategic underground mine planning. We consider a class of problems that can be modeled as a resource-constrained project scheduling problem with optional activities;the objective maximizes net present value. We provide a computational review of math programming and constraint programming techniques for this problem, describe and implement novel problem-size reductions, and introduce an aggregated linear program that guides a list scheduling algorithm running over unaggregated instances. Practical, large-scale planning problems cannot be processed using standard optimization approaches. However, our strategies allow us to solve them to within about 5% of optimality in several hours, even for the most difficult instances.

关键词： underground mine planning resource-constrained project scheduling constraint programming mathematical programming

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A comparative study of modeling and solution approaches for the multi-mode resource-constrained discrete time-cost trade-off problem: Case study of an ERP implementation project

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年第0期169卷

作者： Cakir, Gizem Subulan, Kemal Yildiz, Seyda Topaloglu Hamzadayi, Alper Asilkefeli, Ceren Dokuz Eylul Univ Grad Sch Nat & Appl Sci Izmir Turkey Dokuz Eylul Univ Fac Engn Dept Ind Engn Izmir Turkey Van Yuzuncu Yil Univ Dept Ind Engn Van Turkey

Most knowledge-intensive industries, especially companies developing software engineering projects such as Enterprise Resource Planning (ERP) implementation projects, generally necessitate finding the optimal trade-off between the project duration and total usage cost of the renewable resource costs (e.g., human resource expertise costs). Therefore, the MRC-DTCTP, which integrates classical multi-mode resource-constrained project scheduling (MRCPSP) and discrete time-cost trade-off problems (DTCTP), can be seen as a more applicable problem since it better reflects the objectives and requirements of today's real-life software project applications. The MRC-DTCTP is a much more complex variant of the MRCPSP since it aims to minimize total direct/indirect costs of the resources simultaneously under a pre-specified project deadline. Based on this motivation, a new explicit integer-linear programming (ILP) model of the MRC-DTCTP was first developed based on the implicit non-linear programming model of Wuliang and Chengen (2009). Due to its NP-hard nature, we also proposed a constraint programming (CP) model that includes several search strategies to solve large-sized problem instances within reasonable computation time. In addition, a genetic algorithm (GA) approach in combination with a Modified Serial Schedule Generation scheme (SSGS) is implemented to make further comparisons on several benchmark instances, which are generated based on the existing MRCPSP data sets taken from the project scheduling problem library (PSPLIB) by considering additional problem characteristics. A comprehensive experimental study has shown that the proposed CP model and GA approach can provide superior results in shorter run times for large-sized benchmark instances. Finally, an international Enterprise Resource Planning (ERP) Software Company's real-life application is presented. The ERP projects generally necessitate finding the optimal trade-off between project makespan and human resource

关键词： Multi-mode resource-constrained discrete time-cost tradeoff problem constraint programming Integer-linear programming Genetic algorithm ERP implementation projects

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Web service selection and composition based on uncertain quality of service

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第1期34卷

作者： Zeyneb Yasmina, Remaci Fethallah, Hadjila Fadoua, Lahfa Abou Baker Belkaid Univ LRIT Lab Dept Comp Sci Tilimsen Algeria

Web services are becoming a major utility for accomplishing complex tasks over the Internet. In practice, the end-users usually search for Web service compositions that best meet the quality of service (QoS) requirements (i.e., QoS global constraints). Since the number of services is constantly increasing and their respective QoS is inherently uncertain (due to environmental conditions), the task of selecting optimal compositions becomes more challenging. To tackle this problem, we propose a heuristic based on majority judgment that allows for reducing the search space. In addition, we perform a constraint programming search to select the Top K compositions that fulfill the QoS global constraints. The experimental results demonstrate the high performance of our approach.

关键词： constraint programming majority judgment uncertain quality of service Web service Web service selection

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Metaheuristics with restart and learning mechanisms for the no-idle flowshop scheduling problem with makespan criterion

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COMPUTERS & OPERATIONS RESEARCH 2022年 138卷 105616-105616页

作者： Oztop, Hande Tasgetiren, M. Fatih Kandiller, Levent Pan, Quan-Ke Izmir Demokrasi Univ Dept Ind Engn Izmir Turkey Yasar Univ Dept Int Logist Management Izmir Turkey Yasar Univ Dept Ind Engn Izmir Turkey Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China

The no-idle permutation flowshop scheduling problem (NIPFSP) extends the well-known permutation flowshop scheduling problem, where idle time is not allowed on the machines. This study proposes a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model for the NIPFSP with makespan criterion. To the best of our knowledge, this study presents a CP model for the NIPFSP for the first time in the literature. We also compare the performance of the proposed MILP and CP models with a well-known MILP model from the literature. Since the studied problem is NP-hard, we also develop a new iterated greedy algorithm with restart and learning mechanisms (IG_RL) and a new iterated local search with restart and learning mechanisms (ILS_RL) as metaheuristics for the problem. In the proposed algorithms, all the parameters are determined by a learning mechanism in a self-adaptive way. Furthermore, a restart mechanism is employed in the proposed IG_RL and ILS_RL algorithms to guarantee the variety of the initial solutions and to assist the algorithm in avoiding the local optima. A variable neighborhood descent procedure is also embedded in the proposed algorithms. We use two well-known benchmark sets, i.e., VRF and Ruiz benchmark suites, to evaluate the performance of proposed solution methods. For almost half of the 240 small VRF instances, optimal results are reported by the MILP and CP models, whereas time-limited model results are reported for the rest. The results on small instances show that the proposed MILP and CP models outperform the MILP model from literature, where the CP model performs better than both MILP models. We compare the performance of the proposed IG_RL and ILS_RL algorithms with the state-of-the-art metaheuristics from the literature on both large VRF instances and Ruiz benchmark instances. The computational results show the effectiveness and superiority of the proposed ILS_RL and IG_RL algorithms for solving the NIPFSP. Primar

关键词： No-idle flowshop scheduling Makespan Iterated greedy algorithm Iterated local search Mathematical modeling constraint programming

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Cooperative route planning of multiple fuel-constrained Unmanned Aerial Vehicles with recharging on an Unmanned Ground Vehicle

Cooperative route planning of multiple fuel-constrained Unma...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Ramasamy, Subramanian Reddinger, Jean-Paul F. Dotterweich, James M. Childers, Marshal A. Bhounsule, Pranav A. University of Illinois Department of Mechanical and Industrial Engineering ChicagoIL60607 United States Devcom Army Research Laboratory Aberdeen Proving Grounds AberdeenMD21005 United States

ISBN: (纸本)9780738131153

Multiple small, low cost, multi-rotor Unmanned Aerial Vehicles (UAVs) are ideal for aerial surveillance over large areas. However, their limited battery capacity restricts them to areas in proximity of stationary recharging depots. One solution is to use an Unmanned Ground Vehicle (UGV) to provide a moving recharging depot. The problem is then to find the time-or energy-optimal paths for the multiple fuel-constrained UAVs to visit a set of mission points while being recharged by stopping at the UGV, whose path also needs to be determined. This is a combinatorial optimization problem that is computationally challenging, but may be solved relatively fast using heuristics. In this paper, we present two-level optimization that involves, (1) finding a UGV path by fixing waypoints using K-means and then formulating and solving a traveling salesman problem (TSP), and (2) finding paths for the multiple UAVs using a vehicle routing problem (VRP) formulation with capacity constraints, time windows, and dropped visits. We used constraint programming to solve these problems in less than a minute on a standard desktop computer for up to 25 mission points and 4 UAVs. Our main observation is that increasing the number of UAVs decreases the mission time and refueling stops, but does not decrease the total distance covered or total time taken. © 2021 IEEE.

关键词： constraint programming

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Better solutions for the Thesis Defense Timetabling problem using a three-phase approach 22

Better solutions for the Thesis Defense Timetabling problem ...

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Proceedings of the 26th Pan-Hellenic Conference on Informatics

作者： Angelos Dimitsas Christos Gogos Efthalia Pappa University of Ioannina Greece

ISBN: (纸本)9781450398541

Thesis defense is the procedure where a graduation candidate defends his thesis in front of a committee. This procedure usually involves a committee with special composition, while several other rules have to be enforced too. The problem of scheduling many thesis defenses in a designated period of time is a problem known to the timetabling community and is hard to solve. In this work, we experiment on a benchmark dataset that was released along with a formal description of the Problem. We employ three mathematical models and combine them to create a three phase approach. We test this approach with both Integer and constraint programming solvers and manage to achieve high quality results, while most of them are better than the formerly best known.

关键词： timetabling Integer programming constraint programming thesis defense

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Metaheuristics for multiobjective optimization in energy-efficient job shops

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期115卷

作者： Gonzalez, Miguel A. Rasconi, Riccardo Oddi, Angelo Univ Oviedo Dept Comp Oviedo Spain ISTC CNR Inst Cognit Sci & Technol Rome Italy

Energy awareness is one of the most relevant research directions in scheduling problems. In this paper we consider the minimization of both the makespan and the energy consumption in the classical job shop scheduling problem. The energy model considered allows several possible states for the machines: off, stand-by, idle, setup and processing. To solve this multi-objective problem we propose an NSGA-II based evolutionary algorithm combined with local search and a heuristic procedure to improve the energy consumption of a given schedule. We also propose an advanced constraint programming (CP) approach as well as a Mixed-Integer Linear programming (MILP) model, to the aim of comparing their performances against those obtained with the NSGA-II. The experimental study is performed against a benchmark set that extends by 41 instances of increasing size, the set tackled in the previous literature against the same problem. The experiments demonstrate the superiority of the NSGA-II algorithm over all other methods, despite the utilization of CP and MILP allows to draw interesting conclusions on the overall solution optimality, revealing that there is still room for further optimization.

关键词： Evolutionary algorithms Heuristic search constraint programming Mixed-Integer Linear programming Job-shop scheduling Energy aware scheduling

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An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

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COMPUTERS & OPERATIONS RESEARCH 2022年 142卷 105733-105733页

作者： Karabulut, Korhan Oztop, Hande Kizilay, Damla Tasgetiren, M. Fatih Kandiller, Levent Yasar Univ Dept Software Engn Izmir Turkey Izmir Democracy Univ Dept Ind Engn Izmir Turkey Auburn Univ Dept Ind & Syst Engn Auburn AL USA Yasar Univ Dept Ind Engn Izmir Turkey

This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread, and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST, we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model, which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature, this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard, we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature, i.e., the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization), IGR (another variant of an iterated greedy algorithm with a restart scheme), and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en, IG2S, IGR, and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature, considering various combinations of jobs, machines, factories, and SDST settings, resulting in 2880 benchmark instances. For 216 out of 240 small-size instances, optimal results are reported by solving the propose

关键词： Distributed flowshop scheduling Sequence-dependent setup times Evolution strategy Mixed-integer programming constraint programming

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Globalizing constraint models

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ARTIFICIAL INTELLIGENCE 2022年 302卷 103599-103599页

作者： Leo, Kevin Mears, Christopher Tack, Guido de la Banda, Maria Garcia Monash Univ Fac IT Melbourne Vic Australia Redbubble Melbourne Vic Australia

We present a method to detect implicit model patterns (such as global constraints) that might be able to replace parts of a combinatorial problem model that are expressed at a low-level. This can help non-expert users write higher-level models that are easier to reason about and often yield better performance. Our method generates candidate model patterns by analyzing both the structure of the model - its constraints, variables, parameters and loops - and the input data from one or more data files. Each candidate is scored by comparing a sample of its solution space with that of the part of the model it is intended to replace. The top-scoring candidates are presented to the user through an interactive display, which shows how they could be incorporated into the model. The method is implemented for the MiniZinc modeling language and available as part of the MiniZinc distribution. (C) 2021 Elsevier B.V. All rights reserved.

关键词： constraint programming constraint acquisition Automated modeling constraint modeling Global constraints

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The hybrid flexible flowshop with transportation times 27

The hybrid flexible flowshop with transportation times

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27th International Conference on Principles and Practice of constraint programming, CP 2021

作者： Armstrong, Eddie Garraffa, Michele O'Sullivan, Barry Simonis, Helmut Johnson and Johnson Research Centre Limerick Ireland Confirm SFI Research Centre for Smart Manufacturing Limerick Ireland School of Computer Science University College Cork Ireland

ISBN: (纸本)9783959772112

This paper presents the hybrid, flexible flowshop problem with transportation times between stages, which is an extension of an existing scheduling problem that is well-studied in the literature. We explore different models for the problem with constraint programming, MILP, and local search, and compare them on generated benchmark problems that reflect the problem of the industrial partner. We then study two different factory layout design problems, and use the optimization tool to understand the impact of the design choices on the solution quality. © Eddie Armstrong, Michele Garraffa, Barry O'Sullivan, and Helmut Simonis.

关键词： constraint programming

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