This study develops a column generation-based distributed scheduling algorithm for multi-mode resource constrained project scheduling problem. The proposed distributed algorithm shares less information among independe...
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This study develops a column generation-based distributed scheduling algorithm for multi-mode resource constrained project scheduling problem. The proposed distributed algorithm shares less information among independent decision makers compared with the traditional integrated approach. In this problem, many independent processors, which can produce different types of products, coordinate with a resource manager (or third-party logistic), who provides different types of vehicles to deliver products to the customers. The problem is decomposed into two parts: production planning for individual processors and vehicle scheduling for the resource manager. A total of 1200 instances are randomly generated to verify the effectiveness of the proposed distributed algorithm. Results of computational experiments verify that the proposed distributed algorithm has good solution quality and calculation efficiency compared with the integrated approach, i.e., CPLEX, hybrid Benders Decomposition, and Lagrangian Relaxation. Lastly, a general framework for the distributed algorithm is proposed to solve a generalized problem.
The multi-mode resource constrained project scheduling problem (MRCPSP) is an NP-hard optimisation problem involving scheduling tasks under resource and precedence constraints, while there are several modes for execut...
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The multi-mode resource constrained project scheduling problem (MRCPSP) is an NP-hard optimisation problem involving scheduling tasks under resource and precedence constraints, while there are several modes for executing each task. In this paper, we propose a novel matheuristic based on relax-and-solve (R &S) algorithm to solve MRCPSP. In addition, a mathematical programming model, which is the generalisation of the multi-dimensional knapsack problem is developed. That model conducts the mode selection process for the purpose of generating an initial feasible solution. We evaluate the performance of the proposed algorithm by solving benchmark instances that are widely used in the literature. The results demonstrate that the proposed R &S algorithm outperforms the state-of-the-art methods for solving the MRCPSP.
The research highlights the potential of integrating energy management and the multi-moderesource-constrainedprojectschedulingproblem, which includes material ordering. It addresses the pressing concerns of escala...
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The research highlights the potential of integrating energy management and the multi-moderesource-constrainedprojectschedulingproblem, which includes material ordering. It addresses the pressing concerns of escalating electricity consumption and greenhouse gas emissions by considering renewable energy resources, time-of-use (TOU) electricity tariffs, and carbon taxes. In this regard, a mixed integer linear programming model is developed. To solve the small-sized problem instances, epsilon-constraint method is used. However, since the problem under consideration is NP-hard and exact methods fail to provide solutions for large-sized instances within a reasonable timeframe, two multi-objective meta-heuristic algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and multi-Objective Particle Swarm Optimization (MOPSO), are used as solution methods. Comparing results obtained by NSGA-II and MOPSO demonstrates the superiority of NSGA-II in the majority of performance metrics, regardless of the problem size. Through comprehensive sensitivity analysis of key model parameters, including TOU electricity tariffs and carbon tax rates, the research determines the optimal project duration and cost. Sensitivity analyses offer insights for sustainable project planning, highlighting the role of renewables in electricity consumption. Minimizing carbon taxes and proactive resource management underscore environmental responsibility, while TOU tariffs enhance sustainable energy practices. Overall, strategically integrating renewables in certain scenarios optimizes project execution, aligning with sustainability objectives for efficient and successful outcomes.
Over the last few decades, a number of mathematical models have been introduced for solving multi-mode resource constrained project scheduling problems (MRCPSPs). However the computational effort required in solving t...
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ISBN:
(纸本)9783662452370;9783662452363
Over the last few decades, a number of mathematical models have been introduced for solving multi-mode resource constrained project scheduling problems (MRCPSPs). However the computational effort required in solving those models depends on the number of variables. In this paper, we attempt to reduce the number of variables required in representing MRCPSPs by formulating two new event-based models. A comparative study was conducted by solving standard benchmark instances using a common objective function for the developed as well as the existing mathematical models. The study provided interesting insights about the problem characteristics, model sizes, solution quality, and computational effort of these approaches.
Advances in the technologies of sensors and lightweight robots increasingly enable direct physical interaction between humans and robots. This so-called human-robot collaboration is supposed to offer more flexibility ...
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Advances in the technologies of sensors and lightweight robots increasingly enable direct physical interaction between humans and robots. This so-called human-robot collaboration is supposed to offer more flexibility in production processes, as opposed to fully automated processes. The aim of this contribution is to describe an integer linear programming model which optimally coordinates the distribution of tasks between humans and robots in a realistic production process of printed circuit boards (PCBs), where the objective is to minimise the completion time of a board. In addition, we discuss an extended case wherein a whole set of different boards is to be assembled, which is highly relevant for low volume production with a high degree of customisation. After stating an extended integer linear programming (ILP) formulation, we propose two practical approaches for solving the computationally more complex second scenario: an order-based heuristic approach and a matheuristic applying a truncated variant of the ILP model with different sequencing strategies. The computational evaluation based on a real-world use case from the PCB industry underlines the efficacy of the matheuristic approach for obtaining a good overall makespan.
Up to date, there is no standard guideline to generate schedules capable of withstanding the inherent uncertainty of projects. Moreover, even though the research in scheduling and resource allocation in project manage...
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ISBN:
(纸本)9781509046010
Up to date, there is no standard guideline to generate schedules capable of withstanding the inherent uncertainty of projects. Moreover, even though the research in scheduling and resource allocation in project management is vast, the practice differs significantly. This study aims to deliver a stable, efficient and practical methodology capable of generating robust baseline schedules. To achieve this, the authors use a discrete version of Differential Evolution within a previously proposed and tested framework and the results improve significantly when compared to it.
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