Project Instances Research Articles

A matheuristic for the resource-constrained project scheduling problem

This paper presents a matheuristic solution algorithm to solve the well-known resource-constrained project scheduling problem (RCPSP). The problem makes use of a restricted neighbourhood method using an activity selection and a search space restriction module and implements them as two alternative search algorithms. The first algorithm makes use of the best-performing components of the branch-and-bound procedures from the literature, and embeds them into a greedy neighbourhood search. The second matheuristic implements the exact branch-and-bound procedures into a known and well-performing meta-heuristic search algorithm.Computational experiments have been carried out on seven different datasets consisting of 10,000+ project instances. Experiments reveal that the choice of exact algorithm is key in finding high-quality solutions, and illustrate that the trade-off between selecting an activity set size and search space restriction depends on the specific implementation. The computational tests demonstrate that the matheuristic discovered 24 new best known solutions that could not be found by either a meta-heuristic or an exact method individually. Moreover, a new benchmark dataset has been proposed that can be used to develop new matheuristic search procedures to solve the problem consisting of 461 instances from the literature.

Reducing the feasible solution space of resource-constrained project instances

This paper present an instance transformation procedure to modify known instances of the resource-constrained project scheduling problem to make them easier to solve by heuristic and/or exact solution algorithms. The procedure makes use of a set of transformation rules that aim at reducing the feasible search space without excluding at least one possible optimal solution. The procedure will be applied to a set of 11,183 instances and it will be shown by a set of experiments that these transformations lead to 110 improved lower bounds, 16 new and better schedules (found by three meta-heuristic procedures and a set of branch-and-bound procedures) and even 64 new optimal solutions which were never not found before.

MetaboLights: open data repository for metabolomics.

MetaboLights is a global database for metabolomics studies including the raw experimental data and the associated metadata. The database is cross-species and cross-technique and covers metabolite structures and their reference spectra as well as their biological roles and locations where available. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the continued growth and diversity of submissions and the significant developments in recent years. In particular, we highlight MetaboLights Labs, our new Galaxy Project instance with repository-scale standardized workflows, and how data public on MetaboLights are being reused by the community. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at https://github.com/EBI-Metabolights.

Designing for Social and Cultural Sustainability: Challenges and Opportunities for a Sustainable Future

An approach to design known as "design for social and cultural sustainability" aims to develop systems, places, and products that are socially and culturally responsible and benefit both society and the environment. This paper examines the idea of design for social and cultural sustainability, as well as its significance and difficulties. It emphasizes the part that designers play in advancing social and cultural sustainability and offers instances of innovative design projects that have positively impacted communities and the environment. In order to promote and execute design for social and cultural sustainability, the paper offers guidelines for designers, educators, and policymakers.

Proactive project scheduling using GPU-accelerated simulations under uncertainty environment

ABSTRACT Numerous scheduling generation and revision methods have been developed for project scheduling under uncertain environments in previous research. However, most of these methods have yet to address a practical project with more than a hundred activities involving multiple decisions in scheduling generation and execution phases. This study proposes a local search-based scheduling method that comprehensively evaluates a baseline schedule considering decision-making in both the planning and execution phases. The proposed method performs simulations to evaluate schedule robustness accurately using GPU to find a locally optimal solution for large problem instances in a reasonable time. A series of numerical experiments demonstrate that the proposed method can generate a robust schedule for large-scale project instances. These results conclude that the proposed method utilizing the simulation-based evaluation and the GPU acceleration is effective and provide insight into developing a scheduling method for practical projects.

New resource-constrained project scheduling instances for testing (meta-)heuristic scheduling algorithms

The resource-constrained project scheduling problem (RCPSP) is a well-known scheduling problem that has attracted attention since several decades. Despite the rapid progress of exact and (meta-)heuristic procedures, the problem can still not be solved to optimality for many problem instances of relatively small size. Due to the known complexity, many researchers have proposed fast and efficient meta-heuristic solution procedures that can solve the problem to near optimality. Despite the excellent results obtained in the last decades, little is known why some heuristics perform better than others. However, if researchers better understood why some meta-heuristic procedures generate good solutions for some project instances while still falling short for others, this could lead to insights to improve these meta-heuristics, ultimately leading to stronger algorithms and better overall solution quality.In this study, a new hardness indicator is proposed to measure the difficulty of providing near-optimal solutions for meta-heuristic procedures. The new indicator is based on a new concept that uses the σ distance metric to describe the solution space of the problem instance, and relies on current knowledge for lower and upper bound calculations for problem instances from five known datasets in the literature.This new indicator, which will be called the σD indicator, will be used not only to measure the hardness of existing project datasets, but also to generate a new benchmark dataset that can be used for future research purposes. The new dataset contains project instances with different values for the σD indicator, and it will be shown that the value of the σ distance metric actually describes the difficulty of the project instances through two fast and efficient meta-heuristic procedures from the literature.

A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic

This research introduces a stochastic version of the multi-mode resource-constrained project scheduling problem (MRCPSP) and its mathematical model. In addition, an efficient multi-start iterated local search (MS-ILS) algorithm, capable of solving the deterministic MRCPSP, is adapted to deal with the proposed stochastic version of the problem. For its deterministic version, the MRCPSP is an NP-hard optimization problem that has been widely studied. The problem deals with a trade-off between the amount of resources that each project activity requires and its duration. In the case of the proposed stochastic formulation, the execution times of the activities are uncertain. Benchmark instances of projects with 10, 20, 30, and 50 activities from well-known public libraries were adapted to create test instances. The adapted algorithm proved to be capable and efficient for solving the proposed stochastic problem.

A Hybrid Multiple Models Transfer Approach for Cross-Project Software Defect Prediction

For a new project, it is impossible to get a reliable prediction model because of the lack of sufficient training data. To solve the problem, researchers proposed cross-project defect prediction (CPDP). For CPDP, most researchers focus on how to reduce the distribution difference between training data and test data, and ignore the impact of class imbalance on prediction performance. This paper proposes a hybrid multiple models transfer approach (HMMTA) for cross-project software defect prediction. First, several instances that are most similar to each target project instance are selected from all source projects to form the training data. Second, the same number of instances as that of the defected class are randomly selected from all the non-defect class in each iteration. Next, instances selected from the non-defect classes and all defected class instances are combined to form the training data. Third, the transfer learning method called ETrAdaBoost is used to iteratively construct multiple prediction models. Finally, the prediction models obtained from multiple iterations are integrated by the ensemble learning method to obtain the final prediction model. We evaluate our approach on 53 projects from AEEEM, PROMISE, SOFTLAB and ReLink four defect repositories, and compare it with 10 baseline CPDP approaches. The experimental results show that the prediction performance of our approach significantly outperforms the state-of-the-art CPDP methods. Besides, we also find that our approach has the comparable prediction performance as within-project defect prediction (WPDP) approaches. These experimental results demonstrate the effectiveness of HMMTA approach for CPDP.

Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints

Make-to-order (MTO) or engineer-to-order (ETO) systems produce complex and highly customized products and, therefore, there is a need for advanced project scheduling approaches for production planning in these systems. An important aspect of production scheduling is the assignment of operators with specific human factors to activities in a manufacturing project. This assignment impacts the duration of the activities, the total wage cost of the project and even the energy consumption during production. With increasing concern regarding low-carbon production in manufacturing, the human factors of operators thus cannot be ignored in the decision-making process in production project scheduling. In this context, our study considers an extension of the well-known resource-constrained project scheduling problem for manufacturing. This problem is represented as a bi-objective optimization problem with the conjoint objectives of minimizing the total cost of the project and its carbon footprint. Two variants of a genetic algorithm-based memetic algorithm (MA) are proposed to solve this problem and a set of artificial, realistic project instances are generated to evaluate the proposed solution procedure. Experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithms (NSGA-II and NSGA-III) and its enhanced approach (ENSGA-II) in terms of both solution quality and computational efficiency. The experiments are conducted on both real-life case study data from an MTO project in the furniture industry and a large set of artificial data instances. Our research allows project managers to select appropriate operators to execute activities based on human factors, wage and power consumption with the objectives of minimum total cost and carbon footprint.

The power of protest in the EIA proceedings. Influence of local community on environmental decisions regarding intensive livestock operations - Example of Poland

Planned intensive livestock operations (ILOs) are often the subject of protests from the local communities due to the possible negative impacts. Active participation of local people occurs at an early stage of investment process, i.e. environmental impact assessment (EIA). The aim of this paper is to reveal the strength of influence from the protesting residents on the environmental proceedings for intensive livestock operations, the knowledge that local people bring to the proceedings and whether - finally - environmental conditions are established, giving the green light to the investment. The results showed that the EIA in Poland mainly endorses technocratic rationality and does not include comprehensive assessment of social impact (SIA). Yet, owing to the opposition from the local inhabitants, who bring into the EIA procedure, concerning ILOs, their value-based and experiential knowledge, local authorities often refuse to establish the environmental conditions for the potential investors. In practice the opposition from the inhabitants leads to the broadening of the scope of SIA in the framework of EIA. This means, however, that the environmental procedures get more complex and controversial from the legal point of view. Ultimately, the majority of the analysed instances of ILO projects, against which the local inhabitants protested, got blocked at the procedural stage of EIA.

Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection

<p>In the practice of software project development, the developed project is a brand-new project. Defect prediction for this type of software project requires the use of other similar projects (i.e. source projects) to collect relevant data to build a defect prediction model, and make defect prediction for the project under development (i.e. target project). However, the prediction model built with the relevant data of the source project cannot achieve the ideal prediction performance when predicting the target project. The main reason is that there is a large data distribution difference between the source project and the target project. The data distribution difference is mainly in the distribution of features between projects and differences between instances. In response to the above problems, starting from both features and instances, a cross-project defect prediction method is proposed. This method first aligns the feature distribution based on the data of the existing target project and the source project data. Then, it selects the labeled instance that is similar to the unlabeled instance in the target project, and finally builds a defect prediction model based on the selected source project instances. Cross-project defect prediction experiments were carried out on the Relink datasets and the Promise datasets. Compared with the classic instance-based cross-project defect prediction method, significant improvements have been made in F-measure and AUC; compared with the prediction of within project defect prediction, it has achieved comparable performance.</p> <p> </p>

Bi-objective Optimization for Rebar Cutting Plan Using Symbiotic Organisms Search

The construction industry is one of the biggest industries in the world. One of the most important materials in this industry is the reinforced steel bar. In construction site usually fabricated the reinforced steel bars into the form of one-dimensional stocks and designed according to the structural specification and installed in various structural components such as columns, beam, slab, and foundation. The purpose of cutting stock problems for reinforced steel bars (rebar cutting plan, RCP) is to satisfy the project requirements and also to minimize the cutting losses, which is the major contributor to the construction waste As there are two objective functions, we call the problem bi-objective rebar cutting plan (B-RCP). In the previous research, efforts developed the metaheuristics method to minimize the cutting losses in preliminary engineering designs but it few to consider about simplicity in order to achieve efficient work. Thus, this research applies the bi-optimization with epsilon constraint in order to find the trade-off solution between the material waste and number of cutting pattern for decision making. This study proposed the metaheuristic method Symbiotic Organisms Search (SOS) to solve B-RCP in one framework, to find the feasible cutting patterns along with the minimum total waste of the reinforced steel bar. To test the performance of the proposed SOS framework, the previous study case is set to be a benchmark, and then create a comparison with PSO. For real project instances of RCP are used to evaluate and validate the performance of SOS. The validation results from both cases of RCP show that SOS has better performance than PSO in both minimize the material waste along reducing the number of cutting patterns. Thus, it is validated that SOS is a competitive algorithm for solving the RCP.

Multi-operator immune genetic algorithm for project scheduling with discounted cash flows

The resource constrained project scheduling problem with discounted cash flows (RCPSPDC) is one of the most challenging problems owing to its NP-hard characteristics. This complex combinatorial optimization problem is most relevant to project management, building and construction management, and production planning. Although several solution methods have been suggested to solve the RCPSPDC, no single method has been shown to be the best for a wide range of problems. In this study, a multi-operator immune genetic algorithm, called MO-IGA, is proposed which integrates a genetic algorithm (GA) and an immune algorithm (IA) to solve the RCPSPDC. Two different operators for each crossover, mutation, immunization, and local search operation are utilized dynamically in the MO-IGA framework. Variable insertion neighborhood search (VINS) and forward–backward improvement (FBI) are utilized for local search to enrich the searching behavior and exploration. The algorithm starts with the same probability for each crossover, mutation, immunization, and local search operator; however, the probability is updated dynamically depending on the success of each operator in producing the quality solution. An activity move rule (AMR) has been utilized to delay the task with negative cash flows as much as possible, which further improves the overall project’s net present value (NPV). A standard benchmark dataset, comprising 17,280 project instances ranging from 25 to 100 activities, is used to test the performance and effectiveness of the proposed MO-IGA. The proposed MO-IGA outperforms several datasets based on both a lower value of the average percentage deviation and the number of feasible schedule generations. The proposed MO-IGA is also shown to be more effective than the multi-operator GA (MO-GA) and multi-operator IA (MO-IA). Extensive numerical analysis, statistical tests, and comparisons with state-of-the-art algorithms proves the effectiveness of the proposed approach.

Everyday code: The project for democracy on our ‘desktop’

This paper examines what I call the project for democracy, by which I mean a perpetual individual-and-collective project to manage our affairs ourselves, in all areas of our lives. The goal of the paper is to understand better what that project entails and how we can carry it out well. To do so, I examine a very prosaic empirical case, the software code that organizes the everyday environment of our personal computing devices: operating systems, window managers, wireless interfaces, system trays, and so on. What would the project of democracy look like in that context? The bulk of the paper is spent fleshing out an answer to that question. I then suggest that the project for democracy in the digital realm of the desktop is just one instance of the wider project for democracy. The desktop can be a little model that can guide and inspire the project for democracy in other arenas, such as the household, the neighborhood, the city, and beyond.

Multi‐start iterated local search metaheuristic for the multi‐mode resource‐constrained project scheduling problem

AbstractIn this paper a multi‐start iterated local search (MS‐ILS) algorithm is presented as a new and effective approach to solve the multi‐mode resource‐constrained project scheduling problem (MRCPSP). The MRCPSP is a well‐known project scheduling NP‐Hard optimization problem, in which there is a trade‐off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution. This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well‐known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds.

Research on Construction Technology of External Hanging Scaffold for Prefabricated Concrete Shear Wall

Combined with an instance of a high-rise residential project in Sino-French Wuhan Ecological Demonstration city, the structural characteristics, construction key technology and disassembly process of the auxiliary external rack of the prefabricated high-rise structure are emphatically analyzed. It is safe and reliable to use the external hanging scaffold structure in the prefabricated high-rise building, which can effectively shorten the construction period and has a beautiful appearance. and also obtains good economic and social benefits, which provides Suggestions and references for the similar prefabricated building construction in the future.

Various extensions in resource-constrained project scheduling with alternative subgraphs

In this research, we present several extensions for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). First of all, we investigate more complex variants of the alternative project structure. More precisely, we consider nested alterative subgraphs, linked alternative branches, multiple selection, caused and closed choices, and split choices. Secondly, we introduce non-renewable resources in the RCPSP-AS in order to implicitly avoid certain combinations of alternatives given a limited availability of this resource over the complete project horizon. We formulate both the basic RCPSP-AS and its extensions as an ILP model and solve it using Gurobi. The computational experiments are conducted on a large set of artificial project instances as well as three case studies. The results show the impact of the different extensions on the project makespan and the computational complexity. We observe that combinations of the proposed extensions might imply complex alternative project structures, resulting in an increasing computational complexity or even infeasible solutions. The analysis of the three case studies shows that it is hard to find feasible solutions with a small time limit or optimal solutions with a larger time limit for projects with a realistic size in terms of the number of activities or alternatives.

Large margin metric learning for multi-view vehicle re-identification

In this paper we propose a novel large margin metric learning scheme for vehicle re-identification (re-ID), which aims to maximize the distance margins among different vehicles in the embedding feature space. Inspired by the fact that in the embedding feature space vehicles with the same identity are usually scattered sparsely due to vehicles’ multi-view appearance, our method is designed to make samples with the same identities more compact and meanwhile project instances with different categories to separate regions in the embedding space. To make training process more efficient, in the first stage, only the Softmax Loss is adopted, and in the second stage, by computing classification hyperplanes between different vehicle identities, a large margin loss is defined to maximize the distances between training samples and their corresponding hyperplanes. Besides, a new sampling method is adopted to find the hardest samples which are close to their hyperplanes and a kernelized re-ranking method is applied to further boost the performance. Compared with state-of-the-art approaches, our method achieves superior results with efficient training and inference process and only the identity as supervision signal. Experimental results on three most popular datasets show that our system produce promising results, and notably on the VeRi-776 dataset, our method can reach the best record with Rank1 96.81% and mAP 80.95%.

An analysis of network and resource indicators for resource-constrained project scheduling problem instances

In the past decades, the resource on the resource-constrained project scheduling problem (RCPSP) has grown rapidly, resulting in an overwhelming amount of solution procedures that provide (near)-optimal solutions in a reasonable time. Despite the rapid progress, little is still known what makes a project instance hard to solve. Inspired by a previous research study that has shown that even small instances with only up to 30 activities is sometimes hard to solve, the current study provides an analysis of the project data used in the academic literature. More precisely, it investigates the ability of four well-known resource indicators to predict the hardness of an RCPSP instance.The study introduces a new instance equivalence concept to show that instances might have very different values for their resource indicators without changing any possible solution for this instance. The concept is based on four theorems and a search algorithm that transforms existing instances into new equivalent instances with more compact resources. This algorithm illustrates that the use of resource indicators to predict the hardness of an instance is sometimes misleading.In a set of computational experiment on more than 10,000 instances, it is shown that the newly constructed equivalent instances have values for the resource indicators that are not only different than the values of the original instances, but also often are better in predicting the hardness the project instances. It is suggested that the new equivalent instances are used for further research to compare results on the new instances with results obtained from the original dataset.

An Advanced LSTM Model for Optimal Scheduling in Smart Logistic Environment: E-Commerce Case

At present, most logistics systems, especially those dedicated to e-commerce, are based on artificial intelligence techniques to offer better services and increase outcomes. However, the variety and complexity of resource allocation, as well as task scheduling, denote that dynamic environments have still great challenges to overcome. So advanced models based on strong algorithms are required. Introducing advanced models into scheduling solutions is a promising way to enhance logistics efficiency. As a result, managing system resources remain essential to optimize task scheduling respecting the interactive impacts, and logistics systems requirements. In response to these challenges, in this paper, a powerful solution based on a Long short-term memory (LSTM) model is proposed to optimize resource allocation and to enhance task scheduling in a smart logistics framework. This paper explores some of the most important scheduling techniques and hypothesizes that deep learning techniques might be able to afford accurate approaches. The proposed smart logistics model lays on strong techniques, for that, experimental simulations were conducted using various project instances. The validation tests demonstrated competitive results with important performance rates i.e.: accuracy of 92,44% with a precision of 93,83, a recall of 95.18%, F1-score of 94,92%, and an AUC of 88,17%. These results reveal the proof-of-principle for using LSTM models for effective and truthful logistics operations.