Handbook on Project Management and Scheduling Vol.1

Schwindt, Christoph; Zimmermann, Jürgen

doi:10.1007/978-3-319-05443-8

Cited by 41 publications

(4 citation statements)

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“…Due to that there is a plethora literature about different optimization techniques and solution methods for various manufacturing problems. We refer the keen reader to these surveys and books as a starting point (see, e.g., Baptiste et al [23], Schwindt and Zimmermann [219,220], Ouelhadj and Petrovic [184], Liu et al [154], Wari and Zhu [255]).…”

Section: Constraint Optimizationmentioning

confidence: 99%

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for futuredevelopment and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications.

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Section: Constraint Optimizationmentioning

confidence: 99%

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Baumgartner

Smith

Rana

et al. 2022

Preprint

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“…The activity lists generated by crossover operator JM2 14,2 solution, a priority rule is chosen randomly. As different priority rules create different activity lists (Schwindt and Zimmermann, 2015), the diversity of population increases. We have used some of the priority rules proposed by Talbot (1982) to reorder the list of activities to provide an appropriate initial solution.…”

Section: An Improved Version Of the Non-dominated Sorting Genetic Algorithm IImentioning

confidence: 99%

“…In this case, the availability of resource k is defined as a function denoted as R kt . Thus, the amount of resource k depends on the period t. Furthermore, in timedependent resource profiles, the amount of resources required by each activity relies on the period t. To capture this concept, we define r jkt as the amount of resource k required by activity j in period t (Schwindt and Zimmermann, 2015). Consider a project comprising eight non-dummy activities, which have to be scheduled subject to k =, 2 renewable resources with constant capacities of R 1 = 5 and R 2 = 5.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the time-dependent multi-skilled RCPSP considering learning effect

Hosseinian

Baradaran

Bashiri

2019

JM2

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Purpose The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously. Design/methodology/approach The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method. Findings Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values. Practical implications The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects. Originality/value Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

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“…Literature review. Project scheduling, a classical problem in project management, has gained increasing attention in the literature over decades ( [30]). Within this area of research, project scheduling with the consideration of financial aspects, wherein the classic objective is to maximize the net present value (NPV) of cash flows in projects, is an important branch ( [17]).…”

mentioning

confidence: 99%

Tabu search and simulated annealing for resource-constrained multi-project scheduling to minimize maximal cash flow gap

He¹,

He²,

Wang³

2021

JIMO

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In reality, a contractor may implement multiple projects simultaneously and in such an environment, how to achieve a positive balance between cash outflow and inflow by scheduling is an important problem for the contractor has to tackle. For this fact, this paper investigates a resource-constrained multi-project scheduling problem with the objective of minimizing the contractor's maximal cash flow gap under the constraint of a project deadline and renewable resource. In the paper, we construct a non-linear integer programming optimization model for the studied problem at first. Then, for the NP-hardness of the problem, we design three metaheuristic algorithms to solve the model: tabu search (TS), simulated annealing (SA), and an algorithm comprising both TS and SA (SA-TS). Finally, we conduct a computational experiment on a data set coming from existing literature to evaluate the performance of the developed algorithms and analyze the effects of key parameters on the objective function. Based on the computational results, the following conclusions are drawn: Among the designed algorithms, the SA-TS with an improvement measure is the most promising for solving the problem under study. Some parameters may exert an important effect on the contractor's maximal cash flow gap.

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Handbook on Project Management and Scheduling Vol.1

Cited by 41 publications

References 1 publication

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Modeling of the time-dependent multi-skilled RCPSP considering learning effect

Tabu search and simulated annealing for resource-constrained multi-project scheduling to minimize maximal cash flow gap

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