The aim of this study is to propose a scenario-based approach with utility-entropy decision model to measure the uncertainty related to the evolution of a resource-constrained project scheduling problem with uncertain...
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The aim of this study is to propose a scenario-based approach with utility-entropy decision model to measure the uncertainty related to the evolution of a resource-constrained project scheduling problem with uncertain activity durations (a stochastic RCPSP). The approach consists of two stages. The first is to apply the proposal proposed by Tseng and Ko to convert a stochastic RCPSP into a full scenario tree. In stage two, we introduce the Expected Utility-Entropy (EU-E) decision model, a weighted linear average of expected utility and entropy, to establish an EU-E criterion. Then we apply the criterion to prune the worse branch(es) to lead a reduced scenario tree. Based on an illustrated example, it has been concluded that the reduced scenario tree by the EU-E criterion with larger trade-off coefficient. has less number of possible paths, less uncertainty, and lengthier expected project duration than that with smaller trade-off coefficient.. Thus, this has demonstrated that not only can the whole scenario during the course of a project be obtained, but also the uncertainty related to the evolution of a project can be measured.
In this paper a dynamic cooperative interaction strategy for the A-Team solving the resource-constrained project scheduling problem (RCPSP) is proposed and experimentally validated. The RCPSP belongs to the class of N...
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ISBN:
(纸本)9783319452425;9783319452432
In this paper a dynamic cooperative interaction strategy for the A-Team solving the resource-constrained project scheduling problem (RCPSP) is proposed and experimentally validated. The RCPSP belongs to the class of NP-hard optimization problems. To solve this problem a team of asynchronous agents (A-Team) has been implemented using multiagent environment. An A-Team consist of the set of objects including multiple optimization agents, manager agents and the common memory which through interactions produce solutions of hard optimization problems. In this paper the dynamic cooperative interaction strategy is proposed. The strategy supervises cooperation between agents and the common memory. To validate the proposed approach the preliminary computational experiment has been carried out.
projectscheduling in a real-life scenario often involves multiple-criteria decision-making in which no single solution exists. To solve such a problem, a multi-objective optimization method has been applied to define...
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ISBN:
(纸本)9783031530241;9783031530258
projectscheduling in a real-life scenario often involves multiple-criteria decision-making in which no single solution exists. To solve such a problem, a multi-objective optimization method has been applied to define the satisfying trade-off between different criteria. In this paper, we focus on a specific use case in the defense industry in which the overall mission is to generate a maintenance plan for the transfer operations of power grid consumers to the new service area. The project objectives include restricting the outage duration during transfer operations, grouping operations concerning the proximity between them, moderating the allocation of supporting resource, and regulating human resources intervention outside business hours. To solve this problem, we propose a combination of heuristic approaches starting by defining a sequence of activities based on their complexities to be scheduled. Concerning the obtained order, a serial-schedule generation scheme (S-SGS) is then implemented by iterating through each activity to define the best time period to proceed the operation in accordance with project's multiple objectives. Finally, the output is transferred to our existing parallel scheme-based solver, Optimizio, to finally justify the project planning. The proposed S-SGS solution provides a feasible schedule of 110 transfer operations in 2 s with solution evaluation analysis and information of a Pareto frontier in approximately 15min. The set of Pareto optimal solutions allows the expert to explore potential trade-offs between criteria. Together with a fast execution time of the algorithms that benefits a multi-scenario simulation, our tool demonstrates a potential capacity to get the optimum outcome of the multi-objective optimization project.
PurposeThe aim of this study is to provide a holistic analysis of all possible maritime business logistics processes related to import and export shipments in a fuzzy environment through a case study of a maritime log...
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PurposeThe aim of this study is to provide a holistic analysis of all possible maritime business logistics processes related to import and export shipments in a fuzzy environment through a case study of a maritime logistics company based on the as-is and to-be models within business process management (BPM).Design/methodology/approachThe analyses considered the following perspectives: (i) in the stage of the process identification, the definition of the problem was carried out;(ii) in the stage of the process discovery, ocean department was divided into ocean export/import operation departments;ocean export/import operation were divided into freight collect/prepaid operation processes;ocean export/import logistics activity groups were broken down into sub-activities for freight collect/prepaid operation;the logistics activity groups and their sub-activities were defined;each sub-activity as either operation or documentation process group was classified;the durations of sub-activities were evaluated by decision-makers (DMs) as fuzzy sets (FSs);the monthly total jobs activities were estimated by DMs as FSs;the applied to monthly jobs activities of total shipments were estimated by DMs as FSs;the durations of each sub-activities were aggregated;the duration of the logistics activity groups and the sub-activities for per job were calculated;the cumulative workload of logistics activity groups and sub-activities were calculated;the duration of sub-activities for per job as operation or documentation departments were calculated, (iii) in the stage of the process analysis, cumulative ocean export/import workload as operation or documentation for freight collect/prepaid were calculated;duration of activity groups and sub-activities for per job as operation or documentation were calculated;cumulative workload activity groups and sub-activities as operation or documentation were calculated, (iv) in the stage of the process redesign, cumulative workload, process cycle time as
The majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. Thus, a crucial part of such metaheuristics is a "schedule generation scheme&qu...
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ISBN:
(纸本)9783319694047;9783319694030
The majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. Thus, a crucial part of such metaheuristics is a "schedule generation scheme" - procedure translating the indirect solution representation into a schedule. Schedule generation scheme is used every time a new candidate solution needs to be evaluated. Being relatively slow, it eats up most of the running time of the metaheuristic and, thus, its speed plays significant role in performance of the metaheuristic. Despite its importance, little attention has been paid in the literature to efficient implementation of schedule generation schemes. We give detailed description of serial schedule generation scheme, including new improvements, and propose a new approach for speeding it up, by using Bloom filters. The results are further strengthened by automated control of parameters. Finally, we employ online algorithm selection to dynamically choose which of the two implementations to use. This hybrid approach significantly outperforms conventional implementation on a wide range of instances.
Purpose Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic scheduling (DS) m...
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Purpose Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction. Design/methodology/approach Upon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts. Findings The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices. Practical implications Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste. Social implications Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste. Originality/value The paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.
In order to create a test-bed for Computational Intelligence (CI) methods dealing with complex, non-deterministic and dynamic environments we propose a definition of a new class of problems, based on the real-world ta...
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ISBN:
(纸本)9781479945061
In order to create a test-bed for Computational Intelligence (CI) methods dealing with complex, non-deterministic and dynamic environments we propose a definition of a new class of problems, based on the real-world task of projectscheduling and executing with risk management. Therefore, we define Risk-Aware projectschedulingproblem (RAPSP) as a (significant) modification of the resource-constrained project scheduling problem (RCPSP). We argue that this task is, considering its daunting complexity, sometimes surprisingly well solved by experienced humans, relying both on tools and their intuition. We speculate that a CI-based solver for RAPSP should also employ multiple cognitively-inspired approaches to the problem and we propose three such solvers of varying complexity and inspiration. Their efficacy comparison is in line with our expectations and supports our claims.
To solve the discrete optimization problem, the traditional continuous differential evolution (DE) algorithm has to be modified in individual representation or evolution strategy. Inspired from a pair of reciprocal op...
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ISBN:
(纸本)9781479904549;9781479904532
To solve the discrete optimization problem, the traditional continuous differential evolution (DE) algorithm has to be modified in individual representation or evolution strategy. Inspired from a pair of reciprocal operators (pointer and address-of) in computer programming language, a novel pointer-based discrete differential evolution (PDDE) is presented in this paper. Making use of the permutation of integers as the individual representation, PDDE redefines the addition and subtraction operations of traditional DE to construct discrete mutation operator. In addition, the scaling factor and crossover probability factor are redefined to fit the discrete operation. The performance of PDDE is evaluated through extensively experiments on comparing general searching ability and solving resource-constrained project scheduling problem. The computational results show that the proposed PDDE is efficient.
With the increasing complexity and scale of industrial projects the need of comprehensive and trustworthy methods for planning and scheduling becomes more and more apparent. Being implemented in recent project managem...
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ISBN:
(纸本)9781614993025;9781614993018
With the increasing complexity and scale of industrial projects the need of comprehensive and trustworthy methods for planning and scheduling becomes more and more apparent. Being implemented in recent project management systems, the traditional methods aid in projectscheduling based on activity durations, precedence relationships, explicit timing constraints, and resource limits all assumed by resource-constrained project scheduling problem (RCPSP). However, the methods have many shortcomings for the industrial projects where spatial factors play critically important role. The objective of this paper is to present an advanced Visual scheduling Method (VSM) for solving Generally constrainedprojectschedulingproblem (GCPSP) that extends the classical RCPSP statement by taking into account additional spatial factors such as product element collisions, missing of supporting neighbouring elements, workspace congestion. In the paper we provide a holistic framework of the method as well as illustrate how feasible project schedules can be generated under complex spatio-temporal constraints in highly automatic and visually interpretable way. A software implementation of the method and its prospects for application in industrial practice are discussed too.
The Dutch Railways (NS) is the Dutch national passenger railway operator. They own rolling stock that has to be maintained regularly. One of the maintenance facilities executing regular maintenance on regional trains ...
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The Dutch Railways (NS) is the Dutch national passenger railway operator. They own rolling stock that has to be maintained regularly. One of the maintenance facilities executing regular maintenance on regional trains is the maintenance department (OB) in Leidschendam. The aim of this project is to develop a framework for optimizing the productivity in the combined labor and task scheduling in the maintenance of regional trains on a daily perspective to increase the delivery reliability. A mathematical model has been developed based on the resource-constrained project scheduling problem. To solve the model a hybrid method combining a genetic algorithm and an annealing-like search heuristic is proposed. Three experiments are performed using real data. During the fist experiment the schedule performance of the algorithm is compared with the performance of the schedules generated by the planners of the OB. The second experiment evaluates different qualification distributions for the available workforce. During the last experiment, the effect of the tardiness cost as introduced in this research is evaluated. The experiments show that the scheduled productivity is increased with an average of 18% per day when the algorithm is used and on average 3 more train units are completely scheduled per day.
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