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A Constraint Programming Formulation of the multi-mode resource-constrained project scheduling problem for the Flexible Job Shop scheduling problem

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IEEE ACCESS 2023年 11卷 144928-144938页

作者： Yuraszeck, Francisco Montero, Elizabeth Canut-De-Bon, Dario Cuneo, Nicolas Rojel, Maximiliano Univ Andres Bello Dept Engn Sci Vina Del Mar 2531015 Chile Univ Tecn Federico Santa Maria Dept Comp Sci Valparaiso 2390123 Chile Direcc Programas Invest & Desarrollo Armada Chile Valparaiso 2360035 Chile Univ Andres Bello Fac Engn Vina Del Mar 2531015 Chile

In this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area.

关键词： Task analysis Job shop scheduling Upper bound Metaheuristics Sequential analysis Constraint handling Renewable energy sources Constraint programming flexible job shop FJSSP job shop JSSP multi-mode resource-constrained project scheduling problem MMRCPSP sequencing flexibility

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Experimental Evaluation of Agent-based Approaches to Solving multi-mode resource-constrained project scheduling problem

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CYBERNETICS AND SYSTEMS 2018年第5-6期49卷 296-316页

作者： Ratajczak-Ropel, Ewa Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

In this paper, a rank-based nonparametric statistical test for measuring the effect of cooperation between optimization agents solving the multi-mode resource-constrained project scheduling problem is presented. To solve this NP-hard optimization problem, different methods are applied including population- and agent-based approaches. One of them is a team of asynchronous agents composed of multiple optimization agents, management agents, and common memories, which through interactions produce solutions of hard optimization problems. Optimization agents represent different methods including local search, path relinking, or tabu search. Interactions are managed through various cooperation strategies based on applying heuristics, reinforcement learning, or population learning.

关键词： Agent A-team MRCPSP multi-agent system multi-mode resource-constrained project scheduling problem optimization

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A Continuous-Time MILP Formulation for the multi-mode resource-constrained project scheduling problem

A Continuous-Time MILP Formulation for the Multi-Mode Resour...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Gnagi, M. Rihm, T. Trautmann, N. Univ Bern Dept Business Adm Bern Switzerland

ISBN: (纸本)9781538667866

The well-known multi-mode resource-constrained project scheduling problem aims at selecting for each project task a start time and an execution mode to obtain a precedence-and resource-feasible schedule with minimal project duration. The available execution modes for the tasks differ in their durations and demands for some scarce resources. Numerous problem-specific solution methods and several mixed-integer linear programming (MILP) formulations have been described in the literature. We introduce a new continuous-time MILP formulation that employs continuous start-time variables and three types of binary variables: mode-selection, resource-assignment and sequencing variables. The results of our computational analysis indicate that the proposed formulation achieves superior performance than two formulations from the literature when the range of the tasks' durations is relatively high.

关键词： Operations Research project Management multi-mode resource-constrained project scheduling problem Mixed-Integer Linear Programming

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multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms

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COMPUTING 2019年第6期101卷 547-570页

作者： Tirkolaee, Erfan Babaee Goli, Alireza Hematian, Milad Sangaiah, Arun Kumar Han, Tao Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Islamic Azad Univ Ayatollah Amoli Branch Young Researchers & Elite Club Amol Iran Yazd Univ Dept Ind Engn Yazd Iran Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Dongguan Univ Technol DGUT CNAM Inst Dongguan Guangdong Peoples R China

This study addresses the multi-objective multi-mode resource-constrained project scheduling problem with payment planning where the activities can be done through one of the possible modes and the objectives are to maximize the net present value and minimize the completion time concurrently. Moreover, renewable resources including manpower, machinery, and equipment as well as non-renewable ones such as consumable resources and budget are considered to make the model closer to the real-world. To this end, a non-linear programming model is proposed to formulate the problem based on the suggested assumptions. To validate the model, several random instances are designed and solved by GAMS-BARON solver applying the epsilon-constraint method. For the high NP-hardness of the problem, we develop two metaheuristics of non-dominated sorting genetic algorithm II and multi-objective simulated annealing algorithm to solve the problem. Finally, the performances of the proposed solution techniques are evaluated using some well-known efficient criteria.

关键词： multi-mode resource-constrained project scheduling problem NPV Payment planning epsilon-Constraint method NSGA-II MOSA

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An adaptive large neighbourhood search as a matheuristic for the multi-mode resource-constrained project scheduling problem

An adaptive large neighbourhood search as a matheuristic for...

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15th International Conference on project Management and scheduling (PMS)

作者： Gerhards, Patrick Stuerck, Christian Fink, Andreas Helmut Schmidt Univ Inst Comp Sci Hamburg Germany Helmut Schmidt Univ Inst Operat Res Hamburg Germany

The multi-mode resource-constrained project scheduling problem is a well-known problem in the field of scheduling. The objective of the problem is to find the minimum makespan for the project. Hence, each activity has to be assigned to a mode and a starting time. At the same time, precedence and resource constraints must not be violated. We present a hybrid approach which combines an adaptive large neighbourhood search with mixed integer programming. We applied the procedure to datasets from the MMLIB library with up to 100 activities and nine modes. The computational results show that the approach is competitive with other state-of-the-art heuristics. Moreover, it found 294 new best known solutions and outperformed all other published methods on the MMLIB+ dataset.

关键词： multi-mode resource-constrained project scheduling problem MRCPSP matheuristic adaptive large neighbourhood search MMLIB hybrid metaheuristic

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An optimization model for construction project scheduling by considering CO2 emissions with multi-mode resource constraints under interval-valued fuzzy uncertainty

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2023年第1期20卷 87-102页

作者： Aramesh, S. Mousavi, S. M. Ghasemi, M. Shahabi-Shahmiri, R. Shahed Univ Dept Ind Engn Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

In Iran, the entire energy system relies on fossil fuels, which imposes significant greenhouse gas emissions. Besides, among different greenhouse gases, carbon dioxide (CO2) has the most considerable portion, and Iran is known as one of the top ten CO2 emitting countries, especially in the industry section. In this paper, a new multi-mode resource-constrained project scheduling problem is presented regarding the emitted CO2 as a greenness index. The proposed mathematical model has four objective functions, which are, minimizing the project completion time, project costs, and emitted CO2, and the fourth objective function maximizes the project quality. The time lag and reworking are regarded in the mathematical model. Reworking not only causes an increase in the project quality and more CO2 emissions but also increases the complexity of the problem. Uncertainty is an essential part of any construction project in real situations, so activities duration and cost of non-renewable and renewable resources are under interval-valued fuzzy uncertainty. To solve the mathematical model with uncertainty, a new extended solving method is proposed. Furthermore, a case study in Iran is presented to show the impact of the given model in real projects, and related results along with the analyses are conducted on this case. Additionally, Pareto front solutions are presented to show the trade-off between objectives. The results illustrate that considering CO2 emissions as a greenness index can reduce project costs and improve quality. On the other hand, this index increases the project completion time. This paper has practical implications for project managers and companies to reach their fundamental goals (i.e., time, cost, and quality) alongside minimizing emitted CO2 in an uncertain environment.

关键词： Construction project scheduling multi-mode resource-constrained project scheduling problem CO2 emissions Rework Interval-valued fuzzy sets Mixed-integer linear programming

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modeling and optimizing the evacuation of hospitals based on the MRCPSP with resource transfers

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EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2016年第3-4期4卷 349-380页

作者： Poppenborg, Jens Knust, Sigrid Tech Univ Clausthal Inst Appl Stochast & Operat Res D-38678 Clausthal Zellerfeld Germany Univ Osnabruck Inst Comp Sci D-49069 Osnabruck Germany

In this paper, we consider the problem of hospital evacuation and model it as a multi-mode resource-constrained project scheduling problem (MRCPSP) with additional resource transfers and blockings. Based on this model two heuristic decomposition approaches are proposed. The first uses a tabu search algorithm for the mode assignment problem, the scheduling subproblem is solved by adapted serial and parallel schedule generation schemes using priority rules. The second is based on a decomposition into an evacuation and a routing subproblem where solutions are represented by resource flows. Computational experiments were performed for both approaches using randomly generated instances based on real-world scenarios.

关键词： Hospital evacuation multi-mode resource-constrained project scheduling problem MRCPSP resource transfers Blocking resource flow

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Dynamic search control-based particle swarm optimization for project scheduling problems

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ADVANCES IN MECHANICAL ENGINEERING 2016年第4期8卷 1-12页

作者： Chen, Ruey-Maw Shen, Yin-Mou Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn 57Sec 2Zhongshan Rd Taichung 41170 Taiwan Kun Shan Univ Dept Informat Management Tainan 710 Taiwan

Many machinery manufacturings are categorized as multi-mode resource-constrained project scheduling problems which have attracted significant interest in recent years. It has been shown that such problems are non-deterministic polynomial-time-hard. Particle swarm optimization is one of the most commonly used metaheuristic. multi-mode resource-constrained project scheduling problems comprise two sub-problems, namely, an activity operating priority and an activity operating mode sub-problems;hence, two particle swarm optimizations are used to solve these two sub-problems. In solving the activity priority sub-problem, a designed global guidance ratio is involved to control the particle's search behavior. Restated, guiding a diversification search at the beginning stage and conducting an intensification search at latter stage are controlled by adjusting the global guidance ratio. The particle swarm optimization combined with the global guidance ratio mechanism is named global guidance ratio-particle swarm optimization herein. Meanwhile, a non-fixed global guidance ratio adjustment is also suggested to further enhance the search performance. Moreover, different communication topologies for balancing the convergence of using global and local topologies are also suggested in global guidance ratio-particle swarm optimization to further improve the search efficiency. The performance of the proposed global guidance ratio-particle swarm optimization scheme is evaluated by solving all the multi-mode resource-constrained project scheduling problem instances in project scheduling problem Library. It is shown that the scheduling solutions are in good agreement with those presented in the literatures. Hence, the effectiveness of the proposed global guidance ratio-particle swarm optimization scheme is confirmed.

关键词： Optimization particle swarm optimization scheduling multi-mode resource-constrained project scheduling problem global guidance ratio

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Cooperative Discrete Particle Swarms for multi-mode resource-constrained projects

Cooperative Discrete Particle Swarms for Multi-mode Resource...

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IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Shen, Hong Li, Xiaoping Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467360852;9781467360845

In this paper, the multi-mode resource-constrained project scheduling problem (MRCPSP) is considered for makespan minimization, which leads to even utilization of machine capacity. According to the characteristics of the considered problem, a discrete particle swarm optimization (DPSO) is adapted, based on which a cooperative optimization method with multiple discrete particle swarms (CPSO) is proposed for MRCPSP. Two swarms are distinctively adopted to the mode assignment and activity sequencing sub-problem. Once a mode list and an activity list are obtained, the two swarms cooperatively search for better solutions. In other words, one swarm updates the mode list in terms of the given activity list and the activity list is improved by the updated mode list by the other swarm. As the same time, a local search procedure is investigated to balance the exploration and exploitation. Computation results of project scheduling problem Library (PSPLIB) sets show CPSO is efficient to solve complicated combination optimization problems.

关键词： Particle Swarm Optimization cooperative method local search multi-mode resource-constrained project scheduling problem

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An Inverse S Curve Based Search Mechanism on Particle Swarm Optimization for Solving project scheduling problem

An Inverse S Curve Based Search Mechanism on Particle Swarm ...

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7th International Conference on Computing and Convergence Technology (ICCCT)

作者： Chen, Ruey-Maw Wu, Di-Shiun Natl Chin Yi Univ Technol Taichung 411 Taiwan

ISBN: (纸本)9781467308946;9788994364261

The multi-mode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem, and has been widely studied. The particle swarm optimization is an effective method and well applied to solve all kind of scheduling problems. MRCPSP is regarded as two sub-problems: the activity mode selection and the activity priority sub-problems. Therefore, discrete version PSO and conventional PSO were used for solving these two sub-problems respectively. In discrete version PSO, the velocity update rule based on constriction PSO is applied. Meanwhile, an inverse S curve based inertia weight adjustment mechanism was proposed to enhance both the global search and local search to improve search efficiency. Moreover, a grouped communication topology was designed to avoid premature convergence and slow convergence problems, i.e., to balance convergence rate. Instances of MRCPSP in PSPLIB were tested to verify the performance of the proposed scheme. The experimental results confirmed that the proposed scheme is effective and efficient for solving NRCPSP type scheduling problems.

关键词： multi-mode resource-constrained project scheduling problem particle swarm optimization inverse S-curve grouped topology

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