Application and Evaluation of Bee-Based Algorithms in Scheduling

2018 ◽  
pp. 20-42
Author(s):  
Ayse Aycim Selam ◽  
Ercan Oztemel
Keyword(s):  
Project Scheduling ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Manufacturing Processes ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Multi Mode ◽  
Vital Element

Scheduling is a vital element of manufacturing processes and requires optimal solutions under undetermined conditions. Highly dynamic and, complex scheduling problems can be classified as np-hard problems. Finding the optimal solution for multi-variable scheduling problems with polynomial computation times is extremely hard. Scheduling problems of this nature can be solved up to some degree using traditional methodologies. However, intelligent optimization tools, like BBAs, are inspired by the food foraging behavior of honey bees and capable of locating good solutions efficiently. The experiments on some benchmark problems show that BBA outperforms other methods which are used to solve scheduling problems in terms of the speed of optimization and accuracy of the results. This chapter first highlights the use of BBA and its variants for scheduling and provides a classification of scheduling problems with BBA applications. Following this, a step by step example is provided for multi-mode project scheduling problem in order to show how a BBA algorithm can be implemented.

scholarly journals An Efficient Particle Swarm Optimizer with Application to Man-Day Project Scheduling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Frode Eika Sandnes
Keyword(s):  
Project Scheduling ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Np Hard ◽  
Scheduling Problems ◽  
Particle Swarm Optimizer ◽  
Np Hard Problem ◽  
Hard Problems ◽  
Project Scheduling Problem ◽  
Mode Assignment

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfully.

scholarly journals Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems

2017 ◽  
Author(s):  
Miquel Bofill ◽  
Jordi Coll ◽  
Josep Suy ◽  
Mateu Villaret
Keyword(s):  
Project Scheduling ◽  
Resource Constraints ◽  
Time Dependent ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Binary Decision ◽  
Compact Binary ◽  
Order Of Magnitude ◽  
Project Scheduling Problem ◽  
Multi Mode

Pseudo-Boolean (PB) constraints are usually encoded into Boolean clauses using compact Binary Decision Diagram (BDD) representations. Although these constraints appear in many problems, they are particularly useful for representing resource constraints in scheduling problems. Sometimes, the Boolean variables in the PB constraints have implicit at-most-one relations. In this work we introduce a way to take advantage of these implicit relations to obtain a compact Multi-Decision Diagram (MDD) representation for those PB constraints. We provide empirical evidence of the usefulness of this technique for some Resource-Constrained Project Scheduling Problem (RCPSP) variants, namely the Multi-Mode RCPSP (MRCPSP) and the RCPSP with Time-Dependent Resource Capacities and Requests (RCPSP/t). The size reduction of the representation of the PB constraints lets us decrease the number of Boolean variables in the encodings by one order of magnitude. We close/certify the optimum of many instances of these problems.

Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems

2009 ◽  
Vol 419-420 ◽  
pp. 633-636 ◽  
Author(s):  
James C. Chen ◽  
Wun Hao Jaong ◽  
Cheng Ju Sun ◽  
Hung Yu Lee ◽  
Jenn Sheng Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Total Tardiness ◽  
Scheduling Problems ◽  
Test Bed ◽  
Resource Constrained ◽  
Multiple Resources ◽  
Practical Constraints

Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

A New Decision Support System for Optimal Integrated Project Scheduling and Resource Planning

2021 ◽  
pp. 1367-1383
Author(s):  
Amir Ahrari ◽  
Ali Haghani
Keyword(s):  
Resource Allocation ◽  
Project Scheduling ◽  
Efficient Solution ◽  
Practical Problem ◽  
Mathematical Formulation ◽  
Optimal Solution ◽  
Resource Planning ◽  
Scheduling Problems ◽  
Activity Scheduling ◽  
Integrated Project

Two scheduling practices are commonly used depending on the availability of resources. When resources are not expensive, activities are scheduled and then resources are allocated until the available resources are exhausted. Then, iterative adjustments are applied to the resource allocation plan and the activities sequence to reach a feasible solution. Conversely, when expensive resources are involved, a resource allocation plan based on the economics of the resource is established and then activities are scheduled accordingly. However, Resource Constrained Scheduling Problems (RCSP) are not solved efficiently with either of these approaches. To find the optimal solution, activity scheduling and resource allocation should be formulated as an integrated optimization problem. Such models become numerically cumbersome for practical size problems and difficult to solve. In this article, a novel mathematical formulation and an efficient solution algorithm are proposed for solving RCSPs. Then, this framework is used for solving a practical problem in the context of the construction industry.

scholarly journals Research on Construction Schedule Optimization of Assembly Building Based on NSGA-II

2020 ◽  
Vol 165 ◽  
pp. 06055
Author(s):  
Ru Wang ◽  
Jing Lian
Keyword(s):  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Nsga Ii ◽  
Resource Constrained Project Scheduling ◽  
Project Construction ◽  
Project Scheduling Problem ◽  
Research Achievement ◽  
Construction Schedule ◽  
Multi Mode

Considering multiple possible scenarios in the process of project construction, the prefabricated project scheduling problem is studied in combination with the theory of multi-mode resource-constrained project scheduling problem. Multi-objective multi-mode resource-constrained project scheduling model with time/robustness trad-offs was constructed. Next, the adjusted non-dominated genetic algorithm (NSGA-II) was designed to solve the model. Finally, the proposed model and algorithm were implicated to a real project, in order that research achievement can guide managers to make decisions and invest resources scientifically and reasonably.

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