Integration of multi-mode resource-constrained project scheduling under bonus-penalty policies with material ordering under quantity discount scheme for minimizing project cost

In this paper, a mixed binary integer mathematical programming model is developed for integration of Multimode Resource-Constrainted Project Scheduling Problem (MRCPSP) under bonus-penalty policies and Quantity Discount Problem in Material Ordering (QDPMO) with the objective of minimizing the total...

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Vydáno v:Scientia Iranica. Transaction E, Industrial engineering Ročník 29; číslo 1; s. 427 - 446
Hlavní autor: Akhbari, M
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tehran Sharif University of Technology 01.01.2022
Témata:
Algorithms
Constraints
Costs
Critical path
Genetic algorithms
Heuristic
Heuristic methods
Integer programming
Integration
Linear programming
Material requirements planning
Mathematical programming
Methods
Net present value
Optimization
Policies
Project management
Renewable resources
Resource scheduling
Scheduling
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Shrnutí:In this paper, a mixed binary integer mathematical programming model is developed for integration of Multimode Resource-Constrainted Project Scheduling Problem (MRCPSP) under bonus-penalty policies and Quantity Discount Problem in Material Ordering (QDPMO) with the objective of minimizing the total project cost. By proving a theorem, an important property of the optimum solution of the problem is found, which reduces the search space significantly compared to previous studies. Since the Resource-Constraint Project Scheduling Problem (RCPSP) belongs to the class of problems that are NP-hard, four hybrid meta-heuristic algorithms called COA-GA, GWO-GA, PSO-GA, and GA-GA are developed and tuned to solve the problem. Each of the proposed algorithms consists of outside and inside search components, which determine the best schedule and materials procurement plan, respectively. Finally, a set of standard PROGEN test problems is solved using the proposed hybrid algorithms under fixed CPU time. The results show that the COA-GA algorithm outperforms others.
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DOI:10.24200/sci.2020.54286.3680