Constructive Heuristics for Project Scheduling Resource Availability Cost Problem with Tardiness

Su, Connie Tenin; Santoro, Miguel Cezar; Mendes, André Bergsten

doi:10.1061/(asce)co.1943-7862.0001524

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…In contrast, this definition decreases to 0% in the definition of low tardiness cost. Su, Santoro and Mendes (2018) took this value as an average of 25% of the resource's total unit costs, and these rates are determined as 87.5% in case of high cost and 12.5% in case of low cost. Furthermore, some researchers using test problems assigned random numbers between 1 and 10 (Kolisch, Sprecher and Drexl, 1995), (Afshar-Nadjafi and Shadrokh, 2008), (Pamay, 2011), 10 and 30 (Golestaneh, Jafari, Khalilzadeh and Karimi 2013), 10 and 20 (Afshar-Nadjafi, Basati and Maghsoudlou 2017) for tardiness unit penalty costs.…”

Section: Tardiness Penalty Costsmentioning

confidence: 99%

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

RUHLUSARAÇ

ÇALIŞKAN

2022

bmij

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Real-life project scheduling environments are often dynamic and subject to disruption. Early or late completion of interrupted projects can create costs for the business. At the same time, there is an alternative to producing multiple projects at multiple different costs. In this study, a new mixed integer linear programming model that minimizes the sum of weighted earliness and tardiness penalties and mode selection costs is proposed to solve the real-life problem faced by a boutique furniture company. A proposed dynamic model also considers the cost of deviation from the baseline schedule in case disruption scenarios corrupt the resulting baseline schedule. The problems are solved with the CPLEX solver using the GAMS program. The results show that the interruption scenarios partially change the baseline schedule and increase the total cost. In case of more than one interruption in the same schedule, the number of late completed activities and their delay times increased.

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Section: Tardiness Penalty Costsmentioning

confidence: 99%

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

RUHLUSARAÇ

ÇALIŞKAN

2022

bmij

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“…Su et al [23] used a mixed-integer model and discrete constraints to solve the problems. Maghsoudlou et al [24] and Bibiks et al [25] applied the cuckoo Search algorithm to plan for the multirisk project with three distinct evaluation objectives.…”

Section: Approximation Algorithms For Ms-rcpspmentioning

confidence: 99%

“…)// parameter of the mutation operator (12) for (j � 0; j < n; j++) (14) end for (15) if (f (u i (t)) ≤ f(x i (t))) (16) x i (t+1) � u i (t) (17) else (18) x i (t+1) � x i (t) (19) end if (20) end for (21) Calculate the fitness and bestnest (22) If (makespan > min (fitness)) (23) makespan � min (fitness) (24) End if (25) bestnest ⟵ Reallocate (bestnest) (26) t ⟵ t + 1 (27) Input: currentBest //the best schedule among the current population Output: //the improved schedule (1) Begin (2) makespan � f(best) (3) newbest � currentBest; (4) R b ⟵ maxResource (newbest) //the last resource to finish its job (5) Tb ⟵ set of tasks is performed by resource Rb (6) For i � 1 to size(Tb) // Consider each task in T b , the set of tasks performed by resource R b (7) T i � T b [i]; (8) R i ⟵ set of resource that are skilled enough to execute the task i except R b (9) For j � 1 to size (R i ) // Consider each resource in turn (10)…”

Section: Imopse Datasetmentioning

confidence: 99%

Intelligent Differential Evolution Scheme for Network Resources in IoT

Quoc

Doan²,

Thanh

et al. 2020

Scientific Programming

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Scheduling is a fundamental factor in managing the network resource of the Internet of things. For IoT systems such as production lines, to operate effectively, it is necessary to find an intelligent management scheme, i.e., schedule, for network resources. In this study, we focus on multiskill resource-constrained project scheduling problem (MS-RCPSP), a combinational optimization problem that has received extensive attention from the research community due to its advantages in network resource management. In recent years, many approaches have been utilized for solving this problem such as genetic algorithm and ant colony optimization. Although these approaches introduce various optimization techniques, premature convergence issue also occurs. Moreover, previous studies have only been verified on simulation data but not on real data of factories. This paper formulated the MS-RCPSP and then proposed a novel algorithm called DEM to solve it. The proposed algorithm was developed based on the differential evolution metaheuristic. Besides, we build the reallocate function to improve the solution quality so that the proposed algorithm converges rapidly to global extremum while also avoiding getting trapped in a local extremum. To evaluate the performance of the proposed algorithm, we conduct the experiment on iMOPSE, the simulated dataset used by previous authors in their research studies. In addition, DEM was verified on real dataset supported by a famous textile industry factory. Experimental results on both simulated data and real data show that the proposed algorithm not only finds a better schedule compared with related works but also can reduce the processing time of the production line currently used at the textile industry factory.

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Constructive Heuristics for Project Scheduling Resource Availability Cost Problem with Tardiness

Cited by 2 publications

References 41 publications

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

Dynamic multi-mode resource-constrained multi-project scheduling problem with weighted earliness and tardiness: a real-life boutique furniture implementation

Intelligent Differential Evolution Scheme for Network Resources in IoT

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