Improved discrete cuckoo search for the resource-constrained project scheduling problem

Abstract

An Improved Discrete Cuckoo Search (IDCS) is proposed in this paper to solve resource-constrained project scheduling problems (RCPSPs). The original Cuckoo Search (CS) was inspired by the breeding behaviour of some cuckoo species and was designed specifically for application in continuous optimisation problems, in which the algorithm had been demonstrated to be effective. The proposed IDCS aims to improve the original CS for solving discrete scheduling problems by reinterpreting its key elements: solution representation scheme, Lévy flight and solution improvement operators. An event list solution representation scheme has been used to present projects and a novel event movement and an event recombination operator has been developed to ensure better quality of received results and improve the efficiency of the algorithm. Numerical results have demonstrated that the proposed IDCS can achieve a competitive level of performance compared to other state-of-the-art metaheuristics in solving a set of benchmark instances from a well-known PSPLIB library, especially in solving complex benchmark instances.

Hybrid Flow Shop Scheduling Problems Using Improved Fireworks Algorithm for Permutation

“…The PFSP with seven jobs is taken to explain the mapping rule from continuous space to discrete space. If the randomly generated fireworks are sorted by size and the position sequence is (3,5,4,6,1,7,2), the processing order of the jobs is j 3 → j 5 → j 4 → j 6 → j 1 → j 7 → j 2 . In this way, the sparks in continuous space are uniquely mapped to a solution in discrete space.…”

“…The amount of iterations was set to 200. Given that the number of machines in each stage is different, the upper and lower boundaries were as follows: lathe: (1,4), planer: (1, 3), grinder: (1,4). The specific parameter settings are listed in Table 5.…”

Intelligent Differential Evolution Scheme for Network Resources in IoT

“…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. Lin et al [26] proposed a new solution based on GA and some other heuristic algorithms.…”

“…)// 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)…”

Designing a resource-constrained project scheduling model considering multiple routes for flexible project activities: meta-heuristic algorithms

“…The classification of RCPSP is summarized as follows [13] Since the RCPSP is NP-hard, and for more adaptation of the RCPSP with reality, some researchers have considered various extensions and different methods for solving the problem and working on exact methods using mixed-integer programming, constraint programming, and satisfiability modulo theories [14][15][16][17][18][19][20][21][22]. Bibiks et al [23] presented a discrete cuckoo search (DCS) algorithm for RCPSP and improved the DCS algorithm in their study [24]. Fathallahi and Najafi [25] discussed a fuzzy RCPSP with the aim of maximizing NPV of project cash flows.…”