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This study addresses a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Majority of the traditional scheduling problems in parallel machine environment deal with machine as the only resource. However, other resources such as labors, tools, jigs, fixtures, pallets, dies, and industrial robots are not only required for processing jobs but also are often restricted. Considering other resources makes the scheduling problems more realistic and practical to implement in manufacturing environments. First, an integer mathematical programming model with the objective of minimizing makespan is developed for this problem. Noteworthy, due to NP-hardness of the considered problem, application of meta-heuristic is avoidable. Furthermore, two new genetic algorithms including a pure genetic algorithm and a genetic algorithm along with a heuristic procedure are proposed to tackle this problem. With regard to the fact that appropriate design of the parameters has a significant effect on the performance of algorithms, hence, we calibrate the parameters of these algorithms by using the response surface method. The performance of the proposed algorithms is evaluated by a number of numerical examples. The computational results demonstrated that the proposed genetic algorithm is an effective and appropriate approach for our investigated problem.

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This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.Engineering Optimization 2 M. Afzalirad and J. Rezaeian Hurink and Knust (2001) considered an identical PMSP with precedence constraints and sequence-dependent set-up times. They dealt with the question of whether it is possible to design an efficient list scheduling algorithm to minimize makespan for this problem. Tavakkoli-Moghaddam et al. (2009) presented a two-level mixed-integer programming model for a bi-objective unrelated PMSP with release dates, precedence constraints and sequence-dependent set-up times to minimize the number of tardy jobs and sum of the completion times. In addition, they suggested a genetic algorithm (GA). Gacias, Artigues, and Lopez (2010) proposed an exact branch-and-bound procedure and a climbing discrepancy search heuristic for the identical PMSP with precedence constraints and sequence-dependent set-up times while minimizing the sum of completion times and maximum lateness. Driessel and Monch (2011) addressed an identical PMSP to minimize total weighted tardiness with release dates, precedence constraints and sequence-dependent set-up times, and suggested several variants of variable neighbourhood search schemes to solve this problem. A hybrid intelligent solution system based on a GA and simulated annealing (SA) was proposed by Çakar, Köker, and Sari (2012) for minimizing mean tardiness in parallel robots scheduling with unequal release dates and precedence constraints. As discussed above, none of these studies considered machine eligibility restrictions.Some work has been carried out on unrelated or identical PMSPs with machine eligibility restrictions and other operational constraints. Centeno and Armacost (1997) investigated the identical PMSP with release dates and machine eligibility restrictions to minimize the maximum lateness. They developed a heuristic algorithm that resulted from combining the least flexible job first rule (LFJ) and the least flexible machine first rule (LFM). Later, Centeno and Armacost (2004) developed a heuristic algori...

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