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Scheduling Analysis

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Scheduling is one of the important tools in engineering and management to minimize production time and cost. The scheduling theory is characterized by an infinite number of problem types. A classification for the scheduling problems is based on single-machine problems, multi-machine problems, single-stage problems, and multi-stage problems. A production unit is characterized by multi-stage production flow shop with multiple parallel machines at each stage to perform the same operation, usually referred to as the hybrid flow shop (HFS) model.
There are substantial numbers of HFS models in real world (or can be modeled as a HFS), such as printed circuit board (PCB) manufacturing, thin film transistor-liquid crystal display (TFT-LCD) manufacturing, …show more content…

One of the more commonly used assumptions in scheduling settings is to consider the setup times. In order to produce the parts, work is done to prepare the machine, process, or bench, called setup, which includes acquiring tools, return tooling, positioning work in process material, cleaning, setting the required jigs and fixtures, adjusting tools, and inspecting material. Scheduling problems involving setup times can be divided into two classes; the first class is sequence-independent setup times (SIST) and the second is sequence-dependent setup times (SDST). Setup is sequence-dependent if its duration depends on both the current and the immediately preceding job, and is sequence-independent if its duration depends only on the current job to be processed. SDST HFS scheduling can be found in a vast number of industries. Numerous examples are given in the literature, including wafer testing in semiconductor manufacturing, rolling slitting in the paper industry, and the plastics …show more content…

These solutions can be expressed in terms of non-dominated solutions, non-inferior, admissible, or efficient solutions. Therefore, the aim of this paper is to develop a solution method for the proposed problem that search a set of non-dominated solutions. Since problem under study belong to NP-hard class (Ruiz and Marato, 2006), the exact method is not able to provide feasible solutions even for small instances in a reasonable time. This incapacity justifies the need to employ a variety of heuristics and meta-heuristics to solve these problems to optimality or near optimality. In this paper, a genetic algorithm (GA) is proposed to solve our investigated problem. Different approaches have emerged from the development of GA in the literature of multi-objective optimization problem (MOP), some of them are Vector Evaluated Genetic Algorithm (VEGA), Multi-Objective Genetic Algorithm (MOGA), Niched Pareto Genetic Algorithm (NPGA), Non-dominated Sorting Genetic Algorithm (NSGA & NSGA-II), Pareto Stratum-Niche Cubicle Genetic Algorithm (PS-NC GA), Multiple Objective Genetic Local Search (MOGLS), Elitist Non-dominated Sorting Genetic Algorithm (ENGA) and so

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