This paper describes a polynomial-time heuristic for the permutation flow-shop scheduling problem with the makespan criterion. The proposed method consists of two phases: arranging the jobs in priority order and then constructing a sequence. A fuzzy greedy evaluation function is employed to prioritize the jobs for incorporating into the construction phase of the heuristic. Computational experiments using standard benchmark problems indicate an improvement of the new heuristic over the well-known Nawaz, Enscore and Ham (NEH) heuristic. It will be seen that the NEH heuristic is a special case of our more general heuristic.
Aircraft ground handling is an integral part of airline operations. Although ground handling operations usually are straightforward, it could be very complicated in certain situations, such as troubling cargo loading and unloading incidents, weather conditions or improper use of equipment and breakdowns. Ground handlers need to orchestrate a number of activities within a confined area around airplane in a short period of time. Punctuality is important for airlines and resulting increased efficiencies. In this article, scheduling aircraft ground handling operations with uncertain durations using the critical path analysis, the Monte Carlo simulation is considered with the aim of improving aircraft ground services during the turnaround. Having an accurate estimate of aircraft turnaround time considering its type and load, the recourses would be assigned to the ground operations more efficiently. A case study of a long-range wide-body twin-engine jet aircraft is discussed in detail. The results indicate that the proposed method gives improved scheduling relative to the routines observed at a hub airport.
In recent years, there has been a growth of interest in the development of systematic search methods for solving problems in operational research and artificial intelligence. This chapter introduces a new idea for the integration of approaches for hard combinatorial optimisation problems. The proposed methodology evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism. In other words, a fuzzy solution space is exploited using greedy methods. This seems to be superior to the standard greedy version. The chapter consists of two main parts. The first part focuses on description of the theory and mathematics of the so-called fuzzy greedy evaluation concept. The second part demonstrates through computational experiments the effectiveness and efficiency of the proposed concept for hard combinatorial optimisation problems.
This paper describes a genetic algorithm (GA) for the permutation flow-shop scheduling problem (PFSP) with the makespan criterion. A constructive heuristic is employed to generate an initial population for the proposed GA. Computational experiments using standard benchmark problems indicate that the proposed method is very effective.
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