Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method

Airline crew cost is the second-largest operating cost component and its marginal improvement may translate to millions of dollars annually. Further, it's highly constrained-combinatorial nature brings-in high impact research and commercial value. The airline crew pairing optimization problem (CPOP) is aimed at generating a set of crew pairings, covering all flights from its timetable, with minimum cost, while satisfying multiple legality constraints laid by federations, etc. Depending upon CPOP's scale, several Genetic Algorithm and Column Generation based approaches have been proposed in the literature. However, these approaches have been validated either on small-scale flight datasets (a handful of pairings) or for smaller airlines (operating-in low-demand regions) such as Turkish Airlines, etc. Their search-efficiency gets impaired drastically when scaled to the networks of bigger airlines. The contributions of this paper relate to the proposition of a customized genetic algorithm, with improved initialization and genetic operators, developed by exploiting the domain-knowledge; and its comparison with a column generation based large-scale optimizer (developed by authors). To demonstrate the utility of the above-cited contributions, a real-world test-case (839 flights), provided by GE Aviation, is used which has been extracted from the networks of larger airlines (operating up to 33000 monthly flights in the US).Comment: 7 pages, 3 figure

Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

AbstractThis article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm’s performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates

Rail-freight crew scheduling with a genetic algorithm

peer reviewedThis article presents a novel genetic algorithm designed for the solution of the Crew Scheduling Problem (CSP) in the rail-freight industry. CSP is the task of assigning drivers to a sequence of train trips while ensuring that no driver’s schedule exceeds the permitted working hours, that each driver starts and finishes their day’s work at the same location, and that no train routes are left without a driver. Real-life CSPs are extremely complex due to the large number of trips, opportunities to use other means of transportation, and numerous government regulations and trade union agreements. CSP is usually modelled as a set-covering problem and solved with linear programming methods. However, the sheer volume of data makes the application of conventional techniques computationally expensive, while existing genetic algorithms often struggle to handle the large number of constraints. A genetic algorithm is presented that overcomes these challenges by using an indirect chromosome representation and decoding procedure. Experiments using real schedules on the UK national rail network show that the algorithm provides an effective solution within a faster timeframe than alternative approaches

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

Molecular Techniques Reveal Wide Phyletic Diversity of Heterotrophic Microbes Associated with Discodermia spp. (Porifera: Demospongiae)

Sponges are well known to harbor large numbers of heterotrophic microbes within their mesohyl. Studies to determine the diversity of these associated microbes have been attempted for only a few shallow water species. We cultured various microorganisms from several species of Discodermia collected from deep water using the \u27Johnson-Sea-Link\u27 manned submersibles, and characterised them by standard microbiological identification methods. Characterisation of a small proportion (ca. 10%) of the total and potential eubacterial isolate collection with molecular systematics techniques revealed a wide diversity of microbes. Phylogenetic analyses of 32 small subunit (SSU) 16S-like rRNA gene sequences from different micorbes indicated high levels of taxonomic diversity assoiated with this genus of sponge. For example, bacteria from at least five cubacterial subdivisions - gamma, alpha, beta, Cytophaga and Gram positive - were isolated from the mesohyl of Discodermia. Several strains were unidentifiable from current sequence databases. No overlap was found between sequences of 24 isolates and 8 sequences obtained by PCR and cloning directly from sponge samples. The abundance and diversity of microbes associated with sponges such as Discodermia suggest that they may play important roles in marine microbial ecology, dispersal and evolution

Lack of Chemical Defense in Two Species of Stalked Crinoids: Support for the Predation Hypothesis for Mesozoic Bathymetric Restriction

Methanol/dichloromethane extracts of (1) the arms and pinnules, and (2) the stalk and cirri of the deep water stalked crinoids Endoxocrinus parrae (Gervais) and Neocrinus decorus (Carpenter) were imbedded at ecologically relevant volumetric concentrations in alginate food pellets containing 2% krill as a feeding stimulant and presented in situ to an assemblage of shallow-water reef fish. Experimental pellets were highly palatable to reef fish; no significant differences in pellet consumption occurred between experimental pellets containing extracts from either species of stalked crinoid or control pellets. Small pieces of cirri, stalks, calyx, arms and pinnules of both species were also tested in in situ feeding assays. While immediate consumption by fish was not apparent, Blue Headed Wrasse (Thalassoma bifasciatum (Block)) and Dusky Damselfish (Stegastes fuscus (Cuvier)) bit at pieces of each body component. Similar fish biting behaviors were also observed when two living Endoxocrinus parrae were deployed on the shallow reef. Observations indicate that neither species of stalked crinoid is chemically defended from predation by a natural assemblage of reef fish. This supports the predation hypothesis that restriction of stalked crinoids in deep-water habitats may have resulted from the Mesozoic radiation of durophagous fishes in shallow seas, resulting in a reduction of stalked crinoids from shallow water