Mechanical Engineering

Airline scheduling poses some of the most challenging problems in the entire Operations Research (OR) domain. In that, crew scheduling (CS) constitutes one of the most important and challenging planning activities. Notably, the crew operating cost is the second-largest component of an airline's total operating cost (after the fuel cost). Hence, its optimization promises enormous benefits, and even marginal improvements may translate to annual savings worth millions of dollars for an airline. However, CS is a combination of complex combinatorial optimization problems (with NP-hard computational complexity), namely \textit{crew pairing} and \textit{crew assignment}, which are solved sequentially. Here, \textit{crew pairing optimization} aims at generating a set of flight sequences (each called a crew pairing) to cover a finite set of flight legs from an airline's timetable at minimum cost, while satisfying several legality constraints linked to the federations' safety rule...

Over the last few decades, the Airline Scheduling Process (ASP) has received an unprecedented attention from airliners and operations research society. Conventionally, the Airline Scheduling Process is decomposed into four sub-problems namelySchedule Generation, Fleet Assignment, Aircraft Routing, and Crew Scheduling which are solved sequentially in order to incorporate tractability and feasibility in the overall process. However, sequential decomposition, by construct, fails to capture the inter-dependencies among these sub-problems. To overcome this limitation, the air transport research society has lately started adopting integrated models for modeling and solving the sub-problems of the ASP. But due to the limited computational power and available technology, researchers are only able to address partial-integration among these sub-problems. This paper thoroughly reviews the ASPs literature particularly in the light of the extent to which the inter-dependence has been explored th...

Crew pairing optimization (CPO) is critically important for any airline, since its crew operating costs are second-largest, next to the fuel-cost. CPO aims at generating a set of flight sequences (crew pairings) covering a flight-schedule, at minimum-cost, while satisfying several legality constraints. For large-scale complex flight networks, billion-plus legal pairings (variables) are possible, rendering their offline enumeration intractable and an exhaustive search for their minimum-cost full flight-coverage subset impractical. Even generating an initial feasible solution (IFS: a manageable set of legal pairings covering all flights), which could be subsequently optimized is a difficult (NP-complete) problem. Though, as part of a larger project the authors have developed a crew pairing optimizer (AirCROP), this paper dedicatedly focuses on IFS-generation through a novel heuristic based on divide-and-cover strategy and Integer Programming. For real-world large and complex flight ne...

For an airline, the crew operating cost is second only to the fuel cost, making the crew pairing optimization (CPO) critical for business viability. Its aim is to generate a set of flight sequences (crew pairings) that cover all flights in an airline's schedule, at minimum cost, while satisfying several legality constraints. Being an NP-hard combinatorial optimization problem, CPO is tackled by relaxing the underlying Integer Programming Problem into a Linear Programming Problem, and solving the latter through Column generation (CG) technique. However, with the expansion of airlines' operations lately, the curse of dimensionality renders the exact CG-implementations obsolete, paving the way for heuristic-based CG-implementations. Yet, the much prevalent large-scale complex flight networks involving multiple-crew bases and hub-and-spoke sub-networks, largely remain unaddressed. To bridge the research gap, this paper proposes a novel CG heuristic, which has enabled in-house de...

Crew operating cost is the second largest cost component of an airlines’ total operating cost (second only to the fuel cost) and even marginal cost savings here may amount to millions of dollars, annually. Towards it, a crew needs to be efficiently assigned a sequence of flights starting and ending at the same crew base (a crew pairing). The challenge for an airline is to generate crew pairings which completely cover a finite set of flights over a particular time window, with minimum cost, while satisfying multiple legality constraints linked to airlines’ own regulations, labor laws, and government safety rules etc. The success in solving the associated constrained optimization problem largely depends on solving NP-complete subproblems, linked to the generation of a feasible solution (legal crew pairings covering all flights) and generation of pairings with reasonable cost-quality. In an attempt to address these subproblems in a computationally- and time-efficient manner, the contri...

Airline Crew Pairing Optimization (CPO) aims at generating a set of legal flight sequences (crew pairings), to cover an airline's flight schedule, at minimum cost. It is usually performed using Column Generation (CG), a mathematical programming technique for guided search-space exploration. CG exploits the interdependencies between the current and the preceding CG-iteration for generating new variables (pairings) during the optimization-search. However, with the unprecedented scale and complexity of the emergent flight networks, it has become imperative to learn higher-order interdependencies among the flight-connection graphs, and utilize those to enhance the efficacy of the CPO. In first of its kind and what marks a significant departure from the state-of-the-art, this paper proposes a novel adaptation of the Variational Graph Auto-Encoder for learning plausible combinatorial patterns among the flight-connection data obtained through the search-space exploration by an Airline ...

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 constrainedcombinatorial 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 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 domainknowledge; 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 testcase (839 flights), provided by GE Aviation, is used which has been extracted from the networks of larger airlines (operating upto 33000 monthly flights in the US).

Fused Deposit modeling (FDM) is an additive manufacturing (AM) process that's frequently used to fabricate geometrically complex shaped prototypes and complex parts. It's gaining market as it reduces cycle time for product development without the need for high priced tools. Still, the commercialization of FDM technology in other artificial operations is presently limited due to several failings, alike as inadequate mechanical properties, poor surface quality, and low dimensional accuracy. The rates of FDM-produced products are affected by other process parameters, for illustration, layer thickness, build angle, raster width, or print speed. The process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of other parameters. The optimum setting of parameters is supposed to ameliorate the rates of three-dimensional (3D) printed specimens and may reducepost-production work. This paper intensely reviews state-of-the-art literature on the influence of parameters on part qualities and the being work on process parameter optimization. Also, the failings of being workshop are linked, challenges and openings to work in this field are estimated, and directions for future research and development in this field are suggested

Laser cutting is a popular process, which finds wide application in various manufacturing industries because of its precision of operation and low cost. Laser cutting, being a non-contact process, does not involve any mechanical cutting forces and tool wear. To maintain a high production rate and an acceptable level of quality for the cut parts, there is need to select the optimum combination of process parameters. This paper presents method of optimization for multi performance characteristics of LBC process for mild steel. Surface roughness (SR) and material removal rate (MRR) are multi performance characteristics and nozzle dia., gas pressure, cutting speed and cutting power are taken as input process parameters. Grey relational analysis (GRA) technique is used for optimization. ANOVA technique is also used to find significant and sub significant parameter. This experimental work will be helpful in deciding optimum parameters and to improve LBC process.

The electrical contacts form an integral part of circuit interrupters for which the desirable properties of contact materials are high mechanical strength, high electrical and thermal conductivity, high wear and weld resistance. Despite of different manufacturing techniques like electro-slag crucible melting, vacuum arc smelting, vacuum continuous smelting etc.; the Powder Forging being a solid state process and having high densification factor is an efficient method for the production of electrical contact materials. In the present study Cu-Cr30 wt. % alloys are prepared from elemental powders by sinter-forging. Nb is also added in varying quantities to improve the electrical and mechanical properties and the forging temperature is also varied .The variation in contact material properties due to these changes are tabulated and compared.

This study presents an experimental investigation of the effects of cutting speed, size and volume fraction of particle on the surface roughness in turning of 2024Al alloy composites reinforced with Al 2 O 3 particles. A plan of experiments, based on Taguchi method, was performed machining with different cutting speeds using coated carbide tools K10 and TP30. The objective was to establish a correlation between cutting speed, size and volume fraction of particle with the surface roughness in work-pieces. These correlations were obtained by multiple linear regression. The analysis of variance was also employed to carry out the effects of these parameters on the surface roughness. The test results revealed that surface roughness increased with increasing the cutting speed and decreased with increasing the size and the volume fraction of particles for both cutting tools. The average surface roughness values of TP30 cutting tools were observed to be lower than those of K10 tools. For the average surface roughness values of TP30 tool, cutting speed was found to be the most effective factor while the volume fraction of particle was the most effective factor for those of K10 tool. A good agreement between the predicted and experimental surface roughness was observed within a reasonable limit.