Mustafa Arslan Örnek

The hub-and-spoke (HS) network is a widely used business strategy by airlines to optimize their operations and increase their reach by connecting multiple destinations through a central hub, and the majority of the passengers are transit passengers. The airline schedule planning process starts with the flight scheduling problem (FSP) to generate a timetable in advance of six months. Then, the fleet assignment problem (FAP) is examined to determine aircraft types. This sequential solution approach causes suboptimal solutions, and the schedule must be adjusted to increased demand. Due to the complexity of this problem, this study focuses specifically on incremental schedule design with one hub two-flight leg network. This study presents a Multi-Objective Mixed Integer Programming Model for integrated FSP and FAP to adjust the generated timetable with launching new flights. Two solution approaches are proposed to decide the new proposed flight’s time, aircraft type, and passenger assignment: the Weighted Goal Programming Model (WGPM) and the Lexicographic Goal Programming Model (LGPM). This study shows that there is a conflicting relationship between cost and C0 ₂ emissions in the flight scheduling process. If carbon dioxide emissions are prioritized, airlines need to schedule more flights with small-capacity aircraft.

Airline networks are becoming increasingly complex, particularly at mega-hub airports characterized by high transit volumes. Effective schedule design and fleet assignment are critical for an airline, as they directly influence passenger connectivity and profitability. This study addresses the challenge of introducing a new route from a mega-hub to a new destination, while maintaining the existing flight network and leveraging arrivals from spoke airports to ensure connectivity. First, a mixed-integer nonlinear mathematical model was formulated to produce a global optimal solution at a lower time granularity, but it became computationally intractable at higher granularities due to the exponential growth in constraints and variables. Second, a genetic algorithm (GA) was employed to demonstrate scalability and flexibility, delivering near-optimal, high-granularity schedules with significantly reduced computational time. Empirical validation using real-world data from 37 spoke airports revealed that, while the exact model minimized waiting times and maximized profit at lower granularity, the GA provided nearly comparable profit at higher granularity. These findings guide airline managers seeking to optimize passenger connectivity and cost efficiency in competitive global markets.

Managing yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.