Task Planning with Mixed-Integer Programming for Multiple Cooking Task Using dual-arm Robot

Intuitive dual arm robot programming for assembly operations

Modern production lines rely on the extended use of cooperating robots as a basis of multiple tasks such as assembling, welding, painting, etc. Thus, there is a high need of automated motion generation of the involved industrial robots. This chapter presents an intelligent search algorithm for the motion planning of 13 DOF Dual arm industrial robots. The main purpose of the algorithm is the definition of all the intermediate Dual Arm robot’s configurations which lead the robot’s end effectors from the initial positions to the desired positions and orientations. The search algorithm is based on the gradually approach of all the alternatives configurations and evaluates them. The gradually approach of the configurations is limited from a grid which is based on a set of parameters and aims to reduce the computational time. Grid resolution as well as the values of the parameters are changeable and depends on the estimation level. Automatic motion planning is used in order to automatically generate free collision paths of the robot. In this way the programmer has to define only the end position of the robot and not the intermediate points for ensuring free collision paths. All the selected configurations constitute the desired path of each arm of the dual arm robot from the initial configurations to the final positions. In this way, an opportunity for automatic generation of the desired paths is given to unexperienced programmers.

This paper builds on top of a collaborative ATFM framework previously published, which demonstrated a potential in significant pre-tactical system delay reduction over the current European ATFM practices. In this paper, we aim to raise the technology readiness level (TRL) of the original works, focusing on improving the tractability of its DCB optimizer. To this end, we apply a Dantzig-Wolfe (DW) decomposition approach and use high performance computing (HPC) techniques to attempt the solution in a short time. The main contribution of this paper is to mitigate the computational burden faced by previous approaches, which will make all the contributions demonstrated therein more operationally feasible.

ABSTRACTRailway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions.Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first

The performance of underwater sensor networks (UWSNs) is greatly limited by the low bandwidth and high propagation delay of acoustic communications. Deploying multiple surface-level radio-capable gateways can enhance UWSN performance metrics, reducing end-to-end delays and distributing traffic loads for energy reduction. In this paper, we study the problem of gateway placement for maximizing the cost-benefit of this UWSN architecture. We develop a mixed integer programming (MIP) gateway deployment optimization framework. We analyze the tradeoff between the number of surface gateways and the expected delay and energy consumption of the surface gateway architecture in the optimal case. We used an MIP solver to solve the developed optimization problem and integrated the optimal results to serve as an input for our simulations to evaluate the benefits of surface gateway optimization framework. We investigated the effect of acoustic channel capacity and the underwater sensor node deployment pattern on our solution. Our results show the significant advantages of surface gateway optimization and provide useful guidelines for real network deployment.

During deterministic optimization, only the decision variables are varied whereas all other parameters appearing in the given optimization framework are kept constant. However, some of these parameters are subjected to real life uncertainty and assuming them as constants during the course of optimization leads to suboptimal or infeasible solutions. This paper presents intuitionistic fuzzy expected value model (IFEVM) technique to handle such problems under the category of optimization under uncertainty (OUU). In this approach, uncertain parameters are assumed as intuitionistic fuzzy numbers, which considers not only the degree of membership but also the degree of non-membership to a given set. The concept of IFEVM has been applied to an industrial grinding process where parameters are non-linearly related to one another. Different degrees of risk averseness of a decision maker can be modelled under this framework considering different viewpoints viz., aggressive, conservative and balanced approach. The resultant deterministic equivalent of the multi-objective intuitionistic fuzzy uncertain optimization problem has been solved using a variant of an evolutionary optimization technique, intuitionistic fuzzy expected value non-dominated sorting genetic algorithm II (IFEV-NSGAII), and various scenarios are analyzed.

The aviation industry faces the urgent challenge of reconciling projected traffic growth with stringent climate targets, including the European Union’s goal of climate neutrality by 2050. While techno-logical innovations such as sustainable aviation fuels and hydrogen propulsion are critical for long-term decarbonization, their near-term impact remains limited. In this thesis, we address the immediate opportunity of improving the environmental efficiency of aircraft arrival operations in Terminal Manoeuvring Areas (TMAs), where congestion and complex sequencing often lead to excess fuel burn, emissions and noise. Existing research has demonstrated the benefits of Continuous Descent Operations (CDOs) and structured arrival procedures such as Point Merge (PM). However, for TMA performance evaluations, cur-rent practices are limited in real-world assessments of environmental efficiency beyond Carbon Dioxide (CO2). In terms of optimization of the arrival operations, the exploration of dynamic PM usage and early speed adjustments as a sequencing tool, integrated within the optimization framework, remains relatively unexplored. Furthermore, most optimization frameworks do not fully integrate arrival and departure scheduling in mixed-mode runway environments. The thesis addresses four key research questions: (1) What are the environmental benefits of using fuel-efficient CDOs within TMA and how to quantify them? (2) How to evaluate the arrival aircraft performance within TMAs implementing PM procedures? (3) Can the performance of current operations in a TMA where PM is implemented be improved by using optimization? (4) How can speed adjustment during cruise and descent be used, with or without combining it with PM procedures, to safely separate and sequence arriving flights? The overarching aim is to develop methodologies for assessing and optimizing arrival operations to reduce fuel consumption, emissions and noise while maintaining safety and capacity. The research combines data-driven performance evaluation with mathematical optimization. We use real-world Automatic Dependent Surveillance Broadcast (ADS-B) trajectory data from the OpenSky Network, together with meteorological data from ECMWF ERA5 and the performance modeling capabilities of EUROCONTROL BADA, to quantify inefficiencies in current operations. For optimization, we develop a Mixed-Integer Programming (MIP)-based framework to assign conflict-free, fuel-efficient arrival routes and descent profiles, incorporating wake turbulence separation and runway scheduling constraints. We perform case studies at Dublin and Oslo-Gardermoen airports, both operating with PM procedures. Empirical analysis reveals substantial inefficiencies in current TMA operations, with level-flight segments contributing significantly to excess fuel burn. Our studies show that CDOs reduce fuel consumption and noise exposure, although Carbon Monoxide (CO) and Hydrocarbon (HC) emissions may increase under idle-thrust conditions. Optimization results demonstrate that dynamic PM usage and early speed adjustments improve horizontal and vertical efficiency, reduce time in TMA and decrease fuel burn compared to actual operations. Operational improvements in arrival management can deliver immediate environmental benefits, complementing long-term technological solutions. The proposed frameworks support SESAR and NextGen objectives for greener TMAs and are adaptable to future contexts, including Urban Air Mobility (UAM). By integrating rigorous performance evaluation with advanced optimization, this thesis provides actionable insights for reducing aviation’s environmental footprint while enhancing predictability.

The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant challenge. This paper presents a hybrid Graph Neural Network (GNN)–Linear Programming (LP) framework to accelerate the solution of large-scale Unit Commitment Problems (UCPs) while maintaining the quality of solutions. By analyzing variable stability through historical branch-and-bound (B&B) trajectories, we classify MIP variables into dynamically adjustable stable and unstable groups. We adopt an MIP formulation that includes multiple types of binary variables—such as commitment, startup, and shutdown variables—and extract additional information from these auxiliary binary variables. This enriched representation provides more candidates for stable variable fixation, helping to improve variable refinement, mitigate suboptimality, and enhance computational efficiency. A bipartite GNN is trained offline to predict stable variables based on system topology and historical operational patterns. During online optimization, instance-specific root LP solutions refine these predictions, enabling adaptive variable fixation via a dual-threshold mechanism that integrates GNN confidence and LP relaxations. To mitigate suboptimality risks, we introduce temporally flexible fixation strategies—hard fixation for variables with persistent stability and soft fixation allowing limited temporal adjustments—alongside a GNN-guided branching rule to prioritize unstable variables. Numerical experiments demonstrate that jointly fixing commitment, startup, and shutdown variables yields better performance compared to fixing only commitment variables. Ablation studies further validate the importance of hard fixation and customized branching strategies, especially for large-scale systems.

Multi-fidelity (MF) metamodeling approaches have recently attracted a significant amount of attention in simulation-based design optimization due to their ability to conduct trade-offs between high accuracy and low computational expenses by integrating the information from high-fidelity (HF) and low-fidelity (LF) models. While existing MF metamodel assisted design optimization approaches may yield an inferior or even infeasible solution since they generally treat the MF metamodel as the real HF model and ignore the interpolation uncertainties from the MF metamodel. This situation will be more serious in non-deterministic optimization. Hence, in this work, a MF metamodel assisted robust optimization approach is developed, in which the interpolation uncertainty of the MF metamodel and design variable uncertainty are quantified and taken into consideration. To demonstrate the effectiveness and merits of the proposed approach, two numerical examples and a long cylinder pressure vessel design optimization problem are tested. Results show that for the test cases the proposed approach can obtain a solution that is both optimal and within the feasible region even with perturbation of the uncertain variables.

This paper presents a new general toolpath planning method which considers two important types of constraints: the kinematics constraint and the geometric feature constraints. The contact kinematics theory for rigid bodies is used to incorporate the two types of constraints into our optimization framework. The objective is to maximize the machining efficiency. The proposed method consists of three stages: establishing the mapping between the tool feeding speed with surface curvature, the orientation of the tool and the kinematics of machines; identifying the optimal cutting direction by solving a constrained nonlinear programming problem in the parametric domain; generating streamline-like toolpath and mapping it back to the surface. An example using our toolpath generation method is given to demonstrate its effectiveness.

Crew fatigue management is a critical aspect of airline operations, and layover scheduling plays a key role in ensuring the well-being of flight crew members. The increasing complexity of airline networks and availability of multiple hotels at stations necessitates the development of a framework leveraging efficient assignment of crew to the hotels. Besides efficiency, there are pre-defined rules that should be considered such as group-based assignment and maximum quota. In order to efficiently handle the assignment process, this study proposes a novel optimization framework with Mixed Integer Programming (MIP) formulation that ensures compliance with the associated rules. Meanwhile, the proposed optimization model prioritizes fairness among the hotels at the same station. To validate the effectiveness of the proposed approach, an extensive set of experiments are conducted using real-world airline data and the results are analyzed thoroughly. The experiments depict that the proposed optimization model can be solved efficiently within a short time frame, making it suitable for large-scale airline operations.

Impact of battery degradation on energy cost and carbon footprint of smart homes

Academic scheduling in large universities has long suffered from inefficiencies that compromise faculty well-being, student engagement, and optimal use of institutional resources. Traditional and semi-manual scheduling systems often result in unequal teaching loads, scattered student timetables, and underutilized classrooms—issues that persist even in technologically advanced institutions. This paper addresses these challenges by proposing a Mixed Integer Programming (MIP)-based optimization framework designed specifically for large academic environments like Ankara Medipol University in Türkiye that was used as the case study of this research. The model integrates faculty availability, course enrollment, departmental room assignments, and student learning patterns to generate balanced, conflict-free timetables. Using 30 real-world scheduling instances across five faculties, the model was implemented and validated with institutional data and stakeholder feedback. Findings revealed a 58.3% improvement in workload equity, a 51% reduction in student idle time, and a 29.3% boost in classroom utilization efficiency. Beyond these metrics, the model significantly improved academic satisfaction, reduced operational workload for administrators, and provided university leadership with a data-driven tool for strategic scheduling. This research not only offers a scalable and adaptable scheduling solution but also sets a precedent for integrating mathematical modeling with institutional governance and educational equity. Universities seeking to modernize their planning systems and improve academic quality can adopt this framework as a practical, evidence-informed solution.

With the complications of modern agricultural production, how to formulate scientific and reasonable planting strategies in the limited land resources and highly uncertain market environment has become a key research topic in the agricultural field. The diversity of land types (such as terraced fields, sloping fields, and irrigated land) and the variety of crops in North China make the problem of planting optimization more complicated and practical. In this paper, a bi-level optimization model combining mixed integer programming (MIP) and stochastic programming (SP) is proposed to solve the problems of resource allocation and income maximization in crop planting. Under certain conditions, the planting scheme is constructed by a mixed integer programming model, and the effects of two over-processing strategies, namely “slow sales” and “lower price sales”, are analyzed. Under the condition of uncertainty, various random scenarios are generated by Monte Carlo simulation, and the influence of market demand and yield fluctuation per mu on planting strategy is explored by combining with a stochastic programming model.The experimental results show that the stochastic programming model is superior to the deterministic model in key indicators such as average income, land utilization rate, and income stability. Specifically, compared with the deterministic model, the land utilization rate of the stochastic programming model is increased by about 15% on average, the average income is increased by about 8%, and the fluctuation range of income is reduced by more than 50%. Sensitivity analysis further reveals the significant influence of market demand and the fluctuation range of yield per mu on income, and the stochastic programming model shows strong flexibility and robustness in adapting to uncertainty. At the same time, this study provides multi-dimensional decision support for agricultural operators by introducing multi-scenario weight optimization, which not only performs well in improving economic benefits but also provides an effective reference in resource allocation efficiency and risk management.This paper provides a new theoretical framework for agricultural planting optimization and has important practical value. The stochastic programming method can effectively deal with extreme market conditions and environmental uncertainties, and provide a decision-making basis for agricultural policy makers in subsidy distribution and market regulation. In addition, the research results provide technical support for the digital transformation and intelligent management of agricultural production and promote the development of precision agriculture. The optimization model proposed in this study also provides a reference for resource allocation and optimization in other fields.

Bus tour-based routing and truck deployment for small-package shipping companies