Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

This is the final version. Available on open access from Springer Verlag via the DOI in this recordAdditive manufacturing (AM), also known as 3D printing, has been called a disruptive technology as it enables the direct production of physical objects from digital designs and allows private and industrial users to design and produce their own goods enhancing the idea of the rise of the “prosumer”. It has been predicted that, by 2030, a significant number of small and medium enterprises will share industry-specific AM production resources to achieve higher machine utilization. The decision-making on the order acceptance and scheduling (OAS) in AM production, particularly with powder bed fusion (PBF) systems, will play a crucial role in dealing with on-demand production orders. This paper introduces the dynamic OAS problem in on-demand production with PBF systems and aims to provide an approach for manufacturers to make decisions simultaneously on the acceptance and scheduling of dynamic incoming orders to maximize the average profit-per-unit-time during the whole makespan. This problem is strongly NP hard and extremely complicated where multiple interactional subproblems, including bin packing, batch processing, dynamic scheduling, and decision-making, need to be taken into account simultaneously. Therefore, a strategy-based metaheuristic decision-making approach is proposed to solve the problem and the performance of different strategy sets is investigated through a comprehensive experimental study. The experimental results indicated that it is practicable to obtain promising profitability with the proposed metaheuristic approach by applying a properly designed decision-making strategy.National High Technology Research and Development Program of Chin

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

A new hybrid meta-heuristic algorithm for solving single machine scheduling problems

A dissertation submitted in partial ful lment of the degree of Master of Science in Engineering (Electrical) (50/50) in the Faculty of Engineering and the Built Environment Department of Electrical and Information Engineering May 2017Numerous applications in a wide variety of elds has resulted in a rich history of research into optimisation for scheduling. Although it is a fundamental form of the problem, the single machine scheduling problem with two or more objectives is known to be NP-hard. For this reason we consider the single machine problem a good test bed for solution algorithms. While there is a plethora of research into various aspects of scheduling problems, little has been done in evaluating the performance of the Simulated Annealing algorithm for the fundamental problem, or using it in combination with other techniques. Speci cally, this has not been done for minimising total weighted earliness and tardiness, which is the optimisation objective of this work. If we consider a mere ten jobs for scheduling, this results in over 3.6 million possible solution schedules. It is thus of de nite practical necessity to reduce the search space in order to nd an optimal or acceptable suboptimal solution in a shorter time, especially when scaling up the problem size. This is of particular importance in the application area of packet scheduling in wireless communications networks where the tolerance for computational delays is very low. The main contribution of this work is to investigate the hypothesis that inserting a step of pre-sampling by Markov Chain Monte Carlo methods before running the Simulated Annealing algorithm on the pruned search space can result in overall reduced running times. The search space is divided into a number of sections and Metropolis-Hastings Markov Chain Monte Carlo is performed over the sections in order to reduce the search space for Simulated Annealing by a factor of 20 to 100. Trade-o s are found between the run time and number of sections of the pre-sampling algorithm, and the run time of Simulated Annealing for minimising the percentage deviation of the nal result from the optimal solution cost. Algorithm performance is determined both by computational complexity and the quality of the solution (i.e. the percentage deviation from the optimal). We nd that the running time can be reduced by a factor of 4.5 to ensure a 2% deviation from the optimal, as compared to the basic Simulated Annealing algorithm on the full search space. More importantly, we are able to reduce the complexity of nding the optimal from O(n:n!) for a complete search to O(nNS) for Simulated Annealing to O(n(NMr +NS)+m) for the input variables n jobs, NS SA iterations, NM Metropolis- Hastings iterations, r inner samples and m sections.MT 201

Optimal Mission Planning of Autonomous Mobile Agents for Applications in Microgrids, Sensor Networks, and Military Reconnaissance

As technology advances, the use of collaborative autonomous mobile systems for various applications will become evermore prevalent. One interesting application of these multi-agent systems is for autonomous mobile microgrids. These systems will play an increasingly important role in applications such as military special operations for mobile ad-hoc power infrastructures and for intelligence, surveillance, and reconnaissance missions. In performing these operations with these autonomous energy assets, there is a crucial need to optimize their functionality according to their specific application and mission. Challenges arise in determining mission characteristics such as how each resource should operate, when, where, and for how long. This thesis explores solutions in determining optimal mission plans around the applications of autonomous mobile microgrids and resource scheduling with UGVs and UAVs. Optimal network connections, energy asset locations, and cabling trajectories are determined in the mobile microgrid application. The resource scheduling applications investigate the use of a UGV to recharge wireless sensors in a wireless sensor network. Optimal recharging of mobile distributed UAVs performing reconnaissance missions is also explored. With genetic algorithm solution approaches, the results show the proposed methods can provide reasonable a-priori mission plans, considering the applied constraints and objective functions in each application. The contributions of this thesis are: (1) The development and analysis of solution methodologies and mission simulators for a-priori mission plan development and testing, for applications in organizing and scheduling power delivery with mobile energy assets. Applying these methods results in (2) the development and analysis of reasonable a-priori mission plans for autonomous mobile microgrids/assets, in various scenarios. This work could be extended to include a more diverse set of heterogeneous agents and incorporate dynamic loads to provide power to

Enabling the “Easy Button” for Broad, Parallel Optimization of Functions Evaluated by Simulation

Java Optimization by Simulation (JOBS) is presented: an open-source, object-oriented Java library designed to enable the study, research, and use of optimization for models evaluated by simulation. JOBS includes several novel design features that make it easy for a simulation modeler, without extensive expertise in optimization or parallel computation, to define an optimization model with deterministic and/or stochastic constraints, choose one or more metaheuristics to solve it and run, using massively parallel function evaluation to reduce wall-clock times. JOBS is supported by a new language independent, application programming interface (API) for remote simulation model evaluation and a serverless computing environment to provide massively parallel function evaluation, on demand. Dynamic loop scheduling methods are evaluated in the serverless environment with the opportunity for significant resource contention for master node computing power and network bandwidth. JOBS implements several population-based and single-solution improvement metaheuristics (solvers) for real, discrete, and mixed problems. The object-oriented design is extendible with classes that drastically reduce the amount of code required to implement a new solver and encourage re-use of solvers as building blocks for creating new multi-stage solvers or memetic algorithms