Developing GA-based hybrid approaches for a real-world mixed-integer scheduling problem

Consider optimizing a periodic schedule for an automated production plant as a last step of a more comprehensive design process. In our scenario, each robot’s cyclic sequence of operations and trajectories between potential waiting points have already been fully specified. Further given are those precedences that fix sequence requirements on operations between different robots. It remains to determine the starting time for each operation or movement of each robot within a common cyclic time period so as to avoid collisions of robots that operate in the same space simultaneously. So the task is to find a conflict-resolving schedule that minimizes this common periodic cycle time while observing all precedence relations and collision avoidance constraints. The proposed cycle time minimization problem for robot coordination has, to the best of our knowledge, not been studied before. We develop an approach for solving it by employing binary search for determining the smallest feasible period time of an iso-periodic event scheduling problem (IPESP). This is a variant of the periodic event scheduling problem in which the objects that have to be scheduled need to obey exactly the same period time. The possibility to wait arbitrarily long at waiting points turns out to be essential to justify the use of binary search for identifying the minimum cycle time, thereby avoiding bilinear mixed integer formulations. Special properties of the given scenario admit bounds on the periodic tension variables of an integer programming formulation. Although the IPESP subproblems remain NP-complete in general, these bounds allow solving real-world instances sufficiently fast for the approach to be applicable in practice. Numerical experiments on real-world and randomly generated data are supplied to illustrate the potential and limitations of this approach. Summary of Contribution: When designing automated production plants, a crucial step is to identify the smallest possible per unit period time for the production processes. Based on periodic event scheduling ideas, we develop and analyze mathematical methods for this purpose. We show that the algorithmic implementation of our approach provides an answer to current real-world designs in reasonable time.

Download Full-text

Cable tree wiring - benchmarking solvers on a real-world scheduling problem with a variety of precedence constraints

Constraints ◽

10.1007/s10601-021-09321-w ◽

2021 ◽

Author(s):

Jana Koehler ◽

Josef Bürgler ◽

Urs Fontana ◽

Etienne Fux ◽

Florian Herzog ◽

...

Keyword(s):

State Of The Art ◽

Precedence Constraints ◽

Traveling Salesman ◽

Mixed Integer ◽

Automated Manufacturing ◽

Scheduling Problem ◽

Tasks Scheduling ◽

Optimization Modulo Theories ◽

Coupled Tasks ◽

Np Hardness

AbstractCable trees are used in industrial products to transmit energy and information between different product parts. To this date, they are mostly assembled by humans and only few automated manufacturing solutions exist using complex robotic machines. For these machines, the wiring plan has to be translated into a wiring sequence of cable plugging operations to be followed by the machine. In this paper, we study and formalize the problem of deriving the optimal wiring sequence for a given layout of a cable tree. We summarize our investigations to model this cable tree wiring problem (CTW). as a traveling salesman problem with atomic, soft atomic, and disjunctive precedence constraints as well as tour-dependent edge costs such that it can be solved by state-of-the-art constraint programming (CP), Optimization Modulo Theories (OMT), and mixed-integer programming (MIP). solvers. It is further shown, how the CTW problem can be viewed as a soft version of the coupled tasks scheduling problem. We discuss various modeling variants for the problem, prove its NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark set of 278 instances. The complete benchmark set with all models and instance data is available on github and was included in the MiniZinc challenge 2020.

Download Full-text

Solving the nuclear dismantling project scheduling problem by combining mixed-integer and constraint programming techniques and metaheuristics

Journal of Scheduling ◽

10.1007/s10951-021-00682-x ◽

2021 ◽

Author(s):

Felix Hübner ◽

Patrick Gerhards ◽

Christian Stürck ◽

Rebekka Volk

Keyword(s):

Constraint Programming ◽

Project Scheduling ◽

Computational Study ◽

Real Life ◽

Mixed Integer ◽

Extensive Literature ◽

Scheduling Problem ◽

Multiple Modes ◽

Project Scheduling Problem ◽

Optimisation Techniques

AbstractScheduling of megaprojects is very challenging because of typical characteristics, such as expected long project durations, many activities with multiple modes, scarce resources, and investment decisions. Furthermore, each megaproject has additional specific characteristics to be considered. Since the number of nuclear dismantling projects is expected to increase considerably worldwide in the coming decades, we use this type of megaproject as an application case in this paper. Therefore, we consider the specific characteristics of constrained renewable and non-renewable resources, multiple modes, precedence relations with and without no-wait condition, and a cost minimisation objective. To reliably plan at minimum costs considering all relevant characteristics, scheduling methods can be applied. But the extensive literature review conducted did not reveal a scheduling method considering the special characteristics of nuclear dismantling projects. Consequently, we introduce a novel scheduling problem referred to as the nuclear dismantling project scheduling problem. Furthermore, we developed and implemented an effective metaheuristic to obtain feasible schedules for projects with about 300 activities. We tested our approach with real-life data of three different nuclear dismantling projects in Germany. On average, it took less than a second to find an initial feasible solution for our samples. This solution could be further improved using metaheuristic procedures and exact optimisation techniques such as mixed-integer programming and constraint programming. The computational study shows that utilising exact optimisation techniques is beneficial compared to standard metaheuristics. The main result is the development of an initial solution finding procedure and an adaptive large neighbourhood search with iterative destroy and recreate operations that is competitive with state-of-the-art methods of related problems. The described problem and findings can be transferred to other megaprojects.

Download Full-text

A flexible mixed integer programming-based system for real-world nurse rostering

Journal of Scheduling ◽

10.1007/s10951-021-00705-7 ◽

2021 ◽

Author(s):

Elín Björk Böðvarsdóttir ◽

Niels-Christian Fink Bagger ◽

Laura Elise Høffner ◽

Thomas J. R. Stidsen

Keyword(s):

Integer Programming ◽

Mixed Integer Programming ◽

Real World ◽

Mixed Integer ◽

Nurse Rostering

Download Full-text

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i5.pp2349-2356 ◽

2017 ◽

Vol 7 (5) ◽

pp. 2349 ◽

Cited By ~ 7

Author(s):

Surender Reddy Salkuti

Keyword(s):

Power System ◽

Reactive Power ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Optimization Techniques ◽

Mixed Integer ◽

Integer Programming Problem ◽

Scheduling Problem ◽

Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

Download Full-text

Optimising Daily Fantasy Sports Teams with Artificial Intelligence

International Journal of Computer Science in Sport ◽

10.2478/ijcss-2020-0008 ◽

2020 ◽

Vol 19 (2) ◽

pp. 21-35

Author(s):

Ryan Beal ◽

Timothy J. Norman ◽

Sarvapali D. Ramchurn

Keyword(s):

Real World ◽

National Football League ◽

Mixed Integer ◽

Data Sets ◽

Fantasy Sports ◽

Real World Data ◽

Sports Teams ◽

Novel Approach ◽

Four Seasons ◽

Daily Fantasy Sports

AbstractThis paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.

Download Full-text

Generalized swarm intelligence algorithms with domain-specific heuristics

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i1.pp157-165 ◽

2021 ◽

Vol 10 (1) ◽

pp. 157

Author(s):

P. Matrenin ◽

V. Myasnichenko ◽

N. Sdobnyakov ◽

D. Sokolov ◽

S. Fidanova ◽

...

Keyword(s):

Swarm Intelligence ◽

Minimum Distance ◽

Heuristic Algorithms ◽

Population Based ◽

Scheduling Problem ◽

Domain Specific ◽

Hybrid Approaches ◽

Self Organized ◽

Swarm Algorithms ◽

Tasks Scheduling

<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

Download Full-text

A Two-Stage Offline-to-Online Multiobjective Optimization Strategy for Ship Integrated Energy System Economical/ Environmental Scheduling Problem

Complexity ◽

10.1155/2021/6686563 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Qing An ◽

Jun Zhang ◽

Xin Li ◽

Xiaobing Mao ◽

Yulong Feng ◽

...

Keyword(s):

Multiobjective Optimization ◽

Real World ◽

Operation Time ◽

Energy System ◽

Scheduling Problem ◽

Storage Space ◽

Optimization Strategy ◽

Two Stage ◽

Integrated Energy System ◽

And Storage

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.

Download Full-text

Mathematical models for a batch scheduling problem to minimize earliness and tardiness

Journal of Industrial Engineering and Management ◽

10.3926/jiem.2541 ◽

2018 ◽

Vol 11 (3) ◽

pp. 390 ◽

Cited By ~ 3

Author(s):

Basar Ogun ◽

Çigdem Alabas-Uslu

Keyword(s):

Mathematical Models ◽

Real World ◽

Lean Manufacturing ◽

Programming Model ◽

Computational Study ◽

Batch Scheduling ◽

Scheduling Problem ◽

Scheduling Problems ◽

Earliness And Tardiness ◽

Customer Orders

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem is addressed to provide on-time completion of customer orders in the environment of lean manufacturing. The problem is to optimize partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components.Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid from inventory of final products. The first model is a non-linear integer programming model while the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented.Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times comparing to the other two models. It was also showed that the alternative model can solve moderate sized real-world problems.Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature since it includes new circumstances which may arise in real-world applications. This research, also, contributes the literature of batch scheduling problem by presenting new optimization models.

Download Full-text