Solving Scheduling Problems Research Articles

Decision Support System Applications for Scheduling in Professional Team Sport. The Team's Perspective.

Background: Periodization implies the systematic planning of training and competition with the goal of reaching the best possible performance in the most important competition. In team sports, this consists of finding a flight-and-practice schedule that maximizes the opportunities to perform the periodized contents (e.g., trips, practices, games, and days off). This process is conducted whilst considering known constraints (e.g., competitive schedule, roster availability, weather, especial events, holidays, or emotional effect of days away). The way a scheduling decision support system (DSS) leads users to make a decision should allow for flexibility, whilst minimizing users' confusion and facilitating the understanding of the recommendation given by the scheduling decision support system. Traditional approaches to solving scheduling problems use either simulation models, analytical models, heuristic approaches or a combination of these methods. When it comes to evaluate how the scheduling DSS is performing, three overarching aspects need to be reviewed: context satisfaction, process efficiency, and output quality. Appropriate training periodization and scheduling of trips and training sessions are critical for teams to optimize training and recovery processes in order to maximize health and performance. This article presents a methodological framework for designing decision-support systems for scheduling in professional team sports.

The Unrelated Parallel Machine Scheduling with a Dependent Time Setup using Ant Colony Optimization Algorithm

The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving scheduling problems quickly and precisely, the company can get more profit. On that note, this paper focused on unrelated PMS problems, which did not consider the inherent uncertainty in processing time and set up time by minimizing the makespan and tardiness. This paper aimed to minimize the makespan and tardiness using timing considerations. This paper described how to schedule unrelated parallel machines using the Ant Colony Optimization (ACO) Algorithm approach. The ACO is beneficial for inherent parallelism problems and can provide fast and reasonable solutions. This study revealed that the results of ACO Algorithm scheduling were obtained under a steady condition in iteration 30467. This condition can be interpreted that the makespan and tardiness value is close to 2.75%. By minimizing the makespan and tardiness, the delay of product delivery to consumers can be anticipated. Moreover, a company can maintain customer satisfaction and increase its profit.

A robust multi-response VNS-aiNet approach for solving scheduling problems under unrelated parallel machines environments

In the last few years, a lot of researchers addresses scheduling problems considering unrelated parallel machines to bridge theoretical problems and real-world manufacture environments. Most of these works consider objective function’s characteristics to assist the algorithms in exploiting the search space. This assistance usually leads to a better convergence of the algorithms, but it tends to leave these algorithms restricted only to the own optimization criterion studied. This feature hinders to extend these works to optimization criteria with less theoretical visibility. This work proposes an immuno-inspired algorithm for the resolution of scheduling problems under unrelated parallel environments, evaluating the hypothesis that it is possible to construct a robust approach that is extensible to different optimization criteria, achieving results similar to the state-of-art approaches in the literature for specific criteria. Four case studies are proposed, each one addressing an optimization criterion, which is a benchmark for sequencing problems. The criteria are Total Weighted Tardiness, Makespan, Total Tardiness, and Total Weighted Completion Time. The experiments indicate that the results achieved by the proposed approach are compatible and, in many cases, superior to the state-of-art approaches for the optimization criteria studied, using computational resources compatible with those presented by these approaches.

An Adaptive Iterated Greedy algorithm for distributed mixed no-idle permutation flowshop scheduling problems

Distributed flow shop scheduling is a very interesting research topic. This paper studies the distributed permutation flow shop scheduling problem with mixed no-idle constraints, which have important applications in practice. The optimization goal is to minimize total flowtime. A mixed-integer linear programming model is presented and an Adaptive Iterated Greedy (AIG) algorithm with the sample length changing according to the search process is designed. A restart strategy is also introduced to escape from local optima. Additionally, to further improve the performance of the algorithm, swap-based local search methods and acceleration algorithms for swap neighborhoods are proposed. Referenced Local Search (RLS), which shows better performance in solving scheduling problems, is also used in our algorithm. In the destruction stage, the job to be removed is selected according to the degree of influence on the total flowtime. In the initialization and construction phase, when a job is inserted, the jobs before and after the insertion position are removed and re-inserted into a better position to improve the algorithm search performance. A detailed design experiment is carried out to determine the best parameter configuration. Finally, large-scale experiments show that the proposed AIG algorithm is the best-performing one among all the algorithms in comparison.

Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times

This paper studies the multi-resource-constrained unrelated parallel machine scheduling problem under various operational constraints with the objective of minimising maximum completion time among the scheduled jobs. Sequence-dependent setup times, precedence relations, machine eligibility restrictions and release dates are incorporated into the problem as operational constraints to reflect real-world manufacturing environments. The considered problem is in NP-hard class of problems, which cannot be solved in deterministic polynomial time. Our aim in this study is to develop an exact solution approach based on constraint programming (CP), which shows good performance in solving scheduling problems. In this regard, we propose a CP model and enrich this model by adding lower bound restrictions and redundant constraints. Moreover, to achieve a reduction in computation time, we propose two branching strategies for the proposed CP model. The performance of the CP model is tested using randomly generated and benchmark instances from the literature. The computational results indicate that the proposed CP model outperforms the best solutions with an average gap of 15.52%.

Machine learning‐based scheduling: a bibliometric perspective

In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non-linear in nature, time varying, multi-objective, and subject to interruptions. Ergo, the machine learning-based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning-based algorithms. To reveal the value of machine learning-based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning-based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

• A systematic review on meta-heuristic based task scheduling in cloud is presented. • A novel rigorous taxonomy of meta-heuristic methods in the literature is proposed. • Findings pertaining to involved aspects are verified through an in-depth analysis. • Cloud simulation tools most used in the literature are deeply addressed and analyzed. • Open issues and challenges are outlined, along with a few recommended future trends. Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Particle swarm optimisation has been widely used in solving scheduling problems. This paper proposes a hybrid algorithm namely Hill Climbing with Multi-objective Particle Swarm Optimisation called ...

An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for driving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

Metaheuristics for the multi-task simultaneous supervision dual resource-constrained scheduling problem

This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.

A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem

Teaching-Learning-Based Optimization is one of the well-known metaheuristic algorithm in the research industry. Recently, various population-based algorithms have been developed for solving optimization problems. In this paper, a random scale factor approach is proposed to modify the simple TLBO algorithm. Modified Teaching-Learning-Based Optimization with Opposite-Based-Learning algorithm is applied to solve the Permutation Flow-Shop-Scheduling Problem with the purpose of minimizing the makespan. The OBL approach is used to enhance the quality of the initial population and convergence speed. PFSSP is used extensively for solving scheduling problem, which belongs to the category of NP-hard optimization problems. First, MTLBO is developed to effectively determine the PFSSP using the Largest Order Value rule-based random key, so that individual job schedules are converted into discrete schedules. Second, new initial populations are generated in MTLBO using the Nawaz–Enscore–Ham heuristic mechanism. Finally, the local exploitation ability is enhanced in the MTLBO using effective swap, insert and inverse structures. The performance of proposed algorithm is validated using ten benchmark functions and the Wilcoxon rank test. The computational results and comparisons indicate that the proposed algorithm outperformed over five well-known datasets such as Carlier, Reeves, Heller, Taillards and VRF benchmark test functions, compared to other metaheuristic algorithms. The p-value indicated the significance and superiority of the proposed algorithm over other metaheuristic algorithms.

Scheduling of flexible manufacturing plants with redesign options: A MILP-based decomposition algorithm and case studies

In the last years, the operational research on scheduling problems has been moving away from rigorous optimization approaches into solution strategies being capable of returning practical and fast solutions for large-scale industrial problems. Following this line, this paper proposes a novel MILP-based decomposition procedure for solving scheduling problems arising in flexible manufacturing environments, which generally involve multipurpose units and assembly operations. The solution strategy also considers redesign constraints with the goal of improving the efficiency of the production system, preventing bottlenecks and balancing the equipment utilization. The proposal is validated through the resolution of several instances derived from three real-world case-studies coming from different industrial sectors. The computational results show that the decomposition procedure is capable of generating high quality solutions, sometimes the optimal one, with minimum computational effort for all problem instances considered.

A unified approach for single machine scheduling with position-dependent workloads and positional penalties

In this paper, we provide unified methods for solving scheduling problems with convex resource allocation, concentrating on objective functions that can be expressed as a scalar product of the actual processing times vector and a job-independent and position-dependent penalties vector. We assume general position-dependent workloads, i.e., the workloads are not restricted to be either monotone functions of the job positions or to specific functions. The first unified method focuses on minimizing a scheduling measure subject to a constraint on the resource consumption, whereas the second addresses the complementary problem of minimizing the consumed resource given an upper bound on the scheduling measure. For each unified approach, we provide a methodical analysis and consequently provide a $$O\left( {n^{3} } \right)$$ solution algorithm. Furthermore, to demonstrate the unified schemes, we solve several scheduling measures that involve earliness and tardiness penalties.

Residential load scheduling considering maximum demand using binary particle swarm optimisation

Demand response (DR) programs are gaining importance in smart grid, owing to the continuously increasing energy demand. The primary objective of DR programs is to motivate consumers to change the power consumption pattern to limit the maximum demand. The success of residential DR programs largely depends on schedulable loads and the nature of utility tariff. Binary particle swarm optimisation (BPSO) is an effective tool for solving scheduling problems. In this paper, a BPSO based solution is presented through a case study for the residential load scheduling problem including the consumer demand constraints and maximum demand (MD) limit specified by the utility. The objective of the algorithm is to automatically schedule the consumer's load so as to minimise the energy cost subject to various constraints. Simulation experiments are conducted under different tariff and MD limit conditions. Test results show that the proposed method saves energy cost of the domestic consumer and reduces the maximum demand on the system.

Auction-based algorithm for solving scheduling problem in cross-docking centres

Auction-based algorithm for solving scheduling problem in cross-docking centres

Megelőzési feltételeket tartalmazó ütemezési feladatok párhuzamosítása mohó algoritmusok által generált rendezés-kongruenciák segítségével

Az ütemezési problémák egy alternatív megközelítését jelentik azok az eljárások, amelyek a diszkrét matematika eszközeivel dolgoznak. A részbenrendezett halmazok rendezés-kongruenciáinak tanulmányozásakor világossá vált, hogy a minimális lineáris rendezés-kongruenciák jól alkalmazhatók ütemezési feladatok megoldására. Ez a metódus nemcsak akkor használható, ha egy gép dolgozik, hanem kiterjeszthető olyan esetekre is, amikor egymással párhuzamosan több egységnyi kapacitású gép dolgozhat. Cikkünkben a megelőzési feltételeket tartalmazó ütemezési feladatokra olyan optimális vagy közel optimális megoldást adó algoritmusokat mutatunk be, amelyek a megoldást minimális lineáris kongruencia formájában állítják elő.

Improving genetic algorithm performance by population initialisation with dispatching rules

Scheduling is an important process that is present in many real world scenarios where it is essential to obtain the best possible results. The performance and execution time of algorithms that are used for solving scheduling problems are constantly improved. Although metaheuristic methods by themselves already obtain good results, many studies focus on improving their performance. One way of improvement is to generate an initial population consisting of individuals with better quality. For that purpose a variety of methods can be designed. The benefit of scheduling problems is that dispatching rules (DRs), which are simple heuristics that provide good solutions for scheduling problems in a small amount of time, can be used for that purpose. The goal of this paper is to analyse whether the performance of genetic algorithms can be improved by using such simple heuristics for initialising the starting population of the algorithm. For that purpose both manual and different kinds of automatically designed DRs were used to initialise the starting population of a genetic algorithm. In case of the manually designed DRs, all existing DRs for the unrelated machines environment were used, whereas the automatically designed DRs were generated by using genetic programming. The obtained results clearly demonstrate that using populations initialised by DRs leads to a significantly better performance of the genetic algorithm, especially when using automatically designed DRs. Furthermore, it is also evident that such a population initialisation strategy also improves the convergence speed of the algorithm, since it allows it to obtain significantly better results in the same amount of time. Additionally, the DRs have almost no influence on the execution speed of the genetic algorithm since they construct the schedule in time which is negligible when compared to the execution of the genetic algorithm. Based on the obtained results it can be concluded that initialising individuals by using DRs significantly improves both the convergence and performance of genetic algorithm, without the need of having to manually design new complicated initialisation procedures and without increasing the execution time of the genetic algorithm.

Creating dispatching rules by simple ensemble combination

Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules were obtained. This paper analyses whether combining several dispatching rules into an ensemble leads to performance improvements over the individual dispatching rules. Two methods for creating ensembles of dispatching rules, based on the sum and vote methods applied in machine learning, are used and their effectiveness is analysed with regards to the size of the ensemble, the genetic programming method used to generate the dispatching rules, the size of the evolved dispatching rules, and the method used for creating the ensembles. The results demonstrate that the generated ensembles achieve significant improvements over individual automatically generated dispatching rules.

Auction based algorithm for solving scheduling problem in cross-docking centres.

A cross-docking centre reduces the amount of storage and transportation in a distribution system compared to an ordinary warehouse. This paper proposes an auction-based mechanism for optimum scheduling of inbound trucks in cross-docking centres, thereby reducing the storage and transportation costs. Auction mechanisms are commonly used in different real life applications for better allocation of limited resources. Here, an effort is made to integrate the auction mechanism with the functioning of cross-docking centres. A mathematical model is developed for the optimal scheduling of inbound trucks. As expected, the determination of exact schedule for problems involving large number of variables was found to be computationally complex, and hence a heuristic methodology is also proposed in this study. In our computational experiments, we found that the proposed heuristic method is capable of producing good schedules in minimal time.

Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems

Two conjectures, the use of incomplete chromosome representations and shadow chromosomes may improve the performance of genetic algorithms (GAs), are examined in this study. The examination entails testing distributed flexible job shop scheduling (DFJS) problems subject to preventive maintenance (PM) that involve four scheduling decisions. Genetic algorithms based on a complete chromosome representation that explicitly models the four decisions have been developed previously. By contrast, herein, two incomplete chromosome representations are proposed, whereby the conjectured advantages are two-fold. First, an incomplete chromosome representation models two scheduling decisions, and the remaining two are decoded by heuristic rules designed to ensure the load balance of manufacturing resources. Therefore, scheduling solutions with load imbalance will not be generated, which will help prevent the execution of ineffective searches. Second, a novel method of generating new chromosomes is developed and employed, instead of using traditional genetic operations. These chromosomes, called shadow chromosomes, are generated from good quality scheduling solutions and they may improve performance. Based on these two conjectures, four GAs are proposed. Numerical experiments reveal that each proposed GA outperforms the prior GAs substantially and the two conjectures are thus well justified. These findings shed light on the application of the two conjectures for developing metaheuristic algorithms to solve other high-dimensional space search problems.