Part Of Schedule Research Articles

Robust decision trees for the multi-mode project scheduling problem with a resource investment objective and uncertain activity duration

In this paper, we advocate the use of robust decision trees for a multi-mode resource constrained project scheduling problem with uncertain activity duration and a resource investment objective, coming from an industrial assembly line scheduling application. This work takes place in the context of multi-stage optimization where uncertainty is revealed progressively across a succession of decision time points. In a robust decision tree, a node represents a robust partial schedule from the time origin to a specific decision time point. At this point, the decision maker has access to some information, which partitions the uncertainty scenario set, yielding for each scenario subset a child node and an associated extended partial robust schedule up to the next decision point. Considering that the level of uncertainty is lowered, the new partial schedule is less conservative and improves the robustness guarantee. However, since all accessible information may not be relevant, we turned the information selection part into an optimization problem. An algorithm is proposed to solve the robust decision tree problem. Experimentation is provided to study the influence of decision tree parameters as well as highlighted recommendations. The interest of the decision tree is shown through an experimental comparison with classical approaches of the literature on benchmark instances and industrial instances.

Hepatitis B virus immunity prior to and after administration of a ‘booster’ dose of vaccine among health-care students at a South African university

BackgroundHealth-care students (HCSs) are at risk of occupational exposure to hepatitis B virus (HBV) infection despite an effective hepatitis B vaccine (HepB) being available. The majority of current HCSs are born after HepB was introduced into the South African Expanded Programme on Immunisation in 1995. Thus, it is assumed that having received HepB in infancy, a single ‘booster’ dose would suffice. This study aimed to investigate HBV immunity prior to and after administration of a HepB ‘booster’ dose. MethodsHepatitis B surface antibody (anti-HBs) levels were determined in first year HCSs at the University of the Witwatersrand, before and after receiving the ‘booster’. Participant demographics and HepB history were captured using a structured questionnaire. ResultsBefore receiving the ‘booster’, 56% (101/180) had anti-HBs < 10 mIU/mL and were non-immune. A further 35% had anti-HBs levels of 10 – 99 mIU/mL, and 9% had ≥100 mIU/mL. <30% of HCSs self-reported completion of a three-dose primary series, which was significantly associated with higher baseline anti-HBs levels compared to those with a partial schedule (p = 0.045). Following vaccination, 39% (71/180) returned for follow-up with a significant median (IQR) increase of 476 (151 – 966) mIU/mL (p < 0.001). Of the 45 students who had non-immune baseline levels, 73% (33/45) responded with ≥100 mIU/mL, 16% (7/45) with 10 – 99 mIU/mL and 11% (5/45) remained non-immune. Levels of ≥100 mIU/mL were achieved by 100% of students with baseline levels ≥10 mIU/mL (n = 26). ConclusionMore than half of the HCSs were not immune to HBV prior to receiving the recommended ‘booster’ vaccine. Following vaccination, 7% (5/71) remained unprotected. This study highlights that in the absence of vaccination records and without confirming the immune status of HCSs, it cannot be assumed that HCSs will be protected following a ‘booster’. Policy reform and inclusion of serological tests for immunity prior to HCSs initiating clinical exposure are recommended.

Patient appointment scheduling at hemodialysis centers: An exact branch and price approach

Scheduling patient appointments at a hemodialysis center presents a unique setting. Unlike other appointment scheduling problems in healthcare systems, patients are scheduled for a series of dialysis treatment appointments instead of a single appointment. In this study, we formulate this multiple-appointment system as a set-partitioning problem that allows partial schedules to be feasible. We employ a Branch and Price (BP) algorithm to solve the problem, however, the pricing sub-problem proves to be exceedingly challenging for state-of-the-art dynamic programming algorithms. Therefore, we propose a novel decomposition of the sub-problem and design an efficient embedded Column Generation (CG) algorithm to find the optimal solution. We further design a greedy heuristic that enhances the computational efficiency of the BP algorithm. Our proposed BP algorithms efficiently solve challenging instances that are simulated based on the data from a collaborating hemodialysis center. Specifically, the Enhanced CG-embedded BP algorithm accelerates the CPU time on average by 78% compared to the Base BP algorithm (32% and 59% compared to the Enhanced and the CG-embedded BP algorithms, respectively). We also compare the optimal results with the current scheduling policy at the center. Our proposed Enhanced CG-embedded BP algorithm improves the percentage of leftover appointments by 98% on average and the hours of deviations per patient by 46% on average, compared to the current policy.

Tight necessary feasibility analysis for recurring real-time tasks on a multiprocessor

One of the important design issues for time-critical embedded systems is to derive necessary conditions that meet all job deadlines invoked by a set of recurring real-time tasks under a computing resource (called feasibility). To this end, existing studies focused on how to derive a tight lower-bound of execution requirement (i.e., demand) of a target set of real-time tasks. In this paper, we address the following question regarding the supply provided by a multiprocessor resource: is it possible for a real-time task set to always utilize all the provided supply? We develop a systematic approach that i) calculates the amount of supply proven unusable, ii) finds a partial schedule that yields a necessary condition to minimize the amount of unusable supply, and iii) uses the partial schedule to further reclaim unusable supply. While the systematic approach can be applied to most (if not all) recurring real-time task models, we show two examples how the approach can yield tight necessary feasibility conditions for the sequential task model and the gang scheduling model. We demonstrate the proposed approach finds a number of additional infeasible task sets which have not been proven infeasible by any existing studies for the task models.

A bicriterion single-machine scheduling problem with step-improving processing times

Time-dependent scheduling problems, where the real processing time of jobs is dependent on the starting time, have received growing attention in recent decades. In particular, scheduling problems on a single machine have been widely studied in many facets that address the learning effect and diverse processing environments or time-dependent processing scheduling. Motivated by this observation, we introduce a variant based on the industrial procedure consideration; that is, owing to due date pressure, the processing time of the remaining jobs should be shortened after a period of manufacturing process. We consider a new single-machine scheduling problem with step-improving processing times where the objective function is to find a schedule to minimize a linear combination of the total weighted completion time and total tardiness of all jobs. The proposed problem without a critical date is an NP-hard problem. Therefore, a mixed integer programming model as well as a branch-and-bound (B&B) along with several dominance properties and a lower bound on the completion of an active partial schedule is utilized for solving the problem under study. Subsequently, four variants of the water wave optimization algorithm and four variants of the simulated annealing algorithms were proposed to solve this problem. The simulation results showed that the branch-and-bound method can solve instance problems for up to twelve jobs. The results also showed that all four variants of the wave optimization algorithm did not perform uniformly better than all three variants of the simulated annealing algorithms.

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

AbstractScheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, the Job-shop Scheduling Problem (JSP) is a well known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. Given that already moderately sized JSP instances can be highly combinatorial, and neither optimal schedules nor the runtime to termination of complete optimization methods is known, efficient approaches to approximate good-quality schedules are of interest. In this paper, we propose problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations so that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. Regarding the feasibility and quality of solutions, problem decomposition must respect the precedence of operations within their jobs and partial schedules optimized by time windows should yield better global solutions than obtainable in similar runtime on the entire instance. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract window-wise optimization limitations restricted to partial schedules. Our experiments on JSP benchmark sets of several sizes show that successive optimization by multi-shot ASP solving leads to substantially better schedules within the runtime limit than global optimization on the full problem, where the gap increases with the number of operations to schedule. While the obtained solution quality still remains behind a state-of-the-art Constraint Programming system, our multi-shot solving approach comes closer the larger the instance size, demonstrating good scalability by problem decomposition.

Two-stage no-wait proportionate flow shop scheduling with minimal service time variation and optional job rejection

We consider two-stage no-wait proportionate flow shops with the objective of minimizing service time variation measured by defining the mid-processing point of a job and minimizing the Total Absolute Deviation of mid-processing Points (TADZ). We show that the two-stage no-wait proportionate flow shop with the TADZ objective is solvable in O(nlogn) time. Our findings provide an affirmative answer to an open research question posed by Kovalev et al. (2019) regarding existence of a solvable variant of the two-stage no-wait proportionate flow shop problem with the Total Absolute Deviation of Completion Times (TADC) objective. Moreover, we show when a generic two-stage no-wait proportionate flow shop scheduling problem is solvable in O(nlogn) time. We present practical applications where TADZ is more suitable than TADC as a scheduling objective. We also introduce a new metric defined as the sum of all partial schedule lengths SPSL and show that the two-stage no-wait proportionate flow shop with the SPSL objective is solvable in O(nlogn) time; thus, an additional solvable variant of the two-stage no-wait proportionate flow shop with the SPSL objective is identified and solved. Finally, we consider the option of rejecting a job from the schedule by paying a job-specific penalty for each rejected job and solve the resulting problem with the TADZ objective and the rejection option in O(n3) time by dynamic programming.

Toward Efficient Time-Sensitive Network Scheduling

Time-sensitive networks (TSNs) provide reliable, deterministic message delivery, ideal for networked control systems. Such networks are increasingly implemented in dynamic, resource-constrained avionics environments. This places pressure on the TSN scheduler to compute network flow schedules faster with slower processors, less memory, and limited power consumption. Avionics full-duplex switched Ethernet and IEEE 802.1 Time-Sensitive Networking are briefly compared and found to be largely similar in concept except for mechanisms enforcing determinism (BAG versus IEEE 802.1Qbv gate control) and the added requirement for time synchronization in time-sensitive networking. A new form of reliability known as polymorphic reconfiguration is introduced and contrasted with static, preconfigured redundancy. Scalability issues impacting TSN scheduling for polymorphic reconfiguration are derived. Preconfiguring and storing the TSN schedule for every possible network failure is infeasible in terms of memory storage. IEEE 802.1CB and other redundancy mechanisms exacerbate the problem by further increasing the number of flows that have to be reconfigured and scheduled. The computational complexity of TSN scheduling is reviewed and the notion of a processing-storage tradeoff via a schedule seed is introduced. The resource constrained scheduling challenge is addressed by analyzing tradeoffs in efficient scheduling approaches where qualitative performance metrics are inferred, high-level computational methods are compared, and suggestions for future approaches are outlined. This article contributes to time-sensitive networking scheduling capable of operating in a resource-constrained, dynamically changing network environment rather than in fixed avionics network environments to which they are limited today. Finally, the need for standards for TSN scheduling performance profiling and benchmarks as well as partial schedules and distributed TSN scheduling protocol standards is highlighted.

МЕТОД И АЛГОРИТМ ПЛАНИРОВАНИЯ ОПЕРАЦИЙ НА ОСНОВЕ МОДЕЛИ НЕЧЕТКОГО КОНЕЧНОГО АВТОМАТА

In this paper the planning and scheduling problem as an important optimization problem inmany transportation and robotic applications is discussed. To solve planning problems, the mainapproaches are based on optimization methods, sampling-based methods, and usually such kindsof problems are NP-hard and high dimensional. In this work, the method for planning and schedulingbased on the fuzzy finite state machine model is developed. Fuzzy graph presentation of thescheduling problem and operation planning is given. The paper presents two approaches to theformulation of the planning problem with limited resources and temporal variables: state-oriented(with transitions between states), temporal ordering-oriented (on a time scale). Temporal modelingfor planning problems implies a qualitative approach to managing the distribution of operationsor topological ordering, as well as a quantitative approach to handling imprecise durationsrelationships between operations in multiple parameters. The concepts of fuzzy intervals and fuzzyrelations are introduced for planning operations on a graph. A planning algorithm based on thetheory of automata and temporal modeling under uncertainty has been developed. Using this formalism,a path planning problem is solved by successively altering a state using various operationsuntil a solution is found. The idea of temporal-ordered partial schedule associated with theplanning state of the system is discussed. A model of a finite automaton for a planning system underconditions of uncertainty is proposed. A method and algorithm for scheduling operationsbased on a non-deterministic finite automaton and an enumeration scheme have been developed.The non-deterministic computation for a scheduling problem is a decision tree whose root correspondsto the beginning of the scheduling process, and each branch point in the tree correspondsto a computation point at which the machine has multiple choices. And the finite state machinemodel (automata) for the planning system under uncertainty is suggested.

On the Fine-grained Parameterized Complexity of Partial Scheduling to Minimize the Makespan

We study a natural variant of scheduling that we call partial scheduling: in this variant an instance of a scheduling problem along with an integer k is given and one seeks an optimal schedule where not all, but only k jobs, have to be processed. Specifically, we aim to determine the fine-grained parameterized complexity of partial scheduling problems parameterized by k for all variants of scheduling problems that minimize the makespan and involve unit/arbitrary processing times, identical/unrelated parallel machines, release/due dates, and precedence constraints. That is, we investigate whether algorithms with runtimes of the type f(k)n^{{mathcal {O}}(1)} or n^{{mathcal {O}}(f(k))} exist for a function f that is as small as possible. Our contribution is two-fold: First, we categorize each variant to be either in {mathsf {P}}, {{mathsf {N}}}{{mathsf {P}}}-complete and fixed-parameter tractable by k, or {mathsf {W}}[1]-hard parameterized by k. Second, for many interesting cases we further investigate the runtime on a finer scale and obtain run times that are (almost) optimal assuming the Exponential Time Hypothesis. As one of our main technical contributions, we give an {mathcal {O}}(8^kk(|V|+|E|)) time algorithm to solve instances of partial scheduling problems minimizing the makespan with unit length jobs, precedence constraints and release dates, where G=(V,E) is the graph with precedence constraints.

Efficient automatic scheduling of imaging and vision pipelines for the GPU

We present a new algorithm to quickly generate high-performance GPU implementations of complex imaging and vision pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels. We address the scalability challenge of extending search-based automatic scheduling to map large real-world programs to the deep hierarchies of memory and parallelism on GPU architectures in reasonable compile time. We achieve this using (1) a two-phase search algorithm that first ‘freezes’ decisions for the lowest cost sections of a program, allowing relatively more time to be spent on the important stages, (2) a hierarchical sampling strategy that groups schedules based on their structural similarity, then samples representatives to be evaluated, allowing us to explore a large space with few samples, and (3) memoization of repeated partial schedules, amortizing their cost over all their occurrences. We guide the process with an efficient cost model combining machine learning, program analysis, and GPU architecture knowledge. We evaluate our method’s performance on a diverse suite of real-world imaging and vision pipelines. Our scalability optimizations lead to average compile time speedups of 49x (up to 530x). We find schedules that are on average 1.7x faster than existing automatic solutions (up to 5x), and competitive with what the best human experts were able to achieve in an active effort to beat our automatic results.

Joint Scheduling and Resource Allocation for Efficiency-Oriented Distributed Learning Over Vehicle Platooning Networks

The limited communication and computing resources, as well as the rising concerns about the privacy protection, bring significant challenges to the massive data training and analysis in vehicular networks. To address these challenges, in this paper a platoon-based distributed learning framework design for data learning is carried out, where the vacant computation resources of vehicle platooning networks are leveraged. In the proposed framework, a 2-phase Markovian stochastic process is utilized to depict the learning service heterogeneity for each participating vehicle. Meanwhile, we propose a joint scheduling and resource allocation scheme for efficiency-oriented distributed learning to maximize the learning accuracy subject to a given learning time constraint. The optimization problem is solved as follows. First, given the scheduled vehicles, the communication resource allocation is modeled as a minimum-maximum problem to minimize the learning delay of each learning round. Subsequently, an efficiency-oriented unbiased global aggregation policy is proposed to explore the convergence difference between partial scheduling and total scheduling. Considering the learning convergence and remaining time, an on-demand scheduling scheme is introduced to determine the number of scheduled vehicles. Finally, combining the learning efficiency of each vehicle with the scheduled number of vehicles, the scheduled vehicle set is selected. Simulations results show that the proposed scheduling policy can schedule the number of participating vehicles on demand based on the trade-off between learning performance and learning latency.

Large-scale periodic scheduling in time-sensitive networks

The real-time and safety-critical demands posed in a variety of industrial systems can now be handled on the Ethernet architecture by virtue of the IEEE Time-Sensitive Networking task group. In particular, deterministic nature and timeliness guarantees can be achieved by time-triggered communication schedules that are enabled by the IEEE 802.1Qbv standard. However, generating such a schedule is a computationally challenging task due to several harsh constraints that must be satisfied. This problem is usually transformed into some existing formalism, e.g., Satisfiability Modulo Theories, and solved using a general third-party solver, which has the drawback of limited scalability. Except for some one-pass heuristics, such as the earliest deadline first or as-soon-as-possible scheduling, the current literature does not provide any algorithm tailored to this specific problem of scheduling time-triggered traffic in time-sensitive networks. In this paper, we develop an efficient algorithm that does not use any third-party solver and is based on the search in the space of partial schedules while being guided by the discovered conflicts. Our algorithm calculates schedules for problem instances consisting of 2000 network nodes and more than 10000 flows.

Performance of Heuristic Priority Rule for Multiskilled Resource-Constrained Scheduling

Nowadays, without a classification system that helps the project manager to decide which heuristic applies when mitigating the multiskilled resource-constrainedscheduling, they must try several of rules until they find one that compares favorably (shortest duration) with the results of the other heuristic priority rules. This study explored the twenty-three existing heuristics’ performance for multiskilled resource-constrained scheduling. The results found that the heuristics with good performance are TIMROS, TIMRES, ACROS, WCS ACS and ACTRES. Overall, they outperform the others in shortening the project duration. It should be pointed out that the heuristics dealing with the use of several information are likely better to get shorter project duration. There are enough statistical evidences to conclude that their criterions have a significant effect on reducing project duration by approximately 1-2 times of the standard deviation. The top four heuristics: TIMROS, TIMRES, ACROS and ACTRESS classified into the composite rule produced the lowest average of project duration. It is also found that Serial Schedule Scheme (SSS) underperformthe Partial Schedule Scheme (PSS). This study has the contribution for the project managers to decide which heuristic applies when mitigating the multiskilled resource overallocation problem in term of minimum project duration

The reversibility property in a job-insertion tiebreaker for the permutational flow shop scheduling problem

The best performing approximate methods proposed for the permutational flow shop scheduling problem with makespan minimization are the well known NEH constructive heuristic and the iterated greedy algorithm. Both methods are based on the successive insertion (or reinsertion) of jobs into a partial schedule, evaluating the makespan of the resulting schedule for all insertion positions, and selecting the insertion position that presents the shortest makespan. Frequently, there are many tied insertion positions that produce such shortest makespan. Thus, a tiebreaker must be used to discern a selection among the tied insertion positions. Many tiebreakers have been proposed in the literature for this case. These tiebreakers improve the results produced by approximate methods when embedded into them. In this paper we propose two new tiebreakers that use a weighted and an unweighted approximation of the idle time increment produced by inserting the job into each tied insertion position. They were designed considering the reversibility property of the PFSSP. Our computational experiments show that the proposed tiebreakers outperform tiebreakers from the literature when evaluated within the NEH heuristic and within the iterated greedy algorithm. The iterated greedy algorithms with the proposed tiebreakers embedded are the best approximate methods so far for the permutational flow shop scheduling problem.

An effective MCTS-based algorithm for minimizing makespan in dynamic flexible job shop scheduling problem

In the past several decades, most of the research methods are designed to solve the static flexible job shop scheduling problem. However, in real production environments, some inevitable dynamic events such as new jobs arrival and machine breakdown may occur frequently. In this paper, we study a dynamic flexible job shop scheduling problem (DFJSP) considering four dynamic events, which are new jobs arrival, machine breakdown, jobs cancellation and change in the processing time of operations. A rescheduling method based on Monte Carlo Tree Search algorithm (MCTS) is designed to solve the proposed DFJSP with the objective of minimizing the makespan. Several optimization techniques such as Rapid Action Value Estimates heuristic and prior knowledge are adopted to enhance the performance of the MCTS-based rescheduling method. The response time to dynamic events is critical in DFJSP but has not been solved very well. To greatly reduce the response time to dynamic events, when dynamic events occur, multiple continuous specified time windows are designed for the proposed method, according to which the corresponding subsequent partial schedule for the remaining unprocessed operations is progressively generated. Some experiments have been conducted to compare the proposed method with the commonly used completely reactive scheduling methods and the GA-based rescheduling method. The experiment results indicate that the proposed method is an efficient and promising method for dynamic scheduling both on solution quality and computation efficiency.

Wdrażanie narzędzi zarządzania projektowego do procesu organizacji wystaw. Studium przypadku Muzeum Narodowego w Krakowie (2016–2019)

Implementation of Project Management Tools to the Exhibition Organization Process. Case Study of the National Museum in Krakow (2016–2019) How to thoroughly improve the process of organizing exhibitions in a cultural institution? Is project management in a museum possible? How to implement new tools? What to do after initiating a change? This article provides answers to these questions through a case study prepared by the Head of the Exhibition Organization Department and the Head of the Coordinators Section of the National Museum in Krakow, directly responsible for the exhibition organization process. The article describes the events from 2016 to 2019. This includes an audit of the existing process of exhibition organization, implementation of corrective measures, introduction of new tools, their updating and verification, taking into account such solutions as treating the exhibition as a project, PRINCE2 and Scrum methods, a detailed description of the process, and creating standardized templates of project documents, i.e. a list of objects, project charter, project schedule, partial schedule, project budget, evaluation. These activities resulted in the improvement of team cooperation, the elimination of avoidable delays, the automation and acceleration of the process of preparing project documentation, making the obligations of individual team members public, submitting project data to the stakeholders of a given project on an ongoing basis, reducing the amount of manual work, and reducing the cost of the implementation of individual projects.

Partial flexible job shop scheduling considering preventive maintenance and priorities

In this paper, a new mathematical programming model is proposed for a partial flexible job shop scheduling problem with an integrated solution approach. The purpose of this model is the assignment of production operations to machines with the goal of simultaneously minimizing operating costs and penalties. These penalties include delayed delivery, deviation from a fixed time point for preventive maintenance, and deviation from the priorities of each machine. Considering the priorities for machines in partial flexible job shop scheduling problems can be a contribution in closer to the reality of production systems. For validation and evaluation of the effectiveness of the model, several numerical examples are solved by using the Baron solver in GAMS. Sensitivity analysis is performed for the model parameters. The results further indicate the relationship between scheduling according to priorities of each machine and production scheduling.

Dominance conditions determination based on machine idle times for the permutation flowshop scheduling problem

In this study, we investigate the lower bound and dominance conditions of the N-job and M-machine permutation flowshop scheduling problem to minimize the makespan based on machine idle times. We have developed and validated three dominance conditions and a lower bound based on a new approach that minimizes the machine idle times in a flowshop to facilitate the efficiency of the branch and bound approach. Most importantly, we have developed a dominance condition that is independent of the preceding and subsequent partial schedules, which has been considered critical but has not previously been developed in flowshop scheduling research. Finally, the efficiency of our developed branch and bound approach based on machine idle times is compared with the efficiencies of some previous branch and bound algorithms from the literature. We show that the dominance conditions that we developed can significantly improve the efficiency of the branch and bound approach.

HRHS: A High-Performance Real-Time Hardware Scheduler

This article represents an on-line time-predictable distributed hardware scheduler solution, suitable for many-core systems. We have partitioned the Main scheduler into uniform Partial schedulers to achieve a significant gain in term of performance and scalability, while software scheduling solutions impose excessive delays (in order of thousands of clock cycles) to a system. Although we have considered the implementation of the Earliest Deadline First (EDF) algorithm for each Partial scheduler, one can use customized scheduling policies, as needed. Designers can also modify different parts of our proposed architecture to obtain more suitable hardware for their design. HRHS outperforms conventional schedulers, in terms of resource utilization (LUT, register), delay and energy consumption by 36.83, 22.93, 46.36 and 59.26 percent on average, respectively. It also overpowers clustering solutions by circumventing their intrinsic off-line characteristics. The presented designs are also implemented in ASIC with 45-nanometer technology, in which the HRHS design excels in power, area and critical path length by 49.33, 50.67, and 53.33 percent on average, respectively, over other designs implemented in this article.