Journal of Scheduling最新文献

This paper considers a recoverable robust single-machine scheduling problem under polyhedral uncertainty with the objective of minimising the total flow time. In this setting, a decision-maker must determine a first-stage schedule subject to the uncertain job processing times. Then following the realisation of these processing times, they have the option to swap the positions of up to $\Delta$ disjoint pairs of jobs to obtain a second-stage schedule. We first formulate this scheduling problem using a general recoverable robust framework, before we examine the incremental subproblem in further detail. We prove a general result for max-weight matching problems, showing that for edge weights of a specific form, the matching polytope can be fully characterised by polynomially many constraints. We use this result to derive a matching-based compact formulation for the full problem. Further analysis of the incremental problem leads to an additional assignment-based compact formulation. Computational results on budgeted uncertainty sets compare the relative strengths of the three compact models we propose.

The university course timetabling problem is a challenging problem to solve. As universities have evolved, the features of this problem have changed. One emerging feature is hybrid teaching where classes can be taught online, in-person or a combination of both in-person and online. This work presents a multi-objective binary programming model that includes common university timetabling features, identified from the literature, as well as hybrid teaching features. A lexicographic solution method is outlined and computational experiments using benchmark data are used to demonstrate the key aspects of the model and explore trade-offs among the objectives considered. The results of these experiments demonstrate that the model can be used to find demand-driven schedules for universities that include hybrid teaching. They also show how the model could be used to inform practitioners who are involved in strategic decision-making at universities.

We consider a two-agent scheduling problem with interfering job sets. Agent A—which can be considered as the resource manager—is associated with the whole set of jobs, and agent B—which can be considered as an application master—is associated with a subset of jobs. Each agent aims at minimizing either the maximum or the total completion time of its jobs. Considering an identical parallel machines environment, the goal is to find an assignment and a schedule of jobs which represents the best compromise between the requirements of the agents. The class of multi-agent scheduling problems has drawn a significant interest to researchers in the area of scheduling and operational research. When both agents minimize the makespan, we prove that the number of Pareto solutions is bounded and we show that this bound is reached. Using the (varepsilon )-constraint approach, we propose two integer programming formulations that allow to obtain the exact Pareto front for each problem. Given that the studied problems are NP-hard, we propose genetic algorithms (NSGA-II) to generate approximated Pareto fronts. Computational experiments are conducted to analyze the performances of the proposed methods. The results indicate that the genetic algorithms provide high-quality Pareto fronts and are computationally efficient.

Cloud RAN, a novel architecture for modern mobile networks, relocates processing units from antenna to distant data centers. This shift introduces the challenge of ensuring low latency for the periodic messages exchanged between antennas and their respective processing units. In this study, we tackle the problem of devising an efficient periodic message assignment scheme under the constraints of fixed message size and period without contention nor buffering. We address this problem by modeling it on a common network topology, wherein contention arises from a single shared link servicing multiple antennas. While reminiscent of coupled task scheduling, the introduction of periodicity adds a unique dimension to the problem. We study how the problem behaves with regard to the load of the shared link, and we focus on proving that, for load as high as possible, a solution always exists and it can be found in polynomial time. The main contributions of this article are two polynomial time algorithms, which find a solution for messages of any size and load at most 2/5 or for messages of size one and load at most (phi - 1), the golden ratio conjugate. We also prove that a randomized greedy algorithm finds a solution on almost all instances with high probability, shedding light on the effectiveness of greedy algorithms in practical applications.

The tool’s life statistics module in CNC machining centers typically associates tool’s usage time with the program’s running duration, leading to the tool idle time being logged as a loss in tool life. This often triggers premature tool replacements. To enhance scheduling accuracy across multiple machining centers, we leverage spindle current variations to discern between tool loads and idle periods. Initially, real-time data from the machining center was gathered, and we employed the three-parameter Weibull Distribution method, using 1.351 (A) as the threshold to distinguish between idle and loaded tool states. Subsequently, we proposed a refined method to calculate the tool’s available time, enabling a more precise estimation of its remaining operational lifespan. We further devised a scheduling approach for multiple CNC machining centers based on the tool’s availability time. Ultimately, empirical trials exhibited a 10% increase in average cutting tool utilization efficiency and a 12.5% enhancement in machining center productivity.

We consider the classical machine scheduling, where n jobs need to be scheduled on m machines, and where job j scheduled on machine i contributes (p_{ij}in mathbb {R}) to the load of machine i, with the goal of minimizing the makespan, i.e., the maximum load of any machine in the schedule. We study the inefficiency of schedules that are obtained when jobs arrive sequentially one by one, and the jobs choose the machine on which they will be scheduled, aiming at being scheduled on a machine with a small load. We measure the inefficiency of a schedule as the ratio of the makespan obtained in the worst-case equilibrium schedule, and of the optimum makespan. This ratio is known as the sequential price of anarchy (SPoA). We also introduce two alternative inefficiency measures, which allow for a favorable choice of the order in which the jobs make their decisions. As our first result, we disprove the conjecture of Hassin and Yovel (Oper Res Lett 43(5):530–533, 2015) claiming that the sequential price of anarchy for (m=2) machines is at most 3. We show that the sequential price of anarchy grows at least linearly with the number n of players, assuming arbitrary tie-breaking rules. That is, we show ({textbf {SPoA}} in Omega (n)). At the end of the paper, we show that if an authority can change the order of the jobs adaptively to the decisions made by the jobs so far (but cannot influence the decisions of the jobs), then there exists an adaptive ordering in which the jobs end up in an optimum schedule.

The short-term scheduling of activities in underground mines is an important step in mining operations. This procedure is a challenging optimization problem since it deals with many resources and activities conducted in a confined working space. Moreover, underground mining operations deal with multiple uncertainties such as the variation of activity durations. In this paper, a constraint programming (CP) model is proposed for short-term planning in underground mines. The developed model takes into account the technical requirements of underground operations to build realistic mine schedules. Furthermore, two different approaches are proposed based on the CP model for robust short-term underground mine scheduling. The first approach aims to create a robust schedule using multiple scenarios of the problem. This stochastic CP model enables to find a set of ordered robust sequences of activities performed by each available disjunctive resource over several scenarios. In the second approach, a confidence constraint is introduced in the CP model to specify the probability that the schedule generated would not underestimate the duration of activities. The model allows the mine planner to control the risk level with which an optimized solution should be produced such that it can be implemented given the actual activity durations. The presented approaches are tested on real data sets of an underground gold mine in Canada. An evaluation model is designed to evaluate the robust performance of the proposed models. The experiments demonstrate that both scenario-based and confidence-constraint approaches outperform the deterministic model by generating schedules that are more robust to uncertainties in underground operations.

The paper discusses the organization of the International Timetabling Competition (ITC 2019), which intends to motivate further research on complex university course timetabling problems coming from practice. Thanks to the UniTime timetabling system, we have collected a strong set of benchmark instances with diverse characteristics for the competition. The key novelty lies in the combination of student sectioning with standard time and room assignment of particular course events. The paper analyzes the real-world course timetabling problems present in the competition. The characteristics of thirty competition instances are described together with their representative features, which are discussed institution by institution. The existing solvers are described and compared based on their competition, current, and time-limited results whenever available. As of October 2023, the competition website has about 490 registered users from 66 countries worldwide and is kept up to date with new results.

We consider the medical student scheduling (MSS) problem, which consists of assigning medical students to internships of different disciplines in various hospitals during the academic year to fulfill their educational and clinical training. The MSS problem takes into account, among other constraints and objectives, precedences between disciplines, student preferences, waiting periods, and hospital changes. We developed a local search technique, based on a combination of two different neighborhood relations and guided by a simulated annealing procedure. Our search method has been able to find the optimal solution for all instances of the dataset proposed by Akbarzadeh and Maenhout (Comput Oper Res 129: 105209, 2021b), in a much shorter runtime than their technique. In addition, we propose a novel dataset in order to test our technique on a more challenging ground. For this new dataset, which is publicly available along with our source code for inspection and future comparisons, we report the experimental results and a sensitivity analysis.

Spatiotemporal scheduling of the block assembly process in shipbuilding is to determine temporal information including a time period when each block is under assembly and spatial information including an assigned bay and the placement of the block within the assigned bay. Due to a large number of discrete variables to optimize, finding an optimal schedule in a reasonably short time is almost impossible. Instead, we divide the problem into two phases where the first phase determines the bay assignment and processing start date for each block and the second phase determines the coordinates and rotation of each block in its assigned bay. Our objective is to find a block assembly schedule that minimizes the number of blocks that fail to be processed on time and unfairness in workloads across bays and days. The proposed algorithm is tested on six datasets of block information provided by Korea Shipbuilding & Offshore Engineering Co., Ltd. (KSOE). Our algorithm speeds up the scheduling process and finds schedules of higher quality compared to the original schedules that are manually planned by KSOE.