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In project scheduling, the durations of activities are often uncertain. Delays may cause a massive disorganization if a large number of activities must be rescheduled. In "The Anchor-Robust Project Scheduling Problem," Bendotti, Chrétienne, Fouilhoux, and Pass-Lanneau propose a novel criterion for solution stability in project scheduling under processing times uncertainty. They define anchored jobs as jobs whose starting times can be guaranteed in a baseline schedule. Finding a project schedule with bounded makespan and a max-weight set of anchors is shown to be an NP-hard robust two-stage problem. Taking advantage of the combinatorial structure of project scheduling and budgeted uncertainty, the authors obtain a compact MIP formulation for the problem. Numerical results show that the obtained MIP outperforms standard techniques from the literature. They also showcase the practical interest of anchored jobs in project scheduling. In project scheduling with uncertain processing times, the decision maker often needs to compute a baseline schedule in advance while guaranteeing that some jobs will not be rescheduled later. Standard robust approaches either produce a schedule with a very large makespan or offer no guarantee on starting times of the jobs. The concept of anchor-robustness is introduced as a middle ground between these approaches. A subset of jobs is said to be anchored if the starting times of its jobs in the baseline schedule can be guaranteed. The Anchor-Robust Project Scheduling Problem (AnchRobPSP) is proposed as a robust two-stage problem to find a baseline schedule of bounded makespan and a max-weight subset of anchored jobs. AnchRobPSP is considered for several uncertainty sets, such as box or budgeted uncertainty sets. Dedicated graph models are presented. In particular, the existence of a compact mixed integer programming reformulation for budgeted uncertainty is proven. AnchRobPSP is shown to be NP-hard even for budgeted uncertainty. Polynomial and pseudopolynomial algorithms are devised for box uncertainty and special cases of budgeted uncertainty. According to numerical results, the proposed approaches solve large-scale instances and outperform classical affine decisions rules for AnchRobPSP. Insights on the price of anchor-robustness are given based on computations. [ABSTRACT FROM AUTHOR]

Erratum to "Competitive Two-Agent Scheduling and Its Applications" The paper "Competitive Two-Agent Scheduling and Its Applications" by Leung, Pinedo, and Wan appeared in Operations Research in 2010. Because competitive agent scheduling has become an active area of research, the paper has garnered its share of citations over the years. This particular direction of scheduling research has become so popular that the book Multiagent Scheduling: Models and Algorithms by Agnetis, Billaut, Gawiejnowicz, Pacciarelli, and Soukhal appeared in 2014. However, one of the results in the Leung et al. paper had a minor error in it. The paper considers a two-agent scheduling problem with a single machine, with each one of the two agents having their own set of jobs. The first agent has as the objective the minimization of the total tardiness of her set of jobs, and the second agent has to ensure that the total completion time of her set of jobs is less than or equal to Q. Leung et al. showed, through a dynamic programming formulation, that this problem can be solved in pseudopolynomial time and determined the time complexity of their algorithm. Their proof contains a minor error. The proper algorithm is still pseudopolynomial but of a slightly higher time complexity. Leung et al. (2010) [Leung JY-T, Pinedo M, Wan G (2010) Competitive two-agent scheduling and its applications. Oper. Res. 58:458–469] considered a two-agent nonpreemptive single-machine scheduling problem. Agent A is responsible for n1 jobs with due dates d 1 , ... , d n and has as the objective the minimization of the total tardiness of the n1 jobs. Agent B is responsible for n2 jobs and has as the objective the minimization of the total completion time of the n2 jobs. The problem is to find a schedule for the n 1 + n 2 jobs that minimizes the objective of agent A (with regard to his n1 jobs) while keeping the objective of agent B (with regard to his n2 jobs) below or at a fixed level Q. Leung et al. (2010) [Leung JY-T, Pinedo M, Wan G (2010) Competitive two-agent scheduling and its applications. Oper. Res. 58:458–469], in their theorem 3, showed that this problem can be solved through dynamic programming in pseudopolynomial time. However, in the proof of their theorem and in their dynamic programming formulation, there is an error that requires some changes in their proof. [ABSTRACT FROM AUTHOR]

Jobs in modern parallel-computing frameworks, such as Hadoop and Spark, are subject to several constraints. In these frameworks, the data are typically distributed across a cluster of machines and is processed in multiple stages. Therefore, tasks that belong to the same stage (job) have a collective completion time that is determined by the slowest task in the collection. Furthermore, a task's processing time is machine dependent, and each machine is capable of processing multiple tasks at a time subject to its capacity. In "Scheduling Parallel-Task Jobs Subject to Packing and Placement Constraints," by Mehrnoosh Shafiee and Javad Ghaderi, multiple approximation algorithms with theoretical guarantees are provided to solve the problem under preemptive and nonpreemptive scenarios. The numerical results, using a real traffic trace, demonstrate that the algorithms yield significant gains over the prior approaches. Motivated by modern parallel computing applications, we consider the problem of scheduling parallel-task jobs with heterogeneous resource requirements in a cluster of machines. Each job consists of a set of tasks that can be processed in parallel; however, the job is considered completed only when all its tasks finish their processing, which we refer to as the synchronization constraint. Furthermore, assignment of tasks to machines is subject to placement constraints, that is, each task can be processed only on a subset of machines, and processing times can also be machine dependent. Once a task is scheduled on a machine, it requires a certain amount of resource from that machine for the duration of its processing. A machine can process (pack) multiple tasks at the same time; however, the cumulative resource requirement of the tasks should not exceed the machine's capacity. Our objective is to minimize the weighted average of the jobs' completion times. The problem, subject to synchronization, packing, and placement constraints, is NP-hard, and prior theoretical results only concern much simpler models. For the case that migration of tasks among the placement-feasible machines is allowed, we propose a preemptive algorithm with an approximation ratio of (6 + ϵ). In the special case that only one machine can process each task, we design an algorithm with an improved approximation ratio of four. Finally, in the case that migrations (and preemptions) are not allowed, we design an algorithm with an approximation ratio of 24. Our algorithms use a combination of linear program relaxation and greedy packing techniques. We present extensive simulation results, using a real traffic trace, that demonstrate that our algorithms yield significant gains over the prior approaches. Funding: This work was supported by the National Science Foundation [Grants CNS-1652115 and CNS-1717867]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2021.2198. [ABSTRACT FROM AUTHOR]

We consider queueing systems with general abandonment.Abandonment times are approximated by a particular Cox distribution with all phase exponential rates being the same.We prove that this distribution arbitrarily closely approximates any nonnegative distribution. By explicitly modeling thewaiting time of the first customer in line,we obtain a natural bounded jump Markov process allowing for uniformization. This approach is useful to solve, via dynamic programming, various optimization problems where the objectives and/or constraints involve the distributions of the performance measures, not only their expected values. It is also useful for the performance analysis of queueing systems with general abandonment times. [ABSTRACT FROM AUTHOR]

We consider the problem of appointment scheduling with discrete random durations but under the more realistic assumption that the duration probability distributions are not known and only a set of independent samples is available, e.g., historical data. For a given sequence of appointments (jobs, tasks), the goal is to determine the planned starting time of each appointment such that the expected total underage and overage costs due to the mismatch between allocated and realized durations is minimized. We use the convexity and subdifferential of the objective function of the appointment scheduling problem to determine bounds on the number of independent samples required to obtain a provably near-optimal solution with high probability. [ABSTRACT FROM AUTHOR]

One of the central trends in the optimization community over the past several years has been the steady improvement of general-purpose solvers. A logical next step in this evolution is to combine mixed-integer linear programming, constraint programming, and global optimization in a single system. Recent research in the area of integrated problem solving suggests that the right combination of different technologies can simplify modeling and speed up computation substantially. Nevertheless, integration often requires special-purpose coding, which is time consuming and error prone. We present a general-purpose solver, SIMPL, that allows its user to replicate (and sometimes improve on) the results of custom implementations with concise models written in a high-level language. We apply SIMPL to production planning, product configuration, machine scheduling, and truss structure design problems on which customized integrated methods have shown significant computational advantage. We obtain results that either match or surpass the original codes at a fraction of the implementation effort. [ABSTRACT FROM AUTHOR]

This paper reports on an application of network-flow integer programming to a vacation timeshare exchange problem. A typical timeshare owner has purchased yearly access to a specific week at a specific resort. The resulting lack of vacation variety is mitigated by systems that allow owners to exchange owned weeks for different weeks at different resorts according to their preferences, the assessed value of what they are exchanging, their contractual priority, and resort availability. The timeshare exchange problem is similar to other preference-based assignment problems such as labor scheduling, preferential bidding, and traditional timetabling, but different in the formulation of the objective function. This paper demonstrates how the effectiveness of timeshare exchange processes can be improved through mathematical optimization, as measured by increased satisfaction of participant preferences. Optimization also presents exchange managers with the opportunity to more precisely manage preference and priority trade-offs among various classes of participants. The trade-off decisions are aided by sensitivity analysis utilizing a minmax criterion. [ABSTRACT FROM AUTHOR]