The challenge of user requirements for maintenance scheduling design in large asset-intensive industries suffers from lack of academic and empirical studies. Therefore, using a representative case study, this paper aims to: (1) identify the current practices and complex scheduling requirements; (2) propose a design support tool to optimize the maintenance scheduling process; and (3) report the gained benefits. The results reveal that the proposed tool can decrease the resource requirements, increase the capacity utilization, and reduce the cost while addressing the complex user requirements.

Little research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

Introduction: Emergency physicians (EP) often work at undesirable hours. In response to deleterious effects on quality of life for EPs, traditional 2300-0700 night shifts have been replaced at some centres with staggered 6-hour casino shifts (22:00-04:00 and 04:00-10:00). Though purported to allow for better sleep and recovery patterns, no evidence exists to support the benefits on sleep or quality of life that is used to justify a casino shift model. Using a before and after survey model, this study examines the impact of overhauling night work from a traditional 8-hour shift to casino shifts on the quality of life and job satisfaction of EPs working in an academic emergency department (ED). Methods: In 2010, an initial online, 37-item survey, was sent to all EPs working in the ED, just prior to the transition to casino shifts. 6 years following the transition, a slightly modified 37-item survey was again distributed to all current EPs working at that same centre. Participants rated their level of agreement on a 7-point Likert scale regarding questions related to night work. Results from the two surveys were compared. Results: 43 2010- and 47 2016-surveys were completed. In 2016, recovery to baseline function after a single early shift (22:00-04:00) was most common after 1 day at 52.4%, and after multiple early shifts was ≥2 days at 66.7%. Recovery after a single late shift (04:00-10:00) was most common at 1 day at 54.8%, and after multiple late shifts was ≥2 days at 59.5%. This was in contrast to 2010, when 55.8% recovered from a single traditional night shift after 1 day, and 95.3% required ≥2 days to recover from multiple traditional night shifts. In relation to casino shifts, 40.5% of respondents stated that night shifts are the greatest drawback of their job, compared to 79.1% previously. A minority of respondents felt that teaching (36.5%), diagnostic test interpretation (23.2%), and quality of handover (33.5%) were inferior on early and late night shifts compared to other shifts (74.4%, 58.1%, and 60.5% for traditional night shifts respectively).95.0% of respondents preferred casino over traditional night shifts. Conclusion: There were self-reported improvements in all domains following the implementation of casino shifts.

We study a nonpreemptive scheduling on two parallel identical machines with a dedicated loading server and a dedicated unloading server. Each job has to be loaded by the loading server before being processed on one of the machines and unloaded immediately by the unloading server after its processing. The loading and unloading times are both equal to one unit of time. The goal is to minimize the makespan. Since the problem is NP-hard, we apply the classical list scheduling and largest processing time heuristics, and show that they have worst-case ratios, $8/5$ and $6/5$, respectively.

We analyse a parallel (identical) machine scheduling problem with job delivery to a single customer. For this problem, each job needs to be processed on $m$ parallel machines non-pre-emptively and then transported to a customer by one vehicle with a limited physical capacity. The optimization goal is to minimize the makespan, the time at which all the jobs are processed and delivered and the vehicle returns to the machine. We present an approximation algorithm with a tight worst-case performance ratio of $7/3-1/m$ for the general case, $m\geq 3$.

Nowadays, clusters of multicores are becoming the norm and, although, many or-parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared and distributed memory architectures. In recent work, we have proposed a novel computational model specially designed for such combination which introduces a layered model with two scheduling levels, one for workers sharing memory resources, which we named a team of workers, and another for teams of workers (not sharing memory resources). In this work, we present a first implementation of such model and for that we revive and extend the YapOr system to exploit or-parallelism between teams of workers. We also propose a new set of built-in predicates that constitute the syntax to interact with an or-parallel engine in our platform. Experimental results show that our implementation is able to increase speedups as we increase the number of workers per team, thus taking advantage of the maximum number of cores in a machine, and to increase speedups as we increase the number of teams, thus taking advantage of adding more computer nodes to a cluster.

The problem of scheduling input applications can be examined by extending conventional production function analysis. Using appropriately designed agricultural experiments, it is possible to estimate production function parameters with alternative specifications for input timing (and amount). A study of nitrogen applications to rice is employed to illustrate scheduling via production functions. Alternative specifications and functional forms are simultaneously examined to determine the sensitivity of economic results to these factors. Sensitivity is found to be high, and this finding is hypothesized to be critical for other approaches to input scheduling as well.

In this study, we consider a scheduling environment with m(m ≥ 1) parallel machines. The set of jobs to schedule is divided into K disjoint subsets. Each subset of jobs is associated with one agent. The K agents compete to perform their jobs on common resources. The objective is to find a schedule that minimizes a global objective function f0, while maintaining the regular objective function of each agent, fk, at a level no greater than a fixed value, εk (fk ∈ {fkmax, ∑fk}, k = 0, ..., K). This problem is a multi-agent scheduling problem with a global objective function. In this study, we consider the case with preemption and the case without preemption. If preemption is allowed, we propose a polynomial time algorithm based on a network flow approach for the unrelated parallel machine case. If preemption is not allowed, we propose some general complexity results and develop dynamic programming algorithms.

We consider partial customer flexibility in service systems under two different designs. In the first design, flexible customers have their own queue and each server has its own queue of dedicated customers. Under this model, the problem is a scheduling problem and we show under various settings that the dedicated customers first (DCF) policy is optimal. In the second design, flexible customers are not queued separately and must be routed to one of the server's dedicated queues upon arrival. We extend earlier results about the ‘join the smallest work (JSW)’ policy to systems with dedicated as well as flexible arrivals. We compare these models to a routeing model in which only the queue length is available in terms of both efficiency and fairness and argue that the overall best approach for call centers is JSW routeing. We also discuss how this can be implemented in call centers even when work is unknown.

This paper presents a heuristic approach combining constraint satisfaction, local search and a constructive optimization algorithm for a large-scale energy management and maintenance scheduling problem. The methodology shows how to successfully combine and orchestrate different types of algorithms and to produce competitive results. We also propose an efficient way to scale the method for huge instances. A large part of the presented work was done to compete in the ROADEF/EURO Challenge 2010, organized jointly by the ROADEF, EURO and Électricité de France. The numerical results obtained on official competition instances testify about the quality of the approach. The method achieves 3 out of 15 possible best results.

A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion.

We consider the scheduling of an interval order precedence graph of unit execution time tasks with communication delays, release dates and deadlines. Tasks must be executed by a set of processors partitioned into K classes; each task requires one processor from a fixed class. The aim of this paper is to study the extension of the Leung–Palem–Pnueli (in short LPP) algorithm to this problem. The main result is to prove that the LPP algorithm can be extended to dedicated processors and monotone communication delays. It is also proved that the problem is NP–complete for two dedicated processors if communication delays are non monotone. Lastly, we show that list scheduling algorithm cannot provide a solution for identical processors.

The coupled tasks scheduling problem is a class of scheduling problems introduced for beam steering software of sophisticated radar devices, called phased arrays. Due to increasing popularity of such radars, the importance of coupled tasks scheduling is constantly growing. Unfortunately, most of the coupled tasks problems are NP-hard, and only a few practically usable algorithms for such problems were found. This paper provides a survey of already known complexity results of various variants of coupled tasks problems. Then, it complements previous results by providing experimental results of two new polynomial algorithms for coupled tasks scheduling, which are: an exact algorithm for 1|(1,4,1),strictchains|Cmax problem, and a fast heuristic algorithm for more general 1|(1,2k, 1), strictchains|Cmax problem, where k ∈ ℕ.

This paper deals with the parallel-machine scheduling problem with the aim of minimizing the total (weighted) tardiness under the assumption of different release dates. This problem has been proven to be NP-hard. We introduce some new lower and upper bounds based on different approaches. We propose a branch-and-bound algorithm to solve the weighted and unweighted total tardiness. Computational experiments were performed on a large set of instances and the obtained results showed that our algorithms are efficient.

In this paper, we study the problem of makespan minimization for the multiprocessor scheduling problem in the presence of communication delays. The communication delay between two tasks i and j depends on the distance between the two processors on which these two tasks are executed. Lahlou shows that a simple polynomial-time algorithm exists when the length of the schedule is at most two (the problem becomes 𝒩𝒫-complete when the length of the schedule is at most three). We prove that there is no polynomial-time algorithm with a performance guarantee of less than 4/3 (unless 𝒫 = 𝒩𝒫) to minimize the makespan when the network topology is a chain or ring and the precedence graph is a bipartite graph of depth one. We also develop two polynomial-time approximation algorithms with constant ratio dedicated to cases where the processor network admits a limited or unlimited number of processors.

This paper studies scheduling problems which include a combination of nonlinear job deterioration and a time-dependent learning effect. We use past sequence dependent (p-s-d) setup times, which is first introduced by Koulamas and Kyparisis [Eur. J. Oper. Res.187 (2008) 1045–1049]. They considered a new form of setup times which depend on all already scheduled jobs from the current batch. Job deterioration and learning co-exist in various real life scheduling settings. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and a function of the total normal processing time of jobs scheduled prior to it. The following objectives are considered: single machine makespan and sum of completion times (square) and the maximum lateness. For the single-machine case, we derive polynomial-time optimal solutions.

Dans ce papier, nous traitons le problème de minimisation du makespan dans un flow shop hybride à deux étages avec machines dédiées. En premier lieu, nous présentons des propriétés de base, un ensemble de bornes inférieures et deux cas polynomiaux. En second lieu, nous proposons une nouvelle heuristique qui exploite ces propriétés, et cherche à placer les jobs, en tenant compte pour chaque instance du problème, de la valeur de la borne inférieure. La dernière partie de ce travail présente les résultats expérimentaux d'une étude comparative avec une heuristique de la littérature. L'analyse de ces résultats permet d'apprécier la qualité de notre proposition.

Linear programming techniques can be used in constructing schedules but their application is not trivial. This in particular holds true if a trade-off has to be made between computation time and solution quality. However, it turns out that – when handled with care – mixed integer linear programs may provide effective tools. This is demonstrated in the successful approach to the benchmark constructed for the 2007 ROADEF computation challenge on scheduling problems furnished by France Telecom.