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Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, the issue of determining an optimal age replacement is explored by incorporating minimal repair, preventive replacement, and corrective replacement into a k-out-of-n system subject to shocks. The k-out-of-n system in question consists of n identical components, and functions if at least k components function. Each shock causes the failure of a random number of components; if at least n-k + 1 components fail, the system fails and implements corrective replacement, otherwise the system is minimally repaired. The system is scheduled to implement preventive replacement before failure at age T or at the complement of a random working cycle, whichever occurs first or last. In order to balance the bias time between preventive replacement and corrective replacement, a replacement bias cost is also taken into account for planning replacement policies. The present paper develops a two-phase maintenance methodology and determines the optimal number of components and preventive replacement age that minimize the expected cost rate functions. For each model, the expected cost rate function is formulated analytically and optimized theoretically, and a numerical example is given to illustrate the applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

A non-linear integer programming formulation is developed to solve a production scheduling problem at an industrial consumer goods producer. Its production runs are assigned to pre-set time slots economically sized for long setups and changeovers. The mathematical model applies to a mix of backlogged, or not, sales orders involving multi-product, multi-stage production with finite capacity, material, and storage constraints. It aims at maximizing various strategic objectives (direct margin, resource utilization, and customer service) over rolling workday horizons. Real-life scheduling problems are solved to illustrate the efficiency and applicability of the proposed optimization framework. The results indicate that minimizing stockouts over a 40-workday period during peak season leads to absolute two-digit service level improvements compared to man-made schedules. [ABSTRACT FROM AUTHOR]

A number of results have been reported in the literature for a single machine job scheduling problem with resource constraints. We demonstrate that many of these results and some new results follow from an equivalence of this problem and the classical two-machine flow-shop scheduling problem. We further refine computational complexity of the problem with resource constraints by presenting new NP-hardness proofs. [ABSTRACT FROM AUTHOR]

AbstractThis paper investigates a scheduling imperfect maintenance policy for an operating system that works at random times for multiple jobs (n tandem jobs or n parallel jobs). We consider the system suffers from type-I failure which is corrected by a minimal repair, or type-II failure, which is disaster and is eliminated by a corrective maintenance. To control the deterioration process, preventive maintenance is design to go through at a scheduling time T or the completion of multiple jobs, whichever occurs last. Each maintenance is performed imperfectly, the system improves yet its failure characteristic is also changed after maintenance. Lastly, the system is displaced at the N-th maintenance. On the basis minimizes the mean cost rate, this paper derived the optimal scheduling parameters (T*, n*, N*) analytically and numerically, according to its existence and uniqueness. The models we proposed will provide a general structure for maintenance theory of reliability. [ABSTRACT FROM AUTHOR]

In this study, we consider a system consisting of n different components with failure interaction. Each component suffers from either minor or major failure: the former is removed via minimal repair, while the latter induces complete failure of the system and requires corrective replacement of the system. This study investigates two scheduling problems of preventive replacement policies for a multi-component system that incorporates minimal repair, shortage, and excess costs. First, we consider the scheduling problem of an age replacement policy, in which the system is replaced at a planned time T or upon the occurrence of any major failure. Next, we consider that the system operates N successive random jobs without interruptions. In this scheduling problem, the system is replaced upon the accomplishment of N jobs or upon the occurrence of any major failure. For each scheduling problem, we derive the optimal scheduling parameter ( T * or N * ) analytically and numerically, according to their existence and uniqueness based on minimizing the mean cost rate function. Finally, a numerical example is designed to validate the theoretical results in this article. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have significant potential and sustainability in the future transportation sector due to their less pollution and low operating cost. The uncoordinated charging of these EVs may push the power delivery system towards lower performance and even at the risk of damaging the power system equipment. The proper scheduling and coordination of EVs with the system may lessen the negative impact. In this paper, a multi-objective scheduling approach for individual candidate-based EVs is proposed to relieve the distress in power delivery in the best possible way. Minimization of average demand deviation and system unbalance reduction are selected as the objectives to restore the power delivery performance. The proposed approach has been verified on a realistic three-phase power distribution infrastructure. A simple linear regression model is employed to predict the near future number of EVs in the test region. An optimizitaion algorithm based on particle swarm optimization (PSO) technique is operated to find the finest solution satisfying EV charging constraints. The stochastic nature of load demands at the node points of the test network is analyzed with the help of a probabilistic demand model and it is further incorporated into the optimization framework. It is possible to reduce the system's average demand deviation and unbalance level by 53.74% and 51.36%, respectively, with the proposed EV scheduling approach. A comparative study with the existing methods further claims the efficacy of the proposed approach. Framework for home charging schedule of distinct EVs Compliance with multi-objective power supply efficiency Probabilistic modeling of load demand from real collected data Optimization-driven solution for a test case in Mysore city in India [ABSTRACT FROM AUTHOR]

This paper proposes a new scheduling problem for patient visits with two objectives: minimizing patient waiting time and travel time. It also presents a novel encoding method for Genetic Algorithms (GA) that is well-suited for this problem. Experiments demonstrate that the proposed encoding method reduces optimization iterations by 17% compared to conventional methods, and the GA can decrease waiting time by up to 58.2% and travel time by up to 89.3% for specific examples. The novel scheduling problem and the encoding method are two main contributions of this work. [ABSTRACT FROM AUTHOR]

The modernization of forest harvesting operations has significantly increased the cost of machine ownership and has turned forest harvesting into a capital-intensive process. To increase productivity and profitability, some companies have acquired multi-task harvesting machines. While many previous papers focused on optimizing the harvest scheduling to reduce the costs of harvesting, the assignment of multi-task machines was not considered in their models. In this work, an optimization model is developed for the detailed scheduling of harvesting activities on multiple cut blocks using multi-task machines. This model is a continuation of previous work on detailed harvest scheduling. It prescribes the start time and the end time of operations of each machine at each cut block, the number of machines to be assigned for each harvesting activity at each cut block, the cut block that the machine should move to after completing its operation at a cut block, and the type of activity it should perform. It is applied to a case study of a forest company in Canada. According to the results, the total harvesting cost decreased by Can$ 25,000 when multi-task machines were used compared to exclusive machines, due to less machine movement and the need for fewer machines. [ABSTRACT FROM AUTHOR]

More and more vehicles are connected today via emerging connected and automated vehicle (CAV) technologies. An intriguing application of CAVs is to cross intersections without stops through cooperative scheduling by traffic control infrastructure. Nonetheless, with the increase of CAVs' requests for green, two problems will surface: (I) accommodating too many CAVs' green requests will generate severe interruptions to general traffic; (II) simple scheduling policies like first-come-first serve is inappropriate due to heterogeneous importance of CAVs. To overcome these challenges, we present a mixed-integer linear programming (MILP) formulation for congestion-aware heterogeneous CAV scheduling problems at intersections in this paper. The objective is to ensure that intensive and heterogeneous green requests by CAVs can be scheduled at intersections while the mobility of background traffic is still maintained. The MILP formulation is developed in the context of discrete space-time and phase-time networks whose variables are space-time arc choice variables with respect to individual vehicles and phase-time arc choice variables. We also build an efficient search algorithm based on the "A-D curves" for real-time applications. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware CAV scheduling reveal promising results for real-world applications in the future. [ABSTRACT FROM AUTHOR]

There are many factors to consider when deciding which technologies to use in forest operations and how to plan their use. One important factor is the overall cost when choosing between the established two-machine system (TMS) with a harvester and a forwarder, and a one-machine system with a harwarder in final fellings. Such considerations can be done with different model approaches, all of which have their strengths and weaknesses. The aim of this study was to analyze and compare the TMS and harwarder potential using a Detailed Optimization (DO) approach and an Aggregated Heuristic (AH) approach. The main differences are the aggregation of seasons, including machine system teams, and spatial considerations. The analyses were done for one full year of final fellings for a large forest company's region in central Sweden, containing information necessary for calculating costs for logging, relocation between stands and traveling between the operator's home bases and the stands. The approaches were tested for two scenarios; when only TMS were available, and when both TMS and harwarders were available. The main results were that the approaches coincided well in both potential to decrease total costs when harwarders where available, and distribution of TMS and harwarders. There were some differences in the results, which can be explained by differences in thecalculation approach. It was concluded that the DO approach is more suitable when detailed analyses are prioritized, and the AH approach is more suitable when a more approximate analysis will suffice or the available resources for making the analysis are more limited. [ABSTRACT FROM AUTHOR]

Extreme heat events induced by climate change present a growing risk to transit passenger comfort and health. To reduce exposure, agencies may consider changes to schedules that reduce headways on heavily trafficked bus routes serving vulnerable populations. This paper develops a schedule optimization model to minimize heat exposure and applies it to local bus services in Phoenix, Arizona, using agent-based simulation to inform travel demand and rider characteristics. Rerouting as little as 10% of a fleet is found to reduce network-wide exposure by as much as 35% when operating at maximum fleet capacity. Outcome improvements are notably characterized by diminishing returns, owing to skewed ridership and the inverse relationship between fleet size and passenger wait time. Access to spare vehicles can also ensure significant reductions in exposure, especially under the most extreme temperatures. Rerouting, therefore, presents a low-cost, adaptable resilience strategy to protect riders from extreme heat exposure. [ABSTRACT FROM AUTHOR]

We consider a two-machine no-wait permutation flow shop common due date assignment scheduling problem where the processing time of a job is given as a function of its position in the sequence and its amount of resource allocated to this job. The common due date (CON) assignment method means that all the jobs are given a common due date. We need to make a decision on the common due date, resource allocation and the sequence of jobs to minimise total earliness, tardiness, common due date cost and total resource cost. We show that the problem remains polynomially solvable under the proposed model. [ABSTRACT FROM AUTHOR]

Motivated by the behavioral phenomena that occur while human operators are carrying out tasks, we study multitasking scheduling problems with a rate-modifying activity. In the problems, the processing of a selected task suffers from interruptions by other tasks that are available but unfinished, and the human operators regularly engage rest breaks during work shifts allowing them to recover or mitigate some of the negative effects of fatigue. The objectives are to respectively minimize: makespan, total completion time, maximum lateness, and due-date assignment related cost by determining when to schedule the rate modifying activity and the optimal task sequence in the presence of multitasking. Scheduling models and algorithms are proposed to solve the problems. The numerical examples are presented to illustrate the theorems and algorithms. [ABSTRACT FROM PUBLISHER]

Solving the practical train timetabling problem under complex real-life train operation environment is challenging. This article addresses the train timetabling problem considering the variant parameters (i.e. running time and minimum headway) depending on stop-decisions. Based on a resource-oriented decomposition representation of safety headway, the train timetabling is modeled by cumulative flow variables considering the variant parameters depending on stop-decisions. A Lagrangian relaxation-based approach (LR) is used to decompose the combinatorial train timetabling problem into train-independent shortest path sub-problems, which can be solved simultaneously by parallel computation by relaxing the capacity constraint. A capacity assessment-based heuristic is proposed for improving the feasibility reparing of LR solutions. The solution quality and efficiency are analyzed employing the real-life operational data of Wuhan to Guangzhou high-speed railway in China. The benefits of the improved heuristic and parallel computation are demonstrated in contrast with the existed approach. [ABSTRACT FROM AUTHOR]

The berth scheduling problem (BSP) at container ports has received significant research attention. Nevertheless, few studies considered the BSP under emergent ship arrivals. Thus, this paper proposes an optimization procedure for scheduling and sequencing of ship arrivals at container port under the occurrence of unexpected events; emergent ship arrivals. Three consecutive models are proposed to maximize the number of served emergent ships at minimal disturbance to service schedule of regular ships. The first model schedules and sequences service of emergent ships at available idle berth(s). The remaining unscheduled emergent ships are then assigned by the second model, which attempts to serve those ships in untapped ranges between the scheduled regular ships. If some emergent ships are still unassigned in the untapped ranges, the third model finally resequences the service schedule of regular ships at the berth of the largest free margin to serve those emergent ships. A case study is employed to illustrate the proposed optimization procedure; where the results showed that the proposed models efficiently solved BSP at acceptable satisfaction levels for both regular and emergent ships. In conclusion, the proposed models may provide valuable support to decision makers in dealing with BSP under emergent ship arrivals. [ABSTRACT FROM AUTHOR]

In many industries, production capacity diminishes as machine conditions deteriorate. Maintenance operations improve machine conditions, but also occupy potential production time, possibly delaying the customer orders. Therefore, one challenge is to determine the joint maintenance and production schedule to minimize the combined costs of maintenance and lost production over the long term. In this paper, we address the problem of integrated maintenance and production scheduling in a deteriorating multi-machine production system over multiple periods. Assuming that at the beginning of each period the demand becomes known and machine conditions are observable, we formulate a Markov decision process model to determine the maintenance plan and develop sufficient conditions guaranteeing its monotonicity in both machine condition and demand. We then formulate an integer programming model to find the maintenance and the production schedule in each period. Our computational results show that exploiting online condition monitoring information in maintenance and production decisions leads to 21% cost savings on average compared to a greedy heuristic and that the benefit of incorporating long-term information in making short-term decisions is highest in industries with medium failure rates. [ABSTRACT FROM PUBLISHER]

This article addresses several two-agent scheduling problems in the presence of multitasking. Under multitasking, when a task is being executed, it is inevitably interrupted by the tasks that have not been finished, and the interruption time is proportional to the remaining processing time of the interrupting task. Each agent desires to minimize a certain cost function that depends only on its own tasks' completion times. Different cost functions are considered for both agents, including the maximum of regular cost, total completion time, and (weighted) number of tardy tasks. The purpose is to determine a feasible solution for all tasks of the two agents that minimizes the cost value of the first agent while maintaining the cost value of the other agent, not exceeding a given threshold value. Polynomial and pseudo-polynomial time algorithms are proposed to solve the setting, involving various combinations of cost functions. [ABSTRACT FROM AUTHOR]

With the increasing global demands for energy, fuel supply management is a challenging task of today's industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations. [ABSTRACT FROM AUTHOR]

Network lifetime remains as a significant requirement in Wireless Sensor Network (WSN) exploited to prolong network processing. Deployment of low power sensor nodes in WSN is essential to utilize the energy efficiently. Clustering and sleep scheduling are the two major processes involved in improving network lifetime. However, abrupt and energy unaware selection of cluster head (CH) is non-optimal in WSN which reflects in the drop of energy among sensor nodes. This paper addresses the twofold as utilization of sensor nodes to prolong the node's energy and network lifetime by LEACH-based cluster formation and Time Division Multiple Access scheduling (TDMA). Clusters are constructed by the design of an Enhanced-Low-Energy adaptive Clustering Hierarchy protocol (E-LEACH) that uses parallel operating optimization (Grey Wolf Optimization (GWO) and Discrete Particle Swarm Optimization (D-PSO)) for selecting an optimal CH and helper CH. The fitness values estimation from GWO and D-PSO is concatenated to prefer the best optimal CH. E-LEACH also manages the cluster size which is one of the conventional disadvantages in LEACH. CHs are responsible to perform energy-aware TDMA scheduling which segregates the coverage area into 24 sectors. Alternate sectors are assigned to be operated on any one of the three states as sense, transmit and sleep. Lastly, for mitigating packet loss, a channel is chosen between CH and sink node using the dynamic fuzzy algorithm. The extensive performances are evaluated in terms of energy consumption, throughput, delay, packet loss and network lifetime. [ABSTRACT FROM AUTHOR]

In recent years, the number of stakeholders of construction projects has significantly increased; this has required the simultaneous achievement of competing objectives, such as reductions in the time, cost, resources, and environmental impact of a project, for example. In order to achieve a balance between these objectives, several multiple-objective construction scheduling models have been reported in the literature. However, several challenges have been encountered, due to the complexities of modelling and visualizing more than three objectives simultaneously. Some of these challenges are addressed in this work via the development of a many-objective scheduling model (MOSM) based on a non-dominated sorting genetic algorithm for the simultaneous optimization of four objectives: time, cost, resources and environmental impact. Coordinate plots are used to visualize the trade-offs made between all four of these objectives. A weighted sum is introduced that offers the project team the freedom to choose an optimal solution, depending on the specific priorities of the project, and the practical application of the model is demonstrated via a case study. Our MOSM allows the optimal outcome to be achieved in construction projects with multiple objectives. [ABSTRACT FROM AUTHOR]

Technological developments have propelled the deployment of robots in many applications, which has led to the trend to integrate an increasing number of uncertain processes into robotic and automated equipment. We contribute to this domain by considering the scheduling of a dual-gripper robotic cell. For systems with one potential bottleneck, we determine conditions under which the widely used swap sequence does not guarantee optimality or even feasibility and prove that optimal schedules can be derived under certain conditions when building on two types of slack we introduce. With the addition of a third type of slack and the concept of fixed partial schedules, we develop an offline-online scheduling approach that, in contrast with previous work, is able to deal with uncertainty in all process steps and robot handling tasks, even under pick-up constraints. The approach can deal with single- or multiple-bottleneck systems, and is the first approach that is not restricted to a single predefined sequence such as the swap sequence. Our approach is well suited for real-world applications, since it generates cyclic schedules and allows integration into commonly-used frameworks for robotic-cell scheduling and control. We demonstrate the applicability of our approach to cluster tools in semiconductor manufacturing, showing that our approach generates feasible results for all tested levels of uncertainty and optimal or near-optimal results for low levels of uncertainty. With additional symmetry-breaking constraints, the model can be efficiently applied to industrial-scale test instances. We show that reducing uncertainty to below 10% of the processing time would yield significantly improved cycle lengths and throughput. We also demonstrate that the widely used swap sequence only finds solutions for less than 1% of the instances when strict pick-up constraints are enforced and processing times are heterogeneous. As our approach finds feasible solutions to all of these instances, it enables the application of robotic cells to a significantly broader application environment. [ABSTRACT FROM AUTHOR]

This article addresses an appointment scheduling problem in which the server responds to congestion of the service system. Using waiting time as a proxy for how far behind schedule the server is running, we characterize the congestion-induced behavior of the server as a function of a customer's waiting time. Decision variables are the scheduled arrival times for a specific sequence of customers. The objective of our model is to minimize a weighted cost incurred for a customer's waiting time, server overtime and server speedup in response to congestion. We provide alternative formulations of this problem as a Simulation Optimization (SO) model and a Stochastic Integer Programming (SIP) model, respectively. We show that the SIP model can solve moderate-sized instances exactly under certain assumptions about a server s response to congestion. We further show that the SO model achieves near-optimal solutions for moderate-sized problems while also being able to scale up to much larger problem instances. We present theoretical results for both models and we carry out a series of experiments to illustrate the characteristics of the optimal schedules and to measure the importance of accounting for a server s response to congestion when scheduling appointments using a case study for an outpatient clinic at a large medical center. Finally, we summarize the most important managerial insights obtained from this study. [ABSTRACT FROM AUTHOR]

The paper describes a simulation-based optimization system for manufacturing processes. The principle suitability of simple Threshold Accepting and Great Deluge Algorithms for optimizing practical manufacturing systems is shown, as well as the possibility of parallelizing of these algorithms. A special IP Multicast architecture is introduced which allows the simulation model to be extended to multiple clients and so the efficiency of the optimization algorithms to be increased by concurrent simulation. Each client can log on or log out at an optimization server at any time so that it should be possible to use the idle computer capacity of a single production department or a whole company. The optimization server is implemented as an experimental option in the simulation system simcron MODELLER®, which is particularly used in electronics and semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

From the economical viewpoint in reliability theory, this paper addresses a scheduling replacement problem for a single operating system which works at random times for multiple jobs. The system is subject to stochastic failure which results the imperfect maintenance activity based on some random failure mechanism: minimal repair due to type-I (repairable) failure, or corrective replacement due to type-II (non-repairable) failure. Three scheduling models for the system with multiple jobs are considered: a single work, N tandem works, and N parallel works. To control the deterioration process, the preventive replacement is planned to undergo at a scheduling time T or the job's completion time of for each model. The objective is to determine the optimal scheduling parameters (T* or N*) that minimizes the mean cost rate function in a finite time horizon for each model. A numerical example is provided to illustrate the proposed analytical model. Because the framework and analysis are general, the proposed models extend several existing results. [ABSTRACT FROM AUTHOR]

The most important problem in a Wireless Sensor Network (WSN) is to optimize the use of its limited energy provision, so that it can fulfil its monitoring task as long as possible. Among known available approaches that can be used to improve power management, lifetime coverage optimization provides activity scheduling which ensures sensing coverage while minimizing the energy cost. In this article an approach called Perimeter-based Coverage Optimization protocol (PeCO) is proposed. It is a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion. The novelty of the approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter-coverage level to schedule sensors' activities. Extensive simulation experiments demonstrate that PeCO can offer longer lifetime coverage for WSNs compared to other protocols. [ABSTRACT FROM AUTHOR]

Planning activities of harvest teams (harvesting and forwarding) and transportation is critical for efficient procurement of roundwood from forests to mills. The planning process involves many integrated decisions that consider process, spatial and temporal aspects. The spatial aspect concerns which area to harvest, which machine team to use, the mill to which the timber should be allocated and where to store the timber. The process decisions involve which bucking instruction to use. The temporal aspect concerns when to harvest, when to transport in order to meet specific demand at mills, and when to store the timber. Temporal decisions also include determining a detailed schedule for each harvest team. Such a schedule includes starting time and movement time between harvest areas. This is complicated by the harvest team having different home bases and different machine systems with their specific performance description and capacities. The overall planning problem can be formulated into one optimization model, but such a model is too large for practical use and cannot be solved in a reasonable time. We propose a decomposition scheme where a sequence of aggregated models, or limited parts of the model, is solved to find high-quality solutions quickly. We test the scheduling in cases involving two large Swedish forest companies. [ABSTRACT FROM AUTHOR]

Chemotherapy infusion treatments for cancer have significant and unpredictable variability in duration. This variability can have negative impact on operations – both patient wait time and staff overtime – if not managed well. From an appointment scheduling optimization perspective, this problem has a unique structure because a single server (a nurse) attends to multiple customers (patients) at one time. Based on our observations at the University of Michigan Comprehensive Cancer Center (UMCCC) and collaborations with clinicians there, we present a two-stage stochastic integer program for designing patient appointment schedules under uncertainty in treatment times. The objective is to minimize a trade-off between expected patient wait times and expected total time required to treat patients. We show that solving this optimization problem exactly requires a prohibitive computational time, so we develop a heuristic algorithm to find approximate solutions. We also present an approach to compute lower bounds on the optimal objective value that we use to analyze the performance of our algorithm. Computational experiments based on real-world data are presented and used to draw managerial insights. [ABSTRACT FROM PUBLISHER]

Hospital decision and policy makers are tasked with developing innovative strategies to provide patients with quality healthcare in an effective and efficient manner. Operations research (OR) methods have been applied to hospital operations to improve effectiveness and efficiency. In this three-part article, we review OR applications in hospital environments. In particular, we develop a timeline of events in US healthcare from the late 1940s to 2015 and separate the timeline into four eras:Expansion, Cost Control, Reform, andAccountability. Part I of the article describes the Eras of Expansion and Cost Control, Part II describes the Era of Reform, and Part III describes the Era of Accountability. Research performed during each era is contextualized and stratified by OR method and hospital operations application area. The series of three articles provides a comprehensive review of publications detailing operations research applications in hospital operations. The following is Part III, which details the OR applications in hospital operation area publications from 2010 to January 2015. During the Era of Accountability, the most commonly used OR methods were discrete event simulation and deterministic modeling (optimization), while the most common hospital operation areas where OR methods were applied were staff, room, and patient scheduling, as well as general patient flow assessment. [ABSTRACT FROM PUBLISHER]

This paper presents a computational framework for the optimization of maintenance activities for infrastructure assets, with particular emphasis being placed on road network assets. This framework incorporates degradation and maintenance models for infrastructure assets along with multi-objective optimization for searching optimal maintenance schedules. Given a schedule of maintenance actions, the future performance is estimated by means of a Monte Carlo simulation that enables to account for inherent uncertainties. The design variables of optimization are the types of maintenance actions and their timing over the planning horizon. The objectives are to minimize both the asset degradation and maintenance cost. This includes satisfaction of constraints representing performance demands. The proposed framework is general and can be applied to different types of infrastructure assets. The numerical results, obtained for a road bridge managed by a highway operating agency, demonstrate the validity and usefulness of the proposed framework. [ABSTRACT FROM AUTHOR]

In a recent article, Guo, Cheng and Wang proposed a randomized search algorithm, called modified generalized extremal optimization (MGEO), to solve the quay crane scheduling problem for container groups under the assumption that schedules are unidirectional. The authors claim that the proposed algorithm is capable of finding new best solutions with respect to a well-known set of benchmark instances taken from the literature. However, as shown in this note, there are some errors in their work that can be detected by analysing the Gantt charts of two solutions provided by MGEO. In addition, some comments on the method used to evaluate the schedule corresponding to a task-to-quay crane assignment and on the search scheme of the proposed algorithm are provided. Finally, to assess the effectiveness of the proposed algorithm, the computational experiments are repeated and additional computational experiments are provided. [ABSTRACT FROM PUBLISHER]

In this paper we study the effects of using ruin and recreate methods in a replanning phase of a dynamic dial-a-ride problem. Several such methods are proposed, and a modeling system is used to evaluate how they improve the quality of the solutions. We show that simple changes to existing planning methods can increase the efficiency of the service. Two cases, with different forms of costs inflicted on the vehicles, are evaluated and significant improvements are found in both cases. The best results of our study are found with ruin methods based on removal of sequences of requests. [ABSTRACT FROM AUTHOR]

Scheduling elective surgeries involves sequential decision-making on the part of the operating room (OR) manager, who must continually balance the costs of deferring waiting cases and blocking higher-priority cases. While surgery scheduling has received extensive treatment in the literature, this paper presents the first modeling approach to capture this aspect of the process while incorporating block schedules, block release policies, and surgical waiting lists. The result is a stochastic dynamic programming formulation for the evolution of the schedule for a single day in an OR suite over the days leading up to the day of surgery. A general formulation is presented and theoretical results are obtained for a single-room version. These results demonstrate that optimal waiting list decisions for a single OR follow a threshold policy that preserves a desired amount of OR time for the remaining demand from the room's allocated surgical specialty. An algorithm for determining the optimal thresholds is presented, followed by computational results. [ABSTRACT FROM PUBLISHER]

Store Steel Ltd. faces the problem of producing a huge amount (approximately 1400) of different steel compositions in relatively small quantities (approximately 15 tons). This production is performed in batches of predetermined quantities (50-53 tons). The purpose of this article is to present the methodology for optimizing the production of predetermined steel grades in predetermined quantities before a customer's set deadline in such a way as to reduce the non-planned and ordered quantities with the date ahead of the deadline and to minimize the number of batches. The genetic algorithm method was used for optimization. The results of the genetic algorithm-based batch filling scheduling have been used in practice since 2006. The non-planned and ordered steel quantities with the date ahead of the deadline have been reduced from 17.17% to 10.12% since then. [ABSTRACT FROM AUTHOR]

Five common options for workforce flexibility and their robustness under uncertain demand are investigated. In the first stage, a firm makes optimal staffing decisions according to estimated demand and a given workforce flexibility policy. In the second stage, it reallocates its workforce to react to demand shocks. Numerical results are presented that show that flexibility can lead a firm to staff with too little slack to be flexible to demand shocks, thus leading to higher total costs, i.e., staffing and inventory costs. The forms of flexibility that give robust benefits are identified and an analysis on how different forms of flexibility interact with each other is performed. [Supplemental materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following supplemental resource: Appendix with additional tables of results.] [ABSTRACT FROM AUTHOR]

An automated model is developed to support the optimization of the planning and scheduling of repetitive construction projects. The model provides the capability of optimizing two important objectives commonly sought in scheduling repetitive construction projects: minimizing project duration; and minimizing project cost. The model performs this multi-objective optimization using a genetic algorithm approach. The output of the model is a set of optimal solutions that represent the trade-off between time and cost in planning repetitive construction projects. Furthermore, the model can be utilized to find a single scheduling solution that provides the minimum overall project cost by simply adding project indirect cost to the obtained project direct cost for each of the obtained scheduling solutions on the Pareto optimal curve. Other important time-related costs are also considered in the model including: early completion incentives, late completion penalties and lane rental costs. Providing the planners of repetitive construction projects with an automated set of optimal time-cost trade-off solutions should contribute to cost-effective and speedy delivery of this type of construction project. An application example is analysed to illustrate the use of the model and demonstrate its capabilities in generating optimal trade-off solutions between minimizing the project time and cost for repetitive construction projects. [ABSTRACT FROM AUTHOR]

The ISCI (Industry Standard Commercial Identification) rotator problem requires the assignment of a Set of commercial videotapes to slots such that multiple airings of each tape in the set are as equally spaced as possible with respect to an L[sub1] loss function. The contributions of this research paper are threefold. First, an enhanced branch-and-bound algorithm is presented and shown to produce optimal solutions for each of several problems that were not computationally feasible for a previously developed branch-and-bound implementation. Second, a fast simulated annealing heuristic is developed for larger problems. This heuristic has established new benchmark (best-known) solutions for 16 large ISCI rotator problems from a recently published test suite. Third, we consider alternative loss function metrics, and have found that a branch-and-bound algorithm for the L∞ algorithm is especially scalable. [ABSTRACT FROM AUTHOR]

Every year the English football authorities produce a set of fixtures for the four main divisions in England. Over the Christmas and New Year period every team has to play two fixtures; one being played at their home venue and the other at an opponent's venue. There are various other constraints that also have to be respected with the overall objective being to minimize the total distance travelled by all teams. In this paper, I formally define the problem, discuss the data collection that I have undertaken and present the algorithm (which is based on depth first search, followed by a local search) I have developed. Using data from four seasons, I show that I am able to produce better schedules than those currently used. [ABSTRACT FROM AUTHOR]

In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of assigning employees to a number of work centres taking into account employees' expressed preferences for specific shifts, off-days, and work centres. This particular problem is faced by the Kuwait National Petroleum Corporation that hires a firm to prepare schedules for assigning employees to about 86 stations distributed all over Kuwait. The number of variables in a mixed- integer programming model formulated for this problem is overwhelming, and hence, a direct solution to even the continuous relaxation of this model for relatively large-scale instances is inconceivable. However, we demonstrate that a column generation method, which exploits the special structures of the model, can readily solve the continuous relaxation of the model. Based on this column generation construct, we develop an effective heuristic to solve the problem. Computational results indicate that the proposed approach can facilitate the generation of good-quality schedules for even large-scale problem instances in a reasonable time. [ABSTRACT FROM AUTHOR]

The Royal Australian Navy's Patrol Boat Force carries out essential tasks in the surveillance, policing and defence of Australia's coastal waters. To help the Navy make efficient use of a new generation of boats, the authors have developed optimization procedures to schedule the activities of the boats and their crews. The procedures—embodied in a software system called CBM ('Crews, Boats, Missions')—use simulated annealing and specialized heuristic techniques within a multi-stage problem-solving framework. Tests show that CBM is reliable in terms of solution quality, and flexible with respect to the range of scheduling conditions applied. CBM has proved valuable to the Navy as an investigatory tool, and it is planned that it should be adapted for operational use, as part of a decision support system to aid in the ongoing management of patrol boat operations. [ABSTRACT FROM AUTHOR]

The paper discusses the application of a paradigm for creating scheduling systems for steel reheating furnaces. The proposed paradigm utilizes a modified version of a Genetic Algorithm (GA) to optimize such schedules via new ways of realizing the crossover and mutation operations. The work was conducted in collaboration with the Thrybergh Combination Mill owned by CORUS- Sheffield (UK). The outcome of this research work is a novel scheduling system which links together the scheduling and furnace controls and is also able to accept new and already established mill heuristics. The proposed methodology is 'flexible' as well as 'generic' as it can be augmented easily in order to suit other industrial set-ups. [ABSTRACT FROM AUTHOR]

A scenario where multiple air vehicles are required to prosecute geographically dispersed targets is considered. Furthermore, multiple tasks are to be successively performed on each target, that is, the targets must be classified, attacked, and verified as destroyed. The optimal, for example, minimum time, performance of these tasks requires cooperation among the vehicles such that critical timing constraints are satisfied, that is, a target must be classified before it can be attacked, and an air vehicle is sent to a target area to verify its destruction only after the target has been attacked. In this paper, the optimal task assignment/scheduling problem is posed as a mixed-integer linear program (MILP). The solution of the MILP assigns all tasks to the vehicles and performs the scheduling in an optimal manner, including staged departure times. Coupled tasks involving timing and task order constraints are automatically addressed. When the air vehicles have sufficient endurance, the existence of a solution is guaranteed. [ABSTRACT FROM AUTHOR]

We present a method to increase the utilization of and reduce the waiting times for an under-capacitated diagnostic resource in the presence of uncertain demand with several priority levels. We consider the case of a computed tomography (CT) scanning department that services both high-priority in-patients and lower priority outpatients. Current practice calls for all in-patient demand to be met on the day of the request. Our proposal looks at the benefit of reserving space for carrying over a percentage of non-emergency in-patient demand to the next day and utilizing a pool of on-call outpatients who can respond quickly to available capacity. We formulate and solve an optimization problem that returns a reservation policy that minimizes unused capacity subject to an overtime constraint. We use a simulation to demonstrate a significant reduction in the growth rate of outpatient waiting time resulting from using the proposed method and investigate the sensitivity of results to several model assumptions. [ABSTRACT FROM AUTHOR]

In this paper, we examine crane scheduling for ports. This important component of port operations management is studied when the non-crossing spatial constraint, which is common to crane operations, is considered. We assume that ships can be divided into holds and that cranes can move from hold to hold but jobs are not pre-emptive, so that only one crane can work on one hold or job to complete it. Our objective is to minimize the latest completion time for all jobs. We formulate this problem as an integer programming problem. We provide the proof that this problem is NP- complete and design a branch-and-bound algorithm to obtain optimal solutions. A simulated annealing meta-heuristic with effective neighbourhood search is designed to find good solutions in larger size instances. The elaborate experimental results show that the branch-and-bound algorithm runs much faster than CPLEX and the simulated annealing approach can obtain near optimal solutions for instances of various sizes. [ABSTRACT FROM AUTHOR]

A railway system needs a substantial amount of maintenance. To prevent unexpected breakdowns as much as possible, preventive maintenance is required. In this paper we discuss the preventive maintenance scheduling problem (PMSP), where (short) routine activities and (long) unique projects have to be scheduled in a certain period. To reduce costs and inconvenience for the travellers and operators, these activities should be scheduled together as much as possible. We present two versions of the PMSP, one with fixed intervals between two consecutive executions of the same routine work, and one with only a maximum interval. Apart from giving a math programming formulation for the PMSP and for its extension we also present some heuristics. In addition, we compare the performance of these heuristics with the optimal solution using some randomly generated instances. [ABSTRACT FROM AUTHOR]

This paper deals with discrete-continuous problems of planning and scheduling jobs that cannot be pre-empted The need for reusability and modularity leads us to build a generic simulation and optimization framework, which is described in this contribution. The validation of our platform is presented in terms of an application to an actual highly constrained discrete-continuous problem. [ABSTRACT FROM AUTHOR]

The scheduling of pumps for clean water distribution is a partially discrete non-linear problem with many variables. The scheduling method described in this paper typically produces costs within 1% of a linear program basic solution, and can incorporate realistic non-linear costs that may be hard to incorporate in linear programming formulations. These costs include pump switching and maximum demand charges. A simplified model is derived from a standard hydraulic simulator. An initial schedule is produced by a descent method. Two-stage stimulated annealing produces solutions in a few minutes. Iterative recalibration ensures that the solution agrees closely with the results from a full simulation. [ABSTRACT FROM AUTHOR]

Airlines spend considerable time, effort and financial resources on planning. It is essential to create a competitive timetable and construct a fleet and a crew schedule that utilizes these resources to the maximum. Unfortunately, it is all too common that an airline is faced with the necessity of reconstructing their schedules due to some unforeseen event, for example an aircraft breakdown or a crew member that is indisposed. In this paper, an application that can help airlines solve the complex problem of reconstructing aircraft schedules is presented. A mixed integer multicommodity flow model with side constraints is developed and further reformulated into a set packing model using the Dantzig--Wolfe decomposition. Cancellations, delays and aircraft swaps are used to resolve the perturbation, and the model ensures that the schedule returns to normal within a certain time. Two column generation schemes for heuristically solving the model are tested on real problem data obtained from a Swedish domestic airline. The computational tests show that the application is capable of presenting high quality solutions in a few seconds and therefore can be used as a dynamic decision support tool by the airlines. [ABSTRACT FROM AUTHOR]

An increasing interest in batch processing has been evident in recent years. This renewed interest is explained by the inherent flexibility of such plants that permits a high level of response to uncertain market conditions and requirements. This level of response does require the use of efficient tools to help the decision-making process at the design and operational level. This paper presents a Mixed Integer Linear Program (MILP) model to optimize the scheduling of batch facilities subject to changeovers and distribution constraints so as to guarantee a predefined objective. Such an objective can be defined as the minimum orders' total lateness or the maximum distribution units loading capacity, among others. A continuous-time representation is used as well as the concept of job predecessor and successor to effectively handle changeovers. Facilities having non-identical parallel units/lines, sequence-dependent orders, finite release times for units and orders, restrictions on the suitability of jobs to lines/units and different possible destinations to available distribution units are also considered. Based on these characteristics the proposed model is able to determine the optimal allocation of jobs to production lines/units, the sequence of jobs on every line/unit and the starting and completion production times of each order. Also, the usage and allocation of the distribution resources (eg trucks) to orders and destinations are obtained based on their availability and suitability to the orders. The model led to the development of a prototype information system that can be used as a tool to help the decision-making process at the operational plant level. Finally, the applicability of the proposed system/formulation is shown through the resolution of an industrial real case where the production of polymers is performed. [ABSTRACT FROM AUTHOR]