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Precision and ultra-precision surfaces are crucial for many products – quality optics, joint & cranial implants, turbine blades, and industrial moulds & dies, to name a few. Automation in this context is distinct from standard procedures in industry, where the identical sequence of operations can be repeated over and over again. Ultraprecision tolerances may be tens to hundreds of times tighter, and this is compounded by the hundreds of diverse substrate materials in use. Even with modern computer numerically controlled (CNC) machines, skilled craftspeople are needed to plan a process-chain for a new material or geometry. Processes working at these tight tolerances, fall short of being fully-deterministic, so repeated process-metrology iterations are required. Surface-correction loops may be automated, but expert assessment should be performed at each step to check for unexpected anomalies. The ultimate goal of importing a part, processing autonomously, and delivering a finished part to an “optical” specification with no human intervention, is still a long way off. This paper describes the challenge and why it is important. It then melds together process-monitoring, psychology, artificial intelligence and robotics, to take a far-sighted view of how the ultimate goal can be realised.

Many disasters that have happened in the last decades, including the latest COVID-19 pandemic, have caused a shortage of healthcare resources and change in healthcare operations. Given these impacts, the Operational Research (OR) community has applied various approaches to improve the emergency medical responses. Coordination of healthcare facilities is one of the emergency medical response strategies to ensure the continued provision of medical services during disasters. Although the existing literature reviews of OR approaches have included the perspective of healthcare management, they focused mostly on the application of OR in disaster operations and logistics management. The importance of coordination in healthcare systems during disasters is well recognised in the literature, but to the best of our knowledge there has been no review of the published research in this area. This study provides a focused literature review of the OR contributions in the coordination in healthcare systems during disasters. Definitions of the terms in use in this field are provided. An overall descriptive statistics of the reviewed articles is given, followed by the review of the presented research problems, disaster types, and developed methodologies. The main characteristics of models for the coordination in the healthcare system are described. Measures of coordination effectiveness that denote healthcare resilience are discussed. Based on our findings, we suggest future research directions in the context of existing models extension, and application and development of other methodologies with the aim to provide a solid basis for OR research in the healthcare disaster management.

Introduction. Urrets-Zavalia syndrome (UZS) has been defined as a fixed and dilated pupil accompanied by iris atrophy and occasionally secondary glaucoma. The precise cause of the syndrome is uncertain. Most often it has been described following anterior segment surgery. The objective of this article is to present how to successfully handle patients with UZS after posterior segment surgery. We present all the dilemmas and difficulties we encountered during the diagnostic process. Case outline. This is a case presentation of a patient with UZS following scleral buckle procedure. To our knowledge, this is the first case of UZS following this type of posterior segment surgery. The delay in treatment was mostly due to the lack of knowledge about the linkage of this syndrome with posterior segment surgery. Once the diagnosis was confirmed, parasympathomimetic drops were administered. The patient responded well to the therapy and partial reduction of mydriasis and restoration of pupillary kinetics was observed. Conclusion. Two months after surgery, the treatment of UZS resulted in slight residual anisocoria with signs of iris atrophy. This could indicate reversible mechanism of UZS after posterior segment surgery with iris atrophy as the only permanent consequence.

Four patients with traumatic mydriasis and aphakia following blunt injury of the eye globe were evaluated. Patients with severe glare and photophobia due to wide pupil diameter from 6.0 to 9.0 mm were managed by combined iris cerclage pupilloplasty and retropupillar iris-claw lens implantation. The postoperative anatomic results, visual acuity, subjective degree of glare, photophobia, as well as intraoperative and postoperative complications were evaluated. The mean follow-up time was 32.6 months. Best-corrected visual acuity improved in all patients from preoperative 20/60, 20/30, 20/25, 20/22 to postoperative 20/20, 20/22, 20/20, and 20/20 (Snellen charts). All eyes achieved satisfactory anatomic result with round pupil diameter 3.5–4.5 mm. Glare and photophobia disappeared in all patients. There were no intraoperative or postoperative complications. During the follow-up period, pupils remained round and iris-claw lenses well positioned, without tilting. Combined iris cerclage pupilloplasty with retropupillar iris claw lens implantation appears to be a safe and effective surgical technique in reducing pupil size and improving visual outcomes. It is also a cost- and time-effective procedure, providing great results with a single surgery.

Increasing energy price and requirements to reduce emission are new chal-lenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop envi-ronment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness. Keywords: Energy efficient production planning

Buildings consume a significant amount of energy today and are expected to consume even more in the future. This consumption necessitates the use of fossil fuels such as coal and natural gas, both of which have significant environmental impacts. While renewable energy sources remain promising, the most of the energy supply will still use conventional fuels in the near term. Therefore, improving the energy efficiency in buildings is critical, and one of the central visions of “smart buildings” is to reduce their energy use while maintaining the same level of service and comfort. However, to make the buildings meaningfully “smart”, their envelopes must first be made compliant with the current energy efficiency standards. In this paper we first examine how the public awareness of energy efficiency was risen in Serbia through different demonstration projects, funded by the state budget and through implementation of the energy efficiency measures in public buildings, funded by municipal funds and soft loans from the banks. Then, we describe how the energy efficiency in buildings might further be increased by the use of new technologies and smart networks for control of the energy consumption. We finally argue that these controls should take into account the personal variables (activity, clothing) along with environmental variables (air temperature, velocity, and humidity) for an optimum thermal comfort to be achieved in public and residential buildings.

The paper describes the determination of Ag, Al, B, Ba, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, In, K, Li, Mg, Mn, Na, Ni, Pb, Sr, Tl and Zn, as well as total fat content of milk samples, originated from different sources. The analyzed milk samples were: human milk, fresh cow milk, pasteurized cow milk from a local market, and reconstituted powder milk. The milk samples were obtained from Jablanica District (Serbia) territory. Preparation of samples for macro- and micro-analyses was done by wet digestion. Concentrations of the elements after digestion were determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Total fat content of milk samples was determinate by the Weibull and Stoldt method. The results showed that potassium and calcium concentrations were the highest in all samples: 1840.64 - 2993.26 mg/L and 456.05 - 1318.08 mg/L, respectively. Of all heavy metals from the examined milk samples (copper, zinc, manganese, nickel, cadmium, and lead), the most common were zinc and copper, with approximately similar content in the range of 5 - 12 mg/l, while cadmium nickel and manganese were not detected at all. Samples of fresh cow milk and human milk showed the highest fat content of 3.6 and 4.2 %, respectively. Results for total fat and macro- and micro-analyses showed that fresh cow milk has the highest contents of fat and calcium, making it the most nutritious. [Projekat Ministarstva nauke Republike Srbije, br. TR 34012]

Introduction. Primary acquired iris stromal cyst is rare in adults. In this group, they are generally stable lesions which require no treatment. Case outline. We describe a rare case of a small primary iris cyst in a 39-year-old patient, associated with unusual signs of irritation. Ultrasound biomicroscopy demonstrated iris stromal cyst measuring 3 ? 2 mm. A neodymium-doped yttrium aluminium garnet (Nd:YAG) laser cystotomy was engaged as the least invasive treatment approach. However, the cyst recurred soon after repeated laser treatment and sector iridectomy with excision of the cyst was performed. Five years after surgery there was no evidence of recurrence. Conclusion. Although more benign clinical course of primary stromal iris cyst is generally assumed in adults as compared to children, complete cyst removal seems to be mandatory for preventing cyst recurrence regardless of the cyst size or patient age. To the authors? knowledge this is the first documented report of Nd:YAG laser photodisruption of acquired primary iris stromal cyst in an adult.

Detection and quantification of Candida spp. from subgingival areas using two methods- sterile periodontal curette and sterile paper points.

[Context] Models play an important role in Software and Systems Engineering processes. Reviews are well-established methods for model quality assurance that support early and efficient defect detection. However, traditional document-based review processes have limitations with respect to the number of experts, resources, and the document size that can be applied. [Objective] In this paper, we introduce a distributed and scalable review process for model quality assurance to (a) improve defect detection effectiveness and (b) to increase review artifact coverage. [Method] We introduce the novel concept of Expected Model Elements (EMEs) as a key concept for defect detection. EMEs can be used to drive the review process. We adapt a best-practice review process to distinguish (a) between the identification of EMEs in the reference document and (b) the use of EMEs to detect defects in the model. We design and evaluate the adapted review process with a crowdsourcing tool in a feasibility study. [Results] The study results show the feasibility of the adapted review process. Further, the study showed that inspectors using the adapted review process achieved results for defect detection effectiveness, which are comparable to the performance of inspectors using a traditional inspection process, and better defect detection efficiency. Moreover, from a practical perspective the adapted review process can be used to complement inspection efforts conducted using the traditional inspection process, enhancing the overall defect detection effectiveness. [Conclusions] Although the study shows promising results of the novel process, future investigations should consider larger and more diverse review artifacts and the effect of using limited and different scopes of artifact coverage for individual inspectors.

Many manufacturing companies in China currently are suffering from a Rolling Blackout policy for the industry electricity supply which means that the government electricity is cut off several days in every week resulting in manufacturing companies illegally starting their own diesel generators to maintain production. However, the private generation of electricity is more polluting and costly than the government supplied resource. Thus, the increased price of energy and the requirement to become more environmentally sustainable exert substantial pressures on manufacturing enterprises to reduce energy consumption for cost saving and to become more environmentally friendly. Scheduling of less energy consumption critical operations during Rolling Blackout periods can help minimise the negative effect of this policy. This is a multi-objective optimisation problem as production due dates cannot be ignored and cost is not directly proportional to electricity consumption anymore. Optimal scheduling even of relatively small production orders is clearly beyond the capability of manual tools or common single objective scheduling optimisation methods. Therefore, a multi-objective scheduling optimisation method has been developed which includes reducing electricity consumption and its related cost as part of the objectives in addition to total weighted tardiness. This research focuses on classical job shop environments which are widely used in the manufacturing industry in China and the rest of the world. A mathematical model for the tri-objectives problem that minimises total electricity cost, total electricity consumption and total weighted tardiness has been developed. A specific heuristic has been devised for investigating how the Rolling Blackout policy affects the performance of existing scheduling plans. This heuristic can also be used as a remedial measurement by plant managers if they do not have access to multi-objective optimisation tools. The Non-dominant Sorting Genetic Algorithm has been used as the basis for solving the optimisation problem. Case studies based on four modified job shop instances have been studied to show the effectiveness of the proposed heuristic and the algorithm.

Fruit juice is the product obtained by mechanical processing of one or more types of technologically ripe, fresh, chilled or frozen fruit, which is not but can be fermented, the taste, color and aroma of which must be specific to the type of fruit from which the fruit juice is produced. Fruit juices are important in the human diet since they contain different nutrients, especially vitamins, minerals and carbohydrates. In this paper the content of Ag, Al, B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Sr, Zn, Si, As, P, Mo and Sb in clear orange juices found the local market on the territory of Leskovac was investigated. The preparation of the samples was done by wet digestion. The concentrations of elements after digestion were determined by Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES). Based on the obtained results it can be concluded that all investigated samples of clear orange juices do not have the required level of potassium and phosphorus. On the other side, due to high levels of molybdenum and arsenic, the consumption of clear orange juices in higher amounts could lead to a greater intake of these elements and potentially unwanted adverse effects.

Pellet stoves may play an important role in Serbia in the future when fossil fuel fired conventional heating appliances are replaced by more efficient and environmentally friendly devices. Experimental investigation was conducted in order to examine the influence of wood pellet quality, as well as burner capacity (6, 8 and 10 kW), used in the same stove configuration, on the performance of pellet stove with declared nameplate capacity of 8 kW. The results obtained showed that in case of nominal load and combustion of pellets recommended by the stove manufacturer, stove efficiency of 80.03% was achieved. The use of lower quality pellet caused additional 1.13 kW reduction in heat output in case of nominal load and 0.63 kW in case of reduced load. This was attributed to less favourable properties and lower bulk and particle density of lower quality pellet. The use of different burner capacity has shown to have little effect on heat output and efficiency of the stove when pre-set values in the control system of the stove were not altered. It is concluded that replacement of the burner only is not sufficient to increase/decrease the declared capacity of the same stove configuration, meaning that additional measures are necessary. These measures include a new set up of the stove control system, which needs to be properly adjusted for each alteration in stove configuration. Without the adjustment mentioned, declared capacity of the stove cannot be altered, while its CO emission shall be considerably increased.

Context. Expert sourcing is a novel approach to support quality assurance: it relies on methods and tooling from crowdsourcing research to split model quality assurance tasks and parallelize task execution across several expert users. Typical quality assurance tasks focus on checking an inspection object, e.g., a model, towards a reference document, e.g., a requirements specification, that is considered to be correct. For example, given a text-based system description and a corresponding model such as an Extended Entity Relationship (EER) diagram, experts are guided towards inspecting the model based on so-called expected model elements (EMEs). EMEs are entities, attributes and relations that appear in text and are reflected by the corresponding model. In common inspection tasks, EMEs are not explicitly expressed but implicitly available via textual descriptions. Thus, a main improvement is to make EMEs explicit by using crowdsourcing mechanisms to drive model quality assurance among experts. Objective and Method. In this paper, we investigate the effectiveness of identifying the EMEs through expert sourcing. To that end, we perform a feasibility study in which we compare EMEs identified through expert sourcing with EMEs provided by a task owner who has a deep knowledge of the entire system specification text. Conclusions. Results of the data analysis show that the effectiveness of the crowdsourcing-style EME acquisition is influenced by the complexity of these EMEs: entity EMEs can be harvested with high recall and precision, but the lexical and semantic variations of attribute EMEs hamper their automatic aggregation and reaching consensus (these EMEs are harvested with high precisions but limited recall). Based on these lessons learned we propose a new task design for expert sourcing EMEs.

In the context of Industrie 4.0, we have previously described the roles of robots in optical processing, and their complementarity with classical CNC machines, providing both processing and automation functions. After having demonstrated robotic moving of parts between a CNC polisher and metrology station, and auto-fringe-acquisition, we have moved on to automate the wash-down operation. This is part of a wider strategy we describe in this paper, leading towards automating the decision-making operations required before and throughout an optical manufacturing cycle.

Traditional Software Inspection is a well-established approach to identify defects in software artifacts and models early and efficiently. However, insufficient method and tool support hinder efficient defect detection in large software models. Recent Human Computation and Crowdsourcing processes may help to overcome this limitation by splitting complex inspection artifacts into smaller parts including a better control over defect detection tasks and increasing the scalability of inspection tasks. Therefore, we introduce a Crowdsourcing-Based Inspection (CSI) process with tool support with focus on inspection teams and the quality of defect detection. We evaluate the CSI process in a feasibility study involving 63 inspectors using the CSI process and 12 inspectors using a traditional best-practice inspection process. The CSI process was found useful by the participants. Although the preliminary results of the study were promising, the CSI process should be further investigated with typical large software engineering models.

Recent advances in the column technology have led to the development of small particles as stationary phase carriers in reversed-phase liquid chromatography for separation of large scale of molecules. This work represents an application of UHPLC (ultrahigh performance liquid chromatography with 1.9 μm C-18 particles) coupled with ESI-MS (electro spray ionization-mass spectrometry) and DAD (diode array) analysis for the identification of the mixture of porphyrins such as hemato­porphyrin and protoporphyrin derivatives, widely known photosensitizers used in medicine. Five porphyrin derivatives were separated and identified in the hemato­porphyrin mixture: hematoporphyrin IX, protoporphyrin IX, two hydroxyethyl-vinyl-derivatives and methyl-ester of protoporphyrin IX.

The application of best-practice software inspection processes for early defect detection requires considerable human effort. Crowdsourcing approaches can support inspection activities (a) by distributing inspection effort among a group of human experts and (b) by increasing inspection control. Thus, the application of crowdsourcing techniques aims at making inspection processes more effective and efficient. In this paper, we present a crowdsourcing-supported model inspection (CSI) process and investigate its defect detection effectiveness and efficiency when inspecting an Extended Entity Relationship (EER) model. The CSI process uses so-called Expected Model Elements (EMEs) to guide CSI inspectors during defect detection. We conducted a controlled experiment on defect detection effectiveness, efficiency, and false positives. While CSI effectiveness and efficiency is lower for CSI inspectors, the number of false positives decreases. However, CSI was found promising for increasing the control of defect detection and supports the inspection of large-scale engineering models.

The aim of this work was to give more accurate insight in the interaction of major photosynthesis pigment, chlorophyll (Chl), with copper (II) and zinc (II) in solution by flow injection analysis with electrospray ionization mass spectrometry (FIA-ESI-MS) method, as well as combined with ultra high performance liquid chromatography with DAD detection (UHPLC-DAD). These interactions may potentially, but not necessarily lead to formation of Cu-Chl and Zn-Chl complexes of two different types, which has a lot of - at least - disfunctional implications in plant world. The results based on analysis of full-scan and MS/MS spectra - with or without the UHPLC chromatograms - confirm formation of “central type” Cu-Chl complex, and “central type” Zn-Chl complex as well, along with a proof for formation of “peripheral” Zn-Chl complex, the latter one originating from a very week coordinative interaction at the edge of Chl structure. The used techniques appeared to be an efficient and reliable tool for studying formation and stability of heavy metals complexes with chlorophyll, at least in vitro, with a considerable possibility for assessment of real bio-environmental behavior. [Projekat Ministarstva nauke Republike Srbije, br. TR-34012 i br. OI-172044]

This paper proposes a hyper-heuristic that combines genetic algorithm with mixture experiments to solve multi-objective optimisation problems. At every iteration, the proposed algorithm combines the selection criterion (rank indicator) of a number of well-established evolutionary algorithms including NSGA-II, SPEA2 and two versions of IBEA. Each indicator is called according to an associated probability calculated and updated during the search by means of mixture experiments. Mixture experiments are a particular type of experimental design suitable for the calibration of parameters that represent probabilities. Their main output is an explanatory model of algorithm performance as a function of its parameters. By finding the maximum (probability distribution) of the surface represented by this model, we also find a good algorithm parameterisation. The design of mixture experiments approach allowed the authors to identify and exploit synergies between the different rank indicators at the different stages of the search. This is demonstrated by our experimental results in which the proposed algorithm compared favourably against other well-established algorithms.

In this paper, we propose a novel bee colony optimization approach to the nurse rostering problem. The bee colony optimization algorithm is motivated by the foraging habits of honey bees. In iterations, artificial bees collectively improve their solutions. The developed algorithm alternates constructive and local search phases. In the constructive phase, unscheduled shifts are assigned to available nurses, while the aim of local search phase is to improve the quality of the solution. The algorithm incorporates a novel intelligent discarding of portions of large neighborhoods for which it is predicted that they will not lead to the improvement of the objective function. Performance of the algorithm was evaluated on real-world data from hospitals in Belgium. The results show that the bee colony optimization is able to efficiently find solutions that are competitive compared to the solutions produced by other algorithms reported in the literature.

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.

Purpose: Prostate cancer is the most common cancer in the male population. Radiotherapy is often used in the treatment for prostate cancer. In radiotherapytreatment, the oncologist makes a trade-off between the risk and benefit of the radiation, i.e., the task is to deliver a high dose to the prostate cancer cells and minimize side effects of the treatment. The aim of our research is to develop a software system that will assist the oncologist in planning new treatments. Methods: A nonlinear case-based reasoning system is developed to capture the expertise and experience of oncologists in treating previous patients. Importance (weights) of different clinical parameters in the dose planning is determined by the oncologist based on their past experience, and is highly subjective. The weights are usually fixed in the system. In this research, the weights are updated automatically each time after generating a treatment plan for a new patient using a group based simulated annealing approach. Results: The developed approach is analyzed on the real data set collected from the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. Extensive experiments show that the dose plan suggested by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Conclusions: The developed case-based reasoning system enables the use of knowledge and experience gained by the oncologist in treating new patients. This system may play a vital role to assist the oncologist in making a better decision in less computational time; it utilizes the success rate of the previously treated patients and it can also be used in teaching and training processes.

Radiotherapy planning is a complex problem which requires both expertise and experience of an oncologist. A case based reasoning (CBR) system is developed to generate dose plans for prostate cancer patients. The proposed approach captures the expertise and experience of oncologists in treating previous patients and recommends a dose in phase I and phase II of the treatment of a new patient considering also the success rate of the treatment. The proposed CBR system employs a modified Dempster-Shafer theory to fuse dose plans suggested by the most similar cases retrieved from the case base. In order to mimic the continuous learning characteristic of oncologists, the weights corresponding to each feature used in the retrieval process are updated automatically each time after generating a treatment plan for a new patient. The efficiency of the proposed methodology has been validated using real data sets collected from the Nottingham University Hospitals NHS, City Hospital Campus, UK. Experiments demonstrated that for most of the patients, the dose plan generated by our approach is coherent with the dose plan suggested by an experienced oncologist. This methodology can assist both new and experienced oncologists in the treatment planning.

This article considers the problem of inserting arriving jobs into an existing schedule of a real world manufacturer. A number of match-up strategies, which collect the idle time on machines of a current schedule for the insertion of new jobs, are proposed. Their aim is to obtain new schedules with a good performance which are at the same time highly stable, meaning that they resemble as closely as possible the initial schedule. Basic rescheduling strategies such as ‘total rescheduling’, ‘right shift’ and ‘insertion in the end’ deliver either good performance or stability, but not both. Contrarily, our experimentation and statistical analysis reveal that the proposed match-up strategies deliver high performing schedules with a high stability. An analysis of the problem parameters that determine the behaviour of the proposed match-up algorithms is included in this article.

Research highlights? We developed and implemented three genetic algorithms (GAs) for scheduling radiotherapy treatments for different patient categories: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. Performances of generated schedules were compared using real life data and statistical analysis. KB-GA generated the schedules with best performance considering emergency patients and KB-GA and Weighted-GA generated better performance schedules for emergency and palliative patients simultaneously than Standard-GA. This paper presents a multi-objective optimisation model and algorithms for scheduling of radiotherapy treatments for categorised cancer patients. The model is developed considering real life radiotherapy treatment processes at Arden Cancer Centre, in the UK. The scheduling model considers various real life constraints, such as doctors' rota, machine availability, patient's category, waiting time targets (i.e., the time when a patient should receive the first treatment fraction), and so on. Two objectives are defined: minimisation of the Average patient's waiting time and minimisation of Average length of breaches of waiting time targets. Three genetic algorithms (GAs) are developed and implemented which treat radiotherapy patient categories, namely emergency, palliative and radical patients in different ways: (1) Standard-GA, which considers all patient categories equally, (2) KB-GA, which has an embedded knowledge on the scheduling of emergency patient category and (3) Weighted-GA, which operates with different weights given to the patient categories. The performance of schedules generated by using the three GAs is compared using the statistical analyses. The results show that KB-GA generated the schedules with best performance considering emergency patients and slightly outperforms the other two GAs when all patient categories are considered simultaneously. KB-GA and Weighted-GA generated better performance schedules for emergency and palliative patients than Standard-GA.

Demand for radiotherapy treatment services has increased due to higher cancer incidence, ageing population, and a larger number of prescribed radiotherapy fractions. This higher demand can, in turn, produce longer waiting times for radiotherapy treatment. We introduce a real-world radiotherapy pre-treatment scheduling problem at a hospital in the UK. This scheduling problem is modelled as an optimisation problem with multiple objectives. The objectives are hierarchical, thus the solution approach is based on solving a series of single-objective optimisation scheduling problems. Each of these problems is formulated as a mathematical programming model. Dispatching rules were introduced to produce an initial solution for the mathematical programming model. Their performance is compared to the mathematical programming approach.

Despite decades of research into automated methods for nurse rostering and some academic successes, one may notice that there is no consistency in the knowledge that has been built up over the years and that many healthcare institutions still resort to manual practices. One of the possible reasons for this gap between the nurse rostering theory and practice is that often the academic community focuses on the development of new techniques rather than developing systems for healthcare institutions. In addition, methods suitable for one problem are usually not easily transferable to other problems. In real-world healthcare environments, a personnel manager cannot afford to model a problem and construct a roster using available approaches in order to quantitatively determine which one suits best. There is a lack of criteria for the comparison of approaches to provide a clear picture about their advantages and disadvantages and therefore their suitability to a problem in hand. This paper introduces seven criteria: expressive power, flexibility, algorithmic power, learning capabilities, maintenance, rescheduling capabilities, and parameter tuning, that may offer guidance to researchers and developers of systems for nurse rostering. Two approaches to nurse rostering, which are of very different nature, are evaluated and compared against the introduced criteria. One approach is based on meta-heuristics, while the other employs case-based reasoning. ispartof: Annals of Operations Research vol:194 issue:1 pages:365-384 status: published

In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects appropriate neighbourhoods to use within the biased VNS, produces the best known results in the literature, in terms of solution quality, on some of the benchmark instances. However, it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed.

In this paper, we propose a new statistical approach, also known in biology under the name capture–recapture methods in order to estimate global population statistics from local observations. Evaluating population sizes in P2P systems has received much attention lately as these may be useful to set system parameters, to derive other system statistics, or to predict system performance. As these systems are very large, encompassing several millions of users and since they are highly distributed estimating population sizes is a challenging task. More precisely, we are interested in estimating the number of file replicas in the system, i.e., the size of the population of users possessing given files. To this end, we propose a capture–recapture method which is both computationally efficient and accurate. The method proposed allows deriving global population statistics from local and time-limited observations. We apply the method on a measurement data set of several days on a residential network. We compare the results obtained from direct counting procedures with those derived with the proposed methodology.

This paper investigates the problem of inserting new rush orders into a current schedule of a real world job shop floor. Effective rescheduling methods must achieve reasonable levels of performance, measured according to a certain cost function, while preserving the stability of the shop floor, i.e. introducing as few changes as possible to the current schedule. This paper proposes new and effective match-up strategies which modify only a part of the schedule in order to accommodate the arriving jobs. The proposed strategies are compared with other rescheduling methods such as "right shift" and "insertion in the end", which are optimal with respect to stability but poor with respect to performance, and with "total rescheduling" which is optimal with respect to performance but poor with respect to stability. Our results and statistical analysis reveal that the match-up strategies are comparable to the "right shift" and "insertion in the end" with respect to stability and as good as "total rescheduling" with respect to performance.

Hyper-heuristics or "methodologies to choose heuristics" are becoming increasingly popular given their suitability to solve hard real world combinatorial optimisation problems. Their distinguishing feature is that they operate in the space of heuristics or heuristic components rather than in the solution space. In Dispatching Rule Based Genetic Algorithms (DRGA) solutions are represented as sequences of dispatching rules which are called one at a time and used to sequence a number of operations onto machines. The number of operations that each dispatching rule in the sequence handles is a parameter to which DRGA is notoriously sensitive. This paper proposes a new hybrid DRGA which searches simultaneously for the best sequence of dispatching rules and the number of operations to be handled by each dispatching rule. The investigated DRGA uses the selection mechanism of NSGA-II when handling multi-objective problems. The proposed representation was used to solve different variants of the multi-objective job shop problem as well as the single objective problem with the sum of weighted tardiness objective. Our results, supported by the statistical analysis, confirm that DRGAs that use the proposed representation obtained better results in both the single and multi-objective environment overall and on each particular set of instances than DRGAs using the conventional dispatching rule representation and a GA that uses the more common permutation representation.

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

In this paper we present a novel Case-Based Reasoning (CBR) system called CABAROST (CAsed-BAsed ROSTering) which was developed for personnel scheduling problems. CBR is used to capture and store examples of personnel manager behaviour which are then used to solve future problems. Previous examples of constraint violations in schedules and the repairs that were used to solve the violations are stored as cases. The sequence in which violations are repaired can have a great impact on schedule quality. A novel memetic algorithm is proposed which evolves good quality sequences of repairs generated by CABAROST. The algorithm was tested on instances of the real-world nurse rostering problem at the Queens Medical Centre NHS Trust in Nottingham.

This paper concerns sensitivity analysis of a class of complex job shop scheduling problems which are characterized by: (1) a large number of jobs and machines, (2) uncertain jobs processing times, and (3) multiple measures of schedule performance including average weighted tardiness, the number of tardy jobs, the total setup times, the total idle time of machines, and the total flow times of jobs. The base schedule is generated by applying a new fuzzy multiobjective genetic algorithm which takes into consideration batching of the jobs of a similar type, jobs’ lots sizing and load balancing of the machines. The aim of the proposed sensitivity analysis of a generated schedule is to investigate the consequences of prolongations of job processing times on the measures of schedule performance. The processing times are described by triangular fuzzy numbers and their prolongation is done by expanding the supports of fuzzy numbers. The sensitivity analysis is performed through a series of numerical experiments. ...

Objective We assessed whether peripheral activation of microglia by a nasal proteosome-based adjuvant (Protollin) that has been given safely to humans can prevent amyloid deposition in young mice and affect amyloid deposition and memory function in old mice with a large amyloid load. Methods Amyloid precursor protein (APP) transgenic (Tg) J20 mice received nasal treatment with Protollin weekly for 8 months beginning at age 5 months. Twenty-four-month-old J20 mice were treated weekly for 6 weeks. Results We found reduction in the level of fibrillar amyloid (93%), insoluble β-amyloid (Aβ; 68%), and soluble Aβ (45%) fragments in 14-month-old mice treated with Protollin beginning at age 5 months. Twenty-four-month-old mice treated with nasal Protollin for 6 weeks had decreased soluble and insoluble Aβ (1-40) and (1-42) and improved memory function. Activated microglia (CD11b+ cells) colocalized with Aβ fibrils in the 24-month-old animals, and microglial activation correlated with the decrease in Aβ. No microglial activation was observed in 14-month-old mice, suggesting that once Aβ is cleared, there is downregulation of microglial activation. Both groups had reduction in astrocytosis. Protollin was observed in the nasal cavity and cervical lymph node but not in the brain. Activated CD11b+SRA+ (scavenger receptor A) cells were found in blood and cervical lymph node and increased interleukin-10 in cervical lymph node. No toxicity was associated with treatment. Interpretation Our results demonstrate a novel antibody-independent immunotherapy for both prevention and treatment of Alzheimer's disease that is mediated by peripheral activation of microglia with no apparent toxicity. Ann Neurol 2008;63:591–601

Examination timetabling problems are often solved by a two-phase procedure combining a sequential construction heuristic with a metaheuristic improvement search. There can be many combinations of pairing candidate construction heuristics with a metaheuristic. Different pairings are known to produce solutions of varying quality. In this paper we propose a Case Based Reasoning methodology for selecting the pairing of an appropriate sequential construction heuristic with the Great Deluge metaheuristic. We have thoroughly tested our solution approach by using extensive computational experiments in the domain of examination timetabling, and obtained the best results on a number of benchmark problems. In this research we have addressed the research issues of the representation of timetabling problems, similarity measures for timetabling problems, and the retrieval process.

This paper presents a new tool for multi-objective job shop scheduling problems. The tool encompasses an interactive fuzzy multi-objective genetic algorithm (GA) which considers aspiration levels set by the decision maker (DM) for all the objectives. The GA's decision (fitness) function is defined as a measure of truth of a linguistically quantified statement, imprecisely specified by the DM using linguistic quantifiers such as most, few, etc., that refer to acceptable distances between the achieved objective values and the aspiration levels. The linguistic quantifiers are modelled using fuzzy sets. The developed tool is used to analyse and solve a real-world problem defined in collaboration with a pottery company. The tool provides a valuable support in performing various what-if analyses, for example, how changes of batch sizes, aspiration levels, linguistic quantifiers and the measure of acceptable distances affect the final schedule.

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyper-heuristic framework, a tabu search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the tabu search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-the-art approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems.