AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Evaluation of Hybrid Arc and Volumetric-Modulated Arc Therapy Treatment Plans for Fractionated Stereotactic Intracranial Radiotherapy.

PURPOSE: The study was aimed to compare hybrid arc and volumetric-modulated arc therapy treatment plans for fractionated stereotactic radiotherapy of brain tumors. METHODS: Treatment plans of 22 patients were studied. Hybrid arc and volumetric-modulated arc therapy plans were generated using Brainlab iPlanDose and Varian Eclipse treatment planning systems, respectively, with 6 MV photon beams on a Varian TrueBeam STx linear accelerator (Palo Alto, CA). Prescription dose was 54 Gy. The fractionation was 1.8 Gy per fraction and 30 fractions in total, or 2 Gy per fraction and 27 fractions in total. Planning target volume ranged from 2.4 to 28.6 cm RESULTS: Conformity indexes of hybrid arc and volumetric-modulated arc therapy plans are 1.10 ± 0.10 and 1.14 ± 0.07, respectively ( P = .4); gradient indexes are 5.02 ± 1.20 and 5.64 ± 1.28, respectively ( P = .0001); homogeneity indexes are 1.02 ± 0.01 and 1.05 ± 0.01, respectively ( P = .0001); brainstem maximum doses are 53.87 ± 1.63 Gy and 54.06 ± 3.17 Gy, respectively ( P = .1); and optic chiasm maximum doses are 53.86 ± 1.28 Gy and 53.95 ± 1.81, respectively ( P = .4). The monitor unit efficiencies of hybrid arc and volumetric-modulated arc therapy plans are 2.57 ± 0.25 MU/cGy and 2.68 ± 0.24 MU/cGy, respectively ( P = .2). The differences of conformity index, gradient index, and homogeneity index between hybrid arc and volumetric-modulated arc therapy plans are small: 0.08 ± 0.05, 0.65 ± 0.46, and 0.02 ± 0.01, respectively. The maximum doses in organs at risks are similar between hybrid arc and volumetric-modulated arc therapy plans. Hybrid arc and volumetric-modulated arc therapy plans, which have similar monitor unit efficiencies, present similar dosimetric results in the fractionated intracranial radiotherapy

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Thermal dosimetry for bladder hyperthermia treatment. An overview.

The urinary bladder is a fluid-filled organ. This makes, on the one hand, the internal surface of the bladder wall relatively easy to heat and ensures in most cases a relatively homogeneous temperature distribution; on the other hand the variable volume, organ motion, and moving fluid cause artefacts for most non-invasive thermometry methods, and require additional efforts in planning accurate thermal treatment of bladder cancer. We give an overview of the thermometry methods currently used and investigated for hyperthermia treatments of bladder cancer, and discuss their advantages and disadvantages within the context of the specific disease (muscle-invasive or non-muscle-invasive bladder cancer) and the heating technique used. The role of treatment simulation to determine the thermal dose delivered is also discussed. Generally speaking, invasive measurement methods are more accurate than non-invasive methods, but provide more limited spatial information; therefore, a combination of both is desirable, preferably supplemented by simulations. Current efforts at research and clinical centres continue to improve non-invasive thermometry methods and the reliability of treatment planning and control software. Due to the challenges in measuring temperature across the non-stationary bladder wall and surrounding tissues, more research is needed to increase our knowledge about the penetration depth and typical heating pattern of the various hyperthermia devices, in order to further improve treatments. The ability to better determine the delivered thermal dose will enable clinicians to investigate the optimal treatment parameters, and consequentially, to give better controlled, thus even more reliable and effective, thermal treatments

Development of a hybrid magnetic resonance/computed tomography-compatible phantom for magnetic resonance guided radiotherapy

The purpose of the present study was to develop a hybrid magnetic resonance/computed tomography (MR/CT)-compatible phantom and tissue-equivalent materials for each MR and CT image. Therefore, the essential requirements necessary for the development of a hybrid MR/CT-compatible phantom were determined and the development process is described. A total of 12 different tissue-equivalent materials for each MR and CT image were developed from chemical components. The uniformity of each sample was calculated. The developed phantom was designed to use 14 plugs that contained various tissue-equivalent materials. Measurement using the developed phantom was performed using a 3.0-T scanner with 32 channels and a Somatom Sensation 64. The maximum percentage difference of the signal intensity (SI) value on MR images after adding K2CO3 was 3.31%. Additionally, the uniformity of each tissue was evaluated by calculating the percent image uniformity (%PIU) of the MR image, which was 82.18 ±1.87% with 83% acceptance, and the average circular-shaped regions of interest (ROIs) on CT images for all samples were within ±5 Hounsfield units (HU). Also, dosimetric evaluation was performed. The percentage differences of each tissue-equivalent sample for average dose ranged from -0.76 to 0.21%. A hybrid MR/CT-compatible phantom for MR and CT was investigated as the first trial in this field of radiation oncology and medical physics