Mohammed Fazle Baki

The appointment scheduling problem has been widely studied in various industries, including services, healthcare, finance, and legal advice. Uncertainty in processing times and job no-shows adds complexity. Much of the existing literature relies on unrealistic assumptions vis-à-vis this stochasticity: constant service time, neglecting the uncertainty altogether, usage of vast data to account for the service time distribution, or no account and negligence of no-shows. This research develops a robust appointment scheduling model that applies min-max optimization to generate appointment times for a multi-mode system while accounting for patient no-shows and uncertain service times. Each job may be executed in different service modes with its own cost and processing time range. The objective is to minimize the total expected costs associated with job waiting time and service provider idle time for the worst-case scenario. The proposed method only requires the extreme boundaries of the uncertain parameters, making it broadly applicable in data-scarce contexts. A linear programming model is used to solve the scheduling problem. Computational experiments check the impact of the problem parameters at the end of the scheduling day, including job waiting time, server idle time, and the total overall cost. A novel hybridized heuristic marrying reinforcement learning and a fix-and-optimize mathematical programming method is developed to address the intractable nature of the problem. Q-learning determines job-mode assignments, followed by mathematical programming to solve the restricted model. The hybridized approach is compared to the pure mathematical programming model approach vis-à-vis the overall cost and computational time.

This paper describes an approach for developing decision support systems (DSS) for strategic and tactical operating room (OR) planning and scheduling problems. These problems involve assigning amounts of time and specific time blocks in the ORs to surgical specialties and/or surgeons. A four-phase iterative software development lifecycle (SDLC) approach is used to develop a DSS that has a graphical user interface, a data management system, and optimization and simulation systems that incorporate mathematical programming models, solution methods, and discrete event simulation models. Results from the computational experience show that the plans generated by the DSS utilize at least 78% of the available OR time on average and use the downstream recovery ward (RW) beds in a balanced way that never exceeds the number of available beds.

Background: This literature review delves into the concept of ‘Third-party Reverse Logistics selection’, focusing on its process and functionality using deterministic and uncertain decision-making models. In an increasingly globalized world, Reverse Logistics (RL) plays a vital role in optimizing supply chain management, reducing waste, and achieving sustainability objectives. Deterministic decision-making models employ predefined criteria and variables, utilizing mathematical algorithms to assess factors such as cost, reliability, and capacity across various geographical regions. Uncertain decision-making models, on the other hand, incorporate the unpredictability of real-world scenarios by considering the uncertainties and consequences of decision making and choices based on incomplete information, ambiguity, unreliability, and the option for multiple probable outcomes. Methods: Through an examination of 41 peer-reviewed journal publications between the years 2020 and 2023, this review paper explores these concepts and problem domains within three categories: Literature Reviews (LR), Deterministic Decision-Making (DDM) models, and Uncertain Decision-Making (UDM) models. Results: In this paper, observations and future research directions are discussed. Conclusions: This paper provides a comprehensive review of third-party reverse logistics selection papers.

Surgery demand is an uncertain parameter in addressing the problem of surgery block allocations, and its typical variability should be considered to ensure the feasibility of surgical planning. We develop two models, a stochastic recourse programming model and a two-stage stochastic optimization (SO) model with incorporated risk measure terms in the objective functions to determine a planning decision that is made to allocate surgical specialties to operating rooms (ORs). Our aim is to minimize the costs associated with postponements and unscheduled demands as well as the inefficient use of OR capacity. The results of these models are compared using a case of a real-life hospital to determine which model better copes with uncertainty. We propose a novel framework to transform the SO model based on its deterministic counterpart. Three SO models are proposed with respect to the variability and infeasibility of the measures of the objective function to encode the construction of the SO framework. The analysis of the experimental results demonstrates that the SO model offers better performance under a highly volatile demand environment than the recourse model. The originality of this work lies in its use of SO transformation framework and its development of stochastic models to address the problem of surgery capacity allocation based on a real case.

In this paper, operating room planning and scheduling problems have been studied. In operating room planning, the allocation of patients to operating rooms and their sequencing are critical in determining the performance of operating rooms. In this paper, three surgery scheduling decisions are considered, including the number of operating rooms to open, the allocation of surgeries to operating rooms, and the sequencing of surgeries in allocated operating rooms. All the surgeries under consideration are elective, and surgery durations are considered deterministic. Further, it is considered that the surgeries have different specialties, and each operating room can accommodate a particular specialty of surgeries, i.e., heterogeneous operating rooms are considered in the current study. Before performing a surgery, setup time is required for operating room turnover and sterilization, and it is considered sequence dependent. A mixed integer nonlinear programming (MINLP) model is developed to minimize the overtime costs of operating rooms for allocation and surgery sequencing with sequence dependent setup times. An outer approximation (OA) method is proposed to solve the problem near optimally. Experiments are conducted to compare the performance of the proposed OA method with the standard mixed integer nonlinear programming model. Computational results show the efficiency of the proposed OA method. Later, a case data from a case hospital is collected and a case study is solved.

Modern manufacturing technologies such as Additive Manufacturing are a vital pillar of Industry 4.0 and Smart Manufacturing systems. One of the most promising AM techniques is Directed Energy Deposition (DED) which uses a thermal source to generate a melt pool on a substrate into which metal powder is injected. Laser Cladding (LC), a DED additive process, is widely used in machine parts repair and functional coating due to its advantages, such as the low heat input into the substrate. Compared to conventional welding, this results in less induced stress, lower dilution rate, small heat-affected zone, and good metallurgical bonding between the coating and the substrate. The LC technology is not as robust or standardized. The part quality, mechanical properties, and microstructure of Laser-Cladded parts are neither as commercially predictable nor controllable. This research aims to develop Deep Learning models that predict and classify the LC process for LC coating and single and interfering-bead samples. The ML models will detect faulty process input parameters and predict the quality. This detection and prediction take place in real time, which is vital in producing the additive process's data model component of a Digital Twin (DT). Digital imaging of a coaxial CMOS camera and melted pool temperature measurements using a LASCON LPC04 pyrometer are being used as in-situ sensory devices. A plethora of articles have limited their ML models to individual combinations of substrate and powder and focused on the three main process parameters (laser power, laser beam scanning speed, and Powder feeding rate). This research project expands the scope to include a different substrate and powder combinations, utilize additional parameters, and exploit different powder/substrate combinations. The Inputs

Hybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear; due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0.