Simulation for dynamic patients scheduling based on many objective optimization and coordinator

The Patient Admission Scheduling Problem (PASP) involves scheduling patient admissions, hospital time locations, to achieve certain quality of service and cost objectives, making it a multi-objective combinatorial optimization problem and NP-hard in nature. In addition, PASP is used in dynamic scenarios where patients are expected to arrive at the hospital sequentially, which requires dynamic optimization handling. Taking both aspects, optimization and dynamic utilization, we propose a simulation for dynamic patient scheduling based on multi-objective optimization, window, and coordinator. The role of multi-objective optimization deals with many soft constraints and providing a set of non-dominated solution coordinators. The role of the counter is to collect newly arrived patients and previously unconfirmed patients with the aim of passing them on to the coordinator. Finally, the role of the coordinator is to select a subset of patients from the window and pass them to the optimization algorithm. On the other hand, the coordinator is also responsible for those selected from the non-dominant solutions to activate it in the hospital and decide on unconfirmed employees to place them in the window for the next round. Simulator evaluation and comparison between several optimization algorithms show the superiority of NSGA-III in terms of set criticality and soft constraint values. Therefore, it treats PASP as a multi-objective dynamic optimization of a useful solution. NSGA-II is guaranteed 0.96 percent dominance over NSGA-II and 100 percent dominance of all other algorithms

Window-based multi-objective optimization for dynamic patient scheduling with problem-specific operators

The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS