Agent-Based System Design for Service Process Scheduling: Challenges, Approaches and Opportunities

Compared with traditional manufacturing scheduling, service process scheduling poses additional challenges attributable to the significant customer involvement in service processes. In services, there are typically no inventoried products, which make the service provider's capacity more sensitive to dynamic changes. Service process scheduling objectives are also more complicated due to the consideration of customer preferences, customer waiting costs and human resource costs. After describing the Unified Services Theory and analysing its scheduling implications, this paper reviews the research literature on service process scheduling system design with a particular emphasis on agent-based approaches. Major issues in agent-based service process scheduling systems design are discussed and research opportunities are identified. The survey of the literature reveals that despite of many domain-specific designs in agent-based service process scheduling, there is a lack of general problem formulations, classifications, solution frameworks, and test beds. Constructing these general models for service process scheduling system design will facilitate the collaboration of researchers in this area and guide the effective development of integrated service process scheduling systems

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

Modeling and analysis of hospital facility layout problem

The optimal solution to any facility layout problem is an important aspect and a major concem as it involves significant material handling and transportation cost. The objective is to arrange the departments within the predefined facility boundaries in the way that the interaction between the functions is efficient and the overall movement cost is minimized. While facility layout problems have traditionally focused on manufacturing facilities, there has been little work on analyzing layouts for hospitals. The thesis focuses on hospital facility layout problems (HLP) to (i) minimize the movements of patients and (ii) minimize the movements of accompanying resources such as doctors, nurses, equipment and paramedical staff. The thesis consists of two sections. In the first section, a model for the multi-floor layout problem is presented based on the minimization of movement cost. The model has travel frequency or number of trips, trip difficulty rating, baseline travel cost and distance as parameters for determining the movement cost. In the second section, some additional parameters and constraints are imposed on the model and it is simulated using Microsoft Excel. Simulations are also run to study the effect of different proposed strategies on movement cost. These proposed strategies show a reduction in movement cost from the sample layout strategy in section one. A representative example is used to illustrate the applicability of the proposed formulation

Heuristiken im Service Operations Management

This doctoral thesis deals with the application of operation research methods in practice. With two cooperation companies from the service sector (retailing and healthcare), three practice-relevant decision problems are jointly elicited and defined. Subsequently, the planning problems are transferred into mathematical problems and solved with the help of optimal and/or heuristic methods. The status quo of the companies could be significantly improved for all the problems dealt with.Diese Doktorarbeit beschäftigt sich mit der Anwendung von Operation Research Methoden in der Praxis. Mit zwei Kooperationsunternehmen aus dem Dienstleistungssektor (Einzelhandel und Gesundheitswesen) werden drei praxisrelevante Planungsprobleme gemeinsam eruiert und definiert. In weiterer Folge werden die Entscheidungsmodelle in mathematische Probleme transferiert und mit Hilfe von optimalen und/oder heuristischen Verfahren gelöst. Bei allen behandelten Problemstellungen konnte der bei den Unternehmen angetroffene Status Quo signifikant verbessert werden

Multi-objective Optimization of Hospital Inpatient Bed Assignment

Choosing which bed to assign an admitted patient to in a hospital is a complex problem. There are numerous factors to consider including the patient’s gender and isolation requirements, current bed availability, and unit configurations. This problem must be solved each time a new patient seeks admission resulting in rearrangement of already admitted patients. Each movement of an already admitted patient increases the workload for hospital staff and also increases the risk of nosocomial infections for the patient. In order to alleviate these problems we propose optimizing the patient admission process through a multi-objective model which first maximizes the overall criticality of patients admitted, then minimizes movements of previously admitted patients while creating space for incoming patients. Using this model we perform three sets of experiments. The first experiments seek to determine the ideal number of private and semi-private rooms in a multi-occupancy unit with a fixed number of total rooms. This results in a tool to enable the unit to manage the tradeoffs between moving previously admitted patients and bed utilization. The second experiments seek to determine the ideal timeframe over which to batch patient admissions. These results suggest more frequent admissions have minimal impact on inpatient rearrangement. The third experiments seek to determine the potential benefit of using a centralized admitting entity and finds managing bed assignment from a central perspective far out performs individual units managing their bed assignments

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making