Home healthcare integrated staffing and scheduling

Workforce planning for home healthcare represents an important and challenging task involving complex factors associated with labor regulations, caregivers’ preferences, and demand uncertainties. This task is done manually by most home care agencies, resulting in long planning times and suboptimal decisions that usually fail to meet the health needs of the population, to minimize operating costs, and to retain current caregivers. Motivated by these challenges, we present a two-stage stochastic programming model for employee staffing and scheduling in home healthcare. In this model, first-stage decisions correspond to the staffing and scheduling of caregivers in geographic districts. Second-stage decisions are related to the temporary reallocation of caregivers to neighboring districts, to contact caregivers to work on a day-off, and to allow under- and over-covering of demand. The proposed model is tested on real-world instances, where we evaluate the impact on costs, caregiver utilization, and service level by using different recourse actions. Results show that when compared with a deterministic model, the two-stage stochastic model leads to significant cost savings as staff dimensioning and scheduling decisions are more robust to accommodate changes in demand. Moreover, these results suggest that flexibility in terms of use of recourse actions is highly valuable as it helps to further improve costs, service level, and caregiver utilization.     

Scheduling Problems in a Practical Allocation Model

A parallel computational model is defined which addresses I/O contention,latency, and pipe-lined message passing between tasks allocated to differentprocessors. The model can be used for parallel task-allocation on either anetwork of workstations or on a multi-stage inter-connected parallel machine.To study performance bounds more closely, basic properties are developed forwhen the precedence constraints form a directed tree. It is shown that theproblem of optimally scheduling a directed one-level precedence tree on anunlimited number of identical processors in this model is NP-hard. Theproblem of scheduling a directed two-level precedence tree is also shown tobe NP-hard even when the system latency is zero. An approximation algorithm is then presented for scheduling directedone-level task trees on an unlimited number of processors with anapproximation ratio of 3. Simulation results show that this algorithm is, infact, much faster than its worst-case performance bound. Better simulationresults are obtained by improving our approximation algorithm usingheusistics. Restricting the problem to the case of equal task executiontimes, a linear-time algorithm is presented to find an optimal schedule.     

Decision support flexible manufacturing systems

The justification of flexible manufacturing systems (FMS) is a topic that has gained a lot of attention due to the strategic effect it has on the competitive stature of industrial firms. The decision to convert to FMS should follow a pattern of feasibility assessments, cost/benefit analysis where consideration of issues such as increased quality, productivity and capability as well as tangible benefits takes place. Difficulties, however, arise in the modeling of the behavior of the proposed FMS. This paper introduces the design of a decision support framework that aids the strategic planner in simulating the performance of proposed FMS and in determining the parameters that affect the costs and benefits, tangible and intangible, of such a system. The decision support system (DSS) allows the user to use subjective evaluations of benefits accruing from intangible considerations of new product design, faster turnaround on design-to-market cycles and new marketing strategies in fragmented markets. The DSS enhances the examination of issues such as changing demand, varied tasks and routings, job and machine flexibility, etc. which affect costs and benefits. It performs these important functions by allowing for the development of scenarios that aid in the evaluation of the effect of the conversion from non-flexible to flexible manufacturing on the organization's financial position.     

Decision support systems for production scheduling tasks- Part I of a case study: Analysis and task redesign

The first three steps of a new design method for decision support systems in production scheduling tasks are applied to a large bulk terminal. The operational characteristics of the company largely correspond with those of typical semiprocess industries. The design model used consists of five steps: production analysis, task analysis, task redesign, decision support design and decision support implementation. The results of the application of the design model up to the task redesign step are described.     

Combinatorial Optimization in Real-Time Scheduling: Theory and Algorithms

Real-time computer systems are essential for many applications, such as robot control, avionics, medical instrumentation, manufacturing, etc. The correctness of the system depends on the temporal correctness as well as the functional correctness of the task executions. In order to assure temporal correctness it is necessary that the resources be scheduled to meet the temporal requirements of applications. When we consider the problem of nonpreemptive scheduling of a set of tasks in a processor for which no feasible solution exists, some tasks may have to be rejected so that a schedule can be generated for the rest. In this paper, we consider the problem of generating an optimal schedule such that the number of rejected tasks is minimized, and then the finish time is minimized for the accepted tasks. We propose to use an analytic approach to solve this problem. We first discuss the super sequence based technique which was originally proposed for reducing the search space in testing the feasibility of a task set. Then we show by the Conformation theorem that the super sequence constructed from the task set also provides a valid and reduced search space for the optimization problem. While the complexity of our scheduling algorithm in the worst case remains exponential, our simulation results show that the cost is reasonable for the average case.     

Decomposition algorithms for the integrated process planning and scheduling problem

There are several algorithms to solve the integrated process planning and scheduling (IPPS) problem (i.e., flexible job shop scheduling with process plan flexibility) in the literature. All the existing algorithms for IPPS are heuristic-based search methods and no research has investigated the use of exact solution methods for this problem. We develop several decomposition approaches based on the logic-based Benders decomposition (LBBD) algorithm. Our LBBD algorithm allows us to partition the decision variables in the IPPS problem into two models, master-problem and sub-problem. The master-problem determines process plan and operation-machine assignment, while the sub-problem optimizes sequencing and scheduling decisions. To achieve faster convergence, we develop two relaxations for the optimal makespan objective function and incorporate them into the master-problem. We analyze the performance and further enhance the algorithm with two ideas, a Benders optimality cut based on the critical path and a faster heuristic way to solve the sub-problem. 16 standard benchmark instances available in the literature are solved to evaluate and compare the performances of our algorithms with those of the state-of-the-art methods in the literature. The proposed algorithm either results in the optimal solution or improves the best-known solutions in all the existing instances, demonstrating its superiority to the existing state-of-the-art methods in literature.     

Impact of Price Postponement on Capacity and Flexibility Investment Decisions

Investments in dedicated and flexible capacity have traditionally been based on demand forecasts obtained under the assumption of a predetermined product price. However, the impact on revenue of poor capacity and flexibility decisions can be mitigated by appropriately changing prices. While investment decisions need to be made years before demand is realized, pricing decisions can easily be postponed until product launch, when more accurate demand information is available. We study the effect of this price decision delay on the optimal investments on dedicated and flexible capacity. Computational experiments show that considering price postponement at the planning stage leads to a large reduction in capacity investments, especially in the more expensive flexible capacity, and a significant increase in profits. Its impact depends on demand correlation, elasticity and diversion, ratio of fixed to variable capacity costs, and uncertainty remaining at the times the pricing and production decisions are made.     

Partially Flexible Operating Rooms for Elective and Emergency Surgeries

In hospitals, the management of operating rooms faces a trade‐off between the need to be responsive to emergency surgeries and to conduct scheduled elective surgeries efficiently. Operating rooms can be configured as flexible and handle both electives and emergencies, or as dedicated to focus on either electives or emergencies. With flexible rooms, the prioritization of emergencies over scheduled electives can lead to schedule disruptions. Focused rooms can lead to imbalances between capacity and surgery workload. Whereas hospital administrators typically handle this trade‐off by employing either flexible rooms (complete flexibility) or dedicated rooms (complete focus), we investigate whether a combination of flexible and dedicated rooms (partial flexibility) could be a preferable alternative. The ensuing question is what is the right combination of flexible and dedicated rooms? A versatile simulation model is developed to evaluate different resource allocation policies under various environmental parameters and performance metrics, including patient wait time, staff overtime, and operating room utilization. The main result is that partial flexibility configurations outperform both complete flexibility and complete focus policies by providing solutions with improved values of expected wait time for both emergency and elective patients.     

Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

Supply chain scheduling optimisation in mass customisation based on dynamic profit preference and application case study

Traditionally, the ‘postponement strategy’ is a better way to solve the contradiction between ‘scale production effect’ and ‘customised demand level’ in mass customisation (MC). In higher customisation situation, the conflict between the role of postponement and the higher level of customised demand must be outstanding. When MC operates in supply chain, the excellent flexible characters of the supply chain system will create better conditions to solve the conflict from a new perspective, but the operating combination of MC and the supply chain with uncertain characters will lead to various complicated contradictions and bottlenecks. In this article, we discuss the supply chain scheduling optimisation in MC based on dynamic profit preference to solve these contradictions and bottlenecks, establish a special optimisation model to implement the scheduling. We make a computational industrial case study to illustrate the method application and show the benefits of solving the key contradiction in MC by the supply chain scheduling.     

The shift minimisation personnel task scheduling problem: A new hybrid approach and computational insights

Assigning scheduled tasks to a multi-skilled workforce is a known NP-complete problem with many applications in health care, services, logistics and manufacturing. Optimising the use and composition of costly and scarce resources such as staff has major implications on any organisation׳s health. The present paper introduces a new, versatile two-phase matheuristic approach to the shift minimisation personnel task scheduling problem, which considers assigning tasks to a set of multi-skilled employees, whose working times have been determined beforehand. Computational results show that the new hybrid method is capable of finding, for the first time, optimal solutions for all benchmark instances from the literature, in very limited computation time. The influence of a set of problem instance features on the performance of different algorithms is investigated in order to discover what makes particular problem instances harder than others. These insights are useful when deciding on organisational policies to better manage various operational aspects related to workforce. The empirical hardness results enable to generate hard problem instances. A set of new challenging instances is now available to the academic community.     

Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance

The problem of no‐shows (patients who do not arrive for scheduled appointments) is particularly significant for health care clinics, with reported no‐show rates varying widely from 3% to 80%. No‐shows reduce revenues and provider productivity, increase costs, and limit patient access by reducing effective clinic capacity. In this article, we construct a flexible appointment scheduling model to mitigate the detrimental effects of patient no‐shows, and develop a fast and effective solution procedure that constructs near‐optimal overbooked appointment schedules that balance the benefits of serving additional patients with the potential costs of patient waiting and clinic overtime. Computational results demonstrate the efficacy of our model and solution procedure, and connect our work to prior research in health care appointment scheduling.     

Impacts of demand and supply factors on the capacity scheduling performance of logistics service supply chain with mass customisation service modes: an empirical study from China

This paper studies the influence of the demand–supply environment on the capacity scheduling performance of the logistics service supply chain. According to the key characteristics of the demand–supply environment, the cases of five Chinese companies were introduced and cross-analysed, then research hypotheses were developed. After receiving 154 valid questionnaires and testing our hypotheses, some key conclusions were obtained. From the aspect of demand, there is a positive correlation between the customised levels of demand and the scheduling cost of logistics service integrators but a negative correlation between the customised levels of demand and the scheduling flexibility; From the aspect of supply, a higher self-support ratio of logistics source and a higher sourcing integrity of logistics service integrators will lead to a higher scheduling cost and a lower scheduling flexibility; a whole-process performance evaluation is a moderator, which will positively improve the impact of the whole process scheduling performance.     

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.     

SCHEDULING FLEXIBILITY IN THE SERVICE SECTOR: A POSTAL CASE STUDY

This is a case study of workforce scheduling in the U.S. postal system. We use it to analyze the benefits of scheduling flexibility at postal distribution systems, which can come from several sources. We focus on the additional flexibility deriving from increasing the proportion of part-time employees, as well as from increasing the cross-training of part-time employees. These two dimensions of scheduling flexibility are decision points of particular interest to the postal management. We used a large-scale simulation model of the dynamic functioning of the postal distribution system in conjunction with a staff-scheduling model to obtain insights into policy issues of interest. Our results show that the operating environment at a distribution station affects cost and customer service performance, and that gains from greater scheduling flexibility are situationally dependent. The benefit of cross-training part-time workers is modest over the range of levels considered realistic for distribution stations. Our overall recommendation is for postal managers to increase the proportion of part-time employees, allowing more efficient matching of resources with the varying demands for mail delivery on different routes. For the range of conditions we modeled, cost reductions from more part-time workers average over six percent when capacity utilization is low. Customer service improvements are even larger, and range between 20 and 43% when capacity utilization is high. Improvements at the upper end of the range are achieved when demand variability is also at its highest.     

The Use of Flexible Manufacturing Capacity in Pharmaceutical Product Introductions*

This work considers the value of the flexibility offered by production facilities that can easily be configured to produce new products. We focus on technical uncertainty as the driver of this value, while prior works focused only on demand uncertainty. Specifically, we evaluate the use of process flexibility in the context of risky new product development in the pharmaceutical industry. Flexibility has value in this setting due to the time required to build dedicated capacity, the finite duration of patent protection, and the probability that the new product will not reach the market due to technical or regulatory reasons. Having flexible capacity generates real options, which enables firms to delay the decision about constructing product‐specific capacity until the technical uncertainty is resolved. In addition, initiating production in a flexible facility can enable the firm to optimize production processes in dedicated facilities. The stochastic dynamic optimization problem is formulated to analyze the optimal capacity and allocation decisions for a flexible facility, using data from existing literature. A solution to this problem is obtained using linear programming. The result of this analysis shows both the value of flexible capacity and the optimal capacity allocation. Due to the substantial costs involved with flexibility in this context, the optimal level of flexible capacity is relatively small, suggesting products be produced for only short periods before initiating construction of dedicated facilities.     

Real-time operation selection in an FMS using simulation- a fuzzy approach

Production management and scheduling problems in flexible manufacturing systems (FMSs) are more complicated than those problems in job shops and transfer lines. Due to the flexibility of FMSs, alternative operation is common. When considering the operational problems of FMSs such as scheduling, simulation methodology seems to be useful to address these issues. Real-time scheduling schemes use simulation techniques     

Understanding the emergence of redistributed manufacturing: an ambidexterity perspective

Abstract

This paper extends operations strategy theory on efficiency and flexibility trade-offs to the emergent phenomenon of redistributed manufacturing (RDM). The study adopts a multiple-case design including five small and five large pharmaceutical firms. We propose that organizations can gain the efficiency benefits of centralized manufacturing and the flexibility advantages of RDM by building an ambidexterity capability. To build such a capability, large firms can structurally partition their manufacturing and supply management functions, with one sub-unit managing centralized production and the other RDM. Smaller enterprises can build an ambidexterity capability by creating the right organizational context, where multi-skilled workers switch between efficient and flexible tasks. This paper contributes to theory by explaining the emergence of RDM using an organizational ambidexterity lens, laying the groundwork for new theory development in the field. We provide managers with a practical example of how to build an ambidexterity capability to realize flexibility and efficiency advantages.     

A Comparative Evaluation of Labor Tour Scheduling Methods

This paper presents an initial study of relative performance for a number of the labor tour scheduling heuristic methods proposed in the literature. These heuristic methods were classified as either linear programming (LP) based or construction. Each of the methods was applied to a tour scheduling problem, subject to a variety of labor demand requirements distributions, with the singular objective being the minimization of total labor hours scheduled. Statistical analysis revealed that effective tour schedule solutions were generated by both LP-based and construction methods. Since the performances of the Keith [13], Morris and Showalter [18], and Bechtold and Showalter [5] methods were superior, their solutions were also compared across a number of secondary criteria. An overall analysis of the performances of these three methods resulted in the identification of a number of important managerial and decision-making issues. We conclude that service operations management should consider integrating these heuristic methods into a decision support system. Finally, suggestions for future research are provided.