The impact of non-bottleneck variation in a manufacturing cell

Protective capacity is the 'extra' capacity placed at non-bottleneck resources to absorb random disruptions in planned levels of performance so that the bottleneck resource continues to be effectively utilized. A full factorial experiment with a simulation model was conducted to explore issues associated with the quantity and location of processing variance in a five-station manufacturing cell. The cell's performance was measured using both mean flow time ( MFT ) and bottleneck shiftiness ( SHIFT ) for 3 patterns of variance for the non-bottlenecks at 5 different levels of variation. In order to investigate the importance of the quantity of added capacity on the variation both a low level of protective capacity (10%) and a high level (50%) were considered. The results indicate that having the higher variation work centres close to the bottleneck provides reduced MFT and SHIFT . The performance measures improved at both the low and high setting of protective capacity.     

A finite capacity material requirements planning system

In a discrete parts manufacturing environment, material requirement planning (MRP) is carried out without considering the manufacturing resource capacity. As a result, during implementation, adjustments in planned orders may become necessary. This paper presents a finite capacity material requirements planning algorithm (FCMRP) to obtain capacity-based production plans. These plans need no costly adjustments to satisfy the capacity constraints. Performance of the FCMRP system, when studied through a set of test examples, has been found to be superior to the existing MRP system.     

Bottleneck management: theory and practice

Advocates of TOC believe that bottleneck resource restricts an operation's ability to make money, and the best way to maximise income is to fully exploit the bottleneck resource. Almost all TOC literature focuses on situations where 100% bottleneck utilisation is applied. Based on the implementation experience, the finding is that the optimal level of bottleneck utilisation should be less than 100% and any attempt to increase utilisation beyond the optimal level brings disastrous results for a door manufacturing plant. In order to improve and maintain the performance of the plant effective bottleneck management is critical. The experience provides a deeper understanding of how to design such plants, which could be beneficial for practising managers and academics working with TOC concepts.     

Annualized hours as a capacity planning tool in make-to-order or assemble-to-order environment: An agricultural implements company case

Capacity planning is one of the most important elements for an efficient production planning and control system. It is even more important when the main strategies to fulfil sales requirements are make-to-order (MTO) or assemble-to-order (ATO). Neither MTO or ATO companies keep finished products in stock which means that production level is driven by actual sales figures. Therefore, it is very important to have an available capacity as flexible as possible to meet sales requirements. Annualized hours (AH) is a work time control system that helps in increasing the flexibility of available capacity. The main rule of the AH system is to hire employees to work a certain number of hours on a yearly basis. Following some other pre-defined rules and limits, the AH system allows a company to adjust weekly available time in order to reduce idle capacity or to fulfil requirements that could mean overtime. This paper proposes a linear programming model to plan the operations using AH. This model has been implemented in a company that produces agricultural implements, showing that it is very simple to use. It is producing excellent practical results.     

MULTI-MACHINE,MULTI-PRODUCT PRODUCTION SCHEDULING AND INVENTORY CONTROL

This paper deals with a multi-machine, multi-product lot size determination and scheduling problem. The model developed here considers not only the usual inventory-related operational cost, but also the costs that depend on under- or over-utilization of available men and machines. It penalizes overtime or idle time at any facility. The solution minimizes the inventory and resource-related costs and not just inventory costs. A heuristic is developed to determine the solution from the model and to modify it, as necessary, to obtain a conflict-free, repetitive, and cyclic production schedule for an infinite horizon. Although this model is developed for a manufacturing situation, the words machine, job, and machine shop are used in a symbolic sense, and hence the model can be used in practice in a variety of circumstances.     

A stochastic programming model for outpatient appointment scheduling considering unpunctuality

Many papers on outpatient appointment scheduling assume that patients arrive on time. However, unpunctuality is a stochastic factor that is inevitable in practice, which leads to patients arriving out of order. A schedule may not be reasonable if a clinic neglects the influence of patient unpunctuality. This paper addresses the outpatient scheduling problem considering unpunctuality (OS-U) by developing a stochastic programming model. We compare the performance of the OS-U system with the strict-punctuality (OS-P) system. We illustrate that the model has an exact and unified formula for cases of patients arriving in the appointment order and arriving out of order. The OS-U problem is solved by using Benders decomposition combined with the sample average approximation (BD-SAA) technique to determine the global optimal set of appointment intervals with the goal of minimizing the weighted sum of all patient waiting times, doctor idle times, and overtime. Numerical experiments indicate that the appointment rule changes when considering unpunctuality, although the set of optimal appointment intervals still takes the shape of dome (interval width increases at first, then remains nearly constant and eventually decreases for the last patients). The OS-P system schedules the first two patients together at the start of a session, whereas the OS-U system schedules them with different appointment times and requires a longer slot between the first two patients if patients tend to arrive early rather than late. The variance of unpunctuality has little impact. The no-show probability has a greater influence on system performances in an OS-U system than those in an OS-P system.     

Sleep disturbances in shift-workers: A marker for maladaptation syndrome

Abstract

This study aims to examine whether the relationship between overtime and well-being is influenced by the voluntary vs. involuntary (i.e., compulsory) nature of overtime work and by the presence or absence of rewards for overtime. We also explored the prevalence of these types of overtime and how they were related to work and personal characteristics. A survey was conducted among a representative sample of Dutch full-time employees (N=1612). AN(C)OVA was used to compare rewarded and unrewarded, voluntary and involuntary overtime workers on personal and work characteristics, fatigue, and work satisfaction. Most overtime workers were rewarded (62%). About half of the sample (n=814) could be classified as either voluntary or involuntary overtime workers, or as having “mixed reasons” to work overtime. Voluntary and unrewarded overtime workers had a relatively high income and favourable job characteristics. Involuntary overtime work was associated with relatively high fatigue and low satisfaction, especially for involuntary overtime workers without rewards who can be considered a burnout risk group. Voluntary overtime workers were non-fatigued and satisfied, even without rewards. It can be concluded that control over overtime and rewards for overtime are important for well-being. Moderate overtime work may not be a problem if it is done voluntarily. Moreover, the negative effects of compulsory overtime work may be partly offset by fair compensation for the extra work.     

Time-based goal chasing method for mixed-model assembly line problem with multiple work stations

In sequencing problems for mixed-model assembly line in JIT production system, the Goal Chasing method (GC) is widely used for parts used leveling goal. The difference in assembly time of each product is not taken into consideration in the Goal Chasing method. Assembly time usually varies with product types. In recent years, the Time-Based Goal Chasing method (TBGC) has been proposed. The advantage of TBGC is to consider the influence of different assembly time of each product and idle time in production period. TBGC, however, has been only applied to single work station problems. In this paper, TBGC is applied to an assembly line problem with multiple work stations. Furthermore, the sequencing method and use of Simulated Annealing (SA) or Local Search (LS) for this problem are proposed.     

A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.     

Assessment of Patient Classification in Appointment System Design

This paper investigates two approaches to patient classification: using patient classification only for sequencing patient appointments at the time of booking and using patient classification for both sequencing and appointment interval adjustment. In the latter approach, appointment intervals are adjusted to match the consultation time characteristics of different patient classes. Our simulation results indicate that new appointment systems that utilize interval adjustment for patient class are successful in improving doctors' idle time, doctors' overtime and patients' waiting times without any trade‐offs. Best performing appointment systems are identified for different clinic environments characterized by walk‐ins, no‐shows, the percentage of new patients, and the ratio of the mean consultation time of new patients to the mean consultation time of return patients. As a result, practical guidelines are developed for managers who are responsible for designing appointment systems.     

Appointment Scheduling with No‐Shows and Overbooking

We study an overbooking model for scheduling arrivals at a medical facility under no‐show behavior, with patients having different no‐show probabilities and different weights. The scheduler has to assign the patients to time slots in such a way that she minimizes the expected weighted sum of the patients' waiting times and the doctor's idle time and overtime. We first consider the static problem, where the set of patients to be scheduled and their characteristics are known in advance. We partially characterize the optimal schedule and introduce a new sequencing rule that schedules patients according to a single index that is a function of their characteristics. Then we apply our theoretical results and conclusions from numerical experiments to sequential scheduling procedures. We propose a heuristic solution to the sequential scheduling problem, where requests for appointments come in gradually over time and the scheduler has to assign each patient to one of the remaining slots that are available in the schedule for a given day. We find that the no‐show rate and patients' heterogeneity have a significant impact on the optimal schedule and should be taken under consideration.     

Optimal Sequencing of Unpunctual Patients in High‐Service‐Level Clinics

Even though patients often arrive early and out of turn for scheduled appointments in outpatient clinics, no research has been undertaken to establish whether an available provider should see an early patient right away (preempt) or wait for the patient scheduled next. This problem, which we call the “Wait‐Preempt Dilemma,” is particularly relevant for “high‐service‐level” clinics (such as psychotherapy, chiropractic, acupuncture), where preempting may cause the missing patient to wait for an excessively long time, should she show up soon. Typically, the dilemma is resolved by preemption, where the provider starts serving the patient who has already arrived to avoid staying idle. By contrast, we analytically determine the time intervals where it is optimal to preempt and those where it is optimal to wait, and find that in some cases the provider should in fact stay idle, even in the presence of waiting patients. Our results suggest that the proposed analytical method outperforms the always‐preempt policy in clinics that do not overbook and have service times longer than 30 minutes. In these cases, the analytical method dramatically reduces patient waiting times at the cost of a modest increase in overtime. By contrast, in clinics that overbook or have short service times, the two policies perform similarly, and hence the always‐preempt policy is preferable due to its simplicity. A software application is provided that clinics can readily use to solve the wait‐preempt dilemma.     

Optimal mixed-model sequencing for balanced assembly lines

This paper describes an algorithm for solving optimally, the mixed-model sequencing problem when assembly line stations are balanced for each model. An optimal sequence is obtained with the minimization of the overall assembly line length for zero station idle time.The algorithm incorporates two basic steps. The first involves a search procedure that generates all cycle sequences; i.e. sequences having identical ‘start’ and ‘finish’ positions and whose work content can be executed within a defined station length. The second step uses integer programming (IP) to determine the number and combination of the various cycle sequences, such that the production demand is satisfied.     

Mitigating overtime risk in tactical surgical scheduling

Overtime is a common phenomenon in surgery departments, causing stress to physicians, dissatisfaction to patients, and financial loss to hospitals. We help risk-averse managers of operating rooms (ORs) to mitigate overtime in tactical surgery scheduling, which determines the assignment of elective patients to available ORs in upcoming time periods. We model the uncertain surgical durations via partial, full, or empirical distributions. To mitigate overtime, our model maximizes the risk aversion level of the OR manager (and thus the risk-hedging ability of the solution) while ensuring that the certainty equivalent of surgery duration in each OR at each time period does not exceed the stipulated working hours. The corresponding decision criterion, termed the maximized risk aversion level, is demonstrated in theory and in numerical experiments to be able to mitigate both the overtime probability and the expected overtime duration. To solve the problem, we develop an exact hill-climbing algorithm and demonstrate its convergence and correctness. Numerical experiments based on real-life surgery data show that our method outperforms the existing methods in several indicators that of concern to OR managers. In particular, this method is computationally amiable and hence is applicable to larger-scale instances.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction

Nursing managers are faced with rising turnover and shortages of qualified nursing staff. At the same time they are under increased pressure to simultaneously increase patient care and satisfaction while reducing costs. In this study, we examine the impact of centralizing scheduling decisions across departments in a hospital. By pooling nurses from multiple units and scheduling them in one model, improved costs and reduced overtime result. Reduced overtime improves schedules for nurses. Improved satisfaction levels can positively impact turnover rates among nurses. Our results show that by using a centralized model, nursing managers in hospitals can improve the desirability of nurse schedules by approximately 34% and reduce overtime by approximately 80% while simultaneously reducing costs by just under 11%.     

A TECHNIQUE TO MINIMIZE OVERTIME IN THE CAPACITATED MRP PROBLEM

Since the development of early versions of material requirements planning (mrp) systems, it has been known that a weak link in this technique is the failure to consider the available capacity at the time the lot sizes for individual items are calculated. Ignoring the available capacity may result in infeasible production plans, i.e., those that can only be accomplished with the use of overtime. We present a technique to search for feasible production plans by means of minimizing the total overtime. The technique is based on modifying periodic-order-quantity (poq) lot sizes within a tabu search framework. Computational experiments with the largest problem structure reported in the literature show that the procedure is effective in determining lot sizes for individual items that either minimize or eliminate overtime. Additional experiments reveal that, with appropriate calibration of search parameters, the procedure is also able to deal with more general cost functions (e.g., those that include holding and setup costs).     

An investigation of output flow control,bottleneck flow control and dynamic flow control mechanisms in various simple lines scenarios

Flow control mechanisms have been a topic of academic research for several years. With the growth of business-wide information systems such as enterprise resource planning and supply chain, better planning, scheduling and control of the business transformation process is required in order to achieve increased throughput, reduced inventories, shorter lead times and reduced tardiness. This research compares two new approaches to flow control, output flow control and bottleneck flow control to a real-time flow control system, dynamic flow control. Both output and bottleneck flow control mechanisms are much simpler to implement and manage than dynamic flow control in that they do not require continual feedback and rescheduling. Line characteristics, such as location of breakdowns with respect to the bottleneck, the location of the bottleneck when breakdowns occur, and the impact of variability of processing times on the performance measures (output, WIP level, lateness, and number of tardy jobs) for these three flow control mechanisms are compared. Both output and bottleneck flow control mechanisms perform favourably (particularly bottleneck) under different scenarios and warrant further study across a wider range of scenarios (mixed models, job shops, etc.).     

Overtime usage in a job shop environment

Problems are encountered in a job shop which has a fixed capacity if the total work content of the jobs passing through the shop increases sufficiently. Even the use of effective priority dispatching rules and/or expediting does not adequately shorten the queues which develop if the total work content continually exceeds shop capacity. To avoid losing job orders because the orders are unduly delayed, the job shop might resort to overtime usage. This study examines the efficient and economic use of overtime to relieve the backlog problem and uses overtime as the basic criterion for evaluation of overtime usage. The study employs GPSS V programming language to simulate a hypothetical job shop. The shop is loaded to various proportions of its normal capacity and various levels of overtime are tested. Findings show that overtime should not be assigned indiscriminately but rather should be based on a shop's unique conditions of overtime cost, the priority rule being employed, and the level of capacity utilization. Marginal benefit-cost ratio curves are developed to determine whether overtime usage is economically reasonable. These curves may also be used to determine the maximum or limiting amount of overtime to use under specific shop conditions.     

An appointment scheduling policy for healthcare systems with parallel servers and pre-determined quality of service

The appointment scheduling problem is well-known in the literature. The use of appointment systems has been adopted widely in many different fields, including service industries and especially healthcare.This research focuses on healthcare systems where patients arrive according to pre-assigned appointments. We consider healthcare systems with several parallel servers, where a given sequence of patients, with randomly distributed service durations and a possibility of no-shows, is to be scheduled. The aim is to minimize the end of day and increase resource utilization while a minimal probability of each appointment starting on time (quality of service) is required.We formulated the problem using mathematical programing and developed a multi-server numerical-based (MSN) algorithm to solve it. We conducted some experimental runs and checked the impact of the problem parameters on the end of day, customers’ average waiting time and the percentage of customers that waited for service. We also show how server pooling improves the above system measures. Finally, once the appointments are set, we develop a methodology to determine the shift length so as to balance overtime costs (costs of overtime hours) against undertime costs (costs of regular, unused hours).