QUALITY AND PRODUCTION CONTROL WITH OPPORTUNITIES AND EXOGENOUS RANDOM SHOCKS (original) (raw)

A Comparison of Production Scheduling Policies on Costs, Service Level, and Schedule Changes

Production and Operations Management, 2009

We consider a single product, single level, stochastic master production scheduling (MPS) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the MPS control system: the method used in determining the amount of stock planned for production in each time period. Typically, MPS control systems utilize a single buffer stock. Here, two MPS dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (DEA) methodology to aid in the evaluation of the simulation results, where DEA serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.

Freezing the master production schedule under single resource constraint and demand uncertainty

International Journal of Production Economics, 2003

This paper investigates the impact of freezing the master production schedule (MPS) in multi-item single-level systems with a single resource constraint under demand uncertainty. It also examines the impact of environmental factors on the selection of MPS freezing parameters. A computer model is built to simulate master production scheduling activities in a multi-item system under a rolling time horizon. The result of the study shows that the parameters for freezing the MPS have a significant impact on total cost, schedule instability and the service level of the system. Furthermore, the selection of freezing parameters is also significantly influenced by some environmental factors such as capacity tightness and cost structure. While some findings concerning the performance of MPS freezing parameters without capacity constraints can be generalised to the case of limited capacity, other conclusions under capacity constraints are different from those without capacity constraints. r 2002 Published by Elsevier Science B.V.

OPTIMAL INSPECTION SCHEDULE IN AN IMPERFECT EMQ MODEL WITH FREE REPAIR WARRANTY POLICY(Advanced Planning and Scheduling for Supply Chain Management)

Journal of the Operations Research Society of Japan, 2006

The paper considers a sequential inspectien policy in an imperfect productioll process which shifts randoriLly froiti an 'in-contr'ol' stat,e to an tout-eflcontrol: state fo11owing a ge"eral probability distribution. Two different inspection policies are adopted in the proposed model: (i) no action is taken in the intevniediate of a production run un]ess the process is found in an 'ont-ef-coiitrol' state by. inspectioll and (ii) preventivc repair action is undertaken orice the `in-control' stat,e of the proc:ess is det.ect,ecl by inspectien, The nianuracturer is in a contractual agreeirieTit with tho custoriier to provide frce iniiii}nal repair se,rvice until a certain (warranty) period frorti the tirrLe of initial purchase. The objec:tive is to cletennirie the optirnal nuniber of iiisl}ectioiis and inspectioii tinie sequence during a production run which ininiinize th{] rrianufac;turer's f'utuve expected c'osts ii} presc'iiL teri!i or average cost in distant, f'uture. The proposed i"odel is ft)rrnulated under discount,ed as well as lo}ig-run average cost criteria and sorne structural properties on the optiinal inspectiori poliuy are derived anal.yt,i(/ally. For a nunicrical exariiple, the optirrial inspection policy is deterniined and sevfn'al rnariagerial insights are invest,igated. Keywords: rv'tainte"ance, inspection, EDLCQ niodel, irriperfect repair. fr'ee repair warranty The OpeiationsReseaich Society of Japan optimat Jnspection Schethde 223 may randomly go to Cout,-oiLeontrul'. For a deterioratiiig production proeess with iiicreasing failure rate (IFR), Baneejee and Rahim [1] determined jointly the optimal design parameters on an M control chart and preventive replacement timc. Rahirii [11] and Rahim and Ben-Daya [12] investigated the effect of EMQ on the ecDnomic design of M control chari, for det,eriorating production proeesses where the [in-control' period fo]lows a general probability dist,ribution with IFR. The I)rocess was inspected using a sainpling frequency that increases with t,he age of the system. Tseng [14] inLroduced a prevelltive maintenance policy to enhance the sysLem reliability instea,d of inspectioii policy into an imperfect EMQ model. NIakis [7] considered the joiiit det/erminat,ion of the lot size and the inspection schedule, minimizing the long-run expected cost, per unit time wh ¢ , n in-control periods are generally. dist・ributed and inspections are imperfect.

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Production and Operations Management, 2009

Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in production and supply chain management. The interactions between pricing and production/supply chain performance, however, are not as well understood. Can a firm benefit from knowing the status of the supply chain or production facility when making pricing decisions? How much can be gained if pricing decisions explicitly and optimally account for this status? This paper addresses these questions by examining a make-to-order manufacturer that serves two customer classes-core customers who pay a fixed negotiated price and are guaranteed job acceptance, and "fill-in" customers who make job submittal decisions based on the instantaneous price set by the firm for such orders. We examine four pricing policies that span a range of complexity and required knowledge about the status of the production system at the manufacturer, including the optimal policy of setting a different price for each possible state of the queue. We demonstrate properties of the optimal policy, and we illustrate numerically the financial gains a firm can achieve by following this policy vs. simpler pricing policies. The four policies we consider are (1) state-independent (static) pricing, (2) allowing fill-in orders only when the system is idle, (3) setting a uniform price up to a cutoff state, and (4) general state-dependent pricing. Although general statedependent pricing is optimal in this setting, we find that charging a uniform price up to a cutoff state performs quite well in many settings and presents an attractive trade-off between ease of implementation and profitability. Thus, a fairly simple heuristic policy may actually out-perform the optimal policy when costs of design and implementation are taken into account.

A cost minimisation model for joint production and maintenance planning under quality constraints

In this paper, integrated planning of production, imperfect maintenance and process inspections in a multi-machine system is investigated. This system consists of parallel machines which deteriorate with time and they may shift from a primarily in-control state to a degraded state with a higher defective rate or to a failed state. Maintenance scheduling corresponds to a discrete time age-based imperfect maintenance with a large number of maintenance alternatives. Process inspections are considered to detect the current state of the system. Detecting a deteriorated condition initiates the quality check of the related sub-lots, rework of defective items and a process adjustment that brings the machine in its normal conditions. Production planning includes a capacitated lot-sizing problem with multiple products. We propose a joint approach that coordinates the decisions of the three functions, where the objective function minimises the total cost. Evaluation of costs and interacting factors is presented and two heuristic methods are proposed to solve the problem. The results of the joint model are compared to a non-integrated method and a sensitivity analysis is conducted. 1. Introduction In the competitive business environment of this era, organisations are seeking strategies to improve the quality and system's reliability while reducing the costs. Performance of a system and productivity of its functions are highly related to the coordination and cooperation of the subsystems. Production planning, maintenance scheduling and quality systems are the three functions of manufacturing systems with different goals defined on shared subjects. Despite the conventional approaches in the literature dealing separately with these functions, integrated strategy is an appropriate policy to handle the interactions between them. Lot-sizing decision as a well-studied problem is the determination of production levels, such that the total cost is minimised (Karimi, Fatemi Ghomia, and Wilson 2003). This type of problems arises in a wide range of industries and its complexity corresponds to the problem features such as the number of products, levels and machines, planning horizon, structure of setups , processing times, rework process and customer demands. In the capacitated lot-sizing problem (CLSP), the production levels are restricted for example to the available time and this latter is influenced by maintenance operations and random failures. The well-known Economic Production Quantity (EPQ) model is a subset of CLSP problems. Dohi, Okamura, and Osaki (2001) stated that in the presence of quality deterioration and machine failures, the classic EPQ model losses its usefulness and the uncertainty should be incorporated in the model. The general objective of preventive maintenance (PM) is to maximise the machine availability, or to minimise the system cost (Nourelfath, Ben-Daya, and Nahas 2016). Among the maintenance policies, the age-based maintenance (a subset of the condition-based PM) is an efficient approach employed in several papers (Ben-Daya 2002; El-Ferik 2008; Lu, Zhou, and Li 2016) to link the PM decisions to production or quality plans. PM improves the machine availability and reliability by reducing the rate of failures or increases the quality by enhancing their function. Quality control tools and process inspections are efficiently used to evaluate the hidden state of a system and to detect the process variations. Certain types of deteriorations may originate from internal resources and inspections are employed to signal the need for a maintenance or a process adjustment. Duncan (1956) proposed the economic design of a quality control chart to maximise the unit profit per time in a single machine system. In his model, the optimal determination of the sample size, length of the sampling interval and the control limits of an x-chart are addressed.

Balancing trade-offs in one-stage production with processing time uncertainty

Procedia Manufacturing, 2021

Production scheduling faces three challenges, two of which are trade-offs and the third is processing time uncertainty. The two sources of tradeoffs are between inconsistent key performance indicators (KPIs), and between the expected return and the risk of KPI portfolios. Given the KPIs of total completion time (TCT) and variance of completion times (VCT) are inconsistent for one-stage production, we propose our trade-off balancing (ToB) heuristics. Based on comprehensive case studies, we show that our ToB heuristics efficiently and effectively balance the tradeoffs from these two sources. Daniels and Kouvelis (DK) proposed a scheduling scheme to optimize the worst-case scenarios against processing time uncertainty, and they designed the endpoint product (EP) and endpoint sum (ES) heuristics for robust scheduling accordingly. Using 5 levels of coefficients of variation (CVs) to represent processing time uncertainty, we show that our ToB heuristics are robust as well, and even better than the EP and ES heuristics at high levels of processing time uncertainty. In addition, our ToB heuristics generate undominated solution spaces of KPIs, which provides a solid base in deciding control and specification limits for stochastic process control (SPC). Moreover, based on the normalized deviations from optima, our trade-off balancing scheme can be generalized to balance any inconsistent KPIs.

Machine Downtime Effect on the Warm-Up Period in an Economic Production Quantity Problem

Mathematics

Success in the industrial sector is compromised by diverse conditions such as imperfect product production, manufacturing line interruptions, and unscheduled maintenance. The precise use of common practices in production environments is an available solution to eliminate some of these issues. Applying a warm-up period in a manufacturing process is adequate and cost-effective for almost all companies. It improves the equipment’s productivity and helps the manufacturing line generate fewer defective products. Even though several inventory management studies have included a warm-up phase in their models, its use in economic production quantity (EPQ) models remains largely unexplored. Adding a warm-up phase to the production cycle minimizes maintenance expenses and defective products and increases the machine’s performance. In this study, the dependency between the machine downtime and the warm-up length is examined for the first time. The warm-up time depends on the machine’s off-state...

Analysis of production decisions under budget limitations

Stochastics An International Journal of Probability and Stochastic Processes, 2011

The issue of when to intervene in the evolution of a production system is the focus of this study. The interventions take the form of changes to production depending on the current value of the products. Each change incurs a charge representing costs such as physical expansion, overtime or new hiring when production increases and costs such as severance or shut down when production decreases. The goal is to maximize the expected return subject to these intervention costs over at most a finite number of intervention cycles. This paper determines for a large class of problems an explicit formula for the value function and a set of optimal times at which to increase and decrease production. The optimization is over a very general class of stopping times and proves that an optimal set of times in this general class is given as the hitting times of various levels, depending on the number of remaining interventions. These optimal hitting levels are characterized as a maximizing point for a high-dimensional nonlinear function and can be efficiently and iteratively determined as the solutions of successive one-dimensional nonlinear maximization problems. The solution method is illustrated on some examples, including mean-reverting processes.

On the effect of downtime costs and budget constraint on preventive and replacement policies

Reliability Engineering & System Safety, 2008

This work proposes a general approach to study and improve the effectiveness of the system with respect to its expected life-cycle cost rate. The model we propose considers a production system which is protected against demand fluctuations and failure occurrences with elements like stock piles, line and equipment redundancy, and the use of alternative production methods. These design policies allow to keep or minimize the effect on the nominal throughput, while corrective measures are taken. The system is also subject to an aging process which depends on the frequency and quality of preventive actions. Making decisions is difficult because of discontinuities in intervention and downtime costs and the limited budget. We present a nonlinear mixed integer formulation that minimizes the expected overall cost rate with respect to repair, overhaul and replacement times and the overhaul improvement factor proposed in the literature. The model is deterministic and considers minimal repairs and imperfect overhauls. We illustrate its application with a case based on a known benchmark example.