Order Release Model Research Articles

Order release optimisation for time-dependent and stochastic manufacturing systems

Order release optimisation is essential in production planning, especially in discrete manufacturing. Order release planning models with load-dependent lead times must anticipate the time-dependent work-in-process and output for any given release schedule and thus require an anticipation model that approximates the time-dependent behaviour of queueing systems. We present a generic optimisation model for order release planning in stochastic, non-stationary manufacturing systems that includes a well-defined interface for the anticipation model. We develop two stationary backlog carryover (SBC) approaches to approximate time-dependent queueing behaviour and prove their consistency with the order release model. The resulting nonlinear programming model is shown to be a special case of the well-known clearing function models. A numerical study demonstrates that the optimised order releases for different demand patterns are close to the optimum that results from simulation-based optimisation even for extreme demand and release patterns. The resulting output closely matches the simulated output with some deviations.

DESIGN, DEVELOPMENT AND EVALUATION OF ALOGLIPTIN AND METFORMIN HYDROCHLORIDE BILAYER TABLET

The objective of the present work was to formulate and evaluate bilayer tablets of alogliptin and metformin HCl. Combining alogliptin with metformin HCL gives additional benefits in comparison with either drug alone and could be considered for patients whose quality of life is impaired by diabetes mellitus. The study was performed to design bilayer tablets of alogliptin immediate release layer and metformin HCl sustained release layer by wet granulation method. The immediate release layer comprised Crospovidone superdisintegrant and sustained release layer comprised HPMC K4M as rate release controlling polymers and PVPK30 was used as binder for both layers. The in vitro release of drug from the formulations was studied in 0.1N HCl acidic buffer and pH 6.8 phosphate buffer, and it was found that the prepared sustained release layer tablets were able to sustain the release of the drug up to 12 h and in vitro studies of alogliptin shown more than 80% of drug was released within 30 min. As per ICH guidelines, accelerated stability studies were carried out and results were found within the range. The release of alogliptin follows a zero order release model and the release of metformin HCl follows Higuchi model release.

Average reward adjusted deep reinforcement learning for order release planning in manufacturing

One of the key challenges in production planning, especially in discrete manufacturing, is to determine when to release which orders to the shop floor. The major aim of this planning task is to balance Work-In-Process (WIP) and utilisation levels together with timely completion of orders. The two most crucial attributes of production planning are (i) the highly nonlinear relationship between WIP, flow times and output, and (ii) the dynamically changing environment. Nonetheless, most state-of-the-art models use static lead times to address this problem. Only recently, some papers set lead times dynamically based on the flow time forecasts to react to the dynamic operational characteristics reporting promising results. This paper contributes to this line of research by presenting an order release model that uses reinforcement learning (RL) to set lead times dynamically over time. The applied RL agent is especially designed for processes with periodic feedback and highly variable context. We compare the performance of our new RL algorithm to static order release models and state-of-the-art deep Q-Learning agents by using a multi-stage, multi-product flow-shop simulation model. The results show that, especially for scenarios with high utilisation, our proposed method outperforms the other approaches.

Curcumin solid dispersion based on three model acrylic polymers: formulation and release properties

To investigate structure-property relationship of polymer-based curcumin solid dispersion (SD), three acrylic polymers were used to formulate curcumin SD by solvent evaporation method. Curcumin Eudragit EPO SD (cur@EPO), curcumin Eudragit RS PO SD (cur@RSPO) and curcumin Eudragit RL PO SD (cur@RLPO) showed deep red, golden orange and reddish orange color, respectively. Cur@RSPO entrapped 15.42 wt% of curcumin followed by cur@RL PO and cur@EPO. FTIR spectra indicated that in cur@EPO, curcumin may transfer hydrogen to the dimethylaminoethyl methacrylate group and thus change its color to red. In contrast, curcumin may form hydrogen bonding with Eudragit RS PO and Eudragit RL. Curcumin exists in amorphous state in three SDs as proved by differential scanning calorimetry and X-Ray diffraction measurement. In vitro digestion presented that lower pH value in simulated gastric fluid (SGF) stimulates the curcumin release from cur@EPO while permeability influences the release profile in other two SDs. When in simulated intestinal fluid (SIF), first order release model governs the release behaviors of all three SDs which showed sustained release pattern. Our results are helpful to elucidate how structure of polymer may impact on the major properties of curcumin contained SD and will be promising to broaden its therapeutic applications.

Order release planning with predictive lead times: a machine learning approach

An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. One key parameter is the lead time which is the planned time that elapses between the release of an order and its completion. Lead times are normally determined based on the observed time orders previously took to traverse the production system (flow times). Traditional order release models assume static lead times, although it has been shown that they should be set dynamically to reflect the dynamics of the system. Therefore, we present a flow time estimation procedure to set lead times dynamically using an artificial neural network. Additionally, we implement a safety lead time to incorporate the underlying cost ratio between finished inventory holding and backorder costs in the order release model. We test our proposed approach using a simulation model of a three-stage make-to-order flow-shop and compare the forecast accuracy and the cost performance to other forecast-based order release models from the literature. We show that our proposed model using artificial neural networks outperforms the other tested approaches, especially for scenarios with high utilisation and high variability in processing times.

Rule based workload control in semiconductor manufacturing revisited

An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. A prominent approach is the workload control (WLC) concept which originated from the idea of controlling flow times by controlling order releases. Despite recent advances in rule based WLC models, the recent semiconductor literature has neglected them, although it has been shown that they outperform most other periodic and continuous order release models. Therefore, we adapt the most successful rule based WLC model, the LUMS-COR approach and compare it with two approaches from the semiconductor manufacturing literature: Starvation Avoidance (SA) and ConLOAD approach. We include three pool sequencing rules, namely First-Come First-Served (FCFS), Earliest Due Date (EDD) and Critical Ratio (CR). We analyse their performance using a simulation model of a scaled-down wafer fabrication facility. The results show that, in comparison to the other two order release approaches, the LUMS-COR model yields lower total costs due to a more balanced shop and better timing performance which is robust across different settings. This suggests that the adapted LUMS-COR model has high potential to become a viable alternative to the rule based order release mechanisms used in semiconductor industry to date.

Comparison of two optimization based order release models with fixed and variable lead times.

Manufacturing planning and control systems ideally seek to simultaneously achieve short and predictable flow times as well as high output and due-date performance. One approach to address this problem is the workload control concept. It is based on the idea to control lead times by controlling order releases and thus the level of work-in-process and output. The focus of this paper is on multi-period optimization models for order release planning. We use a simulation study of a make to order manufacturer in a rolling horizon setting and compare the performance of an order release model that assumes fixed lead times and a model that allows for variable lead times: An input output control and a clearing function model, respectively. We analyze two different approaches to cope with the varying and tight due date slack and find that including more foreknowledge of demand is preferable. Finally, we show that the clearing function model outperforms the input output control model in all scenarios by yielding lower inventory levels with shorter shop floor throughput times. This advantage seems not to be affected by the predictability of the demand, but the IOC model narrows the gap when using near optimal parameters especially for a scenario with deterministic demand.

Order release planning by iterative simulation and linear programming: Theoretical foundation and analysis of its shortcomings

Planning future order releases for complex manufacturing systems with substantial lead times must consider limited capacities and the resulting production smoothing problem as well as the highly nonlinear relationship between capacity utilization, work-in-process and flow times. An important solution approach that has been proposed for this problem is to iterate between an order release model with fixed lead times and a lead time estimation (usually simulation) model that estimates the flow times for given order releases, providing the lead times for the next iteration. However, the convergence of this iterative procedure is highly unpredictable, limiting its practical use.The iterative mechanism is analyzed analytically for simplified formulations of the order release and lead time estimation model. We show that an order release procedure of this type that iterates on the lead times is a dual (price) coordination mechanism whose design does not meet the theoretical requirements, and there is no straightforward way to overcome this. The analysis also provides insights into the results of several numerical studies from the literature and suggests a possible research direction to improve the method.Order release mechanisms of this type are a special case of a broader class of production planning methods that iterate between the production planning and the production scheduling level in order to provide realistic values for lead times and planned capacities. Providing theoretical underpinning for this type of production planning methods is an important research objective, and the paper pursues this direction for the special case of order release.

Comparison between rule- and optimization-based workload control concepts: a simulation optimization approach

An important goal of Production Planning and Control systems is to achieve short and predictable flow times, especially where high flexibility in meeting customer demand is required, while maintaining high output and due-date performance. One approach to this problem is the workload control (WLC) concept. Within WLC research two directions have been developed, largely separately, over time: Rule based and optimisation-based models. If a company intends to introduce an order release concept based on WLC it first has to decide which of these two approaches should be applied. Therefore, this paper compares two of the most widely used and considered best performing periodic order release models out of both streams: the LUMS (rule based) and the clearing function model (optimisation based). The parameters of both approaches are set using simulation optimisation. The performance is compared using a simulation study of a hypothetical job shop in a rolling horizon setting. The results show that the optimisation model outperforms the rule-based mechanism in all instances with stochastic demand (exponential inter-arrival times), but is outperformed in aggregate cost of backorders and inventory holding and balancing measures by the LUMS approach for scenarios with high utilisation and seasonal demand.

Adaptive Order Release Planning with Dynamic Lead Times

An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. One key parameter is the lead time which is defined as the planned time that elapses between the release of an order and its completion. Lead times are normally determined based on the actual duration that an order takes to traverse through the production system (flow times). Traditional order release models assume static lead times, although they should be set dynamically to reflect the dynamic operational characteristics of the system. Therefore, we present a cycle time estimation procedure to set lead times dynamically based on exponentially smoothed flow time forecasting. Additionally, we implement a safety lead time to incorporate the underlying cost ratio in the order release model. We use a simulation model of a three-stage flow-shop and show that our approach outperforms other forecast based order release mechanisms from literature.

Formulation and In Vitro Evaluation of Bilayered Matrix Tablets of Pioglitazone for Immediate Release and Glimepiride for Extended Release

Bi-layer tablets are novel drug delivery systems where a combination of two or more drugs in a single unit having different release profiles which improves patient compliance and prolongs the drug action. The study was performed to design bilayer matrix tablets of pioglitazone for immediate release and glimepiride for extended release delivery system. Bilayered matrix tablets composed of two layers, one is immediate release and a second layer is extended release layers. The immediate release layer comprised sodium starch glycolate, and croscarmellose sodium as super disintegrates and extended release layer comprised ethyl cellulose and HPMC K4M as release retardant polymers. Direct compression method was employed for the formulation of bilayer tablets. In vitro studies have shown more than 80% of pioglitazone was released within 60 min. Ethyl cellulose and HPMC K4M retarded the release of glimepiride from the controlled release layer for 12 h. Drug release mechanism exponent (n) was determined for all formulations (0.689-0.789). The release of pioglitazone was found to follow a first order release and the release of glimepiride was followed zero order release model.

FORMULATION AND EVALUATION OF BUCCOADHESIVE SUSTAINED-RELEASE DISCS OF GLIPIZIDE

Glipizide is an oral hypoglycemic agent used in the treatment of type II diabetes mellitus. It is characterized by its poor aqueous solubility and delayed absorption with concomitant food intake. The objective of the present study was to enhance the absorption rate of glipizide and avoid its side effects on stomach by formulating it into buccoadhesive sustained release disc formulations. The discs were prepared by direct compression method. Hydroxypropyl methylcellulose (HPMC 15000), was used as the main hydrophilic matrix forming polymer either alone or in combinations in two ratios (3:2 & 4:1) with various mucoadhesive polymers namely; Sodium alginate (NaAlg), Sodium carboxymethyl cellulose (SCMC), Hydroxyethyl cellulose (HEC), Hydroxypropyl cellulose (HPC) and Chitosan. The discs were evaluated for weight variation test, thickness, diameter, drug content, hardness, friability, swelling index, surface pH, in-vitro bioadhesion, in-vitro release studies and in-vivo bioavailability studies. Invitro release studies demonstrated that formulation F8 which contains HPMC / SCMC (40%: 10%) has sustained the drug release up to 8 hrs which was considered an optimum pattern of drug release. The kinetic studies revealed that all formulations follows zero order release kinetics except F3, F4 and F11 which fitted well in first order release model. Bioavailability parameters including Cmax, Tmax, and AUC0–24 h of F8 and the commercial oral tablets of glipizide (Minidiab® 5 mg) were compared. The selected formulation F8 produced higher Cmax and extended Tmax (P<0.05).

Design and Characterization of Gastroretentive Bilayer Tablet of Amoxicillin Trihydrate and Ranitidine Hydrochloride for H. pylori Infection

Objective: The present study was aimed at developing Gastroretentive Bilayer drug delivery systems containing Amoxicillin Trihydrate and Ranitidine Hydrochloride for the treatment of H. pylori induced peptic ulcer to minimize the side effect, improve the prolongation of action, to reduce the frequency of drug administration. Materials and Method: The tablet is characterized by immediate release layer of Ranitidine Hydrochloride and Gastroretentive layer of Amoxicillin Trihydrate. The formulation containing Gastroretentive layer was designed using HPMC K15M and HPMC K4M as floating agents, sodium bicarbonate and citric acid as gas-generating agent. Crospovidone was used as superdisintegrant for the preparation of immediate release layer. The prepared Gastroretentive layer was evaluated for their precompression parameters, physical characteristics like hardness, friability, uniformity of weight, uniformity of drug content, swelling index, In-vitro floating studies and In-vitro drug release. For the estimation of Ranitidine Hydrochloride and Amoxicillin Trihydrate in a formulation, simultaneous estimation method was employed. Results: The release of the Ranitidine Hydrochloride from the immediate release layer was found to be 94.6% ± 0.02% in 30 minutes. The release of Amoxicillin Trihydrate for the sustained release floating layer was found to be 90.5 ± 0.06% in 12 hours. The data obtained from In-vitro release were fitted into the various kinetic models (Zero Order, Higuchi, First Order and Korsmeyer–Peppas Model). Conclusion: The best fitted model for Amoxicillin Trihydrate and Ranitidine Hydrochloride was found to be zero order release model and first order release model, respectively.

Magnetic glass ceramics for sustained 5-fluorouracil delivery: Characterization and evaluation of drug release kinetics

In the present study, magnetic glass ceramics in the system Fe2O3 ∙ TiO2 ∙ P2O5 ∙ SiO2 ∙ MO (M=Mg, Ca, Mn, Cu, Zn or Ce) are prepared. The effect of adding different cations on the thermal behavior, developed phases, microstructure and magnetic properties is studied using differental thermal analysis (DTA), X-ray diffraction analysis (XRD), transmission electron microscope (TEM), FT-infrared transmission (FT-IR) and vibrating sample magnetometer (VSM) respectively. The magnetic glass ceramics are tested as delivery systems for 5-fluorouracil. Modeling and analysis of release kinetics are addressed. The application of Higuchi square root of time model and the first order release model indicated that, 5-FU is released by diffusion controlled mechanisms, and that its released rate depends greatly on the concentration of loaded drug during the loading stage. The obtained results suggested that, the prepared magnetic glass ceramics can be used for cancer treatment by hyperthermia and/or by localized delivery of therapeutic doses of 5-fluorouracil.

In vitro release characteristics and cellular uptake of poly(D,L-lactic-co-glycolic acid) nanoparticles for topical delivery of antisense oligodeoxynucleotides

The efficacy of antisense oligodeoxynucleotides (AsODNs) is compromised by their poor stability in biological fluids and the inefficient cellular uptake due to their size and negative charge. Since chemical modifications of these molecules have resulted in a number of non-antisense activities, incorporation into particulate delivery systems has offered a promising alternative. The aim of this study was to evaluate various poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles for AsODN entrapment and delivery. PLGA nanoparticles were prepared using the double emulsion solvent evaporation method. The influence of formulation parameters such as PLGA concentration and volume ratio of internal aqueous phase volume (Va1) to organic phase volume (Vo) to external aqueous phase volume (Va2) on particle size, polydispersity index (PDI) and zeta potential (ZP) was investigated using a full factorial study. The particle size increased with increasing PLGA concentrations and volume ratios, with an interaction detectable between the two factors. AsODN entrapment efficiencies ranged between 49.97% and 54.95% with no significant difference between various formulations. By fitting the in vitro release profiles to a dual first order release model it was shown that the AsODN release occurred via two processes: a diffusion controlled process in the early phase (25 to 32% within one day) and a PLGA degradation process in the latter (39 to 70% after 14 days). Cellular uptake studies using primary corneal epithelial cells suggested active transport of nanoparticles via endocytosis. PLGA nanoparticles therefore show potential to successfully entrap AsODNs, transport them into cells and release them over time due to polymer erosion.

Effect of cations on the microstructure and in-vitro drug release of κ- and ι-carrageenan liquid and semi-solid aqueous dispersions

OBJECTIVES The main objective of this study was to determine the effect of potassium and calcium ions on the microstructure and release dynamics of kappa (κ) and iota (ι) carrageenan. The microstructure of the dispersions was imaged using a cryogenic scanning electron microscope. Franz-cell diffusion apparatus was used to determine the release kinetics of a model hydrophilic drug, sodium fluorescein, incorporated in selected polymer dispersions. Release profiles were analysed using Higuchi, Korsmeyer-Peppas and dual first-order models. Cryogenic scanning electron microscope images showed that κ-carrageenan forms hexagonal structures, whereas ι-carrageenan forms rectangular pores at low cation concentrations. In-vitro release studies showed sustained release profiles for all carrageenan systems; however the model drug, fluorescein, diffusion from ι-carrageenan with 0.06% w/v calcium was significantly higher than other ι-carrageenan systems. This may be attributed to improved tortuosity of this system. However further increase in cation concentration led to a reduction in fluorescein release from the matrices. The dual first-order release model illustrated two distinct release rates, an initial rapid release followed by a slow diffusion of fluorescein from the carrageenan matrices. The observed microstructural differences may account for the well known variation in mechanical properties of κ- and ι-carrageenan gels. The dual first order release model adds a new tool in the elucidation of release mechanisms from polymer matrices, where parallel processes contribute to drug release.

Development and evaluation of regioselective bilayer floating tablets of Atenolol and Lovastatin for biphasic release profile

This study was performed to design bilayer regioselective floating tablets of atenolol and lovastatin to give immediate release of lovastatin and sustained release of atenolol. Bilayer floating tablets comprised two layers, i.e immediate release and controlled release layers. The immediate release layer comprised sodium starch glycollate as a super disintegrant and the sustained release layer comprised HPMC K100M and xanthan gum as the release retarding polymers. Sodium bicarbonate was used as a gas generating agent. Direct compression method was used for formulation of the bilayer tablets. Accelerated stability studies were carried out on the prepared tablets inaccordance with ICH guidelines. Roentgenography was carried out to study the in vivo buoyancy of the optimized formulation. All formulations floated for more than 12 h. More than 90% of lovastatin was released within 30 min. HPMC K100M and xanthan gum sustained retarded the release of atenolol from the controlled release layer for 12 h. After stability tests, degradation of both drugs were found but the drugs, contents were found to be within the range. Diffusion exponents (n) were determined for all the formulations (0.53-0.59). The release of atenolol was found to follow a mixed pattern of Korsmeyer-Peppas, Hixson-Crowell and zero order release models. The optimized formulation was found to be buoyant for 8 h in stomach. Therefore, biphasic drug release pattern was successfully achieved through the formulation of floating bilayer tablets inthis study.

Flexibility-driven order releases in job-shop production

Overview. The aim of releasing orders is to transfer jobs from the planning stage to the realization stage under consideration of economic objectives. In a job-shop with order driven production, this decision problem is characterized by time-related, open decision fields. In this context, a possible approach to solve this problem lies in considering flexibility as a decision criterion. This procedure also forms the basis of opportunistic coordination. Here, the basic idea is to utilize the inherent flexibility of a production system to compensate negative consequences of unexpected changes of the decision field. The aim of this article is to • examine the extent in which flexibility aspects are considered in relevant order release models • specify the principles of opportunistic coordination within the order release planning problem to achieve a flexibility-driven order release, and on this basis • develop the structure of a decision model to flexibility-driven order release.

Aggregate order release planning for time-varying demand

Order release is the interface between the control of the material flow through the entire logistic chain and the control of the production units. Order release mechanisms based on the workload control concept usually are based on functional relationships of time averages of work-in-process, output, flow time, etc. This causes severe limitations especially in the case of time-varying demand. The paper shows that order release can be designed as a hierarchical system with aggregate order release planning as the upper level; aggregate order release planning determines the amount of released work in the planning periods and thus decides on the time-dependent state of the production units and on the balance between inventory variation and production level variation. A decision model for the optimization of aggregate order release is presented that takes into account the dependence of the work centre output on the work-in-process and therefore can be considered as a workload control method. Simulation results show the performance of the model. It is concluded that the optimization model leads to improvements over order release models currently available. Further improvements of the model, based on better integration of research on the transient behaviour of queuing networks, are a topic of future research.

Integrating order release control with due-date assignment rules

This study integrates order release control methods with due-date assignment rules and assesses its impact on the accuracy of inter-operation time estimation and performance of due-date assignment. The assessment is made by using an experimental design with three due-date assignment rules, three scheduling rules and three order release models. The three order release models are: (1) Basic model, in which three due-date assignment rules consider the order arrival time as the order release time; (2) Control model, in which three due-date assignment rules integrate with the order release control method developed here; and (3) Adjustment model, in which the control model integrates with the order release control adjustment developed here. Simulation results in this study indicate that integrating the order release control method with due-date assignment rules will significantly enhance not only the accuracy of interoperation time estimation, but also the performance of due-date assignment rules.