Inventory Production Control System Research Articles

Improved cuckoo search optimization for production inventory control systems

The efficient use of the production-inventory control system is of great importance in the industry. In this paper, an investigation study of the impact of an optimal Integral minus Proportional Derivative (I-PD) controller on the dynamic behavior of the production-inventory system is given. The differential equations of the system are first formulated. Then, the I-PD controller is proposed to enhance the responsiveness of the inventory performance. Two swarm optimization techniques, Cuckoo Search Optimization (CSO) and an Improved CSO (ICSO), are employed to tune the adjustable design parameters of the I-PD controller. In order to represent a realistic environment, two simulations are conducted. The first one is when the system is subject to a unit step change in the demand and second is when the demand is randomly. In a comparative performance study for both tuning schemes, the Integral of Time Absolute Error (ITAE) and the Integral of Square Error (ISE) indices were used as evaluation measures. The simulation outcomes using MATLshow the superiority of the ICSO to tune the I-PD controller in terms of reducing the ITAE and ISE indices in comparison with the result obtained from traditional CSO. The result of this improvement indicates that the I-PD controller tuned by ICSO looks likely to better in terms of improving the performance of the system, particularly, by significantly reducing the inventory cost .

Inventory Control of Vegetable Oil Products Using Continuous Review System (Q) Approach and Periodic Review System (P) Methods in Retail Companies: A Case Study of Indonesia

Retail companies are businesses that have a significant role in providing quality staples to improve public health. Every retail company wants the production inventory control system to run well to meet consumer demand. Suppose the amount of inventory ina retail company is too small. In that case, it will disrupt consumer demand for products that consumers want to buy and lose the opportunity to earn profits if an order is more significant than forecast. Meanwhile, if product inventory exceeds the need for a product, it will cause high costs, and if stock is stored for too long, it will result in damage and expiration. This retail company sells quality products and provides a wide range of excellent and complete products and makes it easier for customers to find their needs as one of the best. A company that needs to have a product control system to maintain sales and sell quality products and finally compete with other national and foreign retail companies. However, the current product inventory processing is not optimal because it is only based on previous inventory data. It can be seen from the varying amount of inventory and the high frequency of ordering. This study aims to obtain the most optimal comparison results by using the minimum total cost criteria and minimizing the cost of purchasing the product. This study compares two methods, namely the Continuous Review System (Q) method and the Periodic Review System (P) method. The most suitable method was the Q method with the total inventory cost of Vegetable Oil Products of Rp in this study. 13,371,600 with an efficiency of 42% of the entire inventory costs incurred by retail companies.

New Adaptive Surrogate-Based Approach Combined Swarm Optimizer Assisted Less Tuning Cost of Dynamic Production-Inventory Control System

Today, most manufacturing control systems are complex and expensive, so they are limited to employ a small number of function evaluations for optimal design. Yet, looking for optimization methods with the less-computational cost is an open issue in engineering control systems. This paper aims to propose an effective adaptive optimization approach by integrating Kriging surrogate and Particle Swarm Optimization (PSO). In this method, a novel iterative adaptive approach is utilized using two sets of training samples including initial training and adaptive sample points. The initial training points are designed by space-filling design, while the adaptive points are generated using a new jackknife resampling approach. The proposed approach can effectively convergence towards the global optimal point using a small number of function evaluations. The efficiency and applicability of the proposed algorithm are evaluated using the optimal design of the fractional-order PID (FOPID) controller for some benchmark transfer functions. Then, the introduced approach is applied for tuning the parameters and the sensitivity analysis of the FOPID controller for a dynamic production-inventory control system. The results are in good agreement with the results reported in the literature, while the proposed approach is executed with a lower computational burden.

Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts

The increasing globalisation process has led to a radical change in the production concept, moving from a mass production paradigm towards one of mass customisation (MC), and focusing on value creation by pursuing customers’ needs and increasing responsiveness. The rapid development of information technologies has also made it possible to create new manufacturing paradigms, such as Industry 4.0 and cloud manufacturing, in which the increased level of autonomy is one of the key concepts for tackling new market challenges. This paper proposes a decentralised scheduling approach that improves the performance of production systems while minimising the usually high work-in-progress (WIP) requirements of the classic centralised scheduling and inventory production control system. Using a semi-heterarchical Manufacturing Planning and Control (MPC) architecture and integrating the Industry 4.0 innovation in a cloud manufacturing environment, this work contributes to the design of the lower level of the MPC architecture. The resulting production controller can allocate jobs following different dispatching rules dynamically. The performances of the proposed approach were assessed for different production scenarios and control parameter settings through an exhaustive experimental campaign based on hybrid simulation tools. The results showed that the proposed low-level controller led to a productivity increase while delivering increased responsiveness.

Multi-objective particle swarm optimisation approach for production-inventory control systems

PurposeThis paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO).Design/method/approachThe production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation.FindingsThe results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions.Research limitations/implicationsThis research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level.Originality/valuePSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems.

Analysing the impact of different classical controller strategies on the dynamics performance of production-inventory systems using state space approach

PurposeThe purpose of this paper is to examine the impact of applying two classical controller strategies, including two proportional (P) controllers with two feedback loops and one proportional–integral–derivative (PID) controller with one feedback loop, on the order and inventory performance within a production-inventory control system.Design/methodology/approachThe simulation experiments of the dynamics behaviour of the production-inventory control system are conducted using a model based on control theory techniques. The Laplace transformation of an Order–Up–To (OUT) model is obtained using a state-space approach, and then the state-space representation is used to design and simulate a controlled model. The simulations of each model with two control configurations are tested by subjecting the system to a random retail sales pattern. The performance of inventory level is quantified by using the Integral of Absolute Error (IAE), whereas the bullwhip effect is measured by using the Variance ratio (Var).FindingsThe simulation results show that one PID controller with one feedback loop outperforms two P controllers with two feedback loops at reducing the bullwhip effect and regulating the inventory level.Originality/valueThe production-inventory control system is broken down into three components, namely: the forecasting mechanism, controller strategy and production-inventory process. A state-space approach is adopted to design and simulate the different controller strategy.

Dynamics analysis of a production-inventory control system with two pipelines feedback

PurposeThe purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance.Design/methodology/approachThe proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system.FindingsThe results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives.Research limitations/implicationsThis research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level.Originality/valueThis present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions.

Performance regulation of event-driven dynamical systems using infinitesimal perturbation analysis

This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

Service levels, system cost and stability of production–inventory control systems

Production-inventory control system models have been analysed in the literature either in terms of their stability against demand fluctuations or in terms of their service level and cost performance under uncertain demand. This article analyses the production-inventory system performance in terms of service level (i.e. order fill rate) and average system costs, under stable settings of the control parameters. The classical automatic pipeline variable inventory and order-based production control system has been modified by explicitly modelling safety stock to help achieve higher services levels in the face of random demand. The stability of the system is affected by the control parameters: fractional rates of adjustment of work-in-process and inventory. However, the service level and average cost are affected by the control parameters as well as the smoothing factor in demand forecasting. This article puts forward five propositions which give light to general system performance based on the parameters selection. Intensive simulation experiments have also been carried out to reveal the performance variations within the stable region, leading to further insights on the system behaviour. The managerial insights which can assist proper tuning of systems to help achieve the desired performances have been discussed.

Inventory control for the supply chain: An adaptive control approach based on the identification of the lead-time

In this paper, an Internal Model Control (IMC) scheme is incorporated in production inventory control systems in a complete supply chain. This control scheme presents a good target inventory tracking under the perfect knowledge of the system. Furthermore, the inventory tracking and load disturbance rejection control problems can be tackled separately. However, the closed-loop performance of the IMC scheme may be degraded due to a mismatch between the modelled and actual delay or to the fact that delays may be time-varying. Thus, the IMC control scheme is enhanced in this work with a novel method for the online identification of lead times based on a multimodel scheme. In this way, all benefits of the IMC scheme can be exploited. A detailed discussion of the proposed production inventory system is provided including a stability and performance analysis as well as the identification capabilities of the algorithm. Several simulation examples illustrate the efficiency of the approach.

Possibility and necessity representations of fuzzy inequality and its application to two warehouse production-inventory problem

In this paper, possibility and necessity representations of fuzzy inequality constraints are presented and then crisp versions of the constraints are derived. Here analogous to chance constraints, real-life necessity and possibility constraints in the context of two warehouse multi-item dynamic production-inventory control system are defined and defuzzified following fuzzy relations. Hence, a realistic two warehouse multi-item production-inventory model with fuzzy constraints has been formulated for a finite period of time and solved for optimal production with the objective of having maximum profit. The rate of production is unknown, assumed to be a function of time and considered as a control variable. Also the present system produces some defective units alongwith the perfect ones and the rate of produced defective units is stochastic in nature. Demand of the good units is stock dependent and known and the defective units are sold at a reduced price. The space required per unit item and available storage space are assumed to be imprecise. The inequality of budget constraints is also imprecise. The space and budget constraints are expressed as necessity and/or possibility types. The model is reduced to an equivalent deterministic model using fuzzy relations and solved for optimum production function using Pontryagin’s optimal control policy, the Kuhn–Tucker conditions and generalized reduced gradient (GRG) technique. The model is illustrated numerically and values of demand, optimal production function and stock level are presented in both tabular and pictorial forms.

Design of a novel adaptive inventory control system based on the online identification of lead time

In this paper, an adaptation method for the online identification of lead time is incorporated in production–inventory control systems. Based on the lead time estimate, the tuning parameters are updated in real time to improve the efficiency of the system. Combination of the adaptive scheme with a proportional control law is able to eliminate the inventory drift that appears when the actual lead time is not known in advance or when it varies with time. A detailed analysis is provided for the proposed production–inventory system, including a stability analysis and the quantification of its bullwhip effect. Several examples and comparison with state-of-the-art alternative approaches illustrate the efficiency of the system.

Nonlinear receding horizon control of production inventory systems with deteriorating items

This paper is concerned with the receding horizon control of the production rate of a deteriorating production system with a nonlinear inventory-level-dependent demand. Both continuous and periodic review policies are discussed and numerical illustrations are provided. .

Possibility and necessity constraints and their defuzzification—A multi-item production-inventory scenario via optimal control theory

In this paper, analogous to chance constraints, real-life necessity and possibility constraints in the context of a multi-item dynamic production-inventory control system are defined and defuzzified following fuzzy relations. Hence, a realistic multi-item production-inventory model with shortages and fuzzy constraints has been formulated and solved for optimal production with the objective of having minimum cost. Here, the rate of production is assumed to be a function of time and considered as a control variable. Also the present system produces some defective units along with the perfect ones and the rate of produced defective units is constant. Here demand of the good units is time dependent and known and the defective units are of no use. The space required per unit item, available storage space and investment capital are assumed to be imprecise. The space and budget constraints are of necessity and/or possibility types. The model is formulated as an optimal control problem and solved for optimum production function using Pontryagin’s optimal control policy, the Kuhn–Tucker conditions and generalized reduced gradient (GRG) technique. The model is illustrated numerically and values of demand, optimal production function and stock level are presented in both tabular and graphical forms. The sensitivity of the cost functional due to the changes in confidence level of imprecise constraints is also presented.

Effect of information update frequency on the stability of production–inventory control systems

Production ordering and inventory dynamics in a manufacturing system are analyzed using function transformation techniques (z-transform) and their conditions for stability are examined. A generic model which captures the mixing and variability in the production process is developed. Variation in the stability of the system operating under sufficient inventory coverage and under limited inventory coverage is highlighted. The effects of the frequency of information update on stability are then examined by relating the update frequency to the sampling interval of the underlying difference equations. System dynamics simulations are used to demonstrate the stable or unstable behaviour of the production–inventory system.

Fuzzy control with limited control opportunities and response delay––a production-inventory control scenario

This paper examines the utility of fuzzy control over crisp in situations where the control opportunities are limited and the system response to control actions is delayed. Such situations are often encountered in production systems where limited resources restrict the control opportunities and the operation time delays the response. The performance of a real-time production-inventory control system is studied with fuzzy control strategy and compared with a corresponding crisp control and no-control strategy. The system consists of a production shop having a number of identical processing machines which produce two products. The output goes into two bins whose inventory is required to be controlled at desired level by varying the number of machines allocated to the products. Real-time inventory variation, output, average inventory and machine usage, number of setups and stock-outs are used as performance measures. The simulation results of the system with various configurations show that the capability of fuzzy control is seriously inhibited by limited opportunities and response delay although fuzzy has distinct advantage over crisp. As control opportunities increase fuzzy control becomes increasingly efficient with diminishing effect of response delay.

Functionalities of production-inventory control systems

Abstract We consider three well-known production-inventory control systems: Material Requirements Planning, Just-In-Time and Bottleneck Scheduling. Each system was developed with a specific production environment in mind, and it would therefore be naive to assume that these systems apply to any situation. Most factories would benefit from a hybrid system which carefully weighs functionalities from different systems to fit the problem at hand. In this paper we discuss functionalities required from production-inventory control systems, and show how the three generic systems mentioned above satisfy these requirements. We then proceed by suggesting the core characteristics of a hybrid system.

Design of production-inventory control systems incorporating fast-sampling control policies with implementation lead time

In this paper, singular perturbation methods are used to synthesize fast-sampling error-actuated control policies for two production-inventory system models in which there is a finite time delay of several sampling periods between the formulation and the implementation of the control policies. In the first model it is assumed that the demand for the product cannot be influenced by the management of the production-inventory system, and in the second model it is assumed that the demand for the product can be influenced by advertising and that the desired instantaneous demand rate is specified by management. In both cases, it is shown that the system will exhibit good transient and steady-state behaviour when controlled by such control policies. Furthermore, it is also shown that non-interacting control can be achieved when effective expenditure on advertising is introduced in order to influence the demand for the product, and that the provision of this extra flexibility thus leads to improved transient beha...

Capacity planning in MRP, JIT and OPT: A critique

This paper discusses how well recent production-inventory control systems such as Material Requirements Planning, Just-In-Time production and Optimized Production Technology, behave in environments where capacity constraints are prevalent. It is assumed that the reader is familiar with the mechanisms of production-inventory systems. Emphasis is on the potential of these systems to deal with capacity constraints (e.g. bottleneck facilities) and on their strengths and weaknesses. It is also shown what the main differences and similarities are and how these production-inventory control systems can complement each other in many situations rather than being mutually exclusive.

Analysis of a production-inventory control system for a diffusion department

Abstract The dynamic behaviour of an actual production-inventory control system, designed for a diffusion department, is analysed by means of classical control theory. The actual control rule acts on the basis of norms for the production progress derived from the application of the line-of-balance technique to the production-inventory system. Attention is paid to the steady-state behaviour, the impulse response and the variance amplification of the controlled system. The cause of the poor dynamic performance observed after implementation of the system in practice is releaved. Especially the variance amplification can be decreased without a general loss of performance with respect to other criteria. An alternative control rule is derived from the application of design notions from classical control theory to the production-inventory problem. The new control rule shows an improved performance in some respects over the actual control rule. The case presented shows that some serious errors in production-inven...