An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

A Decision Support System for Determining Optimal Retention Stocks for Service Parts Inventories

This paper reports on the development and successful implementation of a decision support system (DSS) for analyzing service parts inventory retention stocks. The DSS was implemented in a Fortune 100 company with an initial $50,000,000 investment in service parts inventory. During the last two years of use, approximately $13,000,000 of service parts have been scrapped (disposed) with the help of the DSS. Very few of these parts have had to be repurchased from scrap dealers. This has resulted in approximately $6,000,000 in tax savings alone. The system continues 'to be used regularly by the company.

The paper contributes to the service parts management literature in three ways. First, the paper proposes a new forecasting model for the retention stock problem. Second, the paper develops a new inventory model which captures a richer operating environment. Third, the paper suggests how these models may be integrated in an interactive, menu-driven, databased DSS. Although the forecasting model, inventory model, and DSS are described in the context of a specific company, the DSS and the embedded models are applicable to managing service parts in a wide variety of environments.     

Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions

In retail, distribution centres can forecast the stores’ future replenishment orders by computing planned orders for each stock-keeping-unit. Planned orders are obtained by simulating the future replenishment ordering of each stock-keeping-unit based on information about the delivery schedules, the inventory levels, the order policies and the point-estimate forecasts of consumer demand. Point-estimate forecasts are commonly used because automated store ordering systems do not provide information on the demand distribution. However, it is not clear how accurate the resulting planned orders are in the case of products with low and intermittent demand, which make up large parts of the assortment in retail. This paper examines the added value of modelling consumer demand with distributions, when computing the planned orders of products with low and intermittent demand. We use real sales data to estimate two versions of a planned order model: One that uses point-estimates and another that uses distributions to model the consumer demand. We compare the forecasting accuracies of the two models and apply them to two example applications. Our results show that using distributions instead of point-estimates results in a significant improvement in the accuracy of replenishment order forecasts and offers potential for substantial cost savings.     

Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level

In this paper, a manufacturing system composed of a single-product machine, a buffer and a stochastic demand is considered. Two models are presented: continuous and discrete flow models including constant delivery times, machine failures and random demands. The objective is to determine the value of the optimal buffer level, for a hedging point policy which minimises the total average cost function. The cost function is the sum of inventory, transportation and lost sales costs. Infinitesimal perturbation analysis is used for optimisation of the failure-prone manufacturing system. The trajectories of buffer level are studied for the continuous and discrete cases and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implemented in an optimisation algorithm which determines the optimal buffer level in the presence of constant delivery time. Numerical results are presented for continuous and discrete flow models and then compared in order to evaluate the application of the infinitesimal perturbation analysis on the discrete flow model.     

Multi-objective imperfect preventive maintenance optimisation with NSGA-II

Maintenance optimisation is a multi-objective problem in nature, and it usually needs to achieve a trade-off among the conflicting objectives. In this study, a multi-objective maintenance optimisation (MOMO) model is proposed for electromechanical products, where both the soft failure and hard failure are considered, and minimal repair is performed accordingly. Imperfect preventive maintenance (IPM) is carried out during the preplanned periods, and modelled with a hybrid failure rate model and quasi-renewal coefficient. The initial IPM period and the total number of IPM periods are set as the decision variables, and a MOMO model is developed to optimise the availability and cost rate concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the model. A case study of wind turbine’s gearbox is provided. The results show that there are 30 optimal solutions in the MOMO’s Pareto frontier that can maximise the availability and minimise the cost rate simultaneously. Compared with the single-objective maintenance optimisation, it can provide more choices for maintenance decision, and better satisfy the resource constraints and the customer’s preference. The results of the sensitivity analysis show that the effect of age reduction factor on optimisation results is greater than that of failure rate increase factor.     

Enhancing performance in order picking processes by dynamic storage systems

Increasing productivity and reducing labour cost in order picking processes are two major concerns for most warehouse managers. Particularly picker-to-parts order picking methods lead to low productivity as order pickers spend much of their time travelling along the aisles. To enhance order picking process performance, an increasing number of warehouses adopt the concept of dynamic storage where only those products needed for the current order batch are dynamically stored in the pick area, thereby reducing travel time. Other products are stored in a reserve area. We analyse the stability condition for a dynamic storage system with online order arrivals and develop a mathematical model to derive the maximum throughput a DSS can achieve and the minimum number of worker hours needed to obtain this throughput, for order picking systems with a single pick station. We discuss two applications of dynamic storage in order picking systems with multiple pick stations in series. In combination with simulation modelling, we are able to demonstrate that dynamic storage can increase throughput and reduce labour cost significantly.     

Optimum life test plan for Type-I hybrid censored Weibull distributed products sold under general rebate warranty

The selection of an appropriate life test plan is extremely important for any product as it not just improves the quality of the product but also reduces testing costs. In this approach, however, the choice of suitable costs play an important role. In this paper, a decision model is developed to determine the optimal life testing plan (LTP) by minimising the relevant costs involved for non-repairable products sold under the general rebate warranty. LTP are developed in the presence of Type-I hybrid censoring for products having Weibull distributed lifetimes. A constrained optimisation approach is followed considering both producer's and consumer's risk and suitable analysis techniques are employed in obtaining the optimal solution. Monte Carlo simulation is conducted for numerical illustration. In order to study the sensitivity of the optimal solution due to mis-specification of parameter values and cost components, a well-designed sensitivity analysis is incorporated using a real-life Type-I hybrid censored data set.     

An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing

This paper presents a study on supply chain scheduling from the perspective of networked manufacturing (NM). According to feature analysis of supply chain scheduling based on NM, we comprehensively consider the combined benefits of cost, time, and satisfaction level for customised services. In order to derive a scheduling strategy among supply chain members based on NM, we formulate a three-tier supply chain scheduling model composed of manufacturer, collaborative design enterprise and customer. Three objective functions – time function, cost function and delay punishment function – are employed for model development. We also take into account multi-objective optimisation under the constraint of product capacity. By using an improved ant colony optimisation algorithm, we add different pheromone concentrations to selected nodes that are obtained from feasible solutions and we confine pheromone concentrations τ within the minimum value τ _min and the maximum value τ _max, thus obtaining optimal results. The results obtained by applying the proposed algorithm to a real-life example show that the presented scheduling optimisation algorithm has better convergence, efficiency, and stability than conventional ant colony optimisation. In addition, by comparing with other methods, the output results indicate that the proposed algorithm also has better solutions.     

Robust assignment of customer orders with uncertain configurations in a production network for aircraft manufacturing

Production of multi-variant products in a network requires the assignment of customer orders to locations and periods. This is a highly complex planning task, as requirements of procurement, production, distribution, and sales have to be considered. Providing customers with the flexibility of configuring their ordered products after order assignment further increases the complexity of the planning task by taking uncertainty into account. Therefore, a robust optimisation model, using scenarios representing potential customer-specific order configurations, is introduced. By providing enough flexibility to handle maximum work overload caused by the potential order configurations at locations, a robust assignment of orders can be guaranteed in order to avoid undesirable situations causing delays and additional costs. Therefore, the mid-term adjustments of the flexibility limits are enabled by the changeability of workforce supply by making use of external workers. An industrial application of the model in manufacturing of the Airbus A320 Family of aircrafts is presented. The costs for offering configuration flexibility to customers are quantified by the expected value of perfect information. The explicit consideration of configuration uncertainty through the use of scenarios is discussed based on the value of the stochastic solution in comparison to the results attained by simplistically using the expected value.     

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.     

Planning of capacity,production and inventory decisions in a generic reverse supply chain under uncertain demand and returns

There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.     

Coordinating continuous-time distribution and sales planning of perishable goods with quality grades

A variety of industries handling perishable goods is faced with the challenge of reducing lead times in order to ensure the best possible freshness of products at the point of sales. Considering a short-term multi-day planning horizon, the implementation of quantitative optimisation approaches with a continuous-time representation is most appropriate for planning and scheduling. However, as the quality measures are usually standardised by discrete grades, coordination take thresholds of maximum delivery times into account. For this purpose, a new mixed-integer linear programming model is developed. It enables to assess complete material flows, whose formal composition originates in a method based on power sets on the one hand, but additionally allows for the exact scheduling of partial material flows between sites. Specific transportation conditions that need to be imposed with respect to shelf-life are included. The efficiency of the model is confirmed by comparison with an equivalent mixed-integer linear formulation that uses path variables for modelling complete material flows. Besides an illustrative example motivated by the real-life problem of a fresh produce company supplying a wholesaler group, both model formulations are validated within a numerical analysis composed of 10 scenarios with different numbers of suppliers, warehouses, markets and product variants. Each scenario includes six instances with randomly generated data. As a result of the computations using high-performance hardware and software, it was shown that the formulation based on power sets was superior in each instance, as it enabled determining optimal solutions within significantly reduced computation times.     

Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator

A novel method using particle swarm optimisation (PSO) is proposed for optimising parameters of controllers of a wind turbine (WT) with doubly fed induction generator (DFIG). The PSO algorithm is employed in the proposed parameter tuning method to search for the optimal parameters of controllers and achieve the optimal coordinated control of multiple controllers of WT system. The implementation of the algorithm for optimising the controllers' parameters is described in detail. In the analysis, the generic dynamic model of WT with DFIG and its associated controllers is presented, and the small signal stability model is derived; based on this, an eigenvalue-based objective function is utilised in the PSO-based optimisation algorithm to optimise the controllers' parameters. With the optimised controller parameters, the system stability is improved under both small and large disturbances. Furthermore, the fault ride-through capability of the WT with DFIG can be improved using the optimised controller. Simulations are performed to illustrate the control performance.     

Analogue-based demand forecasting of short life-cycle products: a regression approach and a comprehensive assessment

In several industries, global competition, increasing customer expectations and technological innovations tend to accelerate product life-cycles. In this changing environment, traditional forecasting methods tend to be ineffective as a consequence of the transient and highly uncertain demand of short life-cycle products (SLCP), and the scarcity of sales data. To address this challenge, we present a methodology to forecast SLCP demand using time series of similar products referred to as analogies. Linear regression and clustering techniques are used for the selection and weighting of suitable analogies. The proposed methodology is tested against seven analogue-based forecasting methods, including two implementations of non-linear regression methods. In different sets of time series, our methodology attained more accurate forecasts with short processing times compared with state-of-the-art methods. Such results reveal promising applications of combined regression and clustering techniques as simple and effective forecasting tools for supporting replenishment decisions for SLCP.     

A simulation-based comparison of two goods-to-person order picking systems in an online retail setting

Design and analysis of order picking systems continues to be an active topic of interest both in academia and practice, especially in light of the significant increase in online retail sales. In this paper, we examine two types of well-known, goods-to-person order picking systems, namely, a miniload system and a Kiva system. Using a simulation model, we compare the performance of the two systems on the basis of expected throughput and expected container retrieval times to process the same set of customer orders. We also discuss some of the advantages and limitations of the two systems.     

基于拓展经营的交叉销售产品的订货研究

基于交叉销售构建销售组合,研究组合内产品订货量是库存决策中的重点问题。考虑企业从横向角度拓展产品市场,构建了两产品的不对称交叉销售模型,推导了两产品非线性约束规划模型的最优条件和最优解的表达式。文章证明和分析了随着两产品关联系数波动性的增大以及新产品的需求分布属性的弱化,企业为追求两产品整体利润最大化,在现有销售组合的基础上,会采取收缩原有产品订货量的决策。最后,借助MATLAB软件对实例进行了验证。     

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral?derivative controller in automatic voltage regulator system

Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.     

Bayesian regularisation neural network based on artificial intelligence optimisation

Stock prediction is generally considered to be challenging and known for its high noise and strong nonlinearities in financial time series analysis. However, current forecasting models ignore the importance of model parameter optimisation and the use of recent data. In this article, a novel forecasting approach with a Bayesian-regularised artificial neural networks (BR-ANN) was proposed. The weight of the proposed model (BR-ANN) is determined by the particle swarm optimisation (PSO) algorithm. Daily market prices and financial technical indicators are utilised as inputs to predict the one day future closing price of the Shanghai (in China) composite index. The Bayesian-regularised network uses a probabilistic nature for the network weights and can reduce the potential for over-fitting and over-training. Our empirical study and the results of our K-line theory analysis indicate that PSO is determined to be an effective algorithm to optimise the parameters of the Bayesian neural network compared with other well-known prediction algorithms. In particular, the PSO model is more reliable than the simple Bayesian regularisation neural network near the local maximum value.     

客户关系管理中客户分布的动态模型及其应用

在静态的客户金字塔模型的基础上,应用马尔科夫链的基本原理,建立了动态的客户金字塔模型,包括等级分布的基本方程、客户平均转移周期方程和客户分布预测方程。通过真实的企业销售数据,说明了模型的意义和用法,并验证了该模型的正确性。     

Vibration analysis diagnostics by continuous-time models: A case study

In this paper a forecasting system in condition monitoring is developed based on vibration signals in order to improve the diagnosis of a certain critical equipment at an industrial plant. The system is based on statistical models capable of forecasting the state of the equipment combined with a cost model consisting of defining the time of preventive replacement when the minimum of the expected cost per unit of time is reached in the future. The most relevant features of the system are that (i) it is developed for bivariate signals; (ii) the statistical models are set up in a continuous-time framework, due to the specific nature of the data; and (iii) it has been developed from scratch for a real case study and may be generalised to other pieces of equipment. The system is thoroughly tested on the equipment available, showing its correctness with the data in a statistical sense and its capability of producing sensible results for the condition monitoring programme.