Scheduling methods for the statistical setup adjustment problem: a correction and clarification

In semi-automated workshops producing small or medium runs, the human factor plays a preeminent role in the decision making process. A computerized management is sought nonetheless, considering the great number of items to maintain. In this context, classical scheduling tools using optimization techniques are not flexible enough to provide a feasible plan. Cooperative scheduling, which means a collaboration between a human operator and a system, constitutes a promising alternative for managing production. In this paper, the authors present a methodology to implement cooperative scheduling. It is based on the converging of a top-down approach to functional design and a bottom-up approach to the cognitive design of human-computer interaction. The top-down approach relies on a constraint-based analysis that aims at providing a set of feasible schedules for decision making. The bottom-up approach consists of a knowledge acquisition phase, essentially using brainwriting techniques. An industrial application in an aircraft company has been investigated. The emphasis is on the processing-order release function in a flanging shop which resulted, through constraint logic programming, in a mock-up of a cooperative system.     

Bi-objective facility expansion and relayout considering monuments

In this paper, the unequal area facility expansion and relayout problem is studied. The facility relayout problem is important since both manufacturing and service entities must modify their layouts over time when their operational characteristics change. A bi-objective approach is proposed to solve the relayout problem for cases of both a fixed facility area and an expanded facility area. Material handling costs and relayout costs are minimized using a tabu search meta-heuristic optimizer. This heuristic randomly alternates the objective function between the two objectives of the problem in each step and, by doing so, eliminates the difficulty of weighting and scaling the two objectives. The approach is flexible in handling various aspects of the problem such as stationary portions of departments (i.e., monuments), addition of new departments, and changes in existing department and facility areas. Computational experiments show that the bi-objective tabu search approach is effective and tractable. The use of the Pareto front of designs is demonstrated by showing a few approaches to analyzing the trade-offs between initial costs (relayout cost) and ongoing expenses (material handling costs).     

A GLOBAL FLOOR PLANNING TECHNIQUE FOR VLSI LAYOUT

Over the years, many CAD tools for VLSI macrocelf design have become available that automatically perform some of the physical synthesis phases to reduce development costs. Although floor planning imposes global constraints on the quality of the final layout, state-of-the-art tools do not completely support floor planning synthesis. A new floor planning method for macrocell layout style is presented. The floor plan state space is characterized by an equivalence relation to apply efficient solution techniques. A new pseudo-polynomial area optimization algorithm is proposed that derives from a given hierarchical floor plan tree the optimal slicing tree. The order of this floor plan tree is at least 2 and at most 5. Extensions of this approach to cover non-slicing floor plans are also described. Since floor planning and routing are interdependent tasks, an improved dynamic updating scheme is proposed to consider the interconnection area around each cell during the floor plan assembly. The method has been successfully applied to an industrial design with about 260,000 transistors.     

Nanoelectronics from the bottom up

Electronics obtained through the bottom-up approach of molecular-level control of material composition and structure may lead to devices and fabrication strategies not possible with top-down methods. This review presents a brief summary of bottom-up and hybrid bottom-up/top-down strategies for nanoelectronics with an emphasis on memories based on the crossbar motif. First, we will discuss representative electromechanical and resistance-change memory devices based on carbon nanotube and core-shell nanowire structures, respectively. These device structures show robust switching, promising performance metrics and the potential for terabit-scale density. Second, we will review architectures being developed for circuit-level integration, hybrid crossbar/CMOS circuits and array-based systems, including experimental demonstrations of key concepts such lithography-independent, chemically coded stochastic demultipluxers. Finally, bottom-up fabrication approaches, including the opportunity for assembly of three-dimensional, vertically integrated multifunctional circuits, will be critically discussed.     

A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment

In the era of digitalisation, e-commerce retail sites have become decisive channels for reaching millions of potential customers worldwide. Digital marketing strategies are formulated by the marketing teams in order to increase the traffic on their e-commerce sites, thereby boosting the sales of the products. With the massive amount of data available from the cloud, which were conventionally made with a high degree of intuition based on decision makers’ knowledge and experience, can now be supported with the application of artificial intelligence techniques. This paper introduces a novel approach in applying the fuzzy association rule mining approach and the fuzzy logic technique, for discovering the factors influencing the pricing decision of products launched in e-commerce retail site, and in formulating flexible, dynamic pricing strategies for each product launched in an e-commerce site. A pricing decision support system for B2B e-commerce retail businesses, namely Smart-Quo, is developed and implemented in a Hong Kong-based B2B e-commerce retail company. A six-month pilot run reveals a significant improvement in terms of the efficiency and effectiveness in making pricing decisions on each product. The case study demonstrates the feasibility and potential benefits of applying artificial intelligence techniques in marketing management in today’s digital age.     

An intelligent physarum solver for supply chain network design under profit maximization and oligopolistic competition

We propose an efficient bio-inspired algorithm for design of optimal supply chain networks in a competitive oligopoly markets. The firms compete in manufacture, storage and distribution of a product to several markets. Each firm aims at maximisation of its own profit by optimising the design capacity and product flow in the supply chain. We model the supply chain network as a multi-layer graph of manufacturing nodes, distribution nodes and storage centres. To optimise the network, we adopt the mechanisms of a foraging behaviour of slime mould Physarum polycephalym. First, we extend the original Physarum model to deal with networks with multiple sources and sinks. Second, we develop a novel method to solve the user equilibrium (UE) problem by exploiting the adaptivity of the Physarum model: we update the link costs according to the product flow. Third, we refer to an equivalent transformation between system optimum problem and UE problem to determine the optimal product flows and design capacities of a supply chain. At last, we present an approach to update the amount of product supplied by each firm. By comparing our solutions with that in Nagurney (2010b) on several numerical examples, we demonstrate the efficiency and practicality of the proposed method.     

Multi-facility capacity expansion in R&D projects

This paper considers capacity expansion decisions for accommodating different types of R&D activities which require time-varying areas during a given time horizon. The forecasted area needed by each activity can be met by new construction and/or by conversion of existing constructions. The problem involves sizing and scheduling of capacity expansions and conversions at discrete points in time at minimum cost. A hierarchical bottom-up procedure is used to solve the problem, the best results being further evaluated through a systematic multicriteria approach. It is recommended that the relevant information supplied by the active conversions set in the least costs solutions be translated into improved building flexibility.     

Top-down and bottom-up mass spectrometric characterization of human myoglobin-centered free radicals induced by oxidative damage

In an effort to determine the utility of top-down mass spectrometric methodologies for the characterization of protein radical adducts, top-down approaches were investigated and compared to the traditional bottom-up approaches. Specifically, the nature of the radicals on human myoglobin induced by the addition of hydrogen peroxide and captured by the spin trap 5,5-dimethyl-1-pyrroline N-oxide (DMPO) was investigated. The most abundant ion observed in the electrospray mass spectrum of this reaction mixture corresponds in mass to the human myoglobin plus one DMPO molecule. In addition, a second ion of lower abundance is observed, which corresponds to a second DMPO molecule being trapped on myoglobin. Top-down analyses using Fourier transform ion cyclotron resonance mass spectrometry can be used to characterize proteins and, thus, were performed on several different charge-state ions of both the native and the mono-DMPO nitrone adduct of human myoglobin. Data produced from the top-down analyses are very complex yet information rich. In the case of DMPO-modified human myoglobin, the top-down data localized the DMPO spin trap to residues 97-110 of the myoglobin. The observation of the y43+5 fragment ion arising from C-terminal cleavage to the cysteine-110 residue in the MS/MS spectrum of DMPO-modified myoglobin and not in the unmodified myoglobin implicates a change to this residue, specifically, DMPO adduction. On the other hand, using the traditional bottom-up approach of peptide mapping and MS sequencing methodologies, two DMPO radical adducts on human myoglobin were identified, Cys-110 and Tyr-103. The bottom-up approach is more proven and robust than the top-down methodologies. Nonetheless, the bottom-up and top-down approaches to protein characterization are complementary rather than competitive approaches with each having its own utility.     

A Dynamic Resource Allocation Model for A Machine Requirements Problem

This paper is concerned with the problem of determining the optimum number of machines to have in a production process. The problem is analyzed as a resource allocation problem involving the minimum cost allocation of limited floor space, capital budget, and available overtime among various types of machines. The dynamic nature of the problem is also included in the analysis. A deterministic mixed integer programming model is described. A numerical example is presented to illustrate the approach and its utility as an alternative way to address the machine requirements problem.     

A multi-objective mixed-integer programming model for a multi-floor facility layout

We consider a multi-floor facility layout problem in which the overall length and width of the facility, the size and location of each department, the number and the location of elevators and the number of floors in the facility are all modelled as decision variables. We adapt a linear approximation scheme to represent the area of each department. We consider two objective functions in our model, namely minimising material handling and facility building costs, and propose a lexicographic ordering technique to handle multiple objectives. The numerical experiments show that the slack used in the lexicographic ordering approach has a significant impact on the optimal solution. The experiments also show that the material handling cost can be significantly reduced in a multi-floor facility compared with a single-floor facility.     

Estimation of future breakdowns to determine optimal warranty policies for products with deterioration

Warranty has been played an important role not only for safeguarding the rights and interests of consumers but also for promoting the sales and reputation of manufacturers, since a good warranty policy signifies the image of high-quality products and thus becomes a powerful weapon for marketing products in increasingly competitive markets. It is obvious that a more thoughtful warranty policy of a product will attract more customers to consume the specific product, and increase the product's market share accordingly. However, an unlimited warranty for monopolizing the market is absolutely unrealistic, since the cost will significantly increase for maintaining such a warranty policy with better offers. In this paper, a Bayesian decision model for determining the optimal warranty policy for repairable products is proposed. The successive failure times of the repairable product are assumed to be drawn from a nonhomogeneous Poisson process. Both the repair costs for restoring the product to full functionality after each breakdown and the potential sales increases due to the specific warranty policy are also considered. Finally, an application case is utilized to demonstrate the feasibility and validity of the proposed approach.     

考虑销售努力水平的存货质押融资决策

研究了考虑企业销售努力水平因素下存货质押融资决策问题。建立了资金约束的中小零售企业面临受销售努力水平影响的随机性市场需求模型,研究了该零售企业的最优的存货质押量决策和销售努力水平决策,以及风险中性和损失规避情形下银行的最优的存货质押率决策,并分析了销售努力水平对存货质押量决策的影响。结论表明,零售企业销售努力水平的提升给融资参与各方都带来了积极影响,零售企业因此会加大存货质押量争取更多贷款,银行可通过提高存货质押率来提高贷款额度从而获取更大收益。     

Relational analysis model of weather conditions and sales patterns based on nonnegative tensor factorization

It is necessary to analyze the relationships between the retail sales of various items and weather conditions. However, the relationship between the sales of each item and the weather condition may vary among stores. Additionally, it is necessary to model the statistical relationships between a wide variety of goods and weather conditions by using past sales data. In such a case, it becomes unrealistic to construct a forecast model for every individual item owing to the breadth of items and the number of retail shops. This study proposes a model to analyze the relationships between the sales of various items and weather conditions. This method can be used to decompose the data into three matrices based on the nonnegative tensor factorization (NTF) method. The results of the analysis clarified that the proposed model can identify important items whose demand is strongly influenced by weather conditions, thereby increasing the effectiveness of inventory management. Additionally, the store clusters estimated by the proposed model can facilitate the construction of regression models that demonstrate the relationship between the sales of each item and weather conditions.     

An integrated methodology for automating the determination of layout and materials handling system

There is a severe scanty of models and solution techniques for the determination of layout and the materials handling system when neither are fixed. This is a complex problem for which this paper proposes a new integrated methodology using a knowledge-based/optimisation approach to the problem. The knowledge-base consists of facts and rules to determine the feasibility of using a materials handling equipment type for a given move. The optimisation part determines the layout of machines minimising the materials handling costs and the dead space in the layout using a multi-criteria optimisation model. The methodology aims to minimise materials handling costs, aisle space usage and dead-space in the resulting layout. It is particularly applicable to heavy manufacturing environments. The system outputs the optimum location, configuration and orientation of machines, and the material handling equipment types, their design capacities, utilisations and the assignment of moves to each item of materials handling equipment. The results of a successful application to an example problem are given.     

Clusterwise pricing in stores of a retail chain

Clusterwise pricing is characterized by prices of each brand of a category being equal in stores belonging to the same cluster. Expected sales necessary to compute profits are estimated using coefficients of a multilayer perceptron which performs better than several parametric models. Store-specific coefficients of sales response models are estimated by a MCMC method. Both assignment of stores to clusters and prices of each cluster are determined by means of improving hit-and-run, a stochastic optimization method. The objective function to be optimized includes both profits and adherence to the usual price level at individual stores. Based on empirical data on sales, prices and marginal costs of a retail chain it is demonstrated that for a moderate level of risk aversion clusterwise pricing leads to higher expected utility than micromarketing pricing with different prices of each brand across individual stores. Clusterwise pricing attains a high percentage of the profits generated by micromarketing pricing and entails lower menu costs than micromarketing pricing. H. Hruschka thanks two anonymous reviewers for their interest and their detailed helpful comments.     

An integrated EPQ model based on a control chart for an imperfect production process

The notion of quality control is introduced into the classic economic production quantity (EPQ) model. Based on previous research, this paper contributes to an integrated EPQ model combining the concepts of statistical process control and maintenance. An imperfect production process involving three different conditions is considered. A control chart is adopted to monitor the whole process with Taguchi's loss function estimating the quality cost of each condition. A corresponding maintenance policy is planned for the machine depending on what condition it runs in. Thus the proposed EPQ model takes quality-related costs and maintenance-related costs into account other than storage costs and ordering costs already considered in the classic model. The objective of this model is to minimise the total expected production cost per production cycle while simultaneously determining the optimal parameters of control chart design, the interval between sampling, the sample size and the maintenance decision policy. The pattern search method is used to solve the problem using the MATLAB toolbox. In addition, a case study, sensitivity analysis and comparison analysis are presented to demonstrate the application of the model.     

基于加权硬度特征的仪表数字识别

仪表数字识别是智能仪表应用的关键,针对现有方法对角度倾斜、半字识别效率低的问题,引入硬度特征参数来衡量数字图像目标区域在某方向上的抵抗变形的能力,提出了一种结合数字结构特征和统计特征的识别方法。通过对仪表盘上采集的数字进行分析,建立数字自上而下及自下而上的硬度特征库。并依据每个特征重要程度的不同,引入权重,采用加权特征匹配的方法进行数字识别。实验表明,算法不仅简单高效,对于整字和半字都能够取得很好的分割和识别效果,而且对旋转和畸变有较强的容错。     

双渠道多级分销网络库存整合优化配置

针对拥有线下、线上销售渠道的实体零售企业分销网络运作成本居高不下的现状,研究综合渠道整合和线上订单履行问题下的双渠道库存优化配置问题。在“双渠道库存独立管理、线上订单分散履行”和“双渠道库存整合管理、线上订单集中履行” 2种不同分销网络运作模式下分别建立数学模型,并利用基于实数编码的遗传算法进行求解。通过对比这2种不同运作模式下的运作成本,同时在需求波动率增加条件下对运作成本进行敏感性分析,发现渠道库存整合、线上订单集中履行方法能够将库存总费用降低17.25%,能够将库存总费用从中下游往上游转移,减缓中下游节点的库存总费用。     

Single-period assortment and stock-level decisions for dual sales channels with capacity limits and uncertain demand

This paper addresses a dual channel, clicks-and-mortar retailer's problem of determining which among a set of products with seasonal demand will occupy limited retail shelf space, which products will be offered via an online channel, and which items will be available through both channels. Using a consumer choice model in which the set of products offered influences each product's demand in each channel, we consider stocking and price decisions under uncertain demand in a single-period setting with a constraint on the probability of stocking out. The resulting model is a large-scale, chance-constrained, two-stage stochastic programme. We propose a sample average approximation (SAA) method that permits quickly arriving at near-optimal solutions for this complex problem class. We also exercise the proposed model to gain insights on the problem's key tradeoffs and properties of optimal solutions.     

Data envelopment analysis of AGV fleet sizing at a port container terminal

A decision-making approach based on Data Envelopment Analysis (DEA) for determining the efficient container handling processes (considering the number of employed Automated Guided Vehicles (AGVs)) at a port container terminal (PCT) is presented in this paper. Containers are unloaded from the ship by quay cranes and transported to the storage area by AGVs. We defined performance measures of proposed container handling processes and analysed the effects when changing the number of AGVs. The values of performance measures were collected and/or calculated from simulation. Container handling process, with a fixed number of quay cranes, when a different number of AGVs is used to transport containers from berth to assigned locations within storage area, represents a decision-making unit (DMU). We applied the basic CCR (Charnes, Cooper and Rhodes) DEA model with two inputs: average ship operating delay costs and average operating costs of employed equipment at a PCT, and two outputs: average number of handled import containers per ship and weighted average utilisation rate of equipment at a PCT. DEA method proved to be useful when testing different DMUs and when determining efficient DMUs for planning purposes. This study shows that efficiency evaluation of AGV fleet sizing and operations is useful for planning purposes at PCTs.