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Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP). In existing studies, most storage assignment methods only consider the properties of individual item rather than the item groups that are usually collectively required. This paper introduces the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the SLAP. The model is subsequently reduced using the S-shape routing strategy, and a method for determining DCPs from historical data is proposed. To solve the model, a heuristic and a simulated annealing method are developed. The proposed methods are examined and compared extant methods using both real data collected from an online retailer and numerical instances that are randomly generated. The computational results are discussed.

This study addresses the solid waste collection and transportation problem with split deliveries (SWCTPSD). To minimize the total fleet costs, a mixed-integer programming model is proposed. An improved variable neighborhood search (VNS) algorithm is proposed to obtain a high-quality collection and transportation plan quickly. To enhance the local exploitive ability, the VNS algorithm is hybridized with simulated annealing. The experimental results show that the proposed VNS algorithm is effective.

BACKGROUND Transglutaminase 3 (TGM3) regulates multiple oncogene pathways (GSK-3β/β-catenin pathway, Akt/ERK pathway, etc.) to promote hepatocellular carcinoma (HCC) cell proliferation, migration and invasion, however, its clinical value for HCC management is still limited. Therefore, we conducted this study to compare the TGM3 expression between tumor tissue and paired adjacent noncancerous tissue, aiming to explore the clinical application of TGM3 in HCC patients. METHODS Totally, 208 HCC patients were enrolled and their clinicopathological features were collected. Then, 208 pairs of HCC specimens and adjacent noncancerous specimens were used to detect TGM3 protein expression by IHC assay and assessed by a semi-quantitative scoring method. Besides, 157 pairs were proposed to detect TGM3 mRNA expression by RT-qPCR. RESULTS Both TGM3 protein (P

Spiking neural networks (SNNs) present a promising computing model and enable bio-plausible information processing and event-driven based ultra-low power neuromorphic hardware. However, training SNNs to reach the same performances of conventional deep artificial neural networks (ANNs), particularly with error backpropagation (BP) algorithms, poses a significant challenge due to inherent complex dynamics and non-differentiable spike activities of spiking neurons. In this paper, we present the first study on realizing competitive spike-train level backpropagation (BP) like algorithms to enable on-chip training of SNNs. We propose a novel spike-train level direct feedback alignment (ST-DFA) algorithm, which is much more bio-plausible and hardware friendly than BP. Algorithm and hardware co-optimization and efficient online neural signal computation are explored for on-chip implementation of ST-DFA. On the Xilinx ZC706 FPGA board, the proposed hardware-efficient ST-DFA shows excellent performance vs. overhead tradeoffs for real-world speech and image classification applications. SNN neural processors with on-chip ST-DFA training show competitive classification accuracy of 96.27% for the MNIST dataset with 4× input resolution reduction and 84.88% for the challenging 16-speaker TI46 speech corpus, respectively. Compared to the hardware implementation of the state-of-the-art BP algorithm HM2-BP, the design of the proposed ST-DFA reduces functional resources by 76.7% and backward training latency by 31.6% while gracefully trading off classification performance.

In a paper published in the International Journal of Production Economics (IJPE) [Zhang, R., Kaku, I., Xiao, Y., 2012. Model and heuristic algorithm of the joint replenishment problem with complete backordering and correlated demand. International Journal of Production Economics 139 (1), 33–41], the authors proposed a joint replenishment problem (JRP) model with complete backordering and correlated demand caused by cross-selling. The model was transformed into minimizing a function with respect to multiples of a major item's order cycle, and a heuristic algorithm was developed for near-optimal solutions. In this paper, we reinvestigate the problem and analyze the mathematical property of the model to develop an exact algorithm. The algorithm can obtain global optima and exhibits polynomial complexity.

The important influence of e-service quality (e-SQ) on customer satisfaction and loyalty has been demonstrated in many contexts, but has not been examined in telecom settings yet. The current study aimed to construct a measurement scale for e-SQ in telecom settings, as well as to investigate the relationship between e-SQ, customer satisfaction, and customer loyalty. In this study, we analyzed self-reports from 9249 respondents (74.55% were male) between the ages of 19 and 45. A scale consisting of five user experinece dimensions (functional completeness, performance, interface and interaction quality, content and information, support or service) was developed to measure e-SQ in the telecom industry. The scale was proven reliable and valid. The analysis confirmed a positive relationship between e-SQ, customer satisfaction and loyalty. In addition, e-SQ was found to be a core predictor of customer satisfaction and customer loyalty. Moreover, customer satisfaction emerged as the strongest predictor of customer loyalty.

With the implementation of carbon regulations, firmes are facing pressure on emission reduction and need to consider investing in clean equipment and technology to reduce carbon emissions. However, the resulting uncertainty poses risks to operation management, and decision-makers have different risk preferences. This paper considers a risk-averse firm confronted with cap-and-trade regulation and uncertain market demand. The customers are assumed to be environment-friendly, and thus the demand is influenced by carbon emissions. The joint decisions on order quantity and emission reduction levels are investigated under the conditional value-at-risk (CVaR) framework. This research formulates a joint decision model based on the CVaR criterion and explores how a firm’s risk aversion and investment coefficient influence optimal decisions. The findings show that if the investment coefficient of carbon emission reduction is sufficiently small, firms will not trade any carbon credits. This implies that for the effectiveness of the carbon market, the government should continue decreasing the carbon quota with the development of clean technologies. Additionally, the cap-and-trade regulation encourages firms to invest in carbon emission reduction and does not necessarily deplete the risk-averse firm's profit. Particularly, it is found that in the higher trading price of carbon, the optimal emission reduction level is more sensitive to risk aversion than to the order quantity.

The NV model with non-zero reference point under cumulative prospect theory (CPT).Develop the first-order conditions of the NV model with non-zero reference point.The proposed NV model partially explains the pull-to-center effect. This paper formulates the newsvendor model with a non-zero reference point based on cumulative prospect theory (CPT-based newsvendor model), evaluating the prospect by a piecewise-linear value function (ModelPL). We prove the concavity of the objective function, and therefore, the model is solved by the first-order optimality condition. As a comparison, we further present the newsvendor model based on a piecewise-exponential value function (ModelPE), where the utility curvatures are considered.The results show that for a low-profit item, only ModelPE can explain the pull to center effect that was found by Schweitzer and Cachon (2000), if the reference point is high enough. However, for a high-profit item, both models successfully predict the newsvendors behavior if the newsvendor conceives a non-zero reference point. Thus, prospect theory (PT) should not be excluded as a potential explanation for the pull to center effect of the newsvendors decision.

Using tax revenues to subsidize health insurance and to achieve safe and effective health services has become an important means of health care reform for governments. However, how to select designated hospitals or specific medical facilities that allow as many patients as possible to afford medicine and treatment is a difficult problem that the governments of some countries must face. In this paper, we try to address the problem by constructing a sequential game model and presenting an application under China's new cooperative medical scheme (NCMS). We consider a market with two different hospital quality levels and give the optimal decision of the government. Our results show the following: (1) more NCMS-designated hospitals approved by the government may not guarantee that more patients obtain medical services, which depends on the financial budget and the medical service levels. When the budget is large enough, subsidizing both levels of hospitals is optimal. Otherwise, the government should approve only one level. (2) A larger difference between the medical service levels of different hospitals leads to a smaller number of patients who can obtain medical services. In other words, a large service level difference between hospitals harms the goal of the government. (3) The number of hospital patients increases as the medical budget increases.

In order to protect environment and control carbon emissions, some governments have implemented low-carbon policies including carbon tax. The firms in the supply chain have to make decisions under these policies. We explore the pricing and coordination of a dual-channel supply chain under carbon tax. We obtain optimal prices and corresponding profits in centralized and decentralized systems and show the effects of carbon tax on carbon emissions and profits. In addition, we employ two price discount contracts to improve the performance of decentralized system and propose a transfer payment mechanism to make a win-win for supplier and retailer. This research bridges the gap in existing literature and gives some valuable insights for both firms' decision makers and governments' policy makers.

Demand estimation is often confronted with incomplete information of censored demand because of lost sales. Many estimators have been proposed to deal with lost sales when estimating the parameters of demand distribution. This study introduces the cross-selling effect into estimations, where two items are cross-sold because of the positive externality in a newsvendor-type inventory system. We propose an approach to estimate the parameters of a jointly normally distributed demand for two cross-selling items based on an iterative framework considering lost sales. Computational results based on more than two million numerical examples show that our estimator achieves high precision. Compared with the point estimations without lost sales, all the relative errors of the estimations of demand expectation, standard deviation, and correlation coefficient are no larger than 2% on average if the sample size is no smaller than 800. In particular, for demand expectation, the error is smaller than 1% if the comprehensive censoring level is no larger than four standard deviations (implying a 2σ-level of safety stock for each item), even if the sample size decreases to 50. This implies that the demand estimator should be competent in modern inventory systems that are rich in data.

Fast development of online retail industry requires customer orders to be fulfilled within tight windows, where order-picking, the most time-consuming and labor-intensive activity in warehouses, plays an important role. One of the basic ways to improve order-picking operation is assigning storage locations to appropriate items. The storage location assignment problem is in general NP-hard and is mainly solved by heuristics which usually suffer from limited solution quality or high computational effort, especially for large scale problems. In literature, most studies make the storage assignment decisions according to item properties, such as turnover or correlation, which are statistically extracted from item orders. These storage methods follow a data → concept → assign decision mechanism and may ignore useful data characteristics that are not conceptualized. This paper presents a new approach to improve the order-picking operation, which directly uses item orders to make the decisions without any statistical treatments, i.e., following a data → assign mechanism. The concept of good move pair is introduced to quickly find a better assignment through directly exploiting data characteristics of item orders, and an iterative algorithm is developed to minimize the total travel distance. We evaluate the algorithm on real data and numerical instances, and compare its performance with extant methods in the literature. The results show that the proposed method significantly outperforms other methods in most cases. We also extend the algorithm to the case of high-level warehouses and examine its effectiveness.

An extension of the integrated production-delivery lot sizing model with limited production capacity and transportation cost is investigated. We introduce the factor of overtime work into the model to improve the manufacturer’s production. In addition, when finishing a lot, the manufacturer has maintenance time to maintain and repair equipment for ensuring that the supply chain is operating continuously. By analyzing the integrated model, the solution procedure is provided to determine the optimal delivery and order policy. We conduct a numerical experiment and give sensitive analysis by varying some parameters to illustrate the problem and its solution procedure.

Model the problem of non-permutation flow shop scheduling with order acceptance.The model is transformed to linear MIP that is optimally solved by commercial solver.Theorems that are favorable for developing algorithms are presented.An efficient two-phase genetic algorithm (TP-GA) is proposed.The heuristic yields high quality non-permutation solutions. This paper studies the non-permutation solution for the problem of flow shop scheduling with order acceptance and weighted tardiness (FSS-OAWT). We formulate the problem as a linear mixed integer programming (LMIP) model that can be optimally solved by AMPL/CPLEX for small-sized problems. In addition, a non-linear integer programming (NIP) model is presented to design heuristic algorithms. A two-phase genetic algorithm (TP-GA) is developed to solve the problem of medium and large sizes based on the NIP model. The properties of FSS-OAWT are investigated and several theorems for permutation and non-permutation optimum are provided. The performance of the TP-GA is studied through rigorous computational experiments using a large number of numeric instances. The LMIP model is used to demonstrate the differences between permutation and non-permutation solutions to the FSS-OAWT problem. The results show that a considerably large portion of the instances have only an optimal non-permutation schedule (e.g., 43.3% for small-sized), and the proposed TP-GA algorithms are effective in solving the FSS-OAWT problems of various scales (small, medium, and large) with both permutation and non-permutation solutions.

Products are often demanded in tandem because of the cross-selling effect. The demand for an item can increase if sales of its cross-selling-associated items are achieved or decrease when the associated items are out of stock, resulting in lost sales. Therefore, a joint inventory policy should be pursued in a cross-selling system. This paper introduces customer-driven cross-selling into centralized and competitive newsvendor (NV) models by representing an item’s effective demand as a function of other items’ order quantities. We derive first-order optimality conditions for the centralized model in addition to pure-strategy Nash equilibrium conditions and uniqueness conditions of the equilibria for the competitive model. We further develop gradient-based (GB) and iteration-based (IB) algorithms to solve the centralized and competitive models, respectively. A computational study verifies the effectiveness of the proposed algorithms. The computational results show that a larger cross-selling effect leads to a larger order quantity in a centralized NV model but a smaller order quantity in a competitive NV model, and a larger positive correlation between items’ demands leads to higher profits with smaller order quantities in both models. Moreover, NVs will order more items if the demand variance is greater, however resulting in lower profits. In a competitive situation, one will prefer smaller order quantities than in a centralized decision situation.

To ease the conflict between quick response and product variety, more and more business models are developed in supply chains. Among these, the form postponement (FP) strategy is an efficient tool and has been widely adopted. To the supply chain with FP strategy, the design mostly involves two problems: determination of customer order decoupling point (CODP) position and semi-finished product inventory control. In this paper, we develop a two-stage tandem queueing network with MAP arrival to address this issue. Particularly, we introduce a Markov arrival process (MAP) to characterize the correlation of the demand. By using of matrix geometric method, we derive several performance measure of the supply chain, such as inventory level and unfill rate. Our numerical examples show that both the variance and the correlation coefficient of the demand lead to more delayed CODP position and more total cost.

In this study, we improved the variable neighborhood search (VNS) algorithm for solving uncapacitated multilevel lot-sizing (MLLS) problems. The improvement is twofold. First, we developed an effective local search method known as the Ancestors Depth-first Traversal Search (ADTS), which can be embedded in the VNS to significantly improve the solution quality. Second, we proposed a common and efficient approach for the rapid calculation of the cost change for the VNS and other generate-and-test algorithms. The new VNS algorithm was tested against 176 benchmark problems of different scales (small, medium, and large). The experimental results show that the new VNS algorithm outperforms all of the existing algorithms in the literature for solving uncapacitated MLLS problems because it was able to find all optimal solutions (100%) for 96 small-sized problems and new best-known solutions for 5 of 40 medium-sized problems and for 30 of 40 large-sized problems.

Zn1−xCdxS (x=0–1.0) powders were synthesized by a simple combustion method using cadmium nitrate, zinc nitrate and thiourea as raw materials. The structures, morphologies, absorbance, luminescence and photocatalytic properties of the samples were studied by X-ray powder diffraction, transmission electron microscopy, ultraviolet–visible spectrophotometry and photoluminescence. The results show that all Zn1−xCdxS samples exhibit hexagonal structure and no metastable cubic phase is obtained. With increasing content of Cd, red-shifts are observed in both the absorption edges of UV–vis absorption spectra and emission bands of photoluminescence spectra. Among all Zn1−xCdxS samples, CdS shows the best visible-light photocatalytic performance due to its narrow bandgap.

The form postponement (FP) strategy is an important strategy for manufacturing firms to utilize to achieve a quick response to customer needs while keeping low inventory levels of finished products. It is an important and difficult task to design a supply chain that uses FP strategy to mitigate the conflict between inventory level and service level. To this end, we develop a two-stage tandem queuing network to model the supply chain. The first stage is the manufacturing process of the undifferentiated semi-finished product, which is produced on a Make-To-Stock basis: the inventory is controlled by base-stock policy. The second stage is the customization process based on customers' specified requirements. There are two types of order: ordinary order and special order. The former can be met by customizing from semi-finished product, while the latter must be entirely customized beginning from the first stage. The customer orders arrive according to a Poisson process. We first derive the inventory level and fill rate, and then present a total cost model. It turns out that the model is intractable due to the Poisson distribution in the objective function. To analytically solve the problem, we use normal distribution as an approximation of the Poisson distribution, which works well when the parameter of the Poisson distribution is quite large. Finally, some numerical experiments are conducted and managerial insights are offered based on the numerical results.

Image segmentation algorithm is to divide the images into several regions with specific and unique characteristics, and is an important technology to extract the interested target. Image segmentation is the key step to realize the research from general image processing into image analysis, and is vital preprocessing method of image recognition and computer vision. We cannot obtain correct recognition if we do not have correct segmentation. Nevertheless, the only basis of segmentation process is brightness or color of pixels in an image. In the processing of computer automatic segmentation, we experience several problems, such as uneven illumination, effect of noise, indistinct part in image, and shadow, and these factors may cause false segmentation. In order to overcome the disadvantages of the traditional segmentation algorithm, in this paper, we propose a novel segmentation algorithm based on Markov Random Field. The segmentation algorithm proposed in this paper is based on Markov Random Field Mode and Bayesian theory, and we determine the objective function in image segmentation problem on the basis of optimality criterion of statistical decision and estimation theory. Some optimization algorithms are used to obtain the maximum possible distribution of Markov Random Field which satisfy these conditions. The experimental result reflects the effectiveness and robustness of our algorithm. As a supplement, we analyze the development trend of the Markov Random Field theory.

Many inventory models with partial backordering assume that the backordered demand must be filled instantly after stockout restoration. In practice, however, the backordered customers may successively revisit the store because of the purchase delay behavior, producing a limited backorder demand rate and resulting in an extra inventory holding cost. Hence, in this paper we formulate the inventory model with partial backordering considering the purchase delay of the backordered customers and assuming that the backorder demand rate is proportional to the remaining backordered demand. Particularly, we model the problem by introducing a new inventory cost component of holding the backordered items, which has not been considered in the existing models. We propose an algorithm with a two-layer structure based on Lipschitz Optimization (LO) to minimize the total inventory cost. Numerical experiments show that the proposed algorithm outperforms two benchmarks in both optimality and efficiency. We also observe that the earlier the backordered customer revisits the store, the smaller the inventory cost and the fill rate are, but the longer the order cycle is. In addition, if the backordered customers revisit the store without too much delay, the basic EOQ with partial backordering approximates our model very well.

In practice, demands for items are often correlated with each other because of cross-selling effects. Traditional inventory models hold the assumption that demands are independent, which may lead to the risk of misestimating the inventory cost of associated items. This paper addresses a multi-item inventory system where demands for some minor items are correlated with that for a major item. Because of the cross-selling, demand for minor items may either be raised by the sales of the major item or be pulled down by the unsatisfied demands for the major item. Thus, a joint inventory policy considering correlations across items should be pursued. Considering general integer policy, we propose a joint replenishment problem (JRP) model with complete backordering and correlated demand. Through the use of derivatives the model is transformed to minimize a cost function with respect to multiples of the major item's order cycle. Given that the optimal solution should offer a trade-off between the ordering cost and the inventory holding cost of each item, we develop a heuristic algorithm by adjusting the replenishment frequencies of minor items to solve the model. The heuristic is tested by simulated numerical examples and shows satisfactory results.

In this paper, several neighborhood search techniques for solving uncapacitated multilevel lot-sizing problems are investigated. We introduce three indexes: distance, changing range, and changing level that have great influence on the searching efficacy of neighborhood search techniques. These insights can help develop more efficient heuristic algorithms. As a result, we have developed an iterated neighborhood search (INS) algorithm that is very simple but that demonstrates good performance when tested against 176 benchmark instances under different scales (small, medium, and large), with 25 instances having been updated with new best known solutions in the computing experiments.

Multilevel lot-sizing (MLLS) problems, which involve complicated product structures with interdependence among the items, play an important role in the material requirement planning (MRP) system of modern manufacturing/assembling lines. In this paper, we present a reduced variable neighborhood search (RVNS) algorithm and several implemental techniques for solving uncapacitated MLLS problems. Computational experiments are carried out on three classes of benchmark instances under different scales (small, medium, and large). Compared with the existing literature, RVNS shows good performance and robustness on a total of 176 tested instances. For the 96 small-sized instances, the RVNS algorithm can find 100% of the optimal solutions in less computational time; for the 40 medium-sized and the 40 large-sized instances, the RVNS algorithm is competitive against other methods, enjoying good effectiveness as well as high computational efficiency. In the calculations, RVNS updated 7 (17.5%) best known solutions for the medium-sized instances and 16 (40%) best known solutions for the large-sized instances.

Modern production planning and inventory control has been developed in order to treat more practical and more complicated circumstances, such as researching supply chain instead of single stock point; multi-items with correlation instead of single item and so on. In this paper, how to classify inventory items which are correlated each other is discussed by using the concept of 'cross-selling effect'. In history, the ABC classification is usually used for inventory items aggregation because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. A fundamental principle in ABC classification is that ranking all inventory items with respect to a notion of profit based on historical transactions. The difficulty is that the profit of one item not only comes from its own sales, but also from its influence on the sales of other items or reverse, i.e., the 'cross-selling effect'. We had previously developed a classification approach for inventory items by using the association rules to deal with the 'cross-selling effect' and found that a very different classification can be obtained when comparing with traditional ABC classification. However, the 'cross-selling effect' may be considered in different ways. In this paper, a new consideration of inventory classification based on loss rule is presented. The lost profit of item/itemset with 'cross-selling effect' is discussed and defined as criterion for evaluating of importance of item, based on which new algorithms on classifying inventory items, also on discovering maximum profit item selection, are presented. A simple example is used to explain the new algorithm, and large amount of empirical experiments, both on real database collected from Japanese convenient store and on downloaded benchmark database, are implemented to evaluate the performances on effectiveness and utility. The results show that the proposed approach in this paper can gain a well insight into the cross-selling effect among items and is applicable for large-sized transaction database.

In this paper, an effective approach based on the variable neighborhood search (VNS) algorithm is presented to solve the uncapacitated multilevel lot-sizing (MLLS) problems with component commonality and multiple end-items. A neighborhood structure for the MLLS problem is defined, and two kinds of solution move policies, i.e., move at first improvement (MAFI) and move at best improvement (MABI), are used in the algorithm. A new rule called Setup shifting is developed to conduct a more efficient neighborhood search for the MLLS problems. Computational studies are carried out on two sets of benchmark problems. The experimental results show that the VNS algorithm is capable of solving MLLS problems with good optimality and high computational efficiency as well, outperforming most of the existing algorithms in comparison.

To study the capacity planning problem under uncertainty in which market demand and product price are stochastic, multi period capacity planning model based on scenario was investigated in this paper. Two models were proposed: one is a prearranged planning model in which the capacity investment plan do not change with the stochastic market demand, and the other is an adaptive planning model in which capacity investment plan could trace the evolution progress of the stochastic market demand. The computational study compared the decision results of both models, which reveals that the adaptive planning model could suggest better decision. Moreover, based on downside risk analysis, the investment risk of stochastic capacity planning has been investigated, and a prearranged capacity planning model considering the expected downside risk of the objective revenue was proposed. In the model, a constraint of expected downside risk is added to the initial stochastic model to reflect the decision-maker's risk preference. Whether to consider the risk or not will result in different decisions, which, in the computational study, were compared.

Analyzing the special job shop scheduling problem of a large-scale machine shop, considering workers operational qualification and characteristics of discretely concurrent production, a novel mathematical model has been proposed to meet actual production. In addition, an improved binary particle swarm optimization (BPSO) algorithm has been developed for solving the problem of arranging m workers to process n structures, to optimize the minimum completion time of the jobs. In this improved BPSO, a new method of making initial particles has been presented for searching the optimum particle in the feasible dimensional problem space. Besides, importing memory base, modifying Sig function, and considering constraint condition have been used in the algorithm for making updated particles meet the constraint equation of the mathematical model. The research on the algorithm examples demonstrates that the improved BPSO algorithm is effective and can achieve good results. Moreover, the mathematical model has wide application in discrete manufacture.

The main task of the capacity planning decision is to determine an optimal schedule to replace older machines, equipments or activity centers by newer ones. Traditionally, uncertain factors are seldom considered in capacity expansion because of an approximation view in medium- and long-term decision-making. However, in an uncertain market and stochastic production environment, the influences of stochastic factors should not be neglected entirely. To study the stochastic factors' influences on capacity planning decision, a stochastic capacity expansion model is built in this article. The model's objective is to minimize the production and capacity expansion cost. There are two kinds of stochastic factors to be considered: uncertain market demand and consumption of stochastic production capacity. For solving this model, the constraints of uncertain demand are transformed into equivalent deterministic constraints, using the stochastic two-stage model. The constraints of stochastic capacity are transformed into equivalent deterministic formulation, based on Chance Constrained Programming (CCP). Consequently, the stochastic capacity planning model is transformed into a deterministic one, which has a structure where the Product-Mix decision is nested in the capacity planning model. To solve this two-tier model, a heuristic algorithm that combines the Genetic Algorithm (GA) and the Primal-Dual algorithm of Nonlinear Programming, is produced. In the heuristic, GA is used to optimize the capacity decision variables and the Primal-Dual method is used to solve a Product-Mix decision problem with quadratic constraints. A numerical example is constructed for demonstrating the model and the algorithm. The results and analysis are provided.

With the increase of social productivity, the garment industry changed from the traditional hand-made craft into industrial mass production of processing pipeline, which greatly improve the efficiency of production of clothing and shorten the production cycle to meet the growing market demand for clothing. However, with the improvement of people’s living standards, demand for personalized clothing increases and therefore, a large-scale standardized production of clothing is no longer suitable for the development of the market. Although high varieties and low volume of clothing production model meet the individual needs of people in some extent, this model is making choices after producing, trying to use the series and various styles of clothing to replace personalized products. First, it’s impossible to meet the individual needs of all customers.

As the margin of manufacturing declines and customers’ demand for products rises, more and more manufacturing enterprises in the world no longer just focus on the production of physical products, but also explore a series of services oriented to customers besides the sale of products, so as to create profits and improve customer satisfaction. Service is playing an increasingly significant role in the value chain of manufacturing, creating a new source of profit for manufacturing enterprises. Servitization of manufacturing has become one of the development trend of manufacturing in current world. By adopting a series of measures, IBM has achieved the transition from a manufacturing enterprise into one specializing in information technology and business solutions.

After the Chinese Reform and Open-up, by virtue of the incredible manufacturing capacity and the continuous product innovation, “Made in China” products increased rapidly and have become an indispensable part of the global economy.

Parametric is the most often used method for Life-Cycle Cost Estimation(LCCE), of which the Parameter Selection Problem(PSP) plays a key role in modeling Cost Estimation Relationships(CERs). It is generally computational infeasible to find out the optimal solution by comparing all the combinations of parameters when the problem is large-sized. Expertise was always counted on in the past. In this paper, we employ the modern meta-heuristic algorithm, i.e., an improved Simulated Annealing algorithm, to solve the problem of large-scale. We also present a mathematic optimization model for the PSP aiming at minimizing the average cost prediction error. A case study is given to show the principle of the proposed model and simulation experiments are carried out to demonstrate effectiveness and efficiency of this algorithm. The results show that this algorithm has a high probability in finding the optimal solution just by searching very small portion of solution space, which is satisfying.

At the time of commercial banks verifying and assessing the loan applications of enterprises, there is one important problem faced that is how to choose the qualified ones from the numerous applications, which could be named of the Decision-making Program of Loan-granting (DPL). The members of evaluating and granting loan group make up from different departments of banks and different specialized experts. How to synthesize suggestion of expert, draw and reflect expert panel’s result of suggestion most, it has been a focus question of the decision theory all the time. The problems of loan’s group decision were briefly introduced, and then the authors proposed an entropy-based DSS for examining and approving loan applications. The core mathematics models were emphasized which applied a combination of the multi-attribute group decision-making plural entropy model and traditional group eigenvalue method for the loan evaluation purpose. And then its function, structure modules, key technology and solution method were reviewed in detail. Finally, an example is proposed to show how the entropy method can be applied to the evaluation practice.

In this paper, we analyze the special job shop scheduling problem (JSP) of actual production system in large-scale structure workshops. With regard to this kind of JSP problem, two novel mathematical models (deterministic model and stochastic model) are proposed. In addition, particle swarm optimization (PSO) algorithm is used in the paper because of its high efficiency, and binary PSO algorithm is improved for solving this special scheduling problem, i.e. how to arrange m workers to process n jobs. The results obtained from the simulation study demonstrate that using this heuristics method to solve mathematical models can reach optimal or near-optimal solutions efficiently, and can be widely used in many actual manufactories' workshops.

Due to the uncertainty of future demand, companies have changed their production planning mode from pre-planning to produce according to the accepted orders, which is named as make-to-order (MTO) or build-to-order (BTO). The hypothesis of independent demand, which falls short of the real state, has been widely introduced in the model of traditional production planning. In general, the future demand and current orders are not completely independent. This paper introduces the data mining method of timing association rules to the model of MTO production planning. Through timing association rules, the relevancy of the future demand and current orders is considered and the future order demand can be forecasted from the history order data by association rules. The heuristic algorithm based on genetic algorithm (GA) is proposed to solve the model. A numeric example is built to demonstrate the proposed model and algorithm.

This study considers the non-linear optimal control of the complex manufacturing system. We construct a mathematical model, which decomposes the complex manufacturing system into many correlated working centers, where the number of inventory and production evolves dynamically. It aims to minimize the cost of production control. We can utilize MDE algorithm to solve this model. The model can be easily used in multi-product, multi-center, multi-period manufacturing system.

It's an important problem for aviation maintenance enterprise to assure quality of maintenance activity. Thinking over human factors and organization management, the paper produces a production system continuous improvement model for aviation maintenance quality. First, the model gathers characteristics of human factors and man-made maintenance mistakes. Based on association rules, relationships between human factors and the man-made mistakes are found. So, measures for improving organization and management could be produced, which is more pertinent. And, these measures will be used for production system continuous improvement. In an aviation maintenance firm, we made a case study in maintenance management of a type of military aero-transporter and reach to the anticipative effects