排放量成本度量的合乘交通系统优化研究

为缓解交通拥堵,构建绿色交通系统,研究了以排放量成本度量的交通系统优化问题,建立了针对包括合乘的交通系统的分阶段模型。对出行者交通选择模式进行了方式划分,建立了以排放量衡量的用户均衡和系统最优交通流分配模型,设计了相应的求解算法,并通过一个算例说明了模型和算法的可行性和有效性。通过对比,说明了按时间成本度量的系统最优分配和按排放量成本度量的系统最优分配所能达到的效果;根据不同的排放相对系数说明了考虑合乘的交通系统中交通量、系统总时间和排放量的变化趋势。     

PCA-based branch and bound search algorithms for computing K nearest neighbors

In this paper, the efficiency of branch and bound search algorithms for the computation of K nearest neighbors is studied. The most important aspects that influence the efficiency of the search algorithm are: (1) the decomposition method, (2) the elimination rule, (3) the traversal order and (4) the level of decomposition. First, a theoretical derivation of an efficient decomposition method based on principal component analysis is given. Then, different elimination rules and traversal orders are combined resulting in ten different search algorithms. Since the efficiency is strongly dependent on the level of decomposition, this user specified parameter is optimized first. This optimization is realized by a probabilistic model that expresses the total computation time in function of the node traversal cost and the distance computation cost. All comparisons are based on the total computation time for the optimal level of decomposition.     

A partially integrated production-inventory model with interval valued inventory costs,variable demand and flexible reliability

This paper formulates a production-inventory model to investigate the effects of partially integrated production and marketing policy of a manufacturing firm. Demand is assumed to be variable and dependent on the selling price and marketing cost. Also, different inventory costs are considered as interval valued. Shortages are permitted and partially backlogged with a rate dependent on the waiting time. Considering that manufacturing process generates defective units four possible cases have been identified and studied. Basically, the optimization problems (maximization problem for Marketing department and minimization problems for Production and Research & Development departments) have been formulated and solved. For solving these optimization problems, an efficient soft computing algorithm based on Particle Swarm Optimization-Constriction factor (PSO-CO) is proposed. In order to illustrate and validate the production-inventory model a numerical example is solved. Finally, a sensitivity analysis is done to study the effect of changes of different system parameters on optimal policies.     

持续补货模式下的改良品库存模型研究

改良品的订货问题一直没有引起足够的重视。分别建立持续补货模式下不允许缺货与允许缺货的改良品库存模型,并分析改良率与补货周期、各种费用参数的相关关系,研究表明：改良率与补货周期、单位时间存储费用呈负相关关系,与单位时间订货费用、单位时间改良费用、单位时间缺货费用、单位时间总费用呈正相关关系。同时,对不允许缺货与允许缺货的改良品库存模型单位时间总费用进行比较,并分析库存模型各参数对两种库存模型单位时间总费用差值的影响方式,研究表明：单位时间总费用差值与改良率、补充速率、单次订货费用、单位存储费用、单位改良费用呈正相关关系,与单位缺货费用成呈负相关关系,与需求速率的相关关系取决于补充速率与需求速率的大小关系。     

Dynamic joint pricing and production policy for perishable products

A joint dynamic pricing and production problem for perishable products without shortages is considered. The demand rate is price‐dependent and time‐varying. This paper constructs an optimal control model to maximize the total profit under a general nonlinear production cost function. The feature of the optimal joint dynamic pricing and production policy is analyzed by solving the corresponding optimal control problem on the basis of improved Pontryagin's maximum principle. Then, an effective algorithm is designed to obtain the optimal joint policy. The case of the joint static optimal policy is also investigated and compared with the dynamic one. Finally, numerical examples are presented to illustrate the effectiveness of the proposed methods, and some managerial implications are provided for the management of perishable items.     

A recovery model for a two-echelon serial supply chain with consideration of transportation disruption

Supply chains are becoming increasingly competitive and complex in order to effectively meet customer demands. These characteristics make supply chains vulnerable to various risks, including disruptions. In this study, a recovery model is explored for a two-stage production and inventory system with the possibility of transportation disruption. This model is capable of determining the optimal ordering and production quantities during the recovery window, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule. An efficient heuristic was developed to solve the model. The results showed that the optimal recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters. In addition, the heuristic was able to give quality solutions for the model, with very small deviations of the heuristic solutions from the optimal value. Such tools are useful in assisting managers towards effective decision making, particularly in determining the optimal recovery strategy for the longevity and sustainability of their firms undergoing disruptions.     

Coordinated procurement/inspection and production model under inspection errors

In this study, we investigate integrating the acquisition of input materials, material inspection and production planning, where type I and type II inspection errors are allowed, and the unit acquisition cost is dependent on the average quality level. This study aims to find an optimal purchase lot size (or here, equivalently, the fixed production rate multiplied by the production run time), input quality level and the associated inspection policy that minimize the total cost per item including the order cost, materials purchase cost, setup cost, inventory holding cost, and the quality-related cost. Furthermore, the boundaries, conditions and properties for the optimal production run time are obtained under an optimal inspection policy when the input material quality level is fixed. These findings will facilitate the establishing of an efficient algorithm for an optimal solution. The study demonstrates that a partial inspection approach could dominate over both the commonly used policies of full or no inspection, which is different from a previous report where the optimal inspection policy is either full or no inspection. A numerical example is performed to evaluate the impact of the two types of inspection errors and the process deterioration because of a nonconforming process input on the optimal solution, where a Weibull shift distribution is used to simulate the process failure time. Finally, conclusions are addressed.     

Lot-sizing and cyclic scheduling for multiple products in a flow shop

This paper considers a production-inventory system in which optimal batch sizes are determined for n products that are processed on m machines in a flow shop. The total cost function for this system is derived by considering three cost components: inventory cost in work-in-process, the final products inventory cost and the machine setup labor cost. In order to make the optimal solution realizable, it is assumed that all products have the same processing cycle time. The capacity constraint considered during the derivation of the optimal lot sizes acts as an additional constraint. Two heuristic algorithms are developed in order to obtain the optimal solution. An important part of these algorithms is the modeling of the recursive relations among the production waiting times and machine idle times. These algorithms are not only used in deriving the optimal solution but also in providing the production schedules. A numerical example is also demonstrated along with the conclusion and indication for future research.     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

Power-form demand and cost functions in inventory lot size models

This paper presents a continuous-time inventory model with known time-varying demand and carrying cost functions. Backlogs are prohibited, replenishments are assumed to be instantaneous and the planning horizon is finite. The problem is to find the optimal number and schedule of replenishments, i.e. the time intervals between consecutive orders, which minimizes total carrying and replenishment costs throughout the planning horizon.First, a necessary and sufficient condition on the optimal replenishment times is derived for general demand and carrying cost functions when the number of replenishments is fixed. Then, a complete solution (optimal number and schedule of replenishments) is given for the case of power-form demand and carrying cost functions. The asymptotic properties of the solution as the planning horizon tends to infinity are also investigated. The model lends itself to a tractable parametric analysis, and generalizes several special cases already known in the literature.     

An inventory model for deteriorating items under stock-dependent demand and controllable deterioration rate

In this paper, we formulate a deteriorating inventory model with stock-dependent demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. Moreover, it is assumed that the shortages are allowed and partially backlogged, depending on the length of the waiting time for the next replenishment. The objective is to find the optimal replenishment and preservation technology investment strategies while maximizing the total profit per unit time. For any given preservation technology cost, we first prove that the optimal replenishment schedule not only exists but is unique. Next, we show that the total profit per unit time is a concave function of preservation technology cost when the replenishment schedule is given. We then provide a simple algorithm to find the optimal preservation technology cost and replenishment schedule for the proposed model. Finally, we use some numerical examples to illustrate the model.     

传搁时间约束下的运输与批处理机生产协调调度

针对炼钢模铸系统钢锭高温运作的特点,提出带有传搁时间约束的生产前运输与批处理机生产协调的调度问题.工件的加工时间依赖于其传搁时间,每批工件的加工时间为该批工件中加工时间最大值.目标函数为最小化总完工时间与生产费用的线性组合.通过复杂性分析,证明该问题是强NP难解问题.建立混合整数规划模型,基于动态规划提出两种特殊情况的最优算法,设计原问题的启发式算法并进行最坏情况下性能比分析.实验仿真结果验证了所提出启发式算法的有效性与稳定性.     

决策粗糙集与代价敏感分类

将决策粗糙集与代价敏感学习相结合,提出了一种基于决策粗糙集的代价敏感分类方法。依据决策粗糙集理论和属性约简方法,对待预测样本分别计算最优测试属性集,使得样本在最优测试属性集上计算的分类结果具有最小误分类代价和测试代价,依此给出样本的最小总代价分类结果。针对全局最优测试属性集求解过程中计算复杂度高的问题,提出了局部最优测试属性集的启发式搜索算法。该算法以单个属性对降低总分类代价的贡献率为启发函数,搜索各样本的局部最优测试属性集,并输出在局部最优测试属性集上样本的代价敏感分类结果。在UCI数据上的实验分析显示,所提算法有效地降低了分类结果的总代价和测试属性个数,使得样本分类结果同时具有较小的误分类代价和较小的测试代价。     

A study for optional inspection policies in a flexible manufacturing cell

The objective of the paper is to determine the location of inspection stages in order to minimize the expected total manufacturing cost while satisfying the high-quality product demand of customers in a flexible manufacturing cell (FMC). This allows a designer to examine alternative designs in order to select those which are optimal, or near optimal, in manufacturing cost terms. To accomplish this objective, we have formulated a total manufacturing decision model consisting of machine set-up and processing costs. Inspection set-up and inspection costs, and cost due to defective. A case study and sensitivity analysis are presented to demonstrate the application of the decision model developed.     

Optimal lot sizing with maintenance actions and imperfect production processes

Porteus (1986) explored an economic order quantity model with imperfect production processes that the approximate lot size is derived. Basically, he dealt with the lot size problem is rather meaningful. However, for mathematical simplicity, he adopted a truncated Taylor series expansion to present the approximate expected total cost function that results in overvalue of expected total cost. In this paper, we extend Porteus (1986) to present the optimal lot size model for defective items with a constant probability when the system is out-of-control and taking the maintenance cost into account. We show that there exists a unique optimal lot size such that the expected total cost is minimised. In addition, the bounds of optimal lot size are provided to develop the solution procedure. Finally, numerical examples are given to illustrate the theoretical results and compare optimal solutions obtained by using our approach and Porteus's approach. Numerical results show that our approach is better.     

Buyer's optimal ordering policy and payment policy under supplier credit

This paper tries to incorporate previous studies to develop the buyer's inventory model. That is, we want to investigate the buyer's optimal cycle time and optimal payment time under the supplier's trade credit policy and cash-discount policy. Mathematical models have been derived for obtaining the optimal cycle time and optimal payment time for item under the supplier's trade credit and cash discount so that the annual total relevant cost is minimized. Furthermore, numerical examples are given to illustrate the results developed in this paper and to yield managerial implications.     

异构无线Mesh网络的最优下载算法

建立了异构无线Mesh网络的最优下载模型,基于这一模型,从下载时间和下载费用最小化的角度出发,提出了最优下载算法。构架了不同于传统树形图状混合的覆盖网拓扑结构,提高了算法的适应性和可扩展性。在此基础上,提出了基于网络流理论的最小代价最快下载算法,包括最优源节点选择和文件分块策略。仿真结果验证了算法的有效性,与其他算法相比,该算法能在下载时间最小的同时令节点的下载费用趋于最优解。     

Optimal production lot size and reorder point of a two-stage supply chain while random demand is sensitive with sales teams' initiatives

The paper develops a production-inventory model of a two-stage supply chain consisting of one manufacturer and one retailer to study production lot size/order quantity, reorder point sales teams’ initiatives where demand of the end customers is dependent on random variable and sales teams’ initiatives simultaneously. The manufacturer produces the order quantity of the retailer at one lot in which the procurement cost per unit quantity follows a realistic convex function of production lot size. In the chain, the cost of sales team's initiatives/promotion efforts and wholesale price of the manufacturer are negotiated at the points such that their optimum profits reached nearer to their target profits. This study suggests to the management of firms to determine the optimal order quantity/production quantity, reorder point and sales teams’ initiatives/promotional effort in order to achieve their maximum profits. An analytical method is applied to determine the optimal values of the decision variables. Finally, numerical examples with its graphical presentation and sensitivity analysis of the key parameters are presented to illustrate more insights of the model.     

不确定因素下建筑集群冷热电联供系统多目标优化

为降低建筑楼宇的能源消耗,研究建筑集群中的多个建筑楼宇共享冷热电联供系统、热能存储装置以及电池时的能源调度优化问题.考虑到建筑楼宇的能源需求和能源价格具有随机性,并且每个建筑楼宇以各自的费用最小化为目标,从随机规划和多目标的角度,建立建筑集群供能系统的两阶段多目标随机规划模型.为了提高模型的求解效率,提出将线性规划松弛与Benders分解算法相结合,从而获得建筑楼宇共享能源系统的Pareto最优解集.算例分析中通过CPLEX软件求解,对比分析不同随机因素对最优化建筑集群供能系统总费用以及建筑楼宇各自费用的影响程度,结果表明了所提出算法的有效性以及所构建的模型可以有效提高最优化决策的准确性.     

Two-echelon supply chain inventory model with controllable lead time and service level constraint

This paper considers a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer. In the system under study, a vendor produces a product in a batch production environment and supplies it to a buyer facing a stochastic demand, which is assumed to be normally distributed. Also, buyer’s lead time is controllable which can be shortened at an added cost and all shortages are backordered. A model has been formulated for an integrated vendor–buyer problem to jointly determine the optimal order quantity, lead time and the number of shipments from the vendor to the buyer during a production cycle while minimizing the total expected cost of the vendor–buyer integrated system. It is often difficult to estimate the shortage cost in inventory systems. Therefore, instead of having a shortage cost term in the objective function, a service level constraint (SLC) is included in the model that requires a certain proportion of demands to be met in each cycle. An efficient procedure has been suggested to find the bounds on number of shipments and then, an algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyze the behavior of the model. Finally, the savings of buyer and vendor are investigated from implementation of joint optimization model over the model in which they minimize their own cost independently.