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。     

Joint replenishment with imperfect items and price discount

A joint replenishment problem is presented to determine the ordering policy for multiple items having a certain percentage of defective units. The purpose of this paper is to study the impact of the percentage of defective units on the ordering policy. Two different scenarios are presented for joint replenishment problem: (1) without price discount and (2) with price discount. For each scenario, the total expected cost per unit time is derived and algorithms are presented to determine the family cycle length and the integer number of intervals that the replenishment quantity of each item will last. Numerical examples are presented and the results are discussed.     

Modeling of asphalt concrete via simulated annealing

In this study, 65 asphalt core specimens taken from D100/11 state highway section in Turkey were examined for their physical properties in the laboratory. Analysis of data was conducted to determine the effects of the varying environment temperature and varying length of exposure to these temperatures on the stability of the asphalt core samples using destructive, Marshall, method. The asphalt core samples were determined using SPSS statistical program for modeling. Simulated annealing was implemented to determine a set of unknown parameters which best matched the asphalt concrete model predictions with experimental data. This modeling procedure can be used as a guideline for experiments to improve the stability of the asphalt concrete. The stability of the asphalt concrete is taken as the main objective function with respect to voids of volume, saturated unit volume weight, air dry unit volume weight, environment temperature, and exposure time.     

Joint pricing and replenishment decisions for deteriorating items with lot-size and time-dependent purchasing cost under credit period

In the real world, the purchasing cost would normally decrease as the replenishment lot-size becomes larger. In other words, the quantity discount effect applies. The purchasing cost may also decrease with the passage of time, for example if the supplier has made effective improvements in their production efficiency, in other words due to the effect of the learning curve. In this article we discuss a purchasing cost pattern which considers these phenomena: i.e., lot-size and time-dependence. The objective of the model is to make decisions related to the pricing and replenishment of deteriorating items over a finite time horizon, given variable purchasing cost and credit period. We provide the properties and develop algorithms for solving the problems described. Also, we discuss the influence of the variable purchasing cost, the length of the credit period, the rate of deterioration, etc., on the retailer behavior.     

A supplier-selection model with classification and joint replenishment of inventory items

Since inventory costs are closely related to suppliers, many models in the literature have selected the suppliers and also allocated orders, simultaneously. Such models usually consider either a single inventory item or multiple inventory items which have independent holding and ordering costs. However, in practice, ordering multiple items from the same supplier leads to a reduction in ordering costs. This paper presents a model in capacity-constrained supplier-selection and order-allocation problem, which considers the joint replenishment of inventory items with a direct grouping approach. In such supplier-selection problems, the following items are considered: a fixed major ordering cost to each supplier, which is independent from the items in the order; a minor ordering cost for each item ordered to each supplier; and the inventory holding and purchasing costs. To solve the developed NP-hard problem, a simulated annealing algorithm was proposed and then compared to a modified genetic algorithm of the literature. The numerical example represented that the number of groups and selected suppliers were reduced when the major ordering cost increased in comparison to other costs. There were also more savings when the number of groups was determined by the model in comparison to predetermined number of groups or no grouping scenarios.     

Improving simulated annealing with variable neighborhood search to solve the resource-constrained scheduling problem

The purpose of this paper is to improve the simulated annealing method with a variable neighborhood search to solve the resource-constrained scheduling problem. We also compare numerically this method with other neighborhood search (local search) techniques: threshold accepting methods and tabu search. Furthermore, we combine these techniques with multistart diversification strategies and with the variable neighborhood search technique. A thorough numerical study is completed to set the parameters of the different methods and to compare the quality of the solutions that they generate. The numerical results indicate that the simulated annealing method improved with a variable neighborhood search technique is indeed the best solution method. This research was supported by NSERC grant (OGP 0008312) the first author received a FCAR fellowship during her M.Sc. studies.     

Inventory model with two rates of production for deteriorating items with permissible delay in payments

Some decentralized smoothing problems are solved by applying a forward-pass fixed-interval smoother formula in discrete-time systems. It is assumed that a simple estimation structure consists of a global processor and of two local processors. Two cases are considered for the problems of decentralized smoothing and smoothing update: when the local backward-pass information filtered estimates are available, and when the local-smoothed estimates are available. Some features of present algorithms are discussed from the point of view of data transmissions and numerical computations, etc.     

On the optimal replenishment schedule for an inventory system with deteriorating items and time-varying demand and production rates

In this paper, we present an exact method for finding the optimal replenishment schedule for an inventory system with deteriorating items, and in which demand and production rates are allowed to vary over a finite planning horizon. A numerical example is presented to illustrate the method.     

Minimizing distortion and internal forces in truss structures via simulated annealing

Inaccuracies in the length of members and the diameters of joints of large space structures may produce unacceptable levels of surface distortion and internal forces. We formulate two discrete optimization problems, one to minimize surface distortion (DRMS) and the other to minimize internal forces (FRMS). Both of these problems are based on the influence matrices generated by a small deformation linear analysis. Good solutions are obtained for DRMS and FRMS through the use of a simulated annealing heuristic. Results based on two biobjective (DRMS and FRMS) optimization models are discussed     

An inventory replenishment policy for deteriorating items with shortages and partial backlogging

It has long been assumed that the shortages in inventory systems are either completely backlogged or totally lost. However, it is more reasonable to characterize that the longer the waiting time for the next replenishment, the smaller the backlogging rate would be. Moreover, the opportunity cost due to lost sales should be considered since some customers would not like to wait for backlogging during the shortage periods. Without considering these two realistic conditions, study on the inventory modeling for deteriorating items with shortages and partial backlogging cannot be complete and general. In the present article we define an appropriate time-dependent partial backlogging rate and introduce the opportunity cost due to lost sales. Numerical examples are also presented to illustrate the effects of changes in backlogging parameter and unit opportunity cost on total cost and the optimal number of replenishments.Scope and purposeIn a recent article published in this Journal, Giri et al. (Comput. Oper. Res. 27 (2000) 495–505) implemented an existing procedure to the inventory problem of Hariga and Al-Alyan (Comput. Oper. Res. 24 (1997) 1075–83) which concerns with lot-sizing heuristic for deteriorating items with shortages allowed in all cycles except the last one. Giri et al. deviated from the traditional practice and suggested a new policy allowing shortages in all cycles over a finite planning horizon. Their numerical results indicated the proposed policy is cheaper to operate with a cost reduction up to 15%. However, they did not consider the opportunity cost due to lost sales that happen because customers would not like to wait for backlogging. Moreover, for many products with growing sales, the length of the waiting time for the next replenishment is the main factor for determining whether the backlogging will be accepted or not, and the backlogging rate is expected to be time-dependent. Thus the assumption made in Giri et al. that the backlogging rate is a fixed fraction of the total amount of shortages is not reasonable.The purpose of this paper is to present a more realistic discussion of the inventory problem for deteriorating items with time-varying demands and shortages over a finite planning horizon. In contrast to the model by Giri et al., we define an appropriate partial backlogging rate and introduce the opportunity cost due to lost sales. We attempt to complement their model as a practical and general solution for inventory replenishment problems. With these extensions, the scope of applications of the present results is expanded.     

Coordinating replenishment and pricing policies for non-instantaneous deteriorating items with price-sensitive demand

This article will formulate and solve an inventory system with non-instantaneous deteriorating items and price-sensitive demand. The purpose of this study is to determine the optimal selling price and the length of replenishment cycle such that the total profit per unit time has a maximum value for the retailer. We first establish a proper model for a mathematical formulation. Then we develop several theoretical results and provide the decision-maker with an algorithm to find the optimal solution. Finally, two numerical examples are provided to illustrate the solution procedure, and a sensitivity analysis of the optimal solution with respect to major parameters is carried out.     

Optimal dynamic pricing and replenishment policy for perishable items with inventory-level-dependent demand

An inventory system for perishable items with limited replenishment capacity is introduced in this paper. The demand rate depends on the stock quantity displayed in the store as well as the sales price. With the goal to realise profit maximisation, an optimisation problem is addressed to seek for the optimal joint dynamic pricing and replenishment policy which is obtained by solving the optimisation problem with Pontryagin’s maximum principle. A joint mixed policy, in which the sales price is a static decision variable and the replenishment rate remains to be a dynamic decision variable, is presented to compare with the joint dynamic policy. Numerical results demonstrate the advantages of the joint dynamic one, and further show the effects of different system parameters on the optimal joint dynamic policy and the maximal total profit.     

A hybrid neural network and simulated annealing approach to the unit commitment problem

In this paper, the authors present an approach combining the feedforward neural network and the simulated annealing method to solve unit commitment, a mixed integer combinatorial optimisation problem in power system. The artificial neural network is used to determine the discrete variables corresponding to the state of each unit at each time interval. The simulated annealing method is used to generate the continuous variables corresponding to the power output of each unit and the production cost. The type of neural network used in this method is a multi-layer perceptron trained by the back-propagation algorithm. A set of load profiles as inputs and the corresponding unit commitment schedules as outputs (satisfying the minimum up–down, spinning reserve and crew constraints) are utilized to train the network. A method to generate the training patterns is also presented. The experimental result demonstrates that the proposed approach can solve unit commitment in a reduced computational time with an optimum generation schedule.     

Optimal replenishment policy for perishable items with stock-dependent selling rate and capacity constraint

In this paper, a deterministic inventory model is developed for deteriorating items with stock-dependent demand and finite shelf/display space. Furthermore, we allow for shortages and the unsatisfied demand is partially backlogged at the exponential rate with respect to the waiting time. We provide solution procedures for finding the maximum total profit per unit time. In a specific circumstance, the model will reduce to the case with no shortage. Further, we use numerical examples to illustrate the model.     

A new hybrid particle swarm and simulated annealing stochastic optimization method

A novel hybrid particle swarm and simulated annealing stochastic optimization method is proposed. The proposed hybrid method uses both PSO and SA in sequence and integrates the merits of good exploration capability of PSO and good local search properties of SA. Numerical simulation has been performed for selection of near optimum parameters of the method. The performance of this hybrid optimization technique was evaluated by comparing optimization results of thirty benchmark functions of different dimensions with those obtained by other numerical methods considering three criteria. These criteria were stability, average trial function evaluations for successful runs and the total average trial function evaluations considering both successful and failed runs. Design of laminated composite materials with required effective stiffness properties and minimum weight design of a three-bar truss are addressed as typical applications of the proposed algorithm in various types of optimization problems. In general, the proposed hybrid PSO-SA algorithm demonstrates improved performance in solution of these problems compared to other evolutionary methods The results of this research show that the proposed algorithm can reliably and effectively be used for various optimization problems.     

A replenishment policy for items with price-dependent demand,time-proportional deterioration and no shortages

In this article, an order-level inventory system for deteriorating items has been developed with demand rate as a function of selling price. The demand and the deterioration rate are price dependent and time proportional, respectively. We have considered a perishable item that follows a three-parameter Weibull distribution deterioration. Shortages are not permitted in our model. The optimal solution is illustrated with a numerical example and the sensitivity analysis of parameters is carried out.     

Solving dynamic double row layout problem via combining simulated annealing and mathematical programming

Double row layout problem (DRLP) is to allocate facilities on two rows separated by a straight aisle. Aiming at the dynamic environment of product processing in practice, we propose a dynamic double-row layout problem (DDRLP) where material flows change over time in different processing periods. A mixed-integer programming model is established for this problem. A methodology combining an improved simulated annealing (ISA) with mathematical programming (MP) is proposed to resolve it. Firstly, a mixed coding scheme is designed to represent both of sequence of facilities and their exact locations. Secondly, an improved simulated annealing algorithm is suggested to produce a solution to DDRLP. Finally, MP is used to improve this solution by determining the optimal exact location for each facility. Experiments show that this methodology is able to obtain the optimal solutions for small size problems and outperforms an exact approach (CPLEX) for problems of realistic size.     

An automatic homology modeling method consisting of database searches and simulated annealing

We introduce a method of homology modeling consisting of database searches and simulated annealing. All processes involving searches for homologous proteins, alignment, the construction of Calpha atoms, construction of main-chain atoms, and the construction of side-chain atoms are performed automatically. In this method, main-chain conformations are generated from the weighted average of mainchain coordinates in reference proteins. The weight is defined by the local space homology representing the similarity of environmental residues at topologically equivalent positions in reference proteins. Side-chain conformations are generated for constructed main-chain atoms by database searches, and main-chain atoms are optimized for the fixed side-chain conformations. These two processes, i.e., the side-chain generation and main-chain optimization, are repeated several times. This type of construction provides a structure similar to the x-ray structure, in particular, for main-chain and side-chain atoms in the residues belonging to structurally conserved regions (SCRs). The accuracy of our method was evaluated for 14 proteins whose structures are known. The average root mean square deviation between models and x-ray structures was 2.29 A for all atoms, and the percentage of chi1 angles within 30 degrees was 72.6% for SCRs residues. Some models were in good agreement with their respective x-ray structures. Our method, which has the advantage of being automated, gives results similar to, or better than, published results for three widely used test proteins. Our software, FAMS, is available on the World Wide Web.     

Con／k／n：F系统单元序列最优化的模拟退火算法

n中取链续k(con/k/n:F)系统单元序列最优化的实质是一个组合优化问题，该文设计了求解任意con/k/n:F系统全局最优配置的模拟退火算法，并以计算实例检验了该算法。