高校辅导员艾森克人格问卷调查研究

采用艾森克人格问卷简式量表中国版(EPQ_RSC)对320名年龄在20~23岁之间的东北师范大学各学院11、12、13级辅导员进行人格测查,测查结果显示,与常模相比,E(内-外向性)、L(掩饰性)分显著偏高,P(精神质性)、N(情绪稳定性)分显著偏低;辅导员人格大都趋向于稳定外向型人格;性别因素在P因子上的差异显著,家庭所在、专业、年级三个因素对P、E、N、L四个因子无明显差异,家庭排行和家庭收入只在N因子上有显著影响。     

Theory and methodology on the global optimal solution to a General Reverse Logistics Inventory Model for deteriorating items and time-varying rates

In this paper, we present a unified general inventory model for integrated production of new items and remanufacturing of returned items for an infinite planning horizon. Our model considers a production environment that consists of three shops. The first shop is for remanufacturing returned items, the second shop is for manufacturing new items, while the third shop is for collecting returned items to be remanufactured in the first shop. The system is subject to joint production and remanufacturing options, the first one is to produce new items while the second one is to reproduce/recycle the returned items “as-good-as new”. Items deteriorate while they are in storage, and production, remanufacturing, demand, return, and deterioration rates are arbitrary functions of time. A closed form for the total relevant costs as well as a rigorous mathematical proof, which shows the global optimality of the solution to the underlying inventory system are introduced. Illustrative examples, which explain the application of the theoretical results as well as their numerical verifications, are also given.     

Optimal pricing, lot-sizing and marketing planning in a capacitated and imperfect production system

In the classical economic production quantity (EPQ) problem demand is considered to be known in advance. However, in the real-world, demand of a product is a function of factors such as product’s price, its quality, and marketing expenditures for promoting the product. Quality level of the product and specifications of the adopted manufacturing process also affect the unit product’s cost. Therefore, in this paper we consider a profit maximizing firm who wants to jointly determine the optimal lot-sizing, pricing, and marketing decisions along with manufacturing requirements in terms of flexibility and reliability of the process. Geometric programming (GP) technique is proposed to address the resulting nonlinear optimization problem. Using recent advances in optimization techniques we are able to optimally solve the developed, highly nonlinear, mathematical model. Finally, using numerical examples, we illustrate the solution approach and analyze the solution under different conditions.     

Combining an alternative multi-delivery policy into economic production lot size problem with partial rework

This paper combines an alternative multi-delivery policy into an imperfect economic production quantity (EPQ) model with partial rework, with the purpose of reducing supplier’s stock holding cost. We extend the problem examined by Chiu et al. [Chiu, Y.-S. P, Chiu, S. W., Li, C.-Y., & Ting, C.-K. (2009). Incorporating multi-delivery policy and quality assurance into economic production lot size problem. Journal of Scientific and Industrial Research, 68(6), 505-512] by considering an n + 1 delivery policy in lieu of n multi-delivery plan for the specific EPQ model with partial rework. Under such a policy, an initial delivery of perfect (finished) items is distributed to customer for satisfying product demand during manufacturer’s regular production time and rework time. At the end of rework, fixed quantity n installments of the finished products are delivered to customer at a fixed interval of time. As a result, a closed-form optimal replenishment batch size solution to the problem is obtained. A numerical example with analysis and comparison is provided to show practical usage of the proposed model and demonstrate its significant savings in stock holding cost.     

Optimizing a bi-objective multi-product EPQ model with defective items,rework and limited orders: NSGA-II and MOPSO algorithms

In this paper, a bi-objective multi-products economic production quantity (EPQ) model is developed, in which the number of orders is limited and imperfect items that are re-workable are produced. The objectives of the problem are minimization of the total inventory costs as well as minimizing the required warehouse space. The model is shown to be of a bi-objective nonlinear programming type, and in order to solve it two meta-heuristic algorithms namely, the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithm, are proposed. To verify the solution obtained and to evaluate the performance of proposed algorithms, two-sample t-tests are employed to compare the means of the first objective value, the means of the second objective values, and the mean required CPU time of solving the problem using two algorithms. The results show while both algorithms are efficient to solve the model and the solution qualities of the two algorithms do not differ significantly, the computational CPU time of MOPSO is considerably lower than that of NSGA-II.     

Determination of production run time and warranty length under system maintenance and trade credits

Manufacturers offer a warranty period within which they will fix failed products at no cost to customers. Manufacturers also perform system maintenance when a system is in an out-of-control state. Suppliers provide a credit period to settle the payment to manufacturers. This study considers manufacturer's production and warranty decisions for an imperfect production system under system maintenance and trade credit. Specifically, this study uses the economic production quantity to model the decisions under system maintenance and trade credit. These decisions involve how long the production run time and warranty length should be to maximise total profit. This study provides lemmas for the conditions of optimality and develops a theorem and an algorithm for solving the problems described. Numerical examples illustrate the solution procedures and provide a variety of managerial implications. Results show that simultaneously determining production and warranty decisions is superior to only determining production. This study also discusses the effects of the related parameters on manufacturer's decisions and profits. The results of this study are a useful reference for managerial decision-making and administration.     

Optimal ordering and production policy for a recoverable item inventory system with learning effect

This article presents two models for determining an optimal integrated economic order quantity and economic production quantity policy in a recoverable manufacturing environment. The models assume that the unit production time of the recovery process decreases with the increase in total units produced as a result of learning. A fixed proportion of used products are collected from customers and then recovered for reuse. The recovered products are assumed to be in good condition and acceptable to customers. Constant demand can be satisfied by utilising both newly purchased products and recovered products. The aim of this article is to show how to minimise total inventory-related cost. The total cost functions of the two models are derived and two simple search procedures are proposed to determine optimal policy parameters. Numerical examples are provided to illustrate the proposed models. In addition, sensitivity analyses have also been performed and are discussed.     

Joint determination of the lot size and number of shipments for a family of integrated vendor–buyer systems considering defective products

In this paper we present two solution procedures to obtain the solution for a family of economic production quantity inventory models for an integrated vendor–buyer system considering that production system generates defective products, in which the number of shipments must be a discrete value and that the lot size can take continuous (case 1) or integer (case 2) values. These solution procedures provide a more realistic approach for solving a family of inventory models. Previous works only derive the optimal lot size considering the number of shipments as a given value or derive both the lot size and the number of shipments considering both decision variables as continuous. The number of shipments takes integer values in real life and thus it must be considered as a discrete variable. This paper revisits and solves previous inventory models considering the variables (lot size and number of shipments) according to their real-life nature.     

The EPQ model under conditions of two levels of trade credit and limited storage capacity in supply chain management

An inventory problem involves a lot of factors influencing inventory decisions. To understand it, the traditional economic production quantity (EPQ) model plays rather important role for inventory analysis. Although the traditional EPQ models are still widely used in industry, practitioners frequently question validities of assumptions of these models such that their use encounters challenges and difficulties. So, this article tries to present a new inventory model by considering two levels of trade credit, finite replenishment rate and limited storage capacity together to relax the basic assumptions of the traditional EPQ model to improve the environment of the use of it. Keeping in mind cost-minimisation strategy, four easy-to-use theorems are developed to characterise the optimal solution. Finally, the sensitivity analyses are executed to investigate the effects of the various parameters on ordering policies and the annual total relevant costs of the inventory system.     

Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands

This paper investigates the role of variable lead time, learning in production and screening errors in a vendor–buyer supply chain with defective items. The vendor–buyer supply chain is modelled for supplying a single item considering that the lots from vendor may contain some defective items. It is assumed that demand during lead time follows a normal distribution. Moreover, the production time at vendor’s facility is assumed to follow learning whereas buyer’s screening for defective items is prone to errors as well. Numerical examples are presented to illustrate the impact of different variables in the model. The analysis shows that delay in transportation lead time forces the buyer to carry more inventories to avoid shortages. Further, Type I error has a major impact on this cost. It was found that learning in production keeps on reducing the total cost of the supply chain up to a threshold.