Asia-Pacific Journal of Operational Research

The paper considers a situation when a terrorist with N attack facilities allocates its resource between attacking an object and protecting the facilities that will be used in the attack. A government allocates its resource between protecting the object passively and striking actively and preventively against all or a subset of the attack facilities. The government strikes preventively if the object destruction probability is lower than when choosing passive defense. The paper formulates a zero-sum situation in which the terrorist chooses how to distribute its resource between the attack and protection efforts where the government chooses how to distribute its resource between the (PS) preventive strike and protection efforts and decides how many facilities to strike. It is shown that independently of the number of facilities the government strikes, the sum of the fraction of the terrorist's resource allocated to attack and the fraction of the government's resource allocated to the PS always equals one. The condition of the PS effectiveness is formulated. Examples are provided to illustrate the methodology of the model analysis.

This paper, studies the sensitivity analysis of weakly efficient extreme solutions in multiple objective linear programming (MOLP). The aim of the paper is to compute the set of the parameters (corresponding to one coefficient) for which a given extreme point is a weakly efficient solution. We also focus on the properties of the parameters set by proving convexity and closeness of this set. Moreover, we compare the results of the sensitivity analysis of efficiency and of weak efficiency in MOLP.

This paper examines the impact of financing on the performance of a two-level supply chain consisting of a supplier and a budget-constrained retailer. To carry out our study, we set up a three-stage Stackelberg game under a wholesale price contract with a financial market. We show that financing from a competitive financial market can create value for both the supplier and the retailer. We also demonstrate that in a competitive financial market with symmetric information on the retailer's initial budget, the retailer's operational and financial decisions could be decoupled. We provide numerical results to shed light on additional managerial insights as well.

Due to the effects of uncertain factors such as seasonal weather and plant/animal diseases, the output of agricultural production is uncertain given certain agricultural inputs. This paper addresses the issue of coordinating agri-food chain with perishable goods by revenue-sharing (RS) contract when both output and demand are random. Following a RS contract, a supplier decides on agricultural input (e) and deliveries all goods with a price (ω) lower than unit production cost to the retailer while a retailer decides on the retail price (p) and return 1–Φ share (percentage) of its revenue to the supplier. For such a contract, the necessary condition of coordinating the chain is that the expected revenue function is concave. Under the channel coordination, the relationship between RS contract parameters ω and Φ as well as the range of Φ are given. For both output and demand random factor with uniform distribution, the concavity of the expected revenue function is presented. A numerical example illustrates the correctness of our analysis. The results of numerical example show that RS contract is able to coordinate the agri-food chain when the expected revenue function is concave. The results also show that the optimal decisions of p and e are closely related to the demand price elasticity and the range of Φ is widened when the expected output increases.

In the paper, single machine scheduling problems with a learning effect and a rate-modifying activity are considered. Under the learning effect, the processing time of a job is a decreasing function of its position in the sequence. The rate-modifying activity is an event that can change the speed of the machine, and hence the processing time of jobs after the activity. The following objective functions are considered: the makespan, the total earliness, tardiness and completion time penalty, and the total earliness, tardiness, due-window starting time and due-window size penalty. Polynomial time algorithms are proposed to optimally solve the problems.

In this paper, we present a recurrent neural network for solving mixed linear complementarity problems (MLCPs) with positive semi-definite matrices. The proposed neural network is derived based on an NCP function and has a low complexity respect to the other existing models. In theoretical and numerical aspects, global convergence of the proposed neural network is proved. As an application, we show that the proposed neural network can be used to solve linear and convex quadratic programming problems. The validity and transient behavior of the proposed neural network are demonstrated by using five numerical examples.

In this paper, we report a study of the reliability optimal design of multi-state weighted series-parallel systems. Such a system and its components are capable of assuming a whole range of levels of performance, varying from perfect functioning to complete failure. There is a component utility corresponding to each component state. This system model is more general than the traditional binary series-parallel system model. The so-called component selection reliability optimal design problem which involves selection of components with known reliability characteristics and cost characteristics has been widely studied. However, the problem of determining system cost and system utility based on the relationships between component reliability, cost and utility has not been adequately addressed. We call it optimal component design reliability problem which has been studied in one of our former papers and continued in this paper for the multi-state weighted series-parallel systems. Furthermore, comparing to the traditional single-objective optimization model, the optimization model we proposed in this paper is a multi-objective optimization model which is used to maximize expected system performance utility and system reliability while minimizing investment system cost simultaneously. Genetic algorithm is used to solve the proposed physical programming based optimization model. An example is used to illustrate the flexibility and effectiveness of the proposed approach over the single-objective optimization method.