Asia-Pacific Journal of Operational Research

This paper proposes data envelopment analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in service efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data simultaneously. It also contributes to a future research of a trade-off sensitivity test between FM cost, services performance and reliability.

This paper suggests a new DEA-based approach to the identification of the efficient category value for controllable categorical factors when controllable categorical factors are involved in the DMU's production process. Though the main objective of this approach is to identify the efficient category value for nonhierarchical categorical factors, it can also be employed to identify the efficient category value for hierarchical categorical factors without any large adjustment. This new approach is applied to an empirical data from an Agricultural Resource Management Survey for soybean production in Pennsylvania. The result of the empirical application illustrates how our approach leads us to the efficient category value of categorical factors by using both radial measure and nonradial measure. Moreover, it shows us the existence of substantial heterogeneity in the use of categorical factors and the opportunity of improvement in DMUs' selection of category value for categorical factors.

This paper proposes Data Envelopment Analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in services' efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data, simultaneously. It also contributes to a future research of a tradeoff sensitivity test between FM cost, services performance and reliability.

Reinforcement learning (RL) is a state or action value based machine learning method which solves large-scale multi-stage decision problems such as Markov Decision Process (MDP) and Semi-Markov Decision Process (SMDP) problems. We minimize the makespan of flow shop scheduling problems with an RL algorithm. We convert flow shop scheduling problems into SMDPs by constructing elaborate state features, actions and the reward function. Minimizing the accumulated reward is equivalent to minimizing the schedule objective function. We apply on-line TD(λ) algorithm with linear gradient-descent function approximation to solve the SMDPs. To examine the performance of the proposed RL algorithm, computational experiments are conducted on benchmarking problems in comparison with other scheduling methods. The experimental results support the efficiency of the proposed algorithm and illustrate that the RL approach is a promising computational approach for flow shop scheduling problems worthy of further investigation.

There are two kinds of option pricing. The option pricing in exchange follows the Black–Scholes rule but does not consider the optimizing of supply chain. The traditional supply chain option contract can optimize supply chain but does not meet the Black–Scholes rule. We integrate the assumption of above two kinds of option pricing, and design a model to combine the Black–Scholes rule and traditional option contract of optimizing in a supplier-led supply chain. Our combined model can guide the enterprises to write or buy option considering both option pricing rule in financial market and the optimization of supply chain. Then we simulate and verify the model in Zinc industry of China. It is proved that our option pricing model is equalized and optimal to supply chain and consistent with Black–Scholes rule.

A system subject to internal defects and external shocks is analyzed in this paper. Internal defects initiate following a nonhomogeneous Poisson process (NHPP) and they grow according to deterioration processes modeled as gamma processes. A corrective replacement is performed when the deterioration of a defect exceeds a failure threshold. The system is subject to external shocks. After an external shock, the system is replaced with probability 1 – p and is minimally repaired with probability p. The system is preventively replaced at the age of T. Costs are associated with the maintenance actions. The value of T that minimizes the expected cost rate is obtained analytically. Numerical examples are showed to illustrate the theoretical results.

The paper studies the stability of parametric generalized equations (PGE) defined by adding two set-valued mappings. Under some moderate conditions, the open covering/metric regularity of the solution mapping of PGE are discussed. The results are used to analyze the stability of stochastic generalized equations and parametric mathematical programs with equilibrium constraints.

This paper presents an extraction and expansion approach for the graph coloring problem. The extraction phase transforms a large graph into a sequence of progressively smaller graphs by removing large independent sets from the graph. The expansion phase starts by generating an approximate coloring for the smallest graph in the sequence. Then it expands the smallest graph by progressively adding back the extracted independent sets and determine a coloring for each intermediate graph. To color each graph, a simple perturbation based tabu search algorithm is used. The proposed approach is evaluated on the DIMACS challenge benchmarks showing competitive results in comparison with the state-of-the-art methods.

We consider systems comprised of two interlacing M/M/ • /• type queues, where customers of each queue are the servers of the other queue. Such systems can be found for example in file sharing programs, SETI@home project, and other applications [Arazi, A, E Ben-Jacob and U Yechiali (2005). Controlling an oscillating Jackson-type network having state-dependant service rates. Mathematical Methods of Operations Research, 62, 453–466]. Denoting by L_i the number of customers in queue i(Q_i), i = 1, 2, we assume that Q₁ is a multi-server finite-buffer system with an overall capacity of size N, where the customers there are served by the L₂ customers present in Q₂. Regarding Q₂, we study two different scenarios described as follows: (i) All customers present in Q₁ join hands together to form a single server for the customers in Q₂, with service time exponentially distributed with an overall intensity μ₂L₁. That is, the service rate of the customers in Q₂ changes dynamically, following the state of Q₁. (ii) Each of the customers present in Q₁individually acts as a server for the customers in Q₂, with service time exponentially distributed with mean 1/μ₂. In other words, the number of servers at Q₂ changes according to the queue size fluctuations of Q₁.

We present a probabilistic analysis of such systems, applying both Matrix Geometric method and Probability Generating Functions (PGFs) approach, and derive the stability condition for each model, along with its two-dimensional stationary distribution function. We reveal a relationship between the roots of a given matrix, related to the PGFs, and the stability condition of the systems. In addition, we calculate the means of L_i, i = 1, 2, along with their correlation coefficient, and obtain the probability of blocking at Q₁. Finally, we present numerical examples and compare between the two models.

This paper investigates the impact of partial information sharing in a three-echelon supply chain. Partial information sharing means that information sharing occurs only between the distributor and the retailer, but not between the distributor and the manufacturer. This paper contributes to the literature by summarizing the circumstances in which information sharing between the retailer and the distributor benefits the manufacturer. In addition, our study points out that such information sharing does not always bring benefits to the manufacturer and that in some cases the information sharing may harm the manufacturer. We explain the reasons why this can happen and give managerial intuition for our results. Using numerical analysis, we illustrate the impact of partial information sharing on the agents in the supply chain with the change of the autoregressive coefficient in the demand process.