Asia-Pacific Journal of Operational Research

In this paper, an overview of different analytic queueing models for traffic on road networks is presented. In the literature, it has been shown that queueing models can be used to adequately model uninterrupted traffic flows. This paper gives a broad review on this literature. Moreover, it is shown that the developed published methodologies (which are mainly single node oriented) can be extended towards queueing networks. First, an extension towards queueing networks with infinite buffer sizes is evaluated. Secondly, the assumption of infinite buffer sizes is dropped leading to queueing networks with finite buffer sizes. The impact of the buffer size when comparing the different queueing network methodologies is studied in detail. The paper ends with an analytical application tool to facilitate the optimal positioning of the counting points on a highway.

The constrained rectangle-packing problem is the problem of packing a subset of rectangles into a larger rectangular container, with the objective of maximizing the layout value. It has many industrial applications such as shipping, wood and glass cutting, etc. Many algorithms have been proposed to solve it, for example, simulated annealing, genetic algorithm and other heuristic algorithms. In this paper a new heuristic algorithm is proposed based on two strategies: the rectangle selecting strategy and the rectangle packing strategy. We have applied the algorithm to 21 smaller, 630 larger and other zero-waste instances. The computational results demonstrate that the integrated performance of the algorithm is rather satisfying and the algorithm developed is fairly efficient for solving the constrained rectangle-packing problem.

In this study, we present the problems of shipping a single product from a single supply origin, through a serial system of storage depots, to a single demand destination by trucks or ships, and aim to minimize the sum of ordering, inventory and transportation costs over an infinite time horizon. We formulate the problem consisting of general-integer (GI), stationary and nested replenishment policies for the multistage inventory/distribution system. This paper presents the near-optimal cost-curve structure analysis for this serially distributed storage depot problem (SDSDP) and show that the ordering quantities for each storage depot always comply with the GI replenishment policy. Moreover, we develop a heuristic approach, which is based on an efficient search procedure to obtain satisfactory results. A comparison of the results obtained from the proposed heuristic method has been made with that from the LINGO^® software on randomly generated problems. The proposed heuristic is the first solution approach in the literature to solve the single link problems based on the cost-curve structure analysis. Furthermore, we provide an important foundation for carrying out investigations on lot sizing and distribution problems with other supply chain configurations.

In many countries, freight trains have to share a rail network with passenger trains. In this paper, we consider a situation where passenger trains must adhere to a strict published schedule, whereas freight train movements can be inserted at any convenient time, without disrupting scheduled passenger trains. We propose an algorithm for the problem of routing and scheduling of a single freight train in a passenger rail network. However, the multiple freight train routing and scheduling problem is shown to be NP-complete, even for simplified instances. Specifically, we show that both routing and scheduling of freight trains are difficult, even when only two freight trains are considered. It is also difficult when freight train movements are restricted to reach their destinations with no idling permitted at intermediate stations. We have developed a Stepwise Dispatching Heuristic for routing and scheduling multiple freight trains in a passenger rail network. Computational results confirm the efficacy of our algorithm for single freight train routing and of the proposed Stepwise Dispatching Heuristic.

Aimed at better modeling stock returns and finding robustly optimal investment decisions, a new portfolio selection model is proposed in this paper. The model differs from existing ones in following ways: multiple market frictions are taken into account simultaneously; the adopted multivariate t-distribution can capture the well-recognized fat tails in the return data by adding only one more parameter relative to the normal; the downside loss risk is controlled by a chance constraint which, including VaR as a special case, is flexible in terms of adjusting the threshold return and the loss probability level; one important advantage about the combination of the latter two innovations is that the derived asset allocation model can be transformed into a second-order cone program or a linear program, which can be easily solved in polynomial time. Empirical results based on some S&P 500 component stocks not only demonstrate the practicality of our new model, but show how different model parameters could affect the optimal portfolio selection. This is very useful in guiding investors to choose a correct model and to find the investment strategy most suitable for their specific purpose.

In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, we propose several theorems which are used to obtain optimal solutions of linear programming problems with fuzzy parameters. Finally some examples are given for illustrating the proposed method of solving linear programming problem with fuzzy parameters.

In the classical economic order quantity (EOQ) inventory model, it was assumed that the retailer must pay for the received items immediately. However, in practice, the supplier not only allows retailer to settle the account after a certain fixed period but also may offer a cash discount to encourage the retailer to pay for his purchases as soon as possible. On the other hand, it is common practice in most inventory systems to hold excess stocks in a rented warehouse whenever the storage capacity of the owned warehouse is insufficient. Therefore, the purpose of this paper is to establish an EOQ model with limited storage capacity, in which the supplier provides cash discount and permissible delay in payments for the retailer. In the model, we develop some useful theorems to characterize the optimal solution and provide a simple method to find the optimal replenishment cycle time and payment time. Finally, several numerical examples are given to illustrate the theoretical results and some managerial insights are also obtained.

In an attempt to examine the effect of dependencies in the arrival process on the steady state queue length process in single server queueing models with exponential service time distribution, four different models for the arrival process, each with marginally distributed exponential inter-arrivals to the queueing system, are considered. Two of these models are based upon the upper and lower bounding joint distribution functions given by the Fréchet bounds for bivariate distributions with specified marginals, the third is based on Downton's bivariate exponential distribution and fourthly the usual M/M/1 model. The aim of the paper is to compare conditions for stability and explore the queueing behavior of the different models.