Asia-Pacific Journal of Operational Research

Nowadays, the capital cost of open-pit mining equipment is very high so any mistake in the selection of quantity, type and capacity of equipment may cause irreparable impact on the net present value of mining project. Mine planning engineers generally use their intuition and experience in decision making even though equipment selection is a complex multi criteria decision problem. Considering the tangible along with intangible factors in the mine equipment selection problem, this paper proposes a new method of multi criteria decision making (MCDM) that makes it possible to select the optimal equipment that satisfies the decision maker. In a real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not deterministic but they are usually fuzzy-imprecise. Our proposed model considers objective, critical, and subjective factors as the three main common factors in equipment selection analysis. The last two factors, critical and subjective factors, are defined by decision maker's judgments for more adoption with real world problems. A case study is presented to illustrate the use of the proposed model and to demonstrate the capability of the model. The result of this study shows significant reduction of time consumption of calculation and good precision compared to customary methods such as Chang's fuzzy AHP method.

Joint economic lot size models for purchasers and vendors are more economical than when considering their inventory problems independently. It requires vendors to have a complete knowledge of their purchaser's demands. In such a situation, the vendor shall manage the supply with comparatively more inventory at its end. The operational cost can further decrease provided supply is in sub-batches and logistics cost does not increase substantially. However, determining the right size for these sub-batches becomes an issue. Sub-batches of varying size may cause planning and control problems, but supply in sub-batches of uniform size will not.

In this paper, joint economic lot size models are presented for the two supply situations, namely staggered supply and uniform supply. Cases are employed that describe the inventory situation of a single vendor supplying an item to a manufacturer that is further processed before it is supplied to the end-user. Using illustrative examples, the comparative advantages of the uniform sub-batch supply over the staggered alternative are investigated. From the analysis, uniform supply models are found to be comparatively more beneficial and robust than the staggered sub-batch supply.

The multi-depot vehicle routing problem (MDVRP) is a variant of the classical vehicle routing problem (VRP). Most research on MDVRP and its extensions often do not consider the effect of the freight's weight on the total cost. In this paper, the multi-depot vehicle routing problem with weight-related cost (MDVRPWRC) is introduced. MDVRPWRC considers the cost incurred by the weight of freight to determine the vehicle routine with the objective of minimizing the total cost. A scatter search (SS) for the MDVRPWRC is proposed. The sweep algorithm and the optimal splitting procedure are used to construct the initial trial solutions. An iterative descending algorithm and an arc choosing method are adopted in the SS to improve and combine the solutions, respectively. Computational experiments on a range of test datasets show that the MDVRPWRC can reduce the total cost more efficiently compared with the general MDVRP. Moreover, the proposed SS can solve the MDVRPWRC effectively.

In scheduling environments with deterioration, a job processed later consumes more time than that same job when processed earlier. The deteriorating job scheduling problems have been widely studied in the last two decades. However, no result of the completion time variance has been reported. In this study, we consider the variance of job completion time minimization problem on a single machine. It is assumed that the job processing time is a simple linear function of its starting time. We show that an optimal schedule is V-shaped with respect to the job deteriorating rates. A heuristic algorithm utilized the V-shaped property is then proposed, and a computational experiment shows that the proposed heuristic algorithm is quite accurate.

In this paper, we propose a new sequential systems of linear equations (SSLE) filter algorithm, which is an infeasible QP-free method. The new algorithm needs to solve a few reduced systems of linear equations with the same nonsingular coefficient matrix, and after finitely many iterations, only two linear systems need to be solved. Furthermore, the nearly active set technique is used to improve the computational effect. Under the linear independence condition, the global convergence is proved. In particular, the rate of convergence is proved to be one-step superlinear without assuming the strict complementarity condition. Numerical results and comparison with other algorithms indicate that the new algorithm is promising.

The re-entrant line networks under a first-buffer-first-served (FBFS) service discipline are considered in this paper. Under uniform topology, if the scaled arrival process and the scaled service process converge to the corresponding fluid limit processes with an exponential rate, we prove that the scaled processes characterizing the re-entrant line converge to the corresponding fluid limit processes with the exponential rate. Here the scaled processes include the queue length process, workload process, busy time process and idle time process.

This paper considers single machine scheduling problems with linear deteriorating jobs under predictive disruption. In this model, the actual processing time of a job is a increasing linear function of its starting time; and machine is subject to an availability constraint. We assume that an optimal schedule can be obtained by using some algorithms if machine is available at all time. Because of the machine disruption, the original schedule may become infeasible or too far from optimal. We want to create the new schedule that takes into account both the original objective function and a measure of deviation from the original schedule. We consider two versions of the problem. In the first one, the objective is weighted sum of total completion time and total tardiness while in the second one, the objective is weighted sum of total completion time and total earliness. We first prove some properties of the optimal schedule then dynamic programming algorithms are proposed, respectively.