Asia-Pacific Journal of Operational Research

Major drawbacks of the traditional data envelopment analysis (DEA) method include selecting optimal weights in a flexible manner, lacking adequate discrimination power for efficient decision-making units, and considering only desirable outputs. By introducing the concept of global efficiency optimization, this study proposed a double frontiers DEA approach with undesirable outputs to generate a common set of weights for evaluating all decision-making units from both the optimistic and pessimistic perspectives. For a unique optimal solution, compromise models for individual efficiency optimization were developed as a secondary goal. Finally, as an illustration, the models were applied to evaluate the energy efficiency of the Chinese regional economy. The results showed that the proposed approach could improve discrimination power and obtain a fair result in a case where both desirable and undesirable outputs exist.

This paper analyzes an infinite horizon, single-product, continuous-time inventory model with supply and demand risks to evaluate the impact of coordinating a production policy between a distributor and a supplier. We consider a wholesale-price contract and a shortage penalty contract as a coordination mechanisms in an inventory replenishment system. The demand process is described by a Brownian motion with drift, and this inventory model can be formulated as an impulse control problem with uncertain replenishment quantities. We show that under some assumptions, there exist optimal ordering and production policies. We also provide some numerical examples to investigate the effect of these coordination mechanisms on the expected costs and the optimal policies. The computational experiments reveal that (i) the production quantity in decentralized case is higher than that in the centralized case, (ii) the centralized approach strategy results in the lower joint total cost as compared with independent decision approaches, and (iii) the shortage penalty contract has a beneficial effect on the total cost to the entire supply chain in decentralized system.

In this paper, we investigate the well-known permutation flow shop (PFS) scheduling problem with a particular objective, the minimization of total idle energy consumption of the machines. The problem considers the energy waste induced by the machine idling, in which the idle energy consumption is evaluated by the multiplication of the idle time and power level of each machine. Since the problem considered is NP-hard, theoretical results are given for several basic cases. For the two-machine case, we prove that the optimal schedule can be found by employing a relaxed Johnson’s algorithm within O($n^2$) time complexity. For the cases with multiple machines (not less than 3), we propose a novel NEH heuristic algorithm to obtain an approximate energy-saving schedule. The heuristic algorithms are validated by comparison with NEH on a typical PFS problem and a case study for tire manufacturing shows an energy consumption reduction of approximately $5\%$ by applying the energy-saving scheduling and the proposed algorithms.

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs), based on their multiple inputs and outputs. The original model is based on the assumption that DMUs operate independently of one another. However, this assumption may not apply in some situations, as in the case we present in this paper, in which DMUs can work together to produce joint outputs. What makes it more interesting is the situation in which this characteristic of sharing outputs among some DMUs differs from one DMU to another; this makes it more challenging to determine independent efficiency scores that cater for this phenomenon. To address this, the current paper presents a methodology for measuring efficiency in situations in which DMUs share outputs with other units. We examine the case of a set of research groups in a Mexican university. For this study, the inputs used are professors belonging to various groups, and outputs are the published journal articles, some of which are produced completely within a group, whereas others arise from collaboration with professors from other research groups. Jointly published articles form a link connecting the groups.

Do [European Journal of Operational Research, 247, 672–675] discussed a tollbooth tandem queue with two heterogeneous servers, where customers arrive the system according to a Poisson process and the service times at the two heterogeneous servers follow an exponential distribution. In this present paper, we aim to consider a tollbooth tandem queue with two-class customers and two heterogeneous dedicated servers, i.e., different types of customers should be served by their dedicated servers. Using matrix analytic method and spectral expansion method, we derive the steady state probabilities, which are then used for the computation of other performance measures and the Laplace–Stieltjes transform (LST) of the sojourn time of an arbitrary customer. Finally, some numerical examples are provided to illustrate the impact of several system parameters to the performance measures.

The randomness of the second-order cone programming problems is mainly reflected in the objective function and the constraints both having random vectors. In this paper, we discuss the statistical properties of estimates of the respective optimal value and optimal solutions when the random vectors are estimated by their sample both in the objective function and the constraints, which are based on perturbation analysis theory of second-order cone programming. As an example we consider the problem of minimizing a sum of norms with weights.

The continuous and strong growth of the civil aviation in the world combined with the severe adverse weather problem have made necessary the collaboration between the different civil aviation agents to improve the management of the capacity-demand imbalances in the airspace. In this paper, we consider a stochastic simulation optimization problem for air route selection strategy based on flight delay cost. The problem takes consideration of airspace capacity and demand uncertainty, three strategies, including collaborative reroute strategy (CRS), full information reroute strategy (FIRS) and hybrid stated route preference strategy (HSR), are employed to mitigate the flight delay cost. To find the best strategy, a discrete event simulation model is built by Arena Software, and the Monte Carlo method combined with the OCBA simulation optimization technique is employed for assessing a common severe convective weather scenario in the Central and Southern China airspace. Simulation results imply that HSR schemes show better system-wide performance than CRS and FIRS, these benefits are supposed to come from the batch allocations method. Although the airline can receive full information in advance, FIRS does not show obvious advantage in reducing the total airborne waiting time than CRS. For the system-wide performance FIRS is better than CRS, but not as good as HSR.

We consider a single-machine scheduling problem such that the due dates are assigned not to the jobs but to the position at which the job is processed. We focus on the case with identical due date intervals. The objective is to minimize the weighted number of early and tardy jobs. First, we show that the problem is strongly NP-hard and has no $\rho$-approximation algorithm for any fixed value $\rho >1$. Then, we investigate polynomially solvable cases. Finally, we show that the preemption version is weakly NP-hard through its equivalence to the problem of minimizing the weighted number of tardy jobs.

In this paper, we consider the decision-making and coordination mechanism of a seasonal product supply chain channel over a period consisting of a low season and a high season, where the price of the final product is strictly regulated, and the raw material is subject to a price cap regulation during the high season. The optimal decisions and profits under decentralized and centralized cases are derived for the low season and the high season. Our research indicates that, under the double price regulation, the manufacturer’s raw material order in the channel usually is not completely fulfilled during the high season and the order during the low season is usually small. Based on the results, we propose a mutual subsidy mechanism, in order to encourage the supplier to supply more raw material during the high season and to encourage the manufacturer to order more raw material during the low season.The theoretical results indicate that the mutual subsidy mechanism can effectively coordinate the seasonal product supply chain channel under the double price regulation, which is further verified by a numerical example. Moreover, the negotiation skills of the supplier and the manufacturer are also considered under the mutual subsidy mechanism, so that the spillover profit can be allocated on a win–win basis for both sides.

In this paper, we consider the online single machine scheduling problem to minimize the maximum weighted completion time of the jobs. For the preemptive problem, we show that the LW (Largest Weight first) rule yields an optimal schedule. For the non-preemptive problem, Li [Li, W (2015). A best possible online algorithm for the parallel-machine scheduling to minimize the maximum weighted completion time. Asia-Pacific Journal of Operational Research, 32(4), 1550030 (10 pages)] presented a lower bound 2, and then provided an online algorithm with a competitive ratio of 3. In this paper, we present two online algorithms with the best-possible competitive ratio of $2$ for the non-preemptive problem.