Asia-Pacific Journal of Operational Research

To deal with efficiency assessment in a two-stage production process, Despotis et al. (2016b) proposed an innovative composition approach to calculate the two-stage efficiencies, whose minimum is defined as overall efficiency of the network production process. The underlying proposed programming method in this approach is nonlinear and requires the application of a very time-consuming bi-section procedure to obtain the stage efficiencies. In this paper, we have, therefore, proposed a simple linear network DEA model to carry out the same stage efficiency assessment, which is computationally efficient, and is also readily applicable to any multi-stage production process. Additionally, unlike the Despotis et al.’s (2016b) method, our proposed alternative is extended to deal with dynamic efficiency assessment. Finally, we have considered both synthetic and real-life data to demonstrate the ready applicability of our proposed models for both static and dynamic efficiency assessments.

Cash-back industry is now witnessing surging development. Prior works on cash-back sites focus mainly on the demand side, while we are also interested in the supply side. We develop a game theoretical model with a manufacturer, an online retailer, cash-back site(s), and heterogeneous consumers. We find that when the cash-back channel cannot attract new consumers, the manufacturer raises the wholesale price and the retailer raises the retail price, which may lead to the cash-back paradox where all consumers face higher prices. Therefore, when there exists a cash-back channel, the manufacturer is always worse off and the retailer is better off when low-type consumers’ product valuation is intermediate, and consumer surplus and social welfare are both lower. When the retailer affiliates with two competing cash-back sites, the manufacturer contributes to the mitigation of double marginalization problem by raising the wholesale price to a lesser extent, which drives the surprising result that when there exists downstream competition, cash-back sites enjoy higher commission rate and under some circumstances, offer lower cash-back rate and enjoy higher profit. We also show that only when the cash-back channel makes the size of the low-type segment double will the manufacturer be better off with this channel.

Risks play an important role in supply chain vulnerability, performance maintenance, and competitive advantage. Therefore, appropriate risk assessment and ranking are among the top priorities of managers and experts in the supply chain. Because there are several quantitative and qualitative risks in the supply chain, multi-criteria decision-making (MCDM) and, in particular, multi attribute decision making (MADM) methods are used to rank them. Depending on the type of supply chain, the difference in criteria and information available, it is possible to use existing MCDM methods to rank supply chain risks or introduce new methods. In these methods, options are compared on the basis of different criteria according to mathematical methods. Each of these methods uses their own approaches and assumptions. Therefore, there will be different solutions to the ranking. In this case, one or more methods may be selected as the best method. But it cannot be sure that the superior method is chosen correctly. In this paper, decision making methods are compared with each other for an appropriate method to be chosen. Case examples from the related literature have been compared with the proposed method (three-dimensional integrated mean method). To evaluate the validity of the proposed method, the results were sent to experts in various industries along with the proposed method. For analysis the accuracy of experts’ opinions, One-Sample Kolmogorov-Smirnov, Frequencies and Explore tests have been used in SPSS software with 95% confidence level. The obtained results show high reliability of the proposed method for determining an appropriate decision-making method in supply chain risk assessment. However, the proposed method is used for ranking of supplier selection in this paper, but it can be used for many decision-making procedures.

We consider the total weighted early work maximization problem on identical machines in parallel such that the weights are identical, or the due date is the same. First, we present an approach to solve the case with a fixed number of machines in pseudo-polynomial time. Then, we develop approximation algorithms for the two cases with identical weights and with a common due date. For the case with identical weights, furthermore, we show that the parallel-machine and a single-machine cases are strongly NP-hard and weakly NP-hard, respectively, even if the due date of each job is equal to the processing time multiplied by a constant.

In this paper, different due-window assignment flow shop scheduling problem with learning effect and resource allocation is considered. Under two machine no-wait flow shop setting, the goal is to determine the due-window starting time, due-window size, optimal resource allocation and the optimal sequence of all jobs. A bicriteria analysis of the problem is provided where the first criterion is to minimize the scheduling cost (including earliness-tardiness penalty, due-window starting time and due-window size of all jobs) and the second criterion is to minimize the resource consumption cost. It is shown that four versions about scheduling cost and resource consumption cost can be solved in polynomial time.

This paper analyzes irreversible investments in technology under asymmetric duopoly. Asset prices are defined by a diffusion with Poisson jumps. Assuming negative externalityfor profit flows, we develop a real options and game theoretic valuation model to evaluate the optimal investment strategies under interaction. Three types of equilibrium, i.e., simultaneous equilibrium, preemptive equilibrium, and sequential equilibrium, are attainable depending on the firms’ competitive advantageand first-mover advantage. The role of a firm, as “leader”, “follower”, or “simultaneous entrant”, is analyzed both exogenously and endogenously. We find that preemptive competition lowers both firms’ profits from the investments in the technology. Numerical examples illustrate the key results.

In this paper, we propose a fast algorithm based on a new algorithm to calculate the upper bound for the knapsack problem with conflict graph (KPCG). The KPCG is an extension of the 0-1 knapsack problem. A pre-defined conflict graph defines the incompatibility properties between pairs of items. The goal is to determine which items should be packed into the knapsack to maximize the total profit on the premise of satisfying the capacity constraint and the incompatibility constraints. The experimental results show that for the graph with density greater than or equal to 0.1, the branch-and-bound algorithm based on the new algorithm is 1.6458 and 1.6352 times faster than the state-of-the-art algorithm on random and correlated instances, respectively, and can achieve speedups of up to 4.6477 for some low density instances. Moreover, the branch-and-bound algorithm based on the new algorithm can optimally solve more instances than the state-of-the-art algorithm in a given time limit.

This paper studies online scheduling on an unbounded drop-line parallel batch machine with delivery times and limited restarts. A drop-line parallel batch machine is a system that can process some jobs simultaneously as a batch. Jobs in a batch have the same starting time and the completion time of a job is equal to its starting time plus its processing time. Limited restarts mean that a running batch containing at least one restarted job cannot be restarted again. The objective is to minimize the time by which all jobs have been delivered. We prove that any online algorithm has a competitive ratio of at least $1+\alpha$, where $\alpha (\alpha \approx 0.5188)$ is the positive solution of the equation $x(x+x^2+x^3+1)=1$. We provide a best possible $(1+\alpha )$-competitive online algorithm for the problem. Furthermore, we study the restricted problem with small delivery times and provide a best possible online algorithm with a competitive ratio of $\frac{3}{2}$.

We consider two variants of a time–cost tradeoff problem with multiple assessments on a chain precedence graph. Furthermore, each job can only be started after a release time, and a penalty cost is incurred when a job is not finished before its due date. The motivation is from the project such that a project owner can control the duration of each job and the support level of each project partner to avoid the penalty cost from the tardy jobs. We describe the penalty costs of the first and the second variants as the total weighted number of tardy jobs and the total weighted tardiness, respectively. These can be avoided by compressing the processing times or advancing the release times, which incurs a compression cost or release cost according to the linear and the piecewise constant functions, respectively. The objective is to minimize the total penalty, compression cost and release cost. In this paper, we propose the procedure based on the reduction to a shortest path problem, and show that the procedure can solve two variants in strongly polynomial time.