Asia-Pacific Journal of Operational Research

Thanks to the convenient and hassle-free manner of leasing service, the car leasing industry has flourished in recent years. To leverage commercial opportunities, many well-known car manufacturers have chosen to enter the leasing market rather than focus solely on the selling market. An interesting phenomenon is that some manufacturers establish their own leasing departments to gain revenue from the leasing market (referred to as centralization), while other manufacturers conduct their leasing businesses through an autonomous leasing company (referred to as decentralization). In this paper, we propose a game-theoretic model to explore whether a car manufacturer should manage a leasing department in a centralized channel or have the leasing business done by an autonomous company in a decentralized channel. We demonstrate the optimal pricing policy for the manufacturer, dealer, and leasing department/company under both centralization and decentralization. In the main model, we assume that the consumer experience value preference follows a uniform distribution, and leasing brings consumers a better experience value than purchasing, and we normalize the marginal production cost of the product to zero. Our analysis reveals that neither centralization nor decentralization is always optimal, and the related boundary is analyzed. An interesting finding is that as the consumers’ leasing experience value rises, the dealer obtains higher profit, even though it serves fewer consumers in the decentralized channel. In the extension, we relax our assumptions to examine the impact of a normal distribution of consumer experience value preferences, a large purchasing experience value, and a non-negligible production cost, and we find that most of the main model results still hold qualitatively.

In this paper, we deal with the least distance problem (LDP) in Data Envelopment Analysis (DEA), which is to find a closest efficient target over the whole efficient frontier. To this end, we define the efficient frontier by a linear complementarity system and propose a mixed integer programming (MIP) approach to solve the LDP. Our proposed MIP approach: (1) can solve the LDP based on ${\ell }_p$-norm ($p\ge 1$) by using a state-of-the-art solver and obtain the closest efficient target over the whole efficient frontier instead of a subset of it; (2) can be applied for computing the least distance DEA models satisfying the monotonicity; (3) is more user-friendly, because it allows a decision maker to improve the efficiency of a decision making unit (DMU) by setting the affordable input/output level under his/her circumstance. Thus, the efficient target provided by our approach may be more appropriate from the perspective of the decision makers of DMUs.

This paper aims to develop a DEA-based framework to evaluate the efficiency of the supply chain based on the seller–buyer structure and with respect to win–win strategy. This is a bi-stage model employing the CCR model in the forms of input-oriented and output-oriented considering the intermediate measures for two different conditions under a centralized point of view. The obtained results from the extension of this model to supply chain network lead to introduce “efficient path” concept being a path including different components of the supply chain that are efficient in terms of DEA. Other kinds of proper information provided by the proposed model can help the managers and decision-makers of the supply chain field in supplier selection procedure and making efficient portfolios and collaborations across the supply chain network.

The lexicographic and sum ranking systems are two widely-used instruments to measure Olympics achievements. However, the lexicographic ranking system is typically criticized as overvaluing gold medals, while the sum ranking system is particularly criticized as devaluing gold medals and incapable of discriminating National Olympic Committees (NOCs) with identical total number of medals. Meanwhile, there are controversial scoring schemes to assign points to gold, silver and bronze medals. In addition, measuring Olympics achievements in either the Winter or the Summer Olympic Games is unfair to the NOCs who perform significantly different at both Olympics. These issues are addressed in this study by developing a sophisticated mathematical transformation without the exact weights determination to aggregate the lexicographic ranking system in an interval metric. An interval decision matrix is therefore formulated under the simultaneous consideration of the Winter and the Summer Olympics. A Stochastic multicriteria acceptability analysis method is finally utilized to provide a robust measurement with sufficient discriminating power. Spearman’s rank correlation coefficients are calculated to indicate the merits of our method. The proposed method and relative analysis are demonstrated using the date drawn from the Sochi 2014 Winter Olympics and the Rio 2016 Summer Olympics.

After-sales service on a product could be a lucrative source of profits however it is notoriously difficult to manage due to uncertain demand from consumers, and its complex organization such as either to provide warranty or maintenance contract or repair service. This paper focuses on a problem where the manufacturer sells a repairable product while an agent provides after-sales service under uncertain demand. Due to demand volatility, either manufacturer or agent or both the players are risk-averse toward their decisions. The goal of this paper is to design various after-sales service strategies where the optimal price of repair or maintenance contract for the agent whereas the optimal product price for the manufacturer are explicitly determined such that the utility of both players can be maximized. Moreover, the impacts of risk-preference and demand uncertainty are investigated on both players’ price decisions, demand level, and their utility function. Also, the impacts of the agent’s warranty service on product price as well as repair and maintenance contract price are investigated. The interaction between the manufacturer and agent is performed under non-cooperative game where the manufacturer is the leader, and the agent is the follower. The analytical results show that when both players are risk-averse, they should adjust their prices with demand uncertainty and risk-tolerance level in order to maintain the demand stability and get more utility. Further, we consider some special cases where the risk-tolerance level of the manufacturer has an impact on the risk-neutral agent’s decisions whereas the risk-tolerance level of an agent does not have any impact on the risk-neutral manufacturer’s decisions. The analysis also shows that there is a significant impact of the agent’s free-replacement renewing warranty (FRRW) on repair and maintenance price decision if both players are risk-averse. A numerical example is presented to further illustrate the results and related issues.

Multi-attribute reverse auction has many advantages for the buyer with the multi-dimensional attribute requirements. However, it is hard to design an optimal auction mechanism for the scenario in practice. Therefore, this paper presents a method transforming multi-attribute auction into single-attribute auction by bidding in deposit. The analysis indicates that our method can reduce not only the transaction risk caused by the supplier’s bid abandonment but also the operating cost and complexity of the multi-attribute auction. Besides, our method meets the incentive compatibility and participation constraint conditions by promising that the highest bidding supplier is the winner in the Auction and can obtain higher expected profits than traditional auctions for the buyer.

Admission and service rate control problems in queueing systems have been studied in the literature. For an exponential service time distribution, the optimality of the threshold-type policy has been proved. However, in production systems, the production time follows a general distribution, not an exponential one. In this paper, control of the service speed according to the number of customers is considered. The analytical results of an M/G/1 queue with arrival and service rates that depend on the number of customers in the system, which is called an Mn/Gn/1 queue, are used to compute the performance measure of service rate control. In particular, for the case in which the arrival rates are the same among queue-length intervals, a computation method for deriving stationary distributions is developed. Constant, uniform, exponential, and Bernoulli distributions on the service time are considered via numerical experiments. The results show that the optimal threshold depends on the type of distribution, even if the mean value of the service time is the same. In addition, when the reward rate is small, a case in which a non-threshold-type service rate control policy outperforms all threshold-type policies is identified.

This paper studies two types of reverse 1-center problems under uniform linear cost function where edge lengths are allowed to reduce. In the first type, the aim is that the objective value is bounded by a prescribed fixed value $p$ at minimum cost. The aim of the other is to improve the objective value as much as possible within a given budget. An algorithm based on dynamic programming is proposed to solve the first problem in linear time. Then, this algorithm is applied as a subroutine to design an algorithm to solve the second type of the problem in $O(nlog(nK))$ time in which $K$ is a fixed number dependent on the problem parameters. Under the similarity assumption, this algorithm has a better complexity than the Nguyen algorithm (2013) with quadratic-time complexity. Some numerical experiments are conducted to validate this fact in practice.