Asia-Pacific Journal of Operational Research

In this paper, we extend the work of [Tong, J, J Hu and J Hu (2017). Computing equilibrium prices for a capital asset pricing model with heterogeneous beliefs and margin-requirement constraints. European Journal of Operational Research, 256(1), 24–34] and develop various asynchronous algorithms to calculate the equilibrium asset prices in a heterogeneous capital asset pricing model. These asynchronous algorithms are based on different asynchronous updating schemes such as delayed updating, cyclic updating, fixed-length updating and random updating. In addition to potential benefits of improving computational efficiency, these asynchronous updating schemes also reflect several scenarios in financial markets in which investors may receive asset pricing information with various degrees of delays and their preferences on how and when to rebalance their portfolios may also be different. The proofs for the convergence of these algorithms are given. Numerical experiments are also provided to compare these algorithms and they show that these asynchronous algorithms work quite well.

In this paper, we propose a new online scheduling model with linear lookahead intervals, which has the character that at any time $t$, one can foresee the jobs that will coming in the time interval $(t,\lambda t+\beta ]$ in which $\lambda >1,\beta >0$. In this new lookahead model, the length of the lookahead intervals are variable as the time going on and the number of jobs increasing, and has the tend of steady growth. In this paper, we consider online scheduling of unit length jobs on $m$ identical parallel-batch machines under this new lookahead model to minimize makespan. The batch capacity is unbounded, that is $b=\infty$. We present an optimal online algorithm for $\lambda >1,\beta \ge \frac{\lambda -1}{{\lambda }^m-1}$, and provide a best possible online algorithm of competitive ratio $1+{\alpha }_m$ for $\lambda >1,0<\beta <\frac{\lambda -1}{{\lambda }^m-1}$, where ${\alpha }_m$ is the positive root of ${(1+{\alpha }_m)}^{m+1}{\lambda }^m+(1+\beta -\lambda ){\sum }_{i=1}^m{(1+{\alpha }_m)}^i{\lambda }^{i-1}=2+{\alpha }_m$.

In this paper, we propose a new coordination mechanism of contract farming supply chain (hereinafter refer to as “company + farmer + option premium subsidy” or “the new/proposed mechanism”). In the new mechanism, the farmer’s interests are fully guarded by the protection price. Options are adopted by the company to hedge the downside risk and the government provides the company with option premium subsidies aiming to avoid default risk. The superiorities of the proposed mechanism compared with the traditional mechanism are illustrated in the theory and practical application. The results demonstrate that the proposed innovative subsidy mechanism is superior to the traditional and direct subsidy mechanism in regard of enhancing profits and increasing the order quantity, further to reduce the corporate default rate of the company. We also provide suggestions to determine the order quantity for the company and put forward a policy proposal to design the subsidy rate for the government. We advocate that the contract price and the strike price should be decided by the government rather than the company. We deem that the contract price should be the expected primary product price. The coordination of the proposed mechanism is reflected in making the entire supply chain more stable by balancing the interests of both the farmer and the company. By illustrating the predictions of our model under the case of a sudden change in the underlying parameters, we find that the proposed mechanism is robust even when higher volatility happens.

This paper addresses the complex job shop scheduling problem with the consideration of non-identical job sizes. By simultaneously considering practical constraints of sequence dependent setup times, incompatible job families and job dependent batch processing time, we formulate this problem into a simulation optimization problem based on the disjunctive graph representation. In order to find scheduling policies that minimise the expectation of mean weighted tardiness, we propose a genetic programming based hyper heuristic to generate efficient dispatching rules. And then, based on the nested partition framework together with the optimal computing budget allocation technique, a hybrid rule selection algorithm is proposed for searching machine group specified rule combinations. Numerical results show that the proposed algorithms outperform benchmark algorithms in both solution quality and robustness.

In this paper, loss-averse consumer behavior during purchase decision-making process in the dual-channel supply chain is modeled. Loss-averse consumers prefer avoiding losses to gain utility with respect to their reference point while purchasing the product. Two product categories are classified: (1) basic product and (2) luxury goods which have lower and higher reference utility to consumers, respectively. The research objective is to determine the optimal price strategy in dual-channel supply chains and discuss the decision behind loss-averse consumers. To model consumers’ valuation of a product, prospect theory is adopted to calculate the demands of each channel. Then, the optimal pricing strategy and the corresponding profits are found out in a Stackelberg game manner. The results encourage manufacturers of basic goods to engage in dual-channel strategy. Effect of “double marginalization” is reduced if consumers are loss-averse in the dual-channel supply chain. Furthermore, the direct channel online contributes larger demand to the manufacturer. However, manufacturers of luxury goods are not suggested for dual-channel strategy because the demand for direct channel online is negligible and the demand for the retail channel remains unchanged. Nevertheless, retailers cannot obtain benefit from dual-channel and as a result, the profit of basic goods retailers will be reduced.

This paper investigates the ability of a combined buy-back (BB) and revenue sharing (RS) contract to improve the efficiency of a supply chain involving a risk-neutral supplier and a risk-averse retailer facing stochastic demand. We show that the combined contract can coordinate the supply chain under mild conditions. Further, the effects of risk aversion and contract parameters on the agents’ decision-making are analyzed when the retailer’s risk aversion is modeled by the conditional value-at-risk (CVaR) criterion. In contrast to individual BB and RS contracts, the combined contract is able to mitigate the effect of risk-aversion and allow the supplier to obtain higher expected profit. Moreover, situations exist where the combined contract can coordinate the supply chain when neither the BB nor the RS contract can coordinate it. Numerical experiments conducted further confirm the analytical results derived.

This paper investigates the coordination problem of a supply chain (SC) composed of a manufacturer and a retailer both exhibiting corporate social responsibility (CSR) under generic random demand. Under a centralized decision, the unimodality of the expected profit is proven, and the expected profit of the SC is shown to be larger than that of the SC without CSR. Under a decentralized decision dominated by the manufacturer, the manufacturer determines the wholesale price and its CSR investment, and then the retailer decides the order quantity and its CSR investment. After showing that the revenue-sharing (RS) contract is not able to coordinate the SC, a modified RS (MRS) contract is proposed to coordinate the SC. At last, the numerical examples in which random demands follow normal distribution and uniform distribution are used to illustrate the validity of the theoretical analysis and the coordination effectiveness of the MRS contract.

In this paper, a revised directional triangle-area curve representation method (DTAR) is proposed to address the problem of eyebrow semantic shape characterization via curve representation. First, 11 or 12 DTAR values are selected to describe eyebrows via considering the eyebrow corner information roughly, and then the corresponding DTAR curves are acquired via the cubic spline interpolation based on these selected points. Second, a descriptor of the landmarks is developed to represent selected reference eyebrows, and the corresponding DTAR curves are obtained for the selected reference eyebrows. Lastly, a similarity notion based on AFS is introduced via measuring the membership degrees of each eyebrow shape similar to the given reference shapes, and then one can describe each eyebrow shape by using two given reference eyebrow shapes via computing the membership degrees representing the relative similarities. To illustrate the effectiveness of the proposed approach, we use the AR and BJUT databases for experiments to demonstrate the consistency comparison with human perceptions. The experimental results show that the extracted semantic notions of eyebrow shapes obtained by the proposed approach are much better than those by only utilizing 11 DTAR values or 12 DTAR values directly in terms of the consistency with human perceptions.