Asia-Pacific Journal of Operational Research

Sample day selection method plays an important role in managerial decisions which require analyses that are prohibitively expensive to apply to a large number of days. We develop a general sample day selection model that selects sample days based on the cumulative distributions of airspace conditions and characteristics (C&C) by considering factors such as sampling targets, degree of diversity and coverage of the selected days. We introduce indicators that capture the airspace C&C of the North Atlantic region (NAT) and apply the model to select sample days for the NAT. The results show that the model outperforms the methods used by the U.S. Federal Aviation Administration.

We investigate vulnerable supply chain coordination with an option contract in the presence of supply chain disruption risk caused by external and internal disturbances. The supply chain consists of a single risk-neutral supplier and a risk-averse retailer. We characterize the retailer’s order quantity decision under the Conditional Value-at-Risk (CVaR) criterion and the supplier’s production decision. The results show that facing disruption risk and risk-aversion, both the retailer and the supplier would be more prudent to order and produce less than the risk-neutral scenario, inducing damage to the supply chain performance. The number of options purchased is decreasing in disruption risk and the risk-aversion of the retailer. The supplier will increase production as the disruption risk decreases or the shortage penalty increases. When the supplier does not know the risk-aversion of the retailer, the former will produce more and bear a higher overstock risk. We also investigate conditions that facilitate vulnerable supply chain coordination and find that the existence of risk-aversion and disruption risk restrict the option price and exercise price to lower price levels. Finally, we compare the option contract with wholesale price contract from the supplier’s and retailer’s perspectives through a numerical study.

This study considers a shipping supply chain consisting of an ocean carrier (OC) and two competing feeder carriers (FCs) in which the OC and FCs can canvass for cargos. We propose two contrastive structures: structure O (the OC canvasses for cargos) and structure F (the FCs canvass for cargo) to study the carriers’ cargo canvassing strategies. We employ a mean-risk objective function to reflect the cargo-canvassing carriers’ different risk attitudes. We find that the cargo canvassing carrier’s risk attitude has an opposite influence on the equilibrium freight rate of the OC and FCs, which is referred to as vertical reverse effect. We identify the horizontal consistent effect, which implies that an FC’s equilibrium freight rate increases in its and the rival’s risk sensitivity coefficient. In a market with high demand volatility, the OC and FCs should set high freight rates when they are risk-seeking, while set low freight rates when they are risk-averse. In addition, we highlight that FCs canvass for cargos can lead to an incentive alignment of the OC and FCs on cargo canvassing. Our analytical results provide the operational strategies for risk-sensitive OCs and FCs to canvass for cargos.

Traditional fresh produce logistics involve long transportation times and high loss rates. Cross-docking systems can significantly improve these issues but bring the challenge of mid-term planning in which vehicles have to be synchronized promptly at the selected cross-docks. This study proposes a new location and routing model for the cross-docking of fresh produce. Exact solutions of small-scale cases show that the model can greatly reduce the transportation time and loss rate. For real-world, large-scale cases, a greedy and biogeography-based optimization algorithm (G-BBO) is devised and tested to obtain near-optimal solutions within a reasonable time.

In this paper, we consider the quantitative stability analysis and empirical approximation of risk-averse models induced by two-stage stochastic programs with full random recourse. We first establish the quantitative stability under the mean-coherent risk measure framework and the expected utility framework, respectively, under suitable probability metrics. Based on the obtained quantitative stability results, we then investigate the empirical approximation to these models, and estimate the rates of convergence for the optimal value and optimal solution set with the aid of Ky Fan distance.

We investigate a project scheduling problem in which cash flows are periodically variable, and we aim to maximize the net present value (NPV). For each activity, a set of cash flows is considered where each one pertains to a particular period. This setting is compatible with inflation rates that may well occur in some countries with unstable economic situations where the occurrence times of inflation are sometimes known in advance. In this case, the project can be scheduled more suitably to abate probable pitfalls. In this paper, we investigate the problem in two settings: deterministic and stochastic cash flows. In the deterministic case, in each period we consider the expected value of cash flows of each activity as a constant and develop an integer linear programming model in conjunction with a branch-and-bound algorithm to solve the problem optimally. Moreover, we develop a multi-stage stochastic programming (MSSP) model to formulate the stochastic version of the problem. Using a set of randomly generated test instances and extensive computational results, we analyze the performance of our developed solution approaches. In addition, we compare the deterministic and stochastic models and analyze the sensitivity of the most important parameters.

The purpose of this study is to analytically coordinate joint pricing and periodic review ordering choices for a supply chain (SC) facing stochastic price-dependent demand. In this paper, first, the optimal inventory and pricing strategies made by SC members are obtained under decentralized and centralized models. Thereafter, employing a trade credit contract, it proposes to provide a win–win coordination for the investigated SC. Sensitivity analyses are provided both analytically and numerically which indicate the significance of trade credit contracts in improving both customer service level and customer satisfaction. The results indicate that using trade credit contracts, the coordination of joint pricing and periodic review ordering choices is achievable. Moreover, the proposed trade credit contract has a unique feature in achieving more economic benefit compared to the centralized model under some circumstances. In addition, sensitivity analyses reveal that the coordination of integrated periodic review ordering and pricing choices is beneficial, especially for items with high price sensitivity and high demand uncertainty. Our findings also show the applicability of the trade credit contract for an SC facing highly uncertain demand.

This paper deals with the inverse eccentric vertex location problem on extended star networks in which the aim is to modify the edge lengths at the minimum overall cost within certain modification bounds so that a predetermined vertex becomes the farthest vertex from a given fixed vertex under the new edge lengths. Novel combinatorial algorithms with near-linear time complexities are developed for obtaining the optimal solution of the problem under different cost norms.

Inspired by the universal application of cause marketing (CM) campaign, this paper develops a game theoretical model to investigate cause-related product pricing problem. By considering the customer’s prosociality level and reference behavior, where the overall utility of regular product as a reference point, we separately study the cause-related product pricing strategies for competitor with and without response. Analytical results show that consumers’ purchase behaviors (i.e., differential levels of prosociality and reference recognition) significantly influence the cause-related product pricing decisions. When faced with consumers who have different purchase behaviors, the cause-related product pricing strategies should be adjusted accordingly.

Partner selection problem plays an important role in the coordinated operation of logistics service supply chain. In order to select the optimal partner enterprises of logistics service supply chain, a two-stage partner selection method is proposed from the perspective of supply and demand matching. In the first stage, the logistics project is divided into multiple subtasks. The demand criteria system of subtasks and the supply capabilities criteria system of candidate enterprises are established. The concept of subtask-oriented supply–demand matching degree is proposed. According to the supply–demand matching degree threshold, the qualified enterprises of each subtask are selected. In the second stage, a partner selection model is established. The partner selection model is transformed into a matching problem of weighted bipartite graph between subtasks and qualified enterprises, and the optimal matching combinations are determined by using Kuhn–Munkras algorithm. The feasibility of the two-stage partner selection method is verified by a computational experiment. The sensitivity analysis in the computational experiment provides help for decision makers to set parameters more reasonably.