Asia-Pacific Journal of Operational Research

A supply chain with a supplier and a risk-averse retailer is considered in the paper under trade credit contract. The retailer as newsvendor faces a non-negative random demand and the supplier provides the trade credit for the risk-averse retailer with budget constraints. Different from the existing research, in a conditional value-at-risk (CVaR) framework, the optimal ordering quantity and wholesale price are obtained. Analytical results are obtained for the newsvendor retailer’s optimal ordering quantity and supplier’s optimal wholesale price under CVaR measure. Sensitivity analysis is also yielded. It is found that the optimal ordering quantity decreases as the degree of risk aversion increases. Furthermore, we analyze the effect of the initial budget of retailer and the wholesale price on the order quantity decision. This paper also finds that the trade credit contract could create value for a risk-averse supply chain with budget constraints. Finally, to compare with the existing results the theoretical analysis and numerical examples are illustrated.

In this paper, a discrete-time risk model is considered. We assume that the premium received in each time interval is a positive real-valued random variable, and the sequence of premiums is a Markov chain. In any time interval the probability of a claim occurrence is related to the premium received in the corresponding period. We discuss control strategies for dividends paid periodically to the shareholders under two cases: absence and presence of ceiling restriction for dividend rates. We provide algorithms and some properties for the optimal control strategies by transforming the value function.

In this paper, we consider a coordinated scheduling problem on a single batching machine with transportation and deterioration. The jobs are transported by a vehicle to the single batching machine for further processing. The processing time of a job is a step increasing function of its exposure time which is equivalent to the time interval from the beginning of the transportation to the starting of the job on the machine. The objective is to find Pareto-optimal schedules with two performance criteria (total completion time and the number of batches) to balance the inventory level and the production costs. We prove that the general problem is strongly NP-hard. We further develop polynomial-time algorithms for two special cases with a fixed job sequence and without exposure time limit, respectively. For the general problem, we develop a heuristic algorithm and a branch and bound algorithm. Computational experiments show that the heuristic algorithms perform well on randomly generated problem instances, and the branch and bound algorithm can obtain Pareto-optimal solutions for the small-scaled instances.

In many municipal waste collection systems, it is necessary to extend the planning horizon to more than one working day. This can happen, for example, in the collection of some recyclable articles. In this case, some of the streets must be served every day but others need only once every two days service. In this paper, we focus on planning the routing of the collection vehicles while extending the planning horizon to two working days. We propose a simple, but effective, heuristic approach and we carry out extensive computational experiments to evaluate its performance. We also apply our method to solve a real-case application related to the collection of recyclable wastes in a small Italian city.

Sustainability factors play critical role for long-term achievement of a supply chain management and purchasing process becomes more complicated with social and environmental pressures. Managing supplier selection process is a necessary step for companies seeking to manage their corporate legitimacy and reputations. Data envelopment analysis (DEA) has been widely used for supplier selection problems. In this paper, we propose a new super-efficiency method for evaluating sustainability of suppliers in the presence of dual-role factors and volume discounts. We show that enhanced Russell model (ERM) fails to present a complete ranking of suppliers. Our new model presents a complete ranking and also preserves properties of the ERM. Capabilities of our proposed method are shown using a couple of examples. A case study is presented to illustrate our proposed approach. The proposed method is used to select the best sustainable suppliers.

This paper presents a general software reliability growth model (SRGM) based on non-homogeneous Poisson process (NHPP) and optimal software release policy with cost and reliability criteria. The main motive of this study is to develop a software release time decision model considering maintenance cost and warranty cost under fuzzy environment. In previous studies, maintenance cost has been defined either in terms of warranty cost or fault debugging cost. In reality, maintenance cost includes the cost of free patches, updates, technical support and future enhancement. Also, it is possible that maintenance process causes the removal of software faults in the operational phase including the faults which occur outside the warranty period or warranty definition. In other words, warranty action may be included the maintenance action, but not the converse. Considering this fact, maintenance cost and warranty cost are defined separately in the proposed study. Initially, an SRGM has been proposed with the revised concept of imperfect debugging phenomenon considering fault removal efficiency (FRE). Furthermore, the effect of changes in various environmental factors on models parameters has been taken into account. Numerical examples based on real software failure data sets have been given to analyze the performance of the proposed models.

This paper presents two semi-definite programming (SDP) based methods to solve the Key Player Problem (KPP). The KPP is to identify a set of $k$ nodes (i.e., key players) from a social network of size $n$ such that the number of nodes connected to these $k$ nodes is maximized. The KPP has applications in social diffusion and products adoption as it helps maximizing information diffusion and impact. We first formulate the KPP as an integer program (IP) and then convert it into an SDP formulation, which can be solved efficiently and produce a set of high quality candidate solutions. We develop an IP-based algorithm and a stochastic search (greedy) algorithm to find the final solution for the KPP. We compare our algorithms with existing methods in small and large networks with different network structures, including random graph, scale-free network, and community-based scale-free network (CSN). Computational results show that our algorithms are more efficient in solving the KPP in all networks. In addition, we examine how the network structure influences the nodes coverage. It is found that CSNs allow the highest nodes coverage due to their community and scale-free structure.

Public involvement in transportation planning and decision-making process is a key component for ensuring that decisions are made with consideration of public needs and preferences. In this paper, a weighted Euclidean distance based TOPSIS method (WEDTOPSIS) is developed for modeling such a public decision-making process. The Weber–Fechner psycho-physical law is adopted for behavioral modeling of human judgments. Distances to the positive-ideal and negative-ideal solutions of TOPSIS are converted to value measurement models using the Weber–Fechner law. The proposed method is applied on a case where public approval of two different types of public bus operation systems considering six criteria is sought. A numerical illustration is also provided to demonstrate the applicability of the approach. The method provides plausible results in terms of preferences, and shows a high agreement with the ordinary TOPSIS in terms of rankings. Another example showing disagreement on ranking is further analyzed to outline the discrepancies between the TOPSIS and WEDTOPSIS and to indicate the proposed model’s consistency with the behavioral theory. The results are also compared with the results of the additive multi-attribute value (MAVT) method for assessing the performance of the model. Based on the findings, using the proposed method as a decision support tool can be useful, particularly where public input is needed.