Authors:Jianxin Chen, Yong-Wu Zhou Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:29Z DOI: 10.1142/S0217595917400127

Authors:Hua Gong, Ermei Zhang, Fang Liu Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:29Z DOI: 10.1142/S0217595917400140

Authors:Mohammad Izadikhah, Reza Farzipoor Saen, Kourosh Ahmadi Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:29Z DOI: 10.1142/S0217595917400164

Authors:Chefi Triki Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:26Z DOI: 10.1142/S0217595917400152

Authors:Subhashis Chatterjee, Ankur Shukla Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:25Z DOI: 10.1142/S0217595917400176

Authors:Xixi Yang, Jiyang Tan, Hanjun Zhang, Ziqiang Li Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 03, June 2017. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-22T07:42:24Z DOI: 10.1142/S0217595917400139

Authors:Amal Abdel Razzac, Linda Salahaldin, Salah Eddine Elayoubi, Yezekael Hayel, Tijani Chahed Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. This work presents a strategic investment framework for mobile TV infrastructure. We address the question of whether an operator should enter the mobile TV market and, if yes, how and when to do so. We perform a capacity and QoS analysis that shows that Mobile TV is not a simple added-on service but requires new planning strategies that need heavy investments. We consider two possible settings: a competitive mobile TV network where a digital video broadcasting (DVB) operator and a cellular network operator build their independent networks and a cooperative setting where the TV network is mainly relying on a DVB infrastructure whose coverage can be complemented by a cellular network. Two main issues alter the investment decision of the stakeholders, namely, the market uncertainty and the competition or the willingness of cooperation between the main technological players. We define a game theoretical real options framework and propose a novel bi-level dynamic programming algorithm that solves the underlying profit maximization problem. Our numerical results illustrate the optimal investment decisions of both actors and the impact of the system parameters as well as the degree of uncertainty on the investment dates. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-07-17T10:54:26Z DOI: 10.1142/S0217595917500142

Authors:Moawia Alghalith, Xu Guo, Cuizhen Niu, Wing-Keung Wong Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we analyze the impacts of joint energy and output prices uncertainties on the input demands in a mean–variance framework. We find that an increase in expected output price will surely cause the risk-averse firm to increase the input demand, while an increase in expected energy price will surely cause the risk-averse firm to decrease the demand for energy, but increase the demand for the non-risky inputs. Furthermore, we investigate the two cases with only uncertain energy price and only uncertain output price. In the case with only uncertain energy price, we find that the uncertain energy price has no impact on the demands for the non-risky inputs. We also show that the concepts of elasticity and decreasing absolute risk aversion (DARA) play an important role in the comparative statics analysis. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-07-11T06:36:12Z DOI: 10.1142/S021759591750018X

Authors:Changkyu Park Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. Drop-shipping is a commonly adopted online-order fulfillment strategy in the Internet age. In this practice, online retailers leverage the fulfillment capabilities of suppliers to fulfill orders. On the other hand, purchase dependence is a frequent phenomenon and is characterized by the purchase of certain items together due to their unknown interior associations. Although this concept has been significantly examined in the marketing field (e.g., market basket analysis), it has largely remained unaddressed in operations management. This paper develops an [math] model to address an environment in which unmet demand orders are partially lost and partially backordered when purchase dependence exists. The partial backorders are fulfilled by a drop-shipping option. Through computational analyses, this paper demonstrates the effect of both drop-shipping on a partial backordering and purchase dependence. The results show that more profit can be realized by utilizing a drop-shipping option under purchase dependence. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-07-11T06:36:11Z DOI: 10.1142/S0217595917500166

Authors:Byung-Gyoo Kim, Byung-Cheon Choi, Myoung-Ju Park Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. We consider a two-agent scheduling problem in a two-machine flow shop environment where each agent is responsible for his own set of jobs and wishes to minimize the makespan. The objective is to minimize one agent’s makespan, subject to the other’s objective of not exceeding a given threshold. It is known that the problem is NP-hard. Thus, we consider special cases such that the processing times of each agent have a special structure, and analyze their computational complexity. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-07-03T08:30:44Z DOI: 10.1142/S0217595917500178

Authors:Ata Allah Taleizadeh Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. This study proposes a model for a multi-objective, multi-buyer, multi-vendor, multi-product and multi-constraint supply chain. The buyers’ demand rates are stochastic variables with known probability distribution functions. The classical ([math]) inventory control system is used to manage the inventories of all buyers where the lead times are production rate dependent. Shortage is permitted and is partially backordered where the partial back ordering rate is forecasted. The model is considered as a multi-objective integer nonlinear programming problem including cost, service level and lead time objectives and using a novel hybrid method, a hybrid of Meta Goal Programming (MGP) and Firefly Algorithm (FA) are solved. Numerical examples are given to illustrate the proposed method in the study. The results of the study are compared to other hybrid methods of Meta Goal Programming with other evolutionary algorithms such as Bees Algorithms (BA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-07-03T08:30:44Z DOI: 10.1142/S021759591750021X

Authors:Zhusong Liu, Zhenyou Wang, Yuan-Yuan Lu Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. This paper considers the single machine scheduling with learning effect, resource allocation and deteriorating maintenance activity simultaneously. For the convex resource allocation consumption function, we provide a bicriteria analysis where the first (schedule) criterion is to minimize the total weighted sum of makespan, total completion time and total absolute differences in completion times, and the second (resource) criterion is to minimize the total weighted resource consumption. Our aim is to find the optimal resource allocations and job sequence that minimize the three different models of considering the two criterion. We show that these three models are polynomially solvable respectively. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:25Z DOI: 10.1142/S0217595917500117

Authors:Li-Ching Ma Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. The case-based distance (CBD) methods for screening are helpful for assisting decision makers in filtering out alternatives that are unlikely to be chosen. However, most of these methods based on selected cases and distance measurements from a chosen target point can only solve screening problems with positive criteria weights. This study proposes a two-phase approach based on mixed integer programming to integrate the concept of Data Envelopment Analysis-Discriminant Analysis (DEA-DA) and the extended case-based distance (ECBD) method for screening problems involving uncertain signs of criteria weights and different target points. The results show that the proposed approach can reduce the misclassification rate and address multiple solution problems. In addition, because the proposed approach can solve problems involving negative weights directly, the influences of different target points can be reduced. Therefore, it is helpful for decision makers to conduct scenario analysis based on different chosen target points. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:24Z DOI: 10.1142/S0217595917500130

Authors:Shi-Sheng Li, De-Liang Qian, Ren-Xia Chen Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. We consider the problem of scheduling [math] jobs with rejection on a set of [math] machines in a proportionate flow shop system where the job processing times are machine-independent. The goal is to find a schedule to minimize the scheduling cost of all accepted jobs plus the total penalty of all rejected jobs. Two variations of the scheduling cost are considered. The first is the maximum tardiness and the second is the total weighted completion time. For the first problem, we first show that it is [math]-hard, then we construct a pseudo-polynomial time algorithm to solve it and an [math] time for the case where the jobs have the same processing time. For the second problem, we first show that it is [math]-hard, then we design [math] time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:23Z DOI: 10.1142/S0217595917500154

Authors:Andrea Raiconi, Julia Pahl, Monica Gentili, Stefan Voß, Raffaele Cerulli Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this work, we face a variant of the capacitated lot sizing problem. This is a classical problem addressing the issue of aggregating lot sizes for a finite number of discrete periodic demands that need to be satisfied, thus setting up production resources and eventually creating inventories, while minimizing the overall cost. In the proposed variant we take into account lifetime constraints, which model products with maximum fixed shelflives due to several possible reasons, including regulations or technical obsolescence. We propose four formulations, derived from the literature on the classical version of the problem and adapted to the proposed variant. An extensive experimental phase on two datasets from the literature is used to test and compare the performance of the proposed formulations. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:23Z DOI: 10.1142/S0217595917500191

Authors:Jiawen Hu, Zuhua Jiang, Hong Wang Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. This paper considers the situation where the production planning sector has decision making priority over the maintenance sector. The production rate of each production period (PP) varies with different lot size of each PP. An extended imperfect preventive maintenance (PM) model is proposed for a system running with time-varying production rate. The Stackelberg game theory model is adopted to handle the interactive effect between production plan and PM schedule; meanwhile, it embodies the decision priority of the production planning sector. In the Stackelberg game theory model, the production planning sector is assigned to be the leader with the decision variable of production rate of each PP, and the maintenance sector to be the follower with the decision variable of PM schedule. Firstly, the optimal lot size of each item for each PP is obtained for the production planning sector. Then, the optimal production rate of each PP and PM sequences are obtained simultaneously by regarding the total lot size of each PP as the objective. The availability loss caused by maintenance operations and effect of production rate on hazard rate function of system have been considered. An example is studied to illustrate the effectiveness of the model, also the results are fully analyzed. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-29T04:28:22Z DOI: 10.1142/S0217595917500129

Authors:Wentao Wu, Wai Kin Victor Chan, Lei Chi, Zhiguo Gong Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. 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 [math] nodes (i.e., key players) from a social network of size [math] such that the number of nodes connected to these [math] 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-06-05T06:49:07Z DOI: 10.1142/S0217595917500026

Authors:Turan Arslan Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. 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. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-07T03:51:06Z DOI: 10.1142/S021759591750004X