Asia-Pacific Journal of Operational Research

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.

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.

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.

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.

We consider the problem of scheduling $n$ jobs with rejection on a set of $m$ 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 ${𝒩}{𝒫}$-hard, then we construct a pseudo-polynomial time algorithm to solve it and an $O(n^{3}logn)$ time for the case where the jobs have the same processing time. For the second problem, we first show that it is ${𝒩}{𝒫}$-hard, then we design $O(n^2)$ time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time.

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 $(R,T)$ 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.

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.

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.

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 ($R,Q$) 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).