Asia-Pacific Journal of Operational Research

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as "boosts" to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity.

An M-unit system in dynamic environment with operational and repair times following phase-type distributions and incorporating geometrical processes is considered. A general Markov process with vectorial states is the appropriate structure for modeling this system. A transient analysis is performed for this complex system and the transition probabilities are calculated. Some performance measures of general interest in the study of systems are obtained using an algorithmic approach, and applied to G-out-of-M systems. A numerical example is presented and the transient performance measures are calculated and compared with the stationary ones. This paper extends previous reliability systems, that can be considered as particular cases of this one. Throughout the paper, the mathematical expressions are given by algorithmic methods, that emphasized the utility of phase-type distributions in the analysis of lifetime data.

Gower plots provide a powerful graphical tool to detect cardinal and ordinal inconsistencies in a pairwise preference matrix. However, there is no systematical way to help a decision maker to reduce these inconsistencies. This paper develops a model that can assist in making a consistent decision. Gower plots are used to detect major inconsistencies. A multi-objective program is then formulated to adjust both the cardinal inconsistencies and the preference changes subjected to the constraints of ordinal consistency.

Inventory models with deteriorating items have received considerable attention in recent years. In considering the deteriorating inventory with permissible delay in payments, most researchers pay attention to a single warehouse. Under conditions of permissible delay in payments, this paper develops a model to determine the optimal cycle time for a single deteriorating item that is stored in two different warehouses. A rented warehouse (RW) is used to store the excess units over the fixed capacity W of the owned warehouse (OW). The rented warehouse is assumed to charge higher unit holding cost than the OW. In this paper, we propose a two-warehouse inventory model for deteriorating items under permissible delay in payments. It is assumed that the deterioration rate in RW is the same as in OW, and the holding cost in RW is greater than that in OW. The stocks of RW are transported to OW in continuous release pattern and the transportation cost is ignored. Three theorems are developed to determine the optimal cycle time and numerical examples are given to illustrate these theorems.

A novel monotonization method is proposed for converting a non-monotone programming problem into a monotone programming problem. An equivalent monotone programming problem with only inequality constraints is obtained via this monotonization method. Then the existing convexification and concavification methods can be used to convert the monotone programming problem into an equivalent better-structured optimization problem.

In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

In past two decades, the Japanese successful experience in using Just-In-Time (JIT) production has received a great deal of attention. The underlying goal of JIT is to eliminate waste. This can be achieved through various efforts, such as shortening lead time and improving quality. In this paper, we investigate the impact of investing in quality improvement and lead time reduction on the integrated vendor-buyer inventory model with partial backorders. We assume that the lead time demand probability distribution is unknown, while the mean and standard deviation are known and finite. The minimax distribution-free procedure is applied to solve this problem. Also, numerical examples are given to illustrate the results.