Asia-Pacific Journal of Operational Research

Many researches have been carried out in fuzzy linear regression since the past three decades. In this paper, a fuzzy linear regression model based on goal programming is proposed. The proposed model takes into account the centers of fuzzy data as an important feature as well as their spreads. Furthermore, the model can deal with both symmetric and non-symmetric data. To show the efficiency of proposed model, it is compared with some earlier methods based on simulation studies and numerical examples. Moreover, the sensitivity of the model to outliers is discussed.

In this paper, we consider a seller–buyer channel in which marketing expenditure is an endogenous decision for the buyer. We assume that both the unit marketing expenditure and the unit price charged by the buyer influence the end demand for the product. We model the seller–buyer relationship as a non-cooperative as well as a cooperative game. We investigate the non-cooperative game from two perspectives: the Seller–Stackelberg model and the Buyer–Stackelberg model. In the cooperative game, we provide a procedure for outlining Pareto efficient solutions. For each model, we present a numerical example as well as sensitivity analysis with respect to the two key parameters in the model.

In this paper, we extend to the multi-choice games cases the reduced games introduced by Moulin (1985) and Voorneveld and van den Nouweland (1998), respectively. Also, we provide several axiomatizations of the duplicate core on multi-choice games by means of the properties of consistency and its converse.

Real world scheduling problems are complex in nature. They require satisfying multiple objectives. To get a realistic schedule, consideration of machine reliability and availability is very important to allocate job in machine. This research aims to develop two fuzzy inference systems (FIS) for hybrid flow shop problem. First FIS is used to get priority of each job considering multiple objectives of processing time, due date and cost over time. Second FIS is used to get machine reliability and availability based priority using the information of mean time to failure (MTTF) & mean time to repair (MTTR) of each individual machine at each stage. To distribute the workload depending on their reliability and availability based priority of each machine, maximum utilization target is determined. An algorithm has been developed for grouping, sequencing & allocating the jobs to the machines at every stage in such a way that total percentage of over utilization will minimum. Based on this algorithm, a computing tool has been developed and, explained with a three stage hybrid flow shop scheduling problem.

In this paper we address the problem of batching orders in a warehouse, with the objective of minimizing the total travel time. Order batching is an NP-hard optimization problem that is very difficult to solve exactly in practice. Thus, most implemented solutions are based on elementary heuristic methods that perform a relatively limited exploration of the solution space. As an alternative, we propose a heuristic based on variable neighborhood search, where the emphasis is placed on performing an intensive exploration of the most promising regions of the solution space. Simulations are conducted to study the performance of the method with different warehouse configurations, and an exhaustive comparative analysis, which considers all the best known heuristics, is carried out. The results obtained show that the proposed heuristic is competitive and that it provides a suitable method which can be used in practice. Additionally, since the performance of the algorithms depends heavily on factors such as storage policy, routing strategies, or the structure of the orders, we have developed an ANOVA in order to consider the effect of all the above factors on the different methods tested.

This paper presents an exact decomposition approach for an employee days-off scheduling problem of an homogeneous workforce under serval realistic days-off scheduling constraints. These constraints include the following: (1) each employee is assigned to three or four consecutive days-off per week, (2) each employee is assigned at least four consecutive days-off per month and (3) each employee is assigned at least a weekend off per month. A three-step decomposition method is used to obtain the work assignments while respecting the labor demands and the stated constraints.

Fundamental matrix plays an important role in a finite-state Markov chain to find many characteristic values such as stationary distribution, expected amount of time spent in the transient state, absorption probabilities. In this paper, the fundamental matrix of the finite-state quasi-birth-and-death (QBD) process with absorbing state and level dependent transitions is considered. We show that each block component of the fundamental matrix can be expressed as a matrix product form and present an algorithm for computing the fundamental matrix. Some applications with numerical results are also presented.