Asia-Pacific Journal of Operational Research

In this paper cone convex and related functions have been studied. The concept of cone semistrictly convex functions on topological vector spaces is introduced as a generalization of semistrictly convex functions. Certain properties of these functions have been established and their interrelations with cone convex and cone subconvex functions have been explored. Assuming the functions to be cone subconvex, sufficient optimality conditions are proved for a vector valued minimization problem over topological vector spaces, involving Gâteaux derivatives. A Mond-Weir type dual is associated and weak and strong duality results are proved.

Following the idea of Li (1996), Romero (2004) and Chang (2006), this paper proposes several efficient structures of achievement functions for goal programming and interval goal programming models. These proposed structures of achievement functions are more efficient than the traditional structures of achievement functions such as Lexicographic, Weight, MINMAX (Chebyshev) and interval achievement functions. The structures of achievement functions are comprehensive in terms of the breath of goal programming and interval goal programming. In order to demonstrate the superiority of the proposed methods in terms of the number of iterations and execution time, a computational experiment was conducted on a set of generated test examples.

The analysis of the effects of fertilizer pricing on fertilizer-intensive agriculture and farms behavior ought to be an important topic of research for agricultural and environmental economists in Bangladesh. Several possibilities for fertilizer policy have been debated, in particular for the pricing of fertilizer. Following this observation, this study contributes to that discussion by simulating the impact that various policies based upon the price of fertilizer could have on agricultural production. Specifically, the study analyzes the economic, social and environmental implications of alternative fertilizer policies using a multicriteria model of farmers' behavior under different scenarios. The future agricultural and fertilizer scenarios are described in terms of the combination of policy instruments, policy style and configuration of actors. For the purpose of scenario analysis, narratives and quantitative indicator values have been compiled for each scenario. The quantitative estimates are used as input values in the modeling of fertilizing systems under policy changes. The results show that the increase of fertilizer price causes almost similar impacts as observed in the status quo scenario. The results also stress that fertilizer pricing, as a single instrument for controlling fertilizer use is not a satisfactory tool for significantly reducing fertilizer consumption in agriculture.

In this paper, we consider the rescheduling problem for jobs on a single machine with release dates to minimize total sequence disruption under a limit on the makespan. We show that the considered problem can be solved in polynomial time. Consequently, the rescheduling problem for jobs on a single machine with release dates to minimize makespan under a limit on the total sequence disruption can also be solved in polynomial time.

This paper discusses a decision-making problem related to software creation through Commercial-off-the-shelf (COTS) modules. When different alternatives of the same module are available with variations in the attributes of quality (like reliability, execution time, etc.) and cost, the problem is to select appropriate alternatives and integrate them into a software such that the objectives of the software developer (with respect to the attributes of quality) are satisfied. This calls for the use of multiple criteria decision-making techniques. The possible inconsistency between the objectives calls for prioritization of the objectives. Hence goal-programming (GP) approach is used to formulate the problem. We present a Branch and Bound (B&B) algorithm to solve this problem.

The derivation of mean first passage times in Markov chains involves the solution of a family of linear equations. By exploring the solution of a related set of equations, using suitable generalized inverses of the Markovian kernel I - P, where P is the transition matrix of a finite irreducible Markov chain, we are able to derive elegant new results for finding the mean first passage times. As a by-product we derive the stationary distribution of the Markov chain without the necessity of any further computational procedures. Standard techniques in the literature, using for example Kemeny and Snell's fundamental matrix Z, require the initial derivation of the stationary distribution followed by the computation of Z, the inverse of I - P + eπ^T where e^T = (1, 1, …, 1) and π^T is the stationary probability vector. The procedures of this paper involve only the derivation of the inverse of a matrix of simple structure, based upon known characteristics of the Markov chain together with simple elementary vectors. No prior computations are required. Various possible families of matrices are explored leading to different related procedures.

Alternative optimal solutions can give more choice for practical decision making. Therefore, the provision of methods for finding alternative optimal solutions is an important component part of the solution techniques for optimization models. The aim of this paper is to present a branch-and-bound algorithm for finding all optimal solutions of the linear assignment problem. Numerical experimental results are also given.

In this paper, the notion of ∊-strictly efficient solution for vector optimization with set-valued maps is introduced. Under the assumption of the ic-cone-convexlikeness for set-valued maps, the scalarization theorem, ∊-Lagrangian multiplier theorem, ∊-saddle point theorems and ∊-duality assertions are established for ∊-strictly efficient solution.