Asia-Pacific Journal of Operational Research

One of the most important steps in designing a cellular manufacturing system is cell formation which includes grouping the machines in cells and the parts as part families, so that the costs are minimized. Several aspects of the problem should be taken into account in cell formation; more specifically, machines and their reliability are among the most important issues that should be modelled correctly. Another important facet of a cellular manufacturing system is material handling cost consisting of inter-cellular and intra-cellular movement costs. In addition, setup cost may play a significant role in decision-making in many real world problems of cell formation. Obviously, cell formation cannot be completed without considering the demands for parts. Considering all these aspects of the problem in this research, a generalized model for solving cell formation problem is proposed. Exact methods, e.g., B&B, are cumbersome in solving such complex models for large-size problems. Therefore, in this paper, a modified version of simulated annealing algorithm is designed and numerical examples are provided to show that the proposed method is efficient and effective.

We present two recent integer programming models in molecular biology and study practical reformulations to compute solutions to some of these problems. In extension of previously tested linearization techniques, we formulate corresponding semidefinite relaxations and discuss practical rounding strategies to find good feasible approximate solutions. Our computational results highlight the possible advantages and remaining challenges of this approach especially on large-scale problems.

This paper analyzes noncooperative dynamic game behavior in a two-echelon supply chain composed of one supplier and one retailer. The aim is to give supplier reasonable suggestions on decision making of marketing strategy. The challenge of dealing with this problem is the discreteness of demand. The main contributions of this paper are twofold: one is to address the necessary conditions in which supplier can choose quantity discount policy as marketing strategy; the second is to provide the solving methods of the equilibrium strategy and discount rate under quantity discount policy. Two types of quantity discount marketing strategies are considered. It is concluded that the strategy sets of production and procurement are independent of discount rate via analyzing the optimality properties. Numerical tests illustrate that incremental quantity discount policy is more widely considered than all-unit quantity discount policy, and whether the quantity discount policy can be chosen as the marketing strategy not only relates to supplier's own cost structure but also retailer's cost structure.

A typical approach to handle the complexity of multi-faceted decision-making problems is to use multi-attribute decision-making (MADM) methods based on pairwise comparisons. Fuzzy set theory has also been employed to cope with the uncertainty and vagueness involved in conducting the comparisons between components of a decision model. An important issue regarding the reliability of the output is the consistency of pairwise comparisons provided by the decision maker(s). Using the MADM method developed by Lu et al. (2007) as a foundation, this paper proposes an algorithm for evaluating the consistency level of pairwise comparison matrices, where linguistic data are used. A crisp numeric scale has been introduced to consider the priority of linguistic data and to avoid the complexity of handling fuzzy calculations in consistency evaluation of pairwise comparison matrices. As an advantage, the proposed method of consistency evaluation is capable of assessing the degree of inconsistency among the pairwise comparisons. Therefore, the acceptance or rejection of the pairwise comparisons can be determined based on the desired degree of tolerance in accepting inconsistent judgments. An application of a revised MADM method is then demonstrated in a case study involving flood mitigation project selection in Australia.

In this paper, the problem of minimizing maximum cost and makespan simultaneously on an unbounded parallel-batching machine is considered. An unbounded parallel-batching machine is a machine that can handle any number of jobs in a batch and the processing time of a batch is the largest processing time of jobs in the batch. The main goal of a multicriteria problem is to find Pareto optimal solutions. We present a polynomial-time algorithm to produce all Pareto optimal solutions of this bicriteria scheduling problem.

This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimization problem, several unconstrained optimization problems and one typical simulation-based optimization problem, i.e., (s, S)-inventory problem.

A problem in multi-objective dynamic facility layout is achieving distance- and adjacency-based objectives for arranging facility layouts across multiple time periods. As a non-deterministic polynomial time-hard problem, it resembles the quadratic assignment problem (QAP), which can be solved through meta-heuristics such as ant colony optimization (ACO). This study investigates three multi-objective approaches coupled with ACO to solve this problem. As the experimental design, we apply the proposed methods to solve the dynamic facility layout problem (DFLP), multi-objective facility layout problem, and multi-objective DFLP based on data sets from the literature to test the quality of the solution. The results show that the proposed methods are effective for solving the problem.

We consider m-machine permutation flow shop problems with an outsourcing option for a special case where each job's processing time equals the job's processing requirement plus a characteristic value of the machine. The objective is to minimize the sum of the performance measure for in-house jobs (the total completion time or the makespan) and the total outsourcing cost. We prove that two problems are polynomially solvable when the number of machines is fixed.

Consideration is given to the queueing system with incoming Poisson flows of regular and negative customers. Regular customers await service in buffer of finite size r. Each negative customer upon arrival pushes a regular customer out of the queue in buffer (if it is not empty) and moves it to another queue of finite capacity r (bunker). Customers from both queues are served according to exponential distribution with parameter μ, first-come, first-served discipline, but customers in bunker are served with relative priority. Using method based on Chebyshev and Gegenbauer polynomials the algorithm for computation of stationary blocking probabilities and joint probability distribution of the number of customers in buffer and bunker is obtained. Numerical example is provided.

We consider online scheduling of unit length jobs on m identical parallel-batch machines. Jobs arrive over time. The objective is to minimize maximum flow-time, with the flow-time of a job being the difference of its completion time and its release time. A parallel-batch machine can handle up to b jobs simultaneously as a batch. Here, the batch capacity is bounded, that is b < ∞. In this paper, we provide a best possible online algorithm for the problem with a competitive ratio of .