Asia-Pacific Journal of Operational Research

In this paper, a two-warehouse inventory model for items with imperfect quality and all-unit quantity discounts was developed. In practice, the supplier usually offers quantity discounts to encourage the retailer ordering larger lot sizes and thus, extra storage space is needed for the retailer. Two levels of storages, owned-warehouse and rented warehouse, are considered in this study to store bulk quantities. We develop two algorithms to determine the optimal lot size and purchasing cost: one is based on the work of Render et al. (2003) and the other is similar to the method proposed by Goyal (1995). Two numerical examples are provided for illustration, in which we show that our algorithms require fewer iterations than that of the modified procedures based on the work of Render et al. (2003).

In this paper, we investigate the capacitated two-parallel machines scheduling problem, where one machine is only available for a special period of time after which it can no longer process any job while the other machine is continuously available. Our objective is to minimize the completion time of the machine which is continuously available. The offline version of the problem is equivalent to the minimization version of the Subset-Sum problem. We first show the lower bound of the online version is infinite. We also consider the semi-online version with known the total job processing time in advance, for which both lower bound and semi-online algorithms are given.

Shelf space allocation to products greatly impacts the profitability in a retail store. In this paper, we consider a retail shelf-space allocation problem where retailer wishes to allocate the available spaces of different shelves to a large number of products considering direct space elasticity in the product's demand. There is a great interest to develop efficient heuristics due to NP-hard nature of this problem. We propose a dynamic programming heuristic (DPH) to obtain near optimal solution in a reasonable time to solve this problem. The empirical results found that DPH obtained near optimal solutions for randomly generated instances of problems with size (products, shelves) varying from (100, 30) to (200, 50) within a few seconds of CPU time. The performance of DPH is benchmarked against an existing local search heuristic (LSH). It was found that DPH takes substantially less CPU time and attains a solution close to that obtained by LSH. Thus, DPH has great potential to solve the problem of realistic size within reasonable time. The proposed DPH is also applied to a case of an existing retail store.

This paper describes a tabu search approach for a multiprocessor scheduling problem, where a list of jobs has to be scheduled on identical parallel processors. Each job in the list has a release date, a due date, a processing time and a set of predecessors. The objective is to minimize the number of processors used while respecting the constraints imposed by release dates, due dates and precedences. Two versions of this problem are considered here, in the first one precedence constraints are relaxed, they are taken into account in the second one. The proposed method is used to solve High Level Synthesis problem instances. The results show the effectiveness of our approach for both versions of the problem. A comparison with an exact method is also conducted.

This paper considers the valuation problem of basket CDSs. Based on the construction of total hazard rates, the paper develops the work of Zheng and Jiang Zheng and Jiang (2009) from the homogenous case to the primary-subsidiary heterogenous case in the interacting intensity framework, and obtains the corresponding joint density of the default time. Moreover, the paper derives the valuation formulae for the basket CDSs with and without counterparty risk. Numerical results robustly show that, under certain conditions, using the analytical pricing formulae derived in this paper is more efficient than the Monte Carlo method for the basket CDS valuation.

This paper introduces a model for dealing with selecting third-party reverse logistics (3PL) providers in the presence of both dual-role factors and imprecise data. The proposed model is based on data envelopment analysis (DEA). A numerical example demonstrates the application of the proposed method.

This paper proposes a heuristic with stochastic neighborhood structures (SNS) to solve two-stage and three-stage two-dimensional guillotine bin packing and cutting stock problems. A solution is represented as a sequence of items which are packed into existing or new stacks, shelves or bins according to previously defined criteria. Moreover, SNS comprises three random neighborhood structures based on modifying the current sequence of items. These are called cut-and-paste, split, and swap blocks and are applied one by one in a fixed order to try to improve the quality of the current solution. Both benchmark instances and real-world instances provided by furniture companies were utilized in the computational tests. Particularly, all benchmark instances are bin packing instances (i.e., the demand of each item type is small), and all real-world instances are classified into bin packing instances and cutting stock instances (i.e., the demand of each item type is large). The computational results obtained by the proposed method are compared with lower bounds and with the solutions obtained by a deterministic Variable Neighborhood Descent (VND) meta-heuristic. The SNS provide solutions within a small percentage of the optimal values, and generally make significant improvements in cutting stock instances and slight to moderate improvements in bin packing instances over the VND approach.