Mohd Omar

Another look at the single-vendor single-buyer integrated production-inventory problem

This paper considers a supply chain in which a ‘vendor’ supplies a product to a ‘buyer’. The vendor manufactures the product at a finite rate and periodically ships the output to the buyer. The buyer then consumes the product at a fixed rate. Costs are attached to manufacturing batch set up, the delivery of a shipment and stockholding at the vendor and buyer. The objective is to determine the production and shipment policy which minimizes long-run total average cost—assuming the vendor and buyer collaborate and find a way of sharing the consequent benefits. Most previous work has been based on the assumption that unit stockholding costs increase as stock moves down the supply chain, but recent research has suggested that the opposite may sometimes hold. We show how the optimal batch production and shipment policy may be derived when unit stockholding costs increase as stock moves down the chain and shipments are not necessarily equal in size.

Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized.

An optimal batch size for a production system under linearly increasing time-varying demand process

In this paper we consider a manufacturing system which procures raw materials from suppliers and processes them to make a finished product. The problem is to determine an ordering policy for raw materials and a production policy for the finished product to satisfy a deterministic time-varying demand process. We present an optimal solution procedure, using a dynamic programming approach, and also two heuristic procedures. These procedures are illustrated with numerical examples.

Hybrid Evolutionary Algorithm for job scheduling under machine maintenance

The job scheduling problem (JSP) belongs to the well-known combinatorial optimization domain. After scheduling, if a machine maintenance issue affects the scheduled processing of jobs, the delivery of jobs must be delayed. In this paper, we have first proposed a Hybrid Evolutionary Algorithm (HyEA) for solving JSPs. We have then analyzed the effect of machine maintenance, whether preventive or breakdown, on the job scheduling. For the breakdown maintenance case, it is required to revise the algorithm to incorporate a rescheduling option after the breakdown occurs. The algorithm has been tested by solving a number of benchmark problems and thence comparing them with the existing algorithms. The experimental results provide a better understanding of job scheduling and the necessary rescheduling operations under process interruption.

Stock replenishment policies for a stochastic exponentially-declining demand process

This paper addresses the problem of determining stock replenishment policies to meet the demand for spare parts for items of equipment which are no longer manufactured. The assumptions that the number of items still in use is decreasing and that parts fail randomly lend credence to a Poisson demand process with an underlying mean which is decreasing exponentially. We use a dynamic programming formulation in continuous time to determine that replenishment policy which minimises the mean total discounted cost of set-up/order, unit production/purchase, unsatisfied demand and stock left over at the end of the time horizon.

Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems

The job shop scheduling problem (JSSP) is one of the well-known hard combinatorial scheduling problems. This paper proposes a hybrid genetic algorithm with multiparents crossover for JSSP. The multiparents crossover operator known as extended precedence preservative crossover (EPPX) is able to recombine more than two parents to generate a single new offspring distinguished from common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained, a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces competitive results.

Technical Note: An alternative approach to analyze economic ordering quantity and economic production quantity inventory problems using the completing the square method

In most papers and textbooks, the economic order quantity (EOQ) models and their variants, and the economic production quantity (EPQ) models and their variants are commonly solved using differential calculus. This study proposes an alternative approach that transform the problem into a suitable form and a procedure using the completing the squares method is employed to obtain the optimal solution. The applicability of the method is demonstrated for some selected single and multi-variable problems given in the literature.

A novel data replication and management protocol for mobile computing systems

Mobile computing has enabled users to seamlessly access databases even when they are on the move. Mobile computing environments require data management approaches that are able to provide complete and highly available access to shared data at any time from any where. In this paper, we propose a novel replicated data protocol for achieving such goal. The proposed scheme replicates data synchronously over stationary sites based on three dimensional grid structure while objects in mobile sites are asynchronously replicated based on commonly visited sites for each user. This combination allows the proposed protocol to operate with less than full connectivity, to easily adapt to changes in group membership and not require all sites to agree to update data objects at any given time, thus giving the technique flexibility in mobile environments. The proposed replication technique is compared with a baseline replication technique and shown to exhibit high availability, fault tolerance and minimal access times of the data and services, which are very important in an environment with low-quality communication links.

An Îto stochastic differential equations model for the dynamics of the MCF-7 breast cancer cell line treated by radiotherapy

In this paper, a new mathematical model is proposed for studying the population dynamics of breast cancer cells treated by radiotherapy by using a system of stochastic differential equations. The novelty of the model is essentially in capturing the concept of the cell cycle in the modeling to be able to evaluate the tumor lifespan. According to the cell cycle, each cell belongs to one of three subpopulations G, S, or M, representing gap, synthesis and mitosis subpopulations. Cells in the M subpopulation are highly radio-sensitive, whereas cells in the S subpopulation are highly radio-resistant. Therefore, in the process of radiotherapy, cell death rates of different subpopulations are not equal. In addition, since flow cytometry is unable to detect apoptotic cells accurately, the small changes in cell death rate in each subpopulation during treatment are considered. Subsequently, the proposed model is calibrated using experimental data from previous experiments involving the MCF-7 breast cancer cell line. Consequently, the proposed model is able to predict tumor lifespan based on the number of initial carcinoma cells. The results show the effectiveness of the radiation under the condition of stability, which describes the decreasing trend of the tumor cells population.

Dynamic conditional location allocation model for healthcare facilities

In order to maintain the effectiveness of healthcare delivery, the location of the facilities providing the service to the public is very crucial. It is to ensure that the chosen location network and its demand allocation serve the purpose of minimising social cost or equivalently maximising the benefits of the people. This study will accommodate the changes of demand in time, the need to upgrade the network capacity and the condition of existing facilities into a dynamic conditional location allocation model. Using the new model, a case study is presented.