Noor Hasnah Moin

Inventory routing problems: a logistical overview

This paper presents an overview of Supply Chain Management while focussing on the area of Inventory Routing. We aim to provide the state-of-the-art in this area while highlighting the usefulness of the models in practice as well as their limitations. We have classified the papers based on the planning horizon employed in the models namely single period, multiperiod and infinite horizon models that are then complemented by those with stochastic demand patterns. Future research avenues that we believe to be of interest to the OR/MS community are also presented.

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.

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.

Genetic algorithm based approach for the mufti product multi period inventory routing problem

The inventory routing problems (IRP) is an important component of Supply Chain Management. The IRP refers to the coordination of the inventory management and transportation. The solution in IRP gives the optimum vehicle routing while at the same time minimizes the transportation and inventory costs. The problem addressed is of the many-to-one type with finite horizon, multi-periods, multi-suppliers, single assembly plant, where a fleet of capacitated identical vehicles, housed at a depot, transport parts from the suppliers to meet the demand specified by the assembly plant for each period. We propose a hybrid genetic algorithm based on allocation first, route second method to determine an optimal inventory and transportation policy that minimizes the total cost. We introduce two new representations and design corresponding crossover and mutation operators. It is found that a simple representation produces very encouraging results.

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.

Location Routing Inventory Problem with Transshipment Points Using p-Center

Location Routing Inventory Problem with Transshipment (LRIP-T) is a collaboration of the three components in the supply chain which are location-allocation, vehicle routing and inventory management problems that allows transshipment process, in a way that the total system cost and the total operational time are minimized. This study is to determine a set of customer points to act as the transshipment point as and when it is necessary, based on the surplus quantities it has, the quantities to ship to the needing customers and the sequence in which customers are replenished by homogeneous fleet of vehicles. This LRIP-T is modified by making one or more of the customer points in the distribution system as the transshipment points with the selection of the points are done using p-center. Sensitivity analysis for two cases: 1) unlimited supply, 2) limited supply are presented. Results show important savings achieved when compared to the existing model in solving the supply chain problem.

An introduction to multiple structural breaks estimation with minimum description length approach

There are ample evidences that economics time series are usually non-stationary over a long period of observation time. In many cases, the existence of non-stationarity is apparently due to the presence of structural breaks. Consequently, failure to detect ‘shifts’ or breaks might lead to a serious problem. Early researches on structural breaks focus on methods which were developed only for a single break by using historical and sequential test such as CUSUM test and the sequential test statistics. Recent development considers cases with multiple structural breaks based on the model selection approach. This paper revisits the method of estimating the number of breaks and the problem of model selection using Minimum Description Length (MDL) with Genetic Algorithm (GA) as an optimization approach to search for the best fitting model. The effectiveness and the implementation of this approach will be illustrated by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model.

An alternative heuristic for capacitated p-median problem (CPMP)

The p-median (PMP) has the objective of minimizing total distance, a customer/patient needs to travel in order to get a service. Various algorithms have been developed to solve the problem and in this paper, an alternative approach of Genetic Algorithm based heuristics is used to solve the problem in minimizing the travelled distance for a patient to get healthcare service. Combination of the nearly-optimal number of facilities to open and the random node order assignment is used to minimize the average traveled distance. The approach is used to solve a capacitated PMP (or CPMP) and found to solve all the CPMP in shorter time and in more promising result.

Genetic Algorithm and Variable Neighborhood Search for Point to Multipoint Routing Problem

Routing of data in a telecommunication network is important due to the vast amount of data flow in the network. Message Scheduling Problem (MSP) is an important aspect of data routing. It is the process of scheduling a set of requests where each set has a single source and multiple destinations. In this paper, we propose a hybrid Genetic Algorithm to determine the order of request to be scheduled. The proposed algorithm embeds a different Steiner Tree algorithm than those reported in [3]. The second part of the study involves designing three algorithms based on the Variable Neighborhood Search. These algorithms differ in the local search heuristics employed in searching the neighborhood. The performance of these algorithms are compared on a modified set of data taken from the OR library.