Selecting Ordering Policy and Items Classification Based on Canonical Correlation and Cluster Analysis

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.2

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

Genetic Algorithm-Based Coordinated Replenishment in Multi-Item Inventory Control

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.3

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

Nagasawa, Keisuke,Irohara, Takashi,Matoba, Yosuke,Liu, Shuling Korean Institute of Industrial Engineers 2015 Industrial Engineeering & Management Systems Vol.14 No.1

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

A Review of Relief Supply Chain Optimization

Manopiniwes, Wapee,Irohara, Takashi Korean Institute of Industrial Engineers 2014 Industrial Engineeering & Management Systems Vol.13 No.1

With a steep increase of the global disaster relief efforts around the world, the relief supply chain and humanitarian logistics play an important role to address this issue. A broad overview of operations research ranges from a principle or conceptual framework to analytical methodology and case study applied in this field. In this paper, we provide an overview of this challenging research area with emphasis on the corresponding optimization problems. The scope of this study begins with classification by the stage of the disaster lifecycle system. The characteristics of each optimization problem for the disaster supply chain are considered in detail as well as the logistics features. We found that the papers related to disaster relief can be grouped in three aspects in terms of logistics attributes: facility location, distribution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms proposed in each optimization model in order to discover insights, research gaps and findings. Finally, we offer future research directions based on our findings from the investigation of literature review.

Foreword to the Special Issue on the Frontiers of IEMS in Asia-Pacific Area

Peng, Jin,Irohara, Takashi Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.2

Humanitarian Relief Logistics with Time Restriction: Thai Flooding Case Study

Manopiniwes, Wapee,Nagasawa, Keisuke,Irohara, Takashi Korean Institute of Industrial Engineers 2014 Industrial Engineeering & Management Systems Vol.13 No.4

Shortages and delays in a humanitarian logistics system can contribute to the pain and suffering of survivors or other affected people. Humanitarian logistics budgets should be sufficient to prevent such shortages or delays. Unlike commercial supply chain systems, the budgets for relief supply chain systems should be able to satisfy demand. This study describes a comprehensive model in an effort to satisfy the total relief demand by minimizing logistics operations costs. We herein propose a strategic model which determines the locations of distribution centers and the total inventory to be stocked for each distribution center where a flood or other catastrophe may occur. The proposed model is formulated and solved as a mixed-integer programming problem that integrates facility location and inventory decisions by considering capacity constraints and time restrictions in order to minimize the total cost of relief operations. The proposed model is then applied to a real flood case involving 47 disaster areas and 13 distribution centers in Thailand. Finally, we discuss the sensitivity analysis of the model and the managerial implications of this research.

A Review of Relief Supply Chain Optimization

Wapee Manopiniwes,Takashi Irohara 대한산업공학회 2014 Industrial Engineeering & Management Systems Vol.13 No.1

With a steep increase of the global disaster relief efforts around the world, the relief supply chain and humanitarian logistics play an important role to address this issue. A broad overview of operations research ranges from a principle or conceptual framework to analytical methodology and case study applied in this field. In this paper, we provide an overview of this challenging research area with emphasis on the corresponding optimization problems. The scope of this study begins with classification by the stage of the disaster lifecycle system. The characteristics of each optimization problem for the disaster supply chain are considered in detail as well as the logistics features. We found that the papers related to disaster relief can be grouped in three aspects in terms of logistics attributes: facility location, distribution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms proposed in each optimization model in order to discover insights, research gaps and findings. Finally, we offer future research directions based on our findings from the investigation of literature review.

Applying Genetic Algorithm for Can-Order Policies in the Joint Replenishment Problem

Keisuke Nagasawa,Takashi Irohara,Yosuke Matoba,Shuling Liu 대한산업공학회 2015 Industrial Engineeering & Management Systems Vol.14 No.1

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

Selecting Ordering Policy and Items Classification Based on Canonical Correlation and Cluster Analysis

Keisuke Nagasawa,Takashi Irohara,Yosuke Matoba,Shuling Liu 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.2

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

Foreword to the Special Issue on the Frontiers of IEMS in Asia-Pacific Area

Jin Peng,Takashi Irohara 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.2