Irohara Takashi

<p>In the shipping area of a cross-docking center, it is important to decide the chutes to be assigned each store and the docks each truck should be assigned to in order to reduce the transportation time of items between chutes and docks. However, there are cases that consume more time than anticipated because of congestion. Most of the existing literature does not consider the relaxation of congestion. Some researchers have been studying it, but there is still insufficient knowledge about it under specific situations. In this study, we propose a mathematical model for a chute and dock assignment problem based on a traditional model that not only reduces transportation time of items, but also relaxes congestion. To verify the characteristics of the proposed model, we compare this model with the traditional model in terms of objective function and results of assignment. To solve a large-scale problem that cannot be solved by an exact method, we propose a hybrid metaheuristic based on a standard genetic algorithm and local search algorithm. To evaluate the performance of the proposed metaheuristic, we compare it with the optimal solution and a standard genetic algorithm in a small-scale problem, as well as with a random search algorithm and multi-point local search algorithm in a large-scale problem.</p>

The control and maintenance of inventories are vital for many companies because the effective management of inventories can provide better customer service and improve profitability. In recent years, the number of different stock keeping units (SKUs) that must be delivered is exploding. Meanwhile, due to the spread of the electronic commerce, filling customer orders within a 24-hour period is becoming the new standard in many industries, which means that an increasing number of SKUs has to be delivered more frequently and faster. We consider a warehouse configured with a forward area, where items are stored in cases for easy retrieval by an order picker, and a reserve area, where items are stored by the case for picking and replenishing stocks in the forward area. Based on real data, this paper studies the setting of order-up-to level and reorder level in (s,S) policy and determining the proper maintenance intervals (PMIs) in order to reduce stockouts and excessive stock in a distribution center. Order-up-to and reorder levels should be decided after comprehending the trade-off between inventory space and emergency replenishment. Furthermore, we show maintenance at PMIs can lead to cost savings. Such improvements would allow the distribution center to reduce stock-outs and excessive stock, and to increase productivity.

In most studies dealing with inventory problems, lead time is treated as fixed. However, in several practical situations, the lead time can be reduced at an added cost, which is called a "crashing cost". By shortening the lead time, we can lower the safety stock, save inventory space, reduce the stock-out loss, and improve the customer service level. In fact, there are some studies that consider lead time reduction. Pan et al. (2002) proposed the crashing cost, which is assumed to be a function of both the order quantity and the reduced lead time. Ouyang et al. (2001) and Lee (2005) proposed the backorder rate, which is assumed to be dependent on the amount of shortages. There are no inventory models that consider both crashing cost in the Pan et al. (2002) and backorder rate in the Lee (2005). In this paper, we present an inventory model in which the order quantity, reorder point, and lead time are regarded as decision variables. The crashing cost is represented as a function of both the reduced lead time and the order quantity, and the backorder rate is assumed to be dependent on the amount of shortages. We develop an algorithm to find near optimal solutions and give numerical examples to demonstrate the effectiveness of our proposed model. Furthermore, a sensitivity analysis is also conducted to identify situations where shortening lead time is valuable.

This study was motivated by challenges facing inventory managers when deciding the ordering policy for various items. It is difficult to find an appropriate ordering policy for many types of items. We propose a model that changes conventional multi-criteria ABC analysis so that it is suitable for use by inventory managers.  We indicate that categorizing items based on their statistical characteristics leads to an ordering policy suitable for each item. We propose a method for deciding the ordering policy based on important shipping statistics and a classification technique. For this method, we analyze the relation between shipping statistics and the ordering policy for searching important shipping statistics. We classify items by shipping statistics and then decide the ordering policy for each item. In the numerical experiment, we used actual shipment data to calculate many shipping statistics that represent the characteristics of each item. Next, we found the important shipping statistics from Random Forests and applied them to decide the ordering policy. Finally, for confirming the importance of important shipping statistics, we tested the performance of Random Forests and other classifying methods using the important shipping statistics. It was found that the performance of each classifying method was improved.

In this paper, we consider the effect of lot splitting, which is the procedure of splitting a production order into smaller sub-lots that are free to move independently through stages of the manufacturing process. In this lot splitting, there is a trade-off relationship between the material movement and the lead time. We analyze the effect of the lot splitting in simulation experiments under the model of an open shop environment, where there are no restrictions on the order in which the operations of a job are performed. Generally, the average lead time to manufacture the products becomes shorter as a result of lot splitting of each job. However, we show that there are some cases where lot splitting leads to disadvantages, such as when too much lot splitting increases the average lead time when the machine utilization is high and/or the setup time to process the material is relatively long in proportion to the processing time of the jobs. In addition, we propose a new material handling rule, called the time-based rule, which can create the best balance of shorter lead time and less frequent material movement simultaneously. In this time-based rule, we assume that the processing completion time of each production lot is known using radio frequency identification (RFID) to monitor the production progress. The effectiveness of the proposed method is verified in the simulation experiments.

The resolution of environmental problems has received much attention in recent years. In the transportation sector in particular, the need to reduce CO_2 emissions has necessitated changes in manufacturing and distribution infrastructures. The present study considers a transportation scheduling problem in which loads are transported from an overseas production site to three demand centers in Japan. Demand information is presented in the form of an order table that provides information on delivery dates, demand quantity, and order destinations. In addition, the present study addresses three target areas for reducing CO_2 emissions during the distribution process. First, carrier choices must be carefully considered, because the carrier's transportation time, transportation cost, and total CO_2 emissions are different. Second, less-than carrier loads should be minimized; the present study explores the possibility of consolidating more than two orders and reducing the required number of carrier trips. Third, various options for domestic transportation among demand points are explored. The research model was formulated as a mixed integer programming (MIP) problem. The objective function was to minimize transportation costs and CO_2 emission penalties incurred during transportation. In addition, parameter analysis was performed by changing the values of several parameters, such as transportation cost, CO_2 emission penalty, and number of orders. However, less-than carrier load problems stemming from a large number of orders could not be solved in a reasonable amount of time by the present research model. To address this issue, the reason that long calculation times are required was also investigated. The results indicate that using the present research model, a substantial amount of time is required to consider the issue of less-than carrier load.

In this paper, new solution expression and search range grouping rules are proposed for the Stochastic Facility Layout Problem (SFLP). The solution expression is the expansion of the space partitioning method. The new search range grouping rule suppresses long distance delivery as much as possible by limiting the range where AGV searches for delivery request. The objective function of the SFLP is the minimization of average lead time. The throughput is treated as one of the constraints. The average lead time and throughput is calculated by simulation for each solution during the simulated annealing based optimization process. The results of computational experiments show the effectiveness of the proposed algorithm.