Irohara Takashi

Recently, power shortages have become a major problem all over Japan, due to the Great East Japan Earthquake, which resulted in the shutdown of a nuclear power plant. As a consequence, production scheduling has become a problem for factories, due to considerations of the availability of electric power. For factories, the contract with the electric power company sets the maximum power demand for a unit period, and in order to minimize this, it is necessary to consider the peak power when scheduling production. There are conventional studies on flowshop scheduling with consideration of peak power. However, these studies did not consider fluctuations in the processing time. Because the actual processing time is not constant, there is an increase in the probability of simultaneous operations with multiple machines. If the probability of simultaneous operations is high, the probability of increasing the peak power is high. Thus, we consider inserting idle time (delay in inputting parts) into the schedule in order to reduce the likelihood of simultaneous operations. We consider a robust schedule that limits the peak power, in spite of an unexpected fluctuation in the processing time. However, when we insert idle time, the makespan gets longer, and the production efficiency decreases. Therefore, we performed simulations to investigate the optimal amount of idle time and the best point for inserting it. We propose a more robust production scheduling model that considers random processing times and the peak power consumption. The results of experiments show that the effectiveness of the schedule produced by the proposed method is superior to the initial schedule and to a schedule produced by another method. Thus, the use of random processing times can limit the peak power. (C) 2015 Elsevier B.V. All rights reserved.

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.

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.

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 optimi-zation 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, distri-bution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms pro-posed 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.

One of the objectives of supply planning is to find when and how many productions have to be started to minimize total cost. We aim to find the optimum. Base data like the length of transit time are important parameters to plan for the optimum start of production. In this research, we considered two kinds of transit options: normal transit and emergency transit, and we distinguished between planned and executed transit. The decision regarding which transit option to choose for the execution is trivial because emergency is only used when it is needed since normal transit is more cost efficient. However, for planning purpose, it is more difficult to decide which transit option should be used since the uncertainty in demand and supply between plan and execution can allow to plan an emergency transit but to execute the delivery with normal transit, which is a huge benefit in the competitive capital intensive semiconductor industry. If we planned an emergency, we can save inventory and production cost as we can delay the start of production. In contrast, we need pay additional transit cost in case that emergency transit is actually executed. Many characteristics of the semiconductor industry, which might be the front runner for many other industries, are considered in this model such as demand uncertainty, supply uncertainty and economic volatility. In our numerical experiments, we could gain the optimum, depending on each economic situation. Furthermore, we conducted sensitivity analysis of the effect of demand and supply uncertainties on total cost. (C) 2013 Elsevier B.V. All rights reserved.

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.

This paper proposes a tri-level programming model for disaster preparedness planning. The top level addresses facility location and inventory pre-positioning decisions; the second level represents damage caused by the disaster, while the third level determines response and recovery decisions. We use an interdiction framework instead of a stochastic or chance-constrained model. This allows the extent of damage to be treated as a parameter to facilitate scenario exploration for decision-support. We develop an iterative dual-ascent solution approach. Computational results show that our approach is efficient, and we can also draw some insights on disaster relief planning.

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.

Environmental problems have received a great deal of attention in recent years. In particular, CO2 emissions worsen global warming and other environmental problems. The transport sector accounts for 20% of the total CO2 emissions. Therefore, the CO2 emission reduction of the transport sector is of great importance. In order to reduce emissions effectively, it is necessary to change the distribution and transportation processes. The purpose of this study is to minimize both the transportation costs and CO2 emissions during transportation. Our model considers a transportation scheduling problem in which loads are transported from an overseas production base to three domestic demand centers. The need for time-space networks arises naturally to improve the model. It is possible to know the distance carriers are moving, and also consider the timetables of carriers during transportation. Carrier choice, less-than carrier load, and domestic transportation among demand centers are considered as the three target areas to reduce CO2 emissions during the distribution process. The research model was formulated as a mixed integer programming (MIP) problem. It achieves cost reduction, and will contribute to improvement of the natural environment.

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.

This paper considers an auction-based scheduling system in a job shop equipped with ubiquitous network environment to cope with dynamically changing market demands. Under such environment all machines and jobs are assumed to have computing and communication devices and can serve as intelligent agents. The functions for an auctioneer and for a participant are investigated to install the scheduling system as a distributed multi-agent system. The systems sending and receiving messages for the auction form a distributed system on a network, enabling an autonomous scheduling.

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.

In this paper, we propose a heuristic algorithm for unrelated parallel machine scheduling problems. In an unrelated parallel machine, there is no particular relationship among processing times for each job since they are different depending on the machine. The processing times for the jobs assigned to one machine are not proportional to the processing times that correspond to another. The objective function is to minimize the total weighted tardiness and earliness for each job. An unrelated parallel machine scheduling problem can be divided into two problems: one is the assignment problem, which is to assign the jobs to machine types; and the other is the scheduling problem, which is to schedule the jobs on each machine type. In the proposed method, the former problem is solved by making a random initial solution at first. In the latter problem, we apply the Lagrangian decomposition and coordination method to each machine type. In the relaxed problem using the Lagrangian decomposition and coordination method, the machine capacity constraints are relaxed and the optimal values of the Lagrangian multipliers are searched by solving the Lagrangian dual problem using a subgradient algorithm. Then we solve the former problem again using the Lagrangian multiplier. The Lagrangian multiplier helps to solve the former problem because it represents the degree of congestion of jobs on the term. The series of operations is iterated to search for better solutions. Finally, computational experiments show that this approach can find better solutions than the comparative method based on Genetic Algorithm in various environments.

We propose a new approach to optimize facility layout and buffer space allocation for production systems with a feed-forward configuration and variable processing times. Our objective is to efficiently find Pareto-optimal solutions for both throughput and material handling cost. We assume that work transfer is performed by conveyor belt or unlimited vehicles. Since the facility layout does not affect the throughput under these assumptions, we first calculate the throughput for every buffer space allocation using Markov analysis. Then the set of Pareto optimal facility layouts and buffer space allocations is searched using a genetic algorithm. Numerical examples illustrate the proposed approach.

The facility layout problem (FLP) is defined as needing to find an optimal layout with respect to the minimization of material handling costs (MHC) between departments. An elevator (ELV) is a necessity for multi-floor FLPs in order to transfer materials between departments on different floors, and the ELV location significantly influences MHC. While there are some multi-floor layout techniques that consider the ELV location, they have many problems. For example, there is a technique where the ELV is located within a department. In this paper, a new multi-floor facility layout technique is proposed, in which the detailed locations of input/output (I/O) points are determined in addition to departments and ELVs, using an integrated approach of combinatorial optimization and mathematical programming. In this technique, ELVs can be located in free positions without overlapping with departments. I/O points are also optimized within a defined range, but not using conventional ways. The proposed algorithm is based on Simulated Annealing. The objective function is the minimization of MHC between the I/O points of departments along the aisle with the shortest distance. The results of computational experiments show the effectiveness of the proposed algorithm.