伊呂原 隆

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.

効率的な在庫管理は顧客満足度向上やコスト削減に繋がるため，多くの企業は在庫管理に大きな力を入れている．近年，出荷作業の複雑化に対応するべく，在庫の保管場所をフォワードエリアとリザーヴエリアに分ける物流センターが多い．本研究では，対象事例の物流センターのフォワードエリアの在庫の不足・過剰という問題の改善を目的とし，商品の発注点と補充点を求めるモデルの提案，及び，発注点と補充点の適正な見直し間隔に関する研究を行った．発注点と補充点は，在庫スペースと緊急補充頻度のトレードオフの関係を把握し，決定すべきである．対象の物流センターでは，発注点と補充点を4ヶ月ごとに発注点と補充点を見直すのが適正であった．

多くの在庫管理の研究において，リードタイムは所与として扱われている．しかし，追加費用（クラッシングコスト）を支払うことによって，リードタイムを短縮できる状況は少なくない．実際に，リードタイムの短縮を考慮した研究はいくつか行われている．本研究では，発注量とリードタイムの短縮期間の両方に依存するクラッシングコストと欠品量に依存して変化するバックオーダー率を考慮し，発注量・発注点・リードタイムを決定変数とする在庫モデルを提案する．そして，提案モデルに対する近似解の導出を行い，数値実験により提案モデルの有効性を示す．さらに，感度解析により，リードタイムの短縮を行うべき状況を明らかにする．

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.

本論文では,オープンショップモデルにおいて,ロット分割によるリードタイムとロットの移動回数のトレードオフをシミュレーション実験によって分析する.一般にはロット分割を行うとジョブの平均リードタイムは短縮するが,システムの負荷やセットアップタイムが大きい場合は,ロット分割のデメリットが顕著化し,分割数を増加しすぎると平均リードタイムが増加する場合があることを示す.また,ロット分割の利点を生かしリードタイムを短縮しながら,同時にロットの移動回数の増加も抑制するマテリアルハンドリングルールとして新たにtime based ruleを提案し,数値的にその有効性を検証する.

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.

近年,環境問題への対応が重要視され,輸送分野においてもCO_2排出量を削減することが強く求められている.本研究ではキャリア選択,混載輸送,国内の拠点間輸送をCO_2削減対策として取り入れ,海外から国内の需要地まで製品を輸送する新たな問題をモデル化し,混合整数計画問題としての定式化を示す.目的関数は輸送コスト,CO_2排出ペナルティコストを考えた.パラメータ解析として,これらのコストに関するパラメータをそれぞれ変化させることで輸送計画にどのような影響が出るのかを調べた.また与えるオーダー数を増加させ,大規模問題へのアプローチを試みた.その結果,求解に要する時間を左右する主な原因は複数のオーダーの組み合わせを検討する混載輸送であることが分かった.

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.

本論文では非一様並列機械スケジューリング問題に対する独自の解法を提案する.目的関数は納期ずれ最小化とした.本論文の提案技法では,この問題をジョブのマシンタイプへの割当問題と割当られたマシンタイプでのジョブのスケジューリング問題の2つの部分問題に分解して問題を解く.後者の問題にはラグランジュ分解調整法を採用した.手法選定理由は,前者の問題を解く際にラグランジュ乗数がその期におけるジョブの混雑度を表しているためジョブのマシンタイプへの割当を考える際に重要な指標となるためである.そして,前者と後者の部分問題を交互に解くことにより効率的に問題を解く.数値実験では,小規模問題と大規模問題でGA(Genetic Algorithm)ベースの解法と比較実験を行い,本提案技法の有効性を確認した.

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.

本論文では職場やエレベータ位置に加えて,I/O位置に関しても詳細な配置位置を決定する多層階レイアウト設計技法を提案する.提案技法では,数理計画により職場やエレベータの詳細座標を同時決定し,職場と重なることなくフロア内の自由な位置にエレベータを配置できるようにした.また,従来研究のようなI/O候補地を設けず,I/O位置についても所与の範囲内で最適化している.提案するモデルは,組み合わせ最適化問題により職場とエレベータの相対的位置関係を決定した後,混合整数計画法により詳細な座標を決定し,通路経由の搬入出口間距離を用いた運搬コストを最小化する.組み合わせ最適化問題の解法としては,SA(Simulated Annealing)を適用している.数値実験では,提案技法の有効性を示す結果を得た.