伊呂原 隆

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.

本論文では通路構造と職場内物流を考慮し,詳細な配置を決定する新たなレイアウト設計技法を提案する.提案技法では,通路構造と搬入出口位置の決定及び数理計画による詳細な座標の決定を職場配置決定と同時に行い,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.また,これまでの研究ではほとんど考慮されてこなかった職場内物流についても合わせて考慮し,U字型ラインなど職場内の機械配置によって決定される職場の搬入出口の設定を行えるレイアウトモデルを構築した.提案するモデルは,メインの問題である職場の相対的位置関係を表現する組み合わせ最適化問題と通路構造等を決定する幾つかのサブ問題の集合である.メイン問題の解法としては,模擬焼鈍し法により最適化を行っている.数値実験では,提案技法の有効性を示す結果を得た.

In this paper, a new layout expression based on the location matrix and some heuristics are proposed for the determination of aisle structure and I/O locations of each department in a facility layout problem. The proposed genetic algorithm-based algorithm finds multiple solutions along the Pareto optimal frontier. The objective here is to minimize (1) material handling cost and (2) building area. The material movements assume that they are transferred along the shortest distance aisle between the I/O locations of those departments. The results of computational experiments show the effectiveness of the proposed algorithm.

本論文では通路構造と職場内物流を考慮した新たなレイアウト設計技法を提案する.通路構造決定では, 運搬コストを最小化する通路位置を設定すると同時に, 通路本数を抑えて通路面積の削減を狙っている.また, これまでの研究ではほとんど考慮されてこなかった職場内物流についても合わせて考慮し, U字型ラインなど職場内の機械配置によって決定される職場の搬入出口の設定を行えるレイアウトモデルを構築した.解法としては, 職場の相対的位置関係を表現するロケーション・マトリックスに通路表現を導入し模擬焼鈍し法により最適化を行っている.数値実験では, 職場の搬入出口の設定が工場内全体の運搬効率に与える影響について新たな知見を得た.

生産効率を陽に考慮した新しい職場配置問題の枠組みを提案する.従来の古典的な職場配置問題の枠組みでは, 職場配置は物流コストのみで評価されており, それら職場配置が生産効率に及ぼす影響については考慮されていない.しかしながら, 現実のシステムでは, 職場配置はスループットやリードタイム等の生産効率に影響を及ほす.従って, システムに含まれるばらつきを吸収し, 最大の生産効率を達成するような「最良の」職場配置が存在する.我々の問題は, 生産効率を最大にする職場配置を求めることであり, このような職場配置問題をここでは「確率的職場配置問題」と呼ぶ.本論文では, 最大スループットを達成するための最適な職場配置と在庫スペース容量配分の組み合わせを求めるような確率的職場配置問題の例を示し, 従来の職場配置問題と比較しながら確率的職場配置問題の得失を論ずる.

This paper presents an algorithm to get a solution of the facility layout problem for multiple-floors. In a number of previous research, these problems are formulated as a combinatorial optimization problem to minimize only the cost owing to material flow between departments ignoring the utilization of the elevators, which is a typical transportation equipment for vertical transportation. In our proposed algorithm, the capacity of elevators is taken into consideration, Then the proposed algorithm optimizes the number and location of elevator with the consideration of the assignment of each carriage to the elevators. Numeric results are given to demonstrate the effectiveness of the proposed algorithm. (C) 1999 Elsevier Science Ltd. All rights reserved.

In this paper, a new type of Vehicle Routing Problem in which distribution centers exist between factories and customers, is addressed. Each distribution center has no fixed category, which means all customers can be served from any distribution center. When demands occur, products are transported from factories to distribution centers by large fleets, and distributed to customers by small fleets. This results in an inter-dependent relationship between transportation and distribution. However, to take into consideration both transportation from factories to distribution centers and distribution from distribution centers to customers, the solution space becomes extremely large. Thus, it is utterly impracticable to solve the problem by using optimization. This paper, therefore, proposes an integrated heuristic using Tabu Search and Simulated Annealing with two-phase approaches to solve the problem. Numerical experiments has been carried out to demonstrate an effectiveness of the proposed technique.

In this paper, we proposed a hybrid genetic algorithm (Hybrid GA) approach to a multi-objective vehicle routing problem (MVRP). The objective functions considered in this MVRP are (1). to minimize the number of vehicles used, (2). to minimize the total traveling distance for vehicles, (3). to minimize the total waiting time for vehicles, and (4). to maximize the grade of customer satisfaction with due-time. With respect to customer satisfaction with due-time, we used the concept of fuzzy due-time because it can describe customers' preference with service time better than crisped expression of satisfaction with 0 and 1. To handle such multi-objectivity, a set of Pareto optimal solutions are searched by Hybrid GA. Among Pareto optimal solutions, we furthermore targeted at compromise solutions whose objective functions take almost intermediate values each, in order to produce realistic routing plans for vehicles. In the proposed algorithm, a local search procedure is applied to each solution at each generation for efficient search of solutions. The computational results show that the proposed algorithm is efficient for solving MVRP.

In this paper, we address the issue of a cellular munufacturing system layout design, which, in essence, consists of two main problems, cell formation and cell layout, While a number of studies have been conducted on this issue, the two problems were usually considered sequentially, and the impact of cell layout was ignored when solving cell formation. Moreover, it was often assumed that all cells are equal in area and shape, which is not practical for industry-scaled problems. We propose an integrated approach using the Genetic Algorithm (GA) to solve the problems simultaneously, with an assumption that not all cell areas and shapes are equal. Our numerical experiments and results show that our method performs better compared to a conventional method in which cell formation and cell layout are considered sequentially.

Automated Guided Vehicle systems (AGVs) are important in modern manufacturing systems. One of the important issues in the design of AGVs is to determine the direction of AGV, which is known as AGV flow path design problem. Though a number of significant research has been conducted on this problem, most of them do not take into consideration the effect of machine layout when designing AGV flow path. In this paper, an integrated technique employing Genetic Algorithm (GA) and Simulated Annealing (SA) to solve AGV flow path design problem is proposed. GA is used to search for AGV flow path direction, while SA is simultaneously used to search for machine layout.

前報〔1〕では配置対象物の形状の多様性及びその数という観点から大規模な配置問題に対応するためのレイアウト技法(CARD-SA)を提案した.本報では, このCARD-SAを発展させて石油精製・石油化学プラントの実問題を解くことを試みる.本問題のデータは実績プラントから各機器のStock情報, 機器間のFlow(配管)情報を読み出して作成し, メンテナンスエリアや機器間で保つべき保安距離などの現実的な制約条件を考慮したものである.機器の数は100を越え, 機器サイズのばらつきが大きいゆえ, 従来の技法では実用上満足出来る解が得られない.本報では前報〔1〕における建屋を敷地に職場を機器に対応させ, 目的関数は総配管長の最小化と考える.また, 有効性の検証では特に, 対象とする機器数が多くまたサイズのばらつきが大きいことより, クーリングスケジュールと解の収束性の関係等に注目する.

In this paper, a layout technique for unequal area and shape departments with a main aisle is proposed. In this technique, layout is represented by the flexible aisle structure (FAS),which is an improvement on the flexible bay structure (FBS), by considering the main aisle inside the building. FBS and FAS treat unequal area and unfixed rectangular shape departments. By using FAS, assignments of departments and aisle structure are optimized simultaneously during the search. In the objective function, which is based on the material handling cost between departments, we use the distance between departments along aisles, in order to take into account the existence of the main aisle. Also, a new measurement for evaluating department shapes, which have unequal area, unfixed rectangles, is proposed. Experimental results indicate the effectiveness of controlling department shapes by this new measurement and of using distance along aisles in the objective function.

Recently, due to consumer demands have been varied, it is essential that a facility layout is able to adapt to the demands dynamically. In this paper, we present a technique to solve a dynamic layout problem. We deal with the problem by simulataneously considering two kinds of flexibility. One is involved with the rearrangement and the changing size of departments. The other is for the fixed area and shape of the building. The Bottom-Left (BL) algorithm is integrated in the technique in order to treat rectangular departments. The two steps Geretic Algorithm (GA) to improve the solutions, which involved an operation called GENITOR algorithm, is proposed. Numerical experiments are carried out to demonstrate the effectiveness of the proposed technique.

配置問題に対する数理的アプローチの多くは配置対象物間の距離とコストペナルティ(Flow)の積の総和を最小化する組み合わせ最適化問題として定式化され, 配置対象物数が10程度の問題に対しては分枝限定法により最適解が求められている.また, その数が10を越え計算時間の制約から最適解法が事実上適用できない問題に対してはSimulated Annealing(SA)に配置変更アルゴリズム(CARD)を組み入れた近似解法(CARD-SA)が提案されている.しかしこのCARD-SAでも, プラントレイアウトとして頻繁に発生する配置対象物数が100程度にもおよび, かつ形状の多様性が高い大規模な問題に対しては適用が困難である.そこで本研究では配置変更アルゴリズム及びSAを改良することにより現実の配置問題に対して実用上満足のいく解を得るための技法を示す.