Robust scheduling is aiming at constructing proactive schedules capable of dealing with multiple disruptions during project execution. Insertion a time buffer, before an activity start time, is a method to improve the robustness (stability) of a baseline schedule. In this paper, we introduce new heuristics for inserting time buffers in a given baseline schedule while the project due date is predefined and stochastic activity duration is considered. Computational results obtained from a set of benchmark projects show that the proposed heuristics capable of generating proactive schedules with acceptable quality and solution robustness.
In dynamically changing manufacturing environment, it is quite difficult to optimize interdivisional scheduling which reduces total manufacturing cost of all divisions in a factory following fluctuations in demand. To solve this problem, we proposed an interdivisional scheduling method which integrates two parts, interdivisional scheduling part using Lagrangian decomposition coordination method and divisional scheduling part using real-time scheduling and simulation method, and a co-operation mechanism of these two parts. To clarify the validity of the proposed method, experimental model of Interdivisional Scheduling System is developed and numerical experiments are executed. With this method, we can obtain an effective schedule continuously across all divisions which can respond flexibly to both long and short term fluctuation of the manufacturing environment.
An optimal grinding control scheme for cylindrical plunge grinding is proposed in this paper. The proposed grinding control scheme provides the optimal dressing and grinding parameters for batch production. The proposed control scheme consists of a G.A. (Genetic Algorithm) and dynamic programming. The optimized grinding parameters, in accordance with the state variable per cycle, are determined by the G.A. and dynamic programming is applied to ascertain the optimal grinding and dressing parameters for the overall batch. To evaluate the performance of the proposed scheme, off-line simulations based on the experimental data are conducted.
Recently, attention to the railway transportation has been revived from the viewpoint of alleviating environmental problems such as reduction of CO2 emission amount. Moreover, commuter train services in urban areas and long distance train services such as Shinkansen are indispensable in Japan. In this paper, we formulate the train arrival and departure optimization problem at a terminal station as a 0-1 integer programming problem, and we obtainined solutions by using a solver for the case of Tokyo Station of Tohoku Shinkansen. Moreover, we transform this problem in polynomial time to the Maximum matching problem in a bipartite graph when some realistic conditions are assumed.
A new scheduling method is proposed, in this paper, to consider the statistical distributions of the processing times which are represented by normal distributions for the manufacturing systems where the human operators carry out the machining operations by using manual machine tools. The proposed scheduling method consists of two steps. In the first step, the machining schedules are determined for the individual machine tools based on average processing times by using utility value based method. In the second step, the candidate human operators are assigned to the individual machine tools taking into consideration of the statistical distributions of the processing times. A new scheduling system is also developed to generate suitable schedules based on the proposed scheduling method by using the processing time data executed by the combination of human operators, manual machine tools and machining operations. Some case studies have been carried out to verify the effectiveness of the proposed method.
We consider a repetitive routing problem of a single grasp-and-delivery robot used on a printed circuit board (PCB) assembly line. The robot arranges n identical pins from their current configuration to the next required one by transferring at most one pin at a time. The pins support a PCB from underneath to prevent it from overbending, while an automated manipulator embeds electronic parts in the PCB from above. Given an initial configuration of pins and a sequence of m required configurations, the problem asks to find a transfer route of the robot that minimizes the route length over all m transitions. A polynomial time approximation algorithm with factor two has been proposed by the authors to the problem. In this paper, we design a dynamic programming (DP) procedure to improve its empirical performance, and also conduct numerical experiments to show how well the proposed DP procedure performs.
On many railway lines, not only local trains but also rapid trains are operated. Operating rapid trains can improve the convenience of the passengers, but that outcome strongly depends on the assignment of rapid train stops, the associated timetable, and the number of incoming and outgoing passengers at stops on the way. In particular, to fix assignment of rapid train stops is of great importance. Some previous studies have been conducted to determine optimal assignment of rapid train stops; however, they have relied on approximation methods such as genetic algorithms and local searches. In this study, an enumeration-based algorithm to find optimal assignment of rapid train stops is proposed. Taking actual origin-destination (OD) data of the JR Nambu line, which is a medium-scaled line with 25 stations in Japan, as an example, the proposed algorithm determines optimal assignment of rapid train stops and the associated timetable.
We model for ‘Naiji System’ which is a unique corporation technique between a manufacturer and suppliers in Japan. We propose a two stage solution procedure for a production planning problem with advance demand information, which is called ‘Naiji’. Under demand uncertainty, this model is formulated as a nonlinear stochastic programming problem which minimizes the sum of production cost and inventory holding cost subject to a probabilistic constraint and some linear production constraints. By the convexity and the special structure of correlation matrix in the problem where inventory for different periods is not independent, we propose a solution procedure with two stages which are named Mass Customization Production Planning & Management System (MCPS) and Variable Mesh Neighborhood Search (VMNS) based on meta-heuristics. It is shown that the proposed solution procedure is available to get a near optimal solution efficiently and practical for making a good master production schedule in the suppliers.
To meet higher customer satisfaction and shorter production lead time, assembly lines are shifting to mixed-model assembly lines. Accordingly, sequencing is becoming an increasingly important operation scheduling that directly affects on efficiency of the entire process. In this study, such sequencing problem at the mixed-model assembly line has been formulated as a bi-objective integer programming problem so that decision making through trade-off analysis can bring about significant production improvements. Then we have developed a multi-objective analysis method by hybridizing conventional and recent meta-heuristic methods. After showing its generic idea, the car mixed-model assembly line sequencing problem is concerned as a case study. Certain measures are also introduced to quantitatively evaluate the performances of the method through comparison.
This paper describes a procedure for local modifications of a job shop schedule planned under a no-buffer constraint. The modifications are to correct the infeasibility caused by exchanging two consecutive operations on the same machine. An extended disjunctive graph, which has reverse conjunctive arcs connecting two consecutive processes, is introduced to identify the operations preventing the feasibility of such a schedule. By exchanging the operating order of these operations, the obtained infeasible schedule can be corrected to a feasible schedule. An example of the modifications for a given job shop schedule is shown to discuss the advantages of the proposed procedure.
In this paper, we propose Petri net decomposition approach for bi-objective optimization of conflict-free routing for AGV systems. The objective is minimizing total traveling time and equalizing delivery time simultaneously. The dispatching and conflict-free routing problem for AGVs is represented as a bi-objective optimal firing sequence problem for Petri Net. A Petri net decomposition approach is proposed to solve the bi-objective optimization problem efficiently. The convergence of the proposed algorithm is improved reducing search region by the proposed coordination method. The effectiveness of the proposed method is compared with that of a nearest neighborhood dispatching method. Computational results are provided to show the effectiveness of the proposed method.
In this paper, we propose a heuristic algorithm to solve a practical ship scheduling problem for international crude oil transportation. The problem is considered as a vehicle routing problem with split deliveries. The objective of this paper is to find an optimal assignment of tankers, a sequence of visiting and loading volume simultaneously in order to minimize the total distance satisfying the capacity of tankers. A savings-based meta-heuristic algorithm with lot sizing parameters and volume assignment heuristic is developed. The proposed method is applied to solve a case study with real data. Computational results demonstrate the effectiveness of the heuristic algorithm compared with that of human operators.
Various methods have been proposed to solve the traveling salesman problem, referred to as the TSP. In order to solve the TSP, the cost metric (e.g., the travel time and distance) between nodes is needed. As we do not always have specific criterion for the cost metric we are proposing using a new computation environment that is used all over the world—Google Maps. We think a cost metric obtained from Google maps is a good, impartial value with little room for variation, making it easier and more efficient to make map information visible. Moreover, a scalable computation environment can be prepared by using cloud computing technology. We can even expand the TSP and calculate routes taken by multiple people. The numerical results show this computation environment to be effective.
We propose a framework for describing the Critical Chain Project Management (CCPM) method based on a Max-Plus Linear (MPL) representation for multiple projects. The framework takes into account time buffers in both a single and multi-project environments which play a role for controlling undesirable state change and protecting the completion time for the entire system. The appropriate position and size of these buffers are determined for a large scale multi-project system. Furthermore, the MPL system with the CCPM method applied is redefined to easily comprehend the structure of the system. Finally, we confirm the proposed framework through a numerical example.
It is difficult to calculate the amount of inventory of intermediate products in a material processing factory which has complex multiple processes. If the amount is too great, inventory assets accumulate. On the other hand, if it is too small, tardiness in due-date is caused and the lead time from accepting order to shipping is extended because demand variation and equipment failure cannot be absorbed. In this report we explain a simulation model is explained and results are shown for comparing complementary amount of intermediate products by computing the flow from allocat-ing intermediate products to orders to shipping.
A real-time system receives input from the outside, processes it through a sequence of tasks and outputs the result to the outside. Each process corresponds to a sequence of tasks called path and the time from input to output is called response time. For each path, a response time must always be within a specified time. Our objective is to schedule tasks so that the time constraints for all paths are satisfied. In this paper, we adopt a scheduling method based on a scheduling table, which specifies a schedule of a finite time period and is used repeatedly to define the schedule of longer time periods. In order to calculate response times for a schedule efficiently, we introduce a concept called active path. In our algorithm, a scheduling table is searched by a local search. The neighborhood search is accelerated by using precedence relations that are introduced to preserve active paths of a scheduling table. Finally, we report computational results for sample instances from a company.