There is need for a business model to realize mass customization in the automobile industry. This paper proposes a production planning and management system for implementing mass customization. Firstly, the future collaboration model between a manufacturer and suppliers is introduced. And, a complete designing process for building a production planning and management system is proposed. Secondly, an estimating of unfulfilled order rate which gets the fraction of demand that is not satisfied from components in inventory is derived. Thirdly, we propose an iterative and practical algorithm which solves a production planning problem about mass customization to minimize the inventory holding cost and production cost subject to unfulfilled order rate, production constraints and so on. It is formulated as a probabilistic problem and it is solved effectively by using mathematical programming.
In this paper, we deal with a U-shaped production line with multiple multi-function workers. Each worker takes charge of multiple machines. We consider two types of allocations of workers to machines, a separate allocation and a carousal allocation. In the separate allocation, each worker takes charge of a unique set of machines and therefore at each machine the same worker operates items in every cycle. In the carousel allocation, all workers take charge of all machines in the same order. The processing times of the machines, and the operation and walking times of the workers are assumed to be independent and identically distributed random variables. We examine the influence of the variances of these processing, operation and walking times upon the cycle times of these allocation models by simulation and experimental design. We also examine the influences of the buffer sizes and the number of items in these buffers upon the cycle time, if the U-shaped line has some buffers and some items in them.
Recent globalization of market raises outstandingly the importance of logistic optimization toward just-in-time and agile manufacturing. With this point of view, in this study, we have formulated a site location and route selection problem as a p-Hub problem with capacity constraints. It refers to a non-linear integer programming deciding simultaneously location of hubs and routes from plants to customers via hub facilities. To solve the problem practically as well as efficiently, we have developed a new and novel meta-heuristic method termed hybrid tabu search and implemented it in a hierarchical manner. It relies on a tabu search as the upper level algorithm, and a revised Dijkstra method under Lagrange relaxation for capacity constraints as the lower one. To accelerate the efficiency, we give unique methods to generate an initial hub location based on the minimum spanning tree of the nodes, and to adjust the Lagrange multipliers in imitation of auction mechanism regarding transport cost. Moreover, we adopt a multi-start routine in the aid of ant method to prevent from trapping into the local optimum. Through the numerical experiments that outperformed two popular commercial software, we confirmed effectiveness of the proposed method even for real-life applications.
Semiconductor wafer fabrications are modeled as a re-entrant flow shop problem, in which the processing order of each lot is given, but each lot may enter a production line many times. Hence, a re-entrant flow shop is a very complicated problem, which has a property similar to a job shop problem. In this paper, we consider a scheduling method for a whole production line of re-entrant flow shop. A real wafer fabrication line consists of several hundreds of processes and requires re-scheduling many often. So we propose local search methods instead of seeking exact solutions. Some numerical results indicate that the proposed methods obtain good solutions in a short time.
In this paper, we consider an extended class of flexible shop scheduling problems. First, we translate the problem into a mathematical programming formula, i.e., a mixed-integer programming problem. This makes it possible to apply standard packages of mixed integer programming solvers and, while lots of computational time is required in general, to obtain the optimal schedule. Then, in order to seek the schedules close to the optimal, we newly compose a solution method by adopting genetic algorithms based on the formula, and also we design a hybrid method in which integer programming methods and genetic algorithms are combined. Through some computational experiments, the effectiveness and the potential of the proposed approach are examined.