Engineering Costs and Production Economics最新文献

We consider the selection of optimal lead time variability in continuous review inventory models. The variability level is optimized jointly with the lot size and reorder point, where lower variability can be achieved at an extra cost. Low variabilities may correspond to more reliable sources or production processes, and their attainment is one of the implicit goals of JIT logistics. We analyze the resulting tri-variate models, providing sufficient conditions for joint convexity, and hence unique optimum, an algorithm and illustrative examples.

This paper focuses on the problems of logistics from several viewpoints relevant in practice. Empirical observations are extracted from different branches of industry. Based on these observations we classify a set of approaches. We consider that an appropriate approach in future is the integrated paradigm having a basis on traditional industrial engineering discipline supported by information, communication and calculational technologies. This paper is associated with an ongoing Esprit II project 2277-CMSO (CIM for Multisupplier Operations).

This paper considers the development of a general part sequencing methodology which suits flexible systems. A flexible assembly cell configuration is conceptualized. The cell is characterized by a common buffer and a single flexible transfer device for inter-cell transfer activities. Since the time available for decision making on part sequencing is very short, a fast solution is required. A real-time computerized part sequencing system of modular structure is developed. The system comprises three functions: ENTER, ALLOCATE, and TRANSFER SEQUENCE. Each function is provided with various priority options to satisfy different company strategies. Computer simulation is used to compare alternative sequencing structures of the system developed, and to examine its effectiveness under different operating conditions with reference to cell performance and time response.

Determing the number and type of AGVs and the assignment of AGVs to transport parts between workstations is an important problem encountered in the design of an Automated Guided Vehicle System (AGVS). In this paper, an optimization model is proposed to solve this problem. The model minimizes the overall system cost defined as the sum of the annualized cost of operating the AGVs and the cost of transporting parts between workstations. The model is illustrated using a numerical example.

Maintenance in a process industry, such as chemical and oil companies and food processing, needs frequent part replacements to assure continued operations. These parts represent non-repairable items which are best purchased, such as bearings and gaskets. Based on limited information on the time between failures of such systems, one can forecast the need for spare parts for a given planning horizon, which may be from one to several years. In this paper we discuss the methodology to calculate the spare part requirements for non-repairable systems and purchasing strategies for these parts. These strategies are based on a stochastic characterization of the time between failures of the system. The inventory level to be maintained will depend upon the need of such parts in consecutive time intervals or inventory cycles to satisfy the maintenance requirements with an acceptable risk level.

The objective of this study is to minimize the expected present worth of the production costs incurred over the operational life of a robotic assembly system that is integrated with an Automatic Storage/Retrieval System (AS/RS). Production costs include inventory cost and capital cost of equipment. Inventory cost consists of ordering, holding, and lostproduction costs; and all of these costs depend on the scheduling policy for the assembly robots as well as the inventory policy for the AS/RS. Capital cost of equipment depends on the number of depalletizer robots and the number of assembly robots. To identify the minimum-cost design for a certain automotive assembly operation from a given set of alternative system configurations, we present a simulation experiment in which independent replications of each alternative are controlled by a statistical ranking-and-selection procedure that has been adapted to simulation.

In this paper we consider the problem of scheduling n independent jobs on m parallel and identical machines with preemptions. The objective is to minimize the maximum job completion time (or makespan) while meeting all job due-dates. We present an algorithm which is able to generate a due-date schedule, if one exists, and to find the minimum makespan. We also discuss the relevance of the number of job preemptions as a performance measure for the class of parallel-machine scheduling problems. Finally, we identify some potential areas worthy of further research.

When parts are on the shop floor and in manufacture, they are either waiting for or are being attended by a machine or a material handling equipment. When several workstations are required to process a job, the machining and moving of the parts between the workstations can be very time consuming unless it is properly planned. When poorly planned, the processing and handling decision can significantly affect the length of time the job spends in the shop. In this paper, a methodology for planning the machining and the material handling activities for the manufacture of small to medium size batches in a job shop is presented. The model determines the machining rate at each workstation and the unit-load size to use in handling the batch through the production system in order to minimize the total production time. The procedure is easy to implement on a microcomputer for use in planning shop production activities.