Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp

Clips: a capacity and lead time integrated procedure for scheduling.

We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;

Three heuristics of search for a low price when initial information about the market is obsolete

In traditional economics, buyer behaviour is usually modelled under the assumption of full information either on prices and their locations within the market or at least on the probability distribution of prices in the market. Neither of these assumptions seems appropriate in some cases such as when the buyer enters the specific market only very infrequently (e.g., markets for durables). This paper studies experimentally the search rules that buyers might use in this case of extreme lack of information on prices. The paper identifies three general search heuristics, derives three specific rules from the heuristics and, using data from a small-scale experiment, estimates parameters of the rules.search; heuristics; aspiration level; experiment

Card-based delivery date promising in high-variety manufacturing with order release control

Card-based systems – like Kanban and Constant Work-in-Process (ConWIP) – can be simple yet effective means of controlling production. Existing systems, however, can be criticized for their limited applicability and scope. First, card-based systems have not been successful in the production environments that are arguably most in need of their help: complex job shops that produce low-volume, high-variety products. Second, while most existing systems simplify shop floor control, other planning tasks – such as the estimation of short, feasible due dates during customer enquiry management – are not supported. To overcome these limitations, a card-based version of Workload Control – known as COBACABANA (COntrol of BAlance by CArd-BAsed Navigation) – was recently proposed that uses cards for both due date estimation and order release control. This unique combination makes COBACABANA a potentially important means of controlling production, particularly for small job shops with limited resources. However, the original approach had several shortcomings. This paper refines the due date estimation procedure of COBACABANA to make it more practical and consistent with the order release method applied. It then uses simulation to demonstrate – for the first time – the potential of COBACABANA as an integrated concept that combines customer enquiry management and order release control to improve job shop performance. Results also suggest that the need for processing time estimations can be simplified, further facilitating the implementation of COBACABANA in practice

Does Order Negotiation Improve The Job-Shop Workload Control?

Work flows in a job-shop are determined not only by the release load and the time between release factors, but also by the number of accepted orders. There has been extensive research on workload and input-output control aiming at improving the performance of manufacturing operations in job-shops. This paper explores the idea of controlling the workload since the acceptance/rejection of orders stage. A new acceptance/rejection rule is proposed, and tests are conducted to study the sensitivity of job-shop performance to different order acceptance parameters, like the tolerance of the workload limit and the due date extension acceptance. It also evaluates the effect of the negotiation phase of the proposed acceptance rule on the job-shop performance using a simulation model of a generic random job-shop. The extensive simulation experiments allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. We also conclude that different tolerances of the workload limit affect slightly the performance of the job-shop.job shop, order negotiation, workload control

A Selectivity Model of Household Pork Consumption Behavior in Taipei, Taiwan

Food Consumption/Nutrition/Food Safety,