Abstract
A bilevel facility location problem in which the clients choose suppliers based on their own preferences is studied. It is shown that the coopertative and anticooperative statements can be reduced to a particular case in which every client has a linear preference order on the set of facilities to be opened. For this case, various reductions of the bilevel problem to integer linear programs are considered. A new statement of the problem is proposed that is based on a family of valid inequalities that are related to the problem on a pair of matrices and the set packing problem. It is shown that this formulation is stronger than the other known formulations from the viewpoint of the linear relaxation and the integrality gap. © 2009 Pleiades Publishing, Ltd.

Abstract
Containers have been used in past decades increasingly as one of the most important transportation tools. Containers have revolutionized cargo shipping and thus changed the world trade systematically. Container terminals as the transhipment facility play a valuable role in performance of this transportation system. Improvement of this facility has been widely considered in literatures. Automated container terminals (ACTs) have been introduced to pursue this purpose. In ACTs various transport vehicles are automated and integrated to each other. Automated guided vehicles (AGVs) are used in ACTs to handle containers between quay cranes and storage yards. Usually scheduling of the AGVs is known as the key factor to improve the performance of ACTs. This paper proposed a heuristic algorithm to schedule the AGVs concurrently with quay cranes. A genetic algorithm is proposed to optimize the simultaneous scheduling of AGVs and QCs. The results showed that proposed genetic algorithm can be used in practical implications while its running time is reasonably low.

Abstract
Complex production systems can produce more than one part type. For these systems, production rate and priority of production for each part type is determined by production controllers. In this paper, genetic fuzzy logic control (GFLC) methodology is used to develop two production control architectures namely "genetic distributed fuzzy" (GDF), and "genetic supervisory fuzzy" (GSF) controllers. Previously these controllers have been applied to single-part-type production systems. in the new approach the GDF and GSF controllers are developed to control complex production systems. The methodology is illustrated and evaluated using two test cases; two-part-type production line and re-entrant production systems. Genetic algorithm is used to tune the membership functions of input variables of GSF or GDF controllers. The objective function of the GSF controller minimizes the production cost based on work-in-process (WIP) and backlog costs, while surplus minimization is considered by GDF controller. The results show that GDF and GSF controllers can improve the performance of production systems. GSF controllers decrease the WIP level and its variations. GDF controllers show their abilities in reducing the backlog level but generally, production cost for GDF controller is greater than GSF controller.

Abstract
In this paper, we present a new OLAP Mining method for exploring interesting trend patterns. Our main goal is to mine the most (TOP-K) significant changes in Multidimensional Spaces (MDS) applying a gradient-based cubing strategy. The challenge is then finding maximum gradient regions, which maximises the task of detecting TOP-K gradient cells. Several heuristics are also introduced to prune MDS efficiently. In this paper, we motivate the importance of the proposed model, and present an efficient and effective method to compute it by: • evaluating significant changes by means of pushing gradient search into the partitioning process • measuring Gradient Regions (GR) spreadness for data cubing • measuring Periodicity Awareness (PA) of a change, assuring that it is a change pattern and not only an isolated event • devising a Rank Gradient-based Cubing to mine significant change patterns in MDS. Copyright © 2009, Inderscience Publishers.

Abstract
There has been extensive research on workload and input-output control with the objective of improving manufacturing operations in job-shops. In this paper, a multiple decision-making scheme is proposed to plan and control operations in a general job-shop, and to improve delivery and workload related performance measures. The job-shop characteristics reinforce the need for designing a global system that controls both the jobs entering (order acceptance, due date setting and job release) and the work-in-process (dispatching), leading to an improvement of operational measures. Previous research has concentrated on scheduling a set of orders through the shop floor, according to some decision mechanism, in order to optimise some measure of performance (usually total lead time). This means that, since only a part of the decision-making system is being optimised, the resulting decision may be sub-optimal. In this paper it is shown that the performance of the different decision rules changes when they are considered simultaneously. Hence, a higher level approach, where the four decisions (order acceptance, due date setting, job release and dispatching) are considered at the same time, should be adopted to improve job-shop operational performance.

Abstract
Work flows in a job-shop are determined not only by the release load but also by the number of accepted orders. In this paper the common assumption of accepting all incoming orders regardless of shop condition is relaxed. Instead of placing the orders in a 'pre-shop pool' queue, as in previous research, orders that arrive at the shop, when it is highly congested, may be immediately rejected or their due dates may be negotiated. This paper explores the idea of controlling the workload since the acceptance/rejection 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. The effect of the negotiation phase on the job-shop performance is evaluated using a simulation model of a generic random job-shop that allow us to conclude that having a negotiation phase prior to rejection improves almost all workload performance measures. Different tolerances of the workload limit slightly affect the performance of the job-shop.