Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as o...

We present here a discipline for job shop reschedub ing based on partial order backtracking. We show that partial order backtracking offers the rescheduler a framework for schedule repair, based upon a set of nocdoods, which impose a systematic paxtial order on the set of activities to be repaired but allows non systematic techniques to be used within that framework. We present rescheduling as ...

Abstract A dynamic programming (DP) approach with adaptive state generation and conflicts resolution is developed to address the timetable-rescheduling problem (TRP) at relatively lower computation costs. multi-stage decision-making model first represent procedure in high-speed railways. Then, an method by reordering trains each station proposed dynamically create possible states according of p...

The capacitated vehicle routing problem is to find a routing schedule describing the order in which geographically dispersed customers are visited to satisfy demand by supplying goods stored at the depot, such that the traveling costs are minimized. In many practical applications, a long term routing schedule has to be made for operational purposes, often based on average demand estimates. When...

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies Rescheduling New arrival...

Oftentimes, hospitals encounter disruptions of nurse schedule due to unforeseen absence of nurses from their scheduled shifts. Known as the nurse rerostering problem, this problem is commonplace in most hospitals all over the world. In such uncertain environments, decision analysis need to reconstruct the roster, ensuring minimal changes on the original roster, subject to reported disruptions. ...

Generating and representing knowledge about heuristics for repair-based scheduling is a key issue in any rescheduling strategy to deal with unforeseen events and disturbances. Resorting to a feature-based propositional representation of schedule states is very inefficient and generalization to unseen states is highly unreliable whereas knowledge transfer to similar scheduling domains is difficu...

When confronted by a problem, human designers often work forward from similar previously solved problems to solve the current problem. Based on this principle, we propose a new learning system especially suited for design using case-based reasoning principles to augment genetic algorithm search. When confronted with a problem we seed a genetic algorithm's initial population with solutions to si...

This paper presents preventive and emergency control measures for on line transient stability (security) enhancement. For insecure operating state, generation rescheduling based on a real power generation shift factor (RPGSF) is proposed as a preventive control measure to bring the system back to secure operating state. For emergency operating state, two emergency control strategies namely gene...