This paper addresses the application of AI techniques in a practical OR problem , i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation ra...

We consider several combinatorial optimization problems which combine the classic shop scheduling problems, namely open shop scheduling or job shop scheduling, and the shortest path problem. The objective of the obtained problem is to select a subset of jobs that forms a feasible solution of the shortest path problem, and to execute the selected jobs on the open (or job) shop machines to minimi...

In most real production environment, schedules are usually inevitable with the presence of a variety of unexpected disruptions. Thus it is necessary to develop predictive/reactive schedules which can absorb disruptions while maintaining high shop performance. In this paper, a robust predictive/reactive scheduling method is developed for solving job shop scheduling in dynamic environment. It con...

In this paper, we first introduce a generalized version of open shop scheduling (OSS), called generalized cyclic open shop scheduling (GCOSS) and then develop a hybrid method of metaheuristic to solve this problem. Open shop scheduling is concerned with processing n jobs on m machines, where each job has exactly m operations and operation i of each job has to be processed on machine i . However...

Flexible flow shop scheduling problem (also called blended flow shop scheduling problem) is a sort of complex Job Shop Scheduling problem. For the Flexible Flow Shop scheduling problem, only in very special cases, there are polynomial optimal algorithms. In most of the other cases, the problems are NP-Hard. It is a simplification of the original problem to solve Flexible flow shop scheduling wi...

We discuss the application of economic coordination mechanisms to scheduling problems in manufacturing and logistics. We use job shop scheduling as a sample problem domain. We study economically augmented job shop problems (EJSP) which comprise valuation information. We demonstrate how instances of EJSP can be mapped to combinatorial job shop auction problems (CJSAP). A discussion of the tracta...

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. In this paper, a novel job shop approach is proposed to create a more efficient integrated harvesting and sugarcane transport scheduling system to reduce the cost of sugarcane transport. There are several benefits that can be attained by treating the train scheduling problem as a job shop pro...

A learning stage of scheduling tends to produce knowledge about a benchmark of priority dispatching rules which allows a scheduler to improve the solution quality for a set of similar job-shop problems. Once trained on the sample job-shop problems (usually with small sizes), the adaptive algorithm solves a similar job-shop problem (with a moderate size or a large size) better than heuristics us...

This paper studies the problem of scheduling Job shops in robotic cells with no intermediate buffers, called No Wait Blocking Transport Job Shop Scheduling Problem (NWBT JSSP). This problem is an extension of the classical job shop problem. No Wait Blocking Transport job shop problems arise in many realistic production environments. To tackle this problem, we developed a Mixed Integer Linear Pr...

This paper studies the problem of scheduling Job shops in robotic cells with no intermediate buffers, called No Wait Blocking Transport Job Shop Scheduling Problem (NWBT JSSP). This problem is an extension of the classical job shop problem. No Wait Blocking Transport job shop problems arise in many realistic production environments. To tackle this problem, we developed a Mixed Integer Linear Pr...