Iterative Flattening Search for the Flexible Job Shop Scheduling Problem

This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incrementally recomputed from this partial schedule. This work contributes two separate results: (1) it proposes a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an original relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the execution orders of the activities on the machines and opening the resource options for some activities selected at random. The efficacy of the overall heuristic optimization algorithm is demonstrated on a set of well-known benchmarks

Job Shop Scheduling with Routing Flexibility and Sequence-Dependent Setup Times

This paper presents a meta-heuristic algorithm for solving a job shop scheduling problem involving both sequence dependent setup-times and the possibility of selecting alternative routes among the available machines. The proposed strategy is a variant of the Iterative Flattening Search (IFS ) schema. This work provides three separate results: (1) a constraint-based solving procedure that extends an existing approach for classical Job Shop Scheduling; (2) a new variable and value ordering heuristic based on temporal flexibility that take into account both sequence dependent setup-times and flexibility in machine selection; (3) an original relaxation strategy based on the idea of randomly breaking the execution orders of the activities on the machines with a activity selection criteria based on their proximity to the solution\u27s critical path. The efficacy of the overall heuristic optimization algorithm is demonstrated on a new benchmark set which is an extension of a well-known and difficult benchmark for the Flexible Job Shop Scheduling Problem

A hybrid algorithm for flexible job-shop scheduling problem with setup times

[EN] Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. 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Applying Iterative Flattening Search to the Job Shop Scheduling Problem with Alternative Resources and Sequence Dependent Setup Times

This paper tackles a complex version of the Job Shop Scheduling Problem (JSSP) that involves both the possibility to select alternative resources to activities and the presence of sequence dependent setup times. The proposed solving strategy is a variant of the known Iterative Flattening Search (IFS) metaheuristic. This work presents the following contributions: (1) a new constraint-based solving procedure produced by means of enhancing a previous JSSP-solving version of the same metaheuristic; (2) a new version of both the variable and value ordering heuristics, based on temporal flexibility, that capture the relevant features of the extended scheduling problem (i.e., the flexibility in the assignment of resources to activities, and the sequence dependent setup times); (3) a new relaxation strategy based on the random selection of the activities that are closer to the critical path of the solution, as opposed to the original approach based on a fully random relaxation. The performance of the proposed algorithm are tested on a new benchmark set produced as an extension of an existing well-known testset for the Flexible Job Shop Scheduling Problem by adding sequence dependent setup times to each original testset\u27s instance, and the behavior of the old and new relaxation strategies are compared

Replanning in Predictive-reactive Scheduling

Abstract Achieving optimal results in real-life production scheduling is precluded by a number of problems. One such problem is dynamics of environments with unavailable resources (such as machine breakdowns and ill workers) and new demands (e.g. new orders) coming during the schedule execution. Traditional approach to react to unexpected events occurring on the shop floor is generating a new schedule from scratch. Complete rescheduling, however, may require excessive computation time. Moreover, the recovered schedule may deviate a lot from the ongoing schedule. Some work has focused on tackling these shortcomings, but none of the existing approaches tries to substitute jobs that cannot be executed with a set of alternative jobs. This paper reviews techniques related to predictive-reactive scheduling and suggests the future goal, which is to propose algorithms for dealing with unexpected events using the possibility of alternative processes

Task scheduling for dual-arm industrial robots through Constraint Programming

In a society where more and more production becomes automated it demands robots that are as flexible and versatile as humans. Such flexibility demands automatic scheduling of tasks. In this thesis we approach the problem using Constraint Programming and through a case study we present a model for a dual-armed robot that is able to deal with a more flexible workload. We also introduce filters to cut down the runtime of the solver. To evaluate the model we tested it on 6 solvers; G12/FD, JaCoP, Gecode, or-tools, Opturion CPX and Choco3. The results show that the model can produce a solution as good as the one manually implemented for the case study. We introduce filters on the domains of some of the variables and they made an improvement on the runtime for many of the solvers. We also found that the runtime of the solvers varied a lot and could range from several hours to just a few milliseconds using the same data. Unfortunately, in many of the tests the solvers did not complete their searches within the time limit of 4 hours. In some cases when using MiniZinc version 2.0.1, the solvers were not able to read the FlatZinc files. The fastest solver in our tests was Gecode using MiniZinc version 2.0.1

Study applying simulation to improve a real production process in the context of Industry 4.0

During the thesis development, simulation theories and techniques will be applied to a part of the production process of an Italian manufacturing company. A simulation model of the steaming and washing phases will be developed to outline the automated and manual procedures that are performed in the AS-IS state. Several what-if scenarios will be then envisioned and simulated to analyze how the production activities could be re-engineered in the light of the new technological advancements, such as the introduction of full traceability

Novel models and algorithms for integrated production planning and scheduling

This thesis is concerned with production planning and scheduling in make-to-order manufacturing system. We seek effective modelling and efficient solution techniques that can help increase the productivity and the service level of an enterprise, together with reducing production costs, by supporting the management to make smarter decisions on these two levels of the planning hierarchy. In th

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry