Exploiting Iterative Flattening Search to Solve Job Shop Scheduling Problems with Setup Times

No abstract availableThis paper presents a heuristic algorithm for solving a jobshop scheduling problem with sequence dependent setup times (SDST-JSSP). This strategy, known as Iterative Flattening Search (IFS), iteratively applies two steps: (1) a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and (2) a solving-step, in which a new solution is incrementally recomputed from this partial schedule. The algorithm relies on a core constraint-based search procedure, which generates consistent orderings of activities that require the same resource by incrementally imposing precedence constraints on a temporally feasible solution. Key to the effectiveness of the search procedure is a conflict sampling method biased toward selection of the most critical conflicts. The efficacy of the overall heuristic optimization algorithm is demonstrated empirically on a set of well known SDST-JSSP 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

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

Stochastic procedures for generating feasible schedules

In this paper, we investigate the use of stochastic variable and value ordering heuristics for solving job shop scheduling problems with non-relaxable deadlines and complex metric constraints. Previous research in constraint satisfaction scheduling has developed highly effective, deterministic heuristics for this class of problems based on simple measures of temporal sequencing exibility. However, they are not infallible, and the possibility of search failure raises the issue of how to most productively enlarge the search. Backtracking is one alternative, but such systematicity generally implies high computational cost. We instead design an iterative sampling procedure, based on the intuition that it is more productive to deviate from heuristic advice in cases where the heuristic is less informed, and likewise better to follow the heuristic in cases where it is more knowledgeable. We specify stochastic counterparts to previously developed search heuristics, which are parameterized to calibrate degree of randomness to level of discriminatory power. Experimental results on job shop scheduling CSPs of increasing size demonstrate comparative advantage over chronological backtracking. Comparison is also made to another, recently proposed iterative sampling technique called heuristic-biased stochastic sampling (HBSS). Whereas HBSS assumes a statically speci ed heuristic bias that is utilized at every application of the heuristic, our approach de nes bias dynamically according to how well the heuristic discriminates alternatives.