Minimizing makespan in a two-machine no-wait flow shop with batch processing machines

Production planning for a class of batch processing problem

“…Sung and Kim [10] follows the flow shop problem where a discrete processing machine is followed by a batch processing one. Muthuswamy et al [9] address the problem where a sequential-batch processor follows a parallel-batch processor, they proposes a particle swarm optimization algorithm making more than 30% improvement for production makespan compared with commercial solvers. For parallel SBP problems, Lee et al [6] address the typical model of SBP considering the single batch-processing machine scheduling problem in burn-in process in SM and extend the result to identical parallel machines.…”

Modeling and optimization of batch production based on layout and cutting problems under uncertainty

“…Modeled by a mixed integer programming Solved by heuristic algorithms, including four single-sequence based heuristics, a biased random-key genetic algorithm, and a hybrid bin loading algorithm Liao and Liao [7] Scheduling jobs in a flow shop with two batch processing machines Formulated as Improved Mixed Integer Linear (MILP) Programming models The solution was proposed as a MILP-based heuristic algorithm Lu et al [8] Scheduling of a multiproduct multi-stage batch processing system Modeled as a mixed integer linear programming (MILP) problem The model was divided into several sub-problems as the horizon was rolled forward, of which a fixand-relax strategy was applied Muthuswamy et al [9] Makespan minimization in a two-machine no-wait flow shop with batch processing machines A particle swarm optimization algorithm was proposed Sabzehparvar and Seyed-Hosseini [10] Multi-mode resource constrained project scheduling problem with mode dependent time lags Solved by Floyd-Warshall algorithm Tang and Liu [11] Two-machine flow shop scheduling involving a batching machine with transportation or deterioration consideration Solved by a heuristic algorithm and its worst-case performance was discussed Traumann and Schwindt [12] Scheduling a given set of operations in a multipurpose batch plant Modeled by a novel two-phase approach (algorithm) dealing with two types of constraints separately H. Zhou et al [13] Batch-processing machine scheduling with arbitrary release times and non-identical job sizes A particle swarm optimization algorithm was modified and applied S. Zhou et al [14] Parallel batch processing machine scheduling considering electricity consumption cost A multi-objective differential evolution algorithm was proposed imi : Amount of time needed to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s. r s i1mi : Amount of time needed from machine #1 as a resource to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s. r s i2mi : Amount of time needed from machine #2 as a resource to process the activity (batch) in execution mode m i (processing unit m i ) in scenario s.…”

“…To this end, they presented Improved Mixed Integer Linear Programming (MILP) models for the problem. Muthuswamy et al [9] examined the problem of minimizing makespan in a two-machine no-wait flow shop with two batch processing machines. Lu et al [8] discussed the challenges brought by lead time on a Rolling Horizon Basis in a research which was conducted as a case study on the issues happened in a multi-product multi-stage manufacturing system.…”

A novel approach for product makespan prediction in production life cycle

“…The entire algorithm was tested on benchmark instances and compared to other algorithms. Muthuswamy S et al [23] proposed a mathematical formulation and presented a particle swarm optimization algorithm to minimize makespan in a two-machine nowait flow shop with batch processing machines. Li J Q et al [24] proposed a novel hybrid tabu search algorithm with a fast public critical block neighborhood structure to solve the flexible job shop scheduling problem with criterion to minimize maximum completion time (makespan).…”