A new approach to solving multiobjective flowshop scheduling problems: A multi-MOORA-based genetic algorithm

Flowshop scheduling problems constitute a type of problem that is frequently discussed in the literature, where a wide variety of methods are developed for their solution. Although the problem used to be set as a single purpose, it became necessary to expect more than one objective to be evaluated together with increasing customer expectation and competition, after which studies started to be carried out under the title of multiobjective flowshop scheduling. With the increase in the number of workbenches and jobs, the difficulty level of the problem increases in a nonlinear way, and the solution becomes more difficult. This study proposes a new hybrid algorithm by combining genetic algorithms, which are metaheuristic methods, and the Multi-MOORA method, which is a multicriterion decision-making method, for the solution of multiobjective flowshop scheduling problems. The study evaluates and tries to optimize the performance criteria of maximum completion time, average flow time, maximum late finishing, average tardiness, and the number of late (tardy) jobs. The proposed algorithm is compared to the standard multiobjective genetic algorithm (MOGA), and the Multi-MOORA-based genetic algorithm (MBGA) shows better results.

The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect

This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence.

Stochastic Single Machine JIT Scheduling with Geometric Processing Times and Due Dates

In this paper, we investigate a static stochastic single machine JIT scheduling problem in which the jobs’ processing times are stochastically independent and follow geometric distributions whose mean is provided, due dates are geometrically distributed with a common mean, and both the unit penalty of earliness/tardiness and the fixed penalty of earliness/tardiness are deterministic and different. The objective is to minimize the expected total penalties for quadratic earliness, quadratic tardiness, and early and tardy jobs. We prove that the optimal schedule to minimize this problem is V-shaped with respect to the ratio of mean processing time to unit tardiness penalty under the specific condition. Also, we show a special case and two theorems related to this JIT scheduling problem under specific situations where the optimal solutions exist. Finally, based on the V-shaped characteristic, a dynamic programming algorithm is designed to achieve an optimal V-shaped schedule in pseudopolynomial time.

Simulation Modelling and Analysis of Due Date Assignment Methods and Scheduling Decision Rules in a Flexible Manufacturing System

This paper presents the salient details of a simulation-based study conducted to analyze the effect of due date assignment methods and scheduling decision rules on the performance of a flexible manufacturing system. A typical FMS is considered for investigation in the present study. Three endogenous due date setting methods and one exogenous method are examined in the present study. The scheduling rules considered for experimentation include processing time based rules and due date based rules in addition to the unbiased first-in-first-out rule. The performance measures evaluated in the present study are mean flow time, standard deviation of flow time, mean tardiness, standard deviation of tardiness, percentage of tardy jobs and average flow allowance. Analysis of the simulation results reveal that the dynamic due date setting methods provide better system performance.