Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm

Abstract

This paper studies a job shop scheduling problem with due dates and deadlines in the presence of tardiness and earliness penalties. Due dates are desired completion dates of jobs given by the customer, while deadlines are determined by the manufacturer based on customer due dates. Due dates can be violated at the cost of tardiness, whereas deadlines must be met and cannot be violated. The aforementioned scheduling problem, which is NP-hard, can be formulated with the objective function of minimizing the sum of weighted earliness and weighted tardiness of jobs subject to due dates and deadlines. In order to solve this problem, an enhanced genetic algorithm (EGA) is introduced in this paper. EGA utilizes an operation-based scheme to represent schedules as chromosomes. After the initial population of chromosomes is randomly generated, each chromosome is processed through a three-stage decoder, which first reduces tardiness based on due dates, second ensures deadlines are not violated, and finally reduces earliness based on due dates. After the population size is reached, EGA continues with selection, crossover, and mutation. The proposed algorithm is tested on 180 job shop scheduling problems of varying sizes and its performance is discussed.

Minimizing the total weighted earliness and tardiness for a sequence of operations in job shops

“…Since the above approaches for ETSP use mathematical programming models to solve the problem, they inherently generate the optimal schedules and do not require a separate algorithm for schedule generation. Yang et al [32] presented an enhanced genetic algorithm to solve ETSP with distinct due dates and a common deadline for all the jobs. They used an operation-based scheme to represent the chromosomes.…”

On the Optimization of Aircraft Maintenance Management

“…Just as Ahire et al (2000) mention, the problem of scheduling a set of preventive maintenance tasks with a given available workforce can be treated as a variation of the Job Shop Scheduling Problem (JSSP). The generic JSSP is set as follows: for a set of n jobs and a set of m machines (or resources), where each job consists in a sequence of operations and where each operation is performed continuously over a finite period of time in a given machine, the objective of the problem is to find a schedule that minimizes or maximizes certain performance metrics such as to minimize the total duration of the tasks, or maximize machine utilization (Yang et al 2012). The problem has combinatorial characteristics and is considered NP-hard (Rajkumar et al 2011;Karimi et al 2012).…”

Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

“…Also, many papers addressed the same objective in multimachine scheduling environment such as parallel machines (e.g., Emmons [16], Cheng and Chen [17], Alvarez-Valdes et al [18], Kayvanfar et al [19], Li et al [20], Kubiak et al [21]) and flow shops (e.g., Sarper [22], Sung and Min [23], Mosheiov [24], Gupta et al [25], Lauff and Werner [26], Chandra et al [27], Behnamian et al [28], and M'Hallah [29]). Much less has been published on scheduling problems for job shops (e.g., Lauff and Werner [30], Baptiste et al [31], Yang et al [32], and Wang and Li [33]). Gordon et al [34] have reviewed more recent literature of the / problem with CDD.…”

“…Baptiste et al [31] defined an integer programming model for the JITJSSP problem and proposed methods based on two Lagrangian relaxations of the model to derive lower and upper bounds. Yang et al [32] introduced an enhanced genetic algorithm to solve the job shop scheduling problem of minimizing the weighted tardiness and earliness of jobs in the presence of due dates and deadlines. So far, there is no reported research on the job shop scheduling problem (JSSP) considering / over a CDD, and therefore, the twomachine JSSP is addressed in this paper.…”