Due Dates Of Jobs Research Articles

A Single Machine Group Scheduling Problem with Due Date Assignment and Position-Dependent Costs

In this paper, we study a single machine scheduling problem with due date assignment under a group technology environment. The due dates of jobs are assignable according to the following three methods: the common (CON) due date assignment, the slack (SLK) due date assignment, and the different (DIF) due date assignment. Our objective is to determine a group sequence and job sequences within all groups, together with a due date assignment, to minimize a total cost function including earliness, tardiness, due date assignment and flow time costs, in which all unit costs are different and position-dependent. We present a unified [Formula: see text] time algorithm to solve the problem for all the three due date assignment methods.

Deep Reinforcement Learning for Minimizing Tardiness in Parallel Machine Scheduling With Sequence Dependent Family Setups

Parallel machine scheduling with sequence-dependent family setups has attracted much attention from academia and industry due to its practical applications. In a real-world manufacturing system, however, solving the scheduling problem becomes challenging since it is required to address urgent and frequent changes in demand and due-dates of products. To minimize the total tardiness of the scheduling problem, we propose a deep reinforcement learning (RL) based scheduling framework in which trained neural networks (NNs) are able to solve unseen scheduling problems without re-training even when such changes occur. Specifically, we propose state and action representations whose dimensions are independent of production requirements and due-dates of jobs while accommodating family setups. At the same time, an NN architecture with parameter sharing was utilized to improve the training efficiency. Extensive experiments demonstrate that the proposed method outperforms the recent metaheuristics, rule-based, and other RL-based methods in terms of total tardiness. Moreover, the computation time for obtaining a schedule by our framework is shorter than those of the metaheuristics and other RL-based methods.

Multitasking scheduling and due date assignment with deterioration effect and efficiency promotion

We investigate a multitasking scheduling and due date assignment problem with position-dependent deterioration effect and efficiency promotion. Multitasking scheduling allows the processing of a primary job being interrupted by the waiting jobs that are available but unfinished. The duration of processing a primary job is comprised of three parts: the processing time of the remaining part of the primary job, the interruption time denoting the time of the partial processing of the waiting jobs, and the switching time denoting the time during which no jobs are processed when the primary job being scheduled. We consider two behavioral factors which may affect the production efficiency. One is the position-dependent deterioration effect on primary jobs, and the other is the linear efficiency promotion on waiting jobs. The position-dependent deterioration effect means that the productivity deteriorates over time due to personnel fatigue, thereby increasing the actual processing times of primary jobs depending on their scheduled positions. The efficiency promotion indicates that switching between different jobs can keep them from being bored, thereby leading to an increase in productivity when operators tackle the interruption parts of waiting jobs. The due dates of jobs are decision variables in two well-known due date assignment models, i.e., the common and unrestricted due date assignment models. We study several scheduling criteria including the makespan, the total completion time, and two due date-related criteria. For each considered problem, we design an efficient polynomial time algorithm, and present the structural properties of the optimal schedules under different conditions considering the relationships between deterioration effect and efficiency promotion.

Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

This paper deals with unrelated parallel machines scheduling problem with sequence dependent setup times under fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties, which belongs to NP-hard class. Due to inherent uncertainty in Processing times, setup times and due dates of jobs, they are considered here with triangular and trapezoidal fuzzy numbers in order to take into account the unpredictability of parameters in practical settings. Although this study is not the first one to study on fuzzy parallel machines scheduling problem, it advances this area of research in three fields: (1) it selects a fuzzy environment to cover the whole area of the considered problem not just part of it, and also, it chooses an appropriate fuzzy method based on an in-depth investigation of the effect of spread of fuzziness on the variables; (2) It introduces a mathematical programming model for the addressed problem as an exact method; and (3) due to NP-hardness of the problem, it develops an existing algorithm in the literature for the considered problem through extensive simulated experiments and statistical tests on the same benchmark problem test by proposing a genetic algorithm (GA) and a modified simulated annealing (SA) methods to solve this hard combinatorial optimization problem. The result shows the superiority of our modified SA.

Some due date determination scheduling problems with two agents on a single machine

This paper addresses some scheduling problems with two competing agents, called agents A and B, respectively, each of which has a set of independent nonpreemptive jobs to be scheduled for processing on a common machine. The due dates of jobs in each job set are considered as given parameters and must be assigned to individual jobs. Each agent wants to minimize a certain objective function that depends on the completion times and due dates of its jobs only. The goal is to find a feasible schedule and determine the due dates for all jobs of the two agents that minimizes the objective of agent A while keeping the objective of agent B below or at a fixed level Q. We consider nine problems arising from different criteria combinations for the two agents, depending on the criterion of each agent including the maximum lateness, total (weighted) tardiness, (weighted) number of tardy jobs, and total weighted tardiness and earliness, and state the complexity results for most of problems under consideration.

Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment

The current paper investigates a non-identical parallel machine multi-objective scheduling problem in which both the deterioration and learning effects have been considered. Due to uncertainty of the parameters in real-world systems, processing times and due dates of jobs are represented here with triangular fuzzy numbers. Using the credibility measure, a nonlinear mathematical model is provided based on fuzzy chance-constrained programming (FCCP) with the aim to minimize two objective functions, namely total earliness/tardiness (ET) and maximum completion time of jobs (makespan). Since it is a mixed integer nonlinear mathematical model, there is no guarantee that the solution will obtain a global optimum. Therefore, a multi-objective branch and bound algorithm is provided by introducing an effective lower bound in order to obtain a Pareto optimal front. Computational results show that the algorithm proposed is especially useful to solve large-scale problems.

Scheduling Problems with Due Date Assignment

This chapter is devoted to scheduling problems with due date assignment in the context of two-agent scheduling. The main feature of these problems is that the due dates of jobs from either one or both of the two agents are decision variables, which are part of the decision process along with sequence decision. We focus on the following three different scenarios: (i) The due dates of jobs from both of the two agents are decision variables, which are determined using the DIF, CON, or SLK due date assignment method; and (ii) the due dates of the jobs from one agent are decision variables, whereas the due dates of the jobs from the other agent are exogenously given.

109 GAと優先規則の融合による能力調整を考慮したジョブショップスケジューリング : ジョブのクリティカル値に基づく残業決定法(OS4 : 生産スケジューリング・生産管理(2))

We have already proposed a job shop scheduling method to meet due-dates of jobs and to minimize overtime as far as possible. The proposed method can efficiently search schedules using the mixture of genetic algorithm and priority rule. In the method, however, a priority rule was used only for the decision of job sequence. The decision for overtime was carried out using the genetic algorithm alone. In this paper, we propose the more efficient scheduling method by applying the effective knowledge of priority rule not only for the decision of job sequence but also 11)「that of overtime. Numerical experiments show the effectiveness of the proposed method.

Approximate trade-off between minimisation of total weighted tardiness and minimisation of carbon dioxide (CO2) emissions in bi-criteria batch scheduling problem

The quantity of carbon dioxide (CO2) emissions is one of the most widely recognised measures of environmental sustainability. Given the mounting concern about climate change and global warming, managers are facing growing pressure to reduce CO2 emissions. In practice, other than CO2 emissions, managers may be concerned with other objectives when making a scheduling decision. This work develops the ε-archived genetic algorithm (ε-AGA) to examine two batch scheduling problems with the goal of minimising CO2 emissions and the traditional due date-based objective of minimising total weighted tardiness (TWT). Experimental results show that in terms of both quality and diversity of solutions, ε-AGA outperforms NSGA-II for same computation time limit as the stopping criteria. Several interesting observations are made. (1) These two objectives conflict with each other; (2) jobs that arrive soon after each other reduce makespan, and so reduce CO2 emissions; (3) given a set of m identical batching machines, the due dates of jobs do not seem to substantially influence CO2 emissions; and (4) in purchasing a machine, the variation in power consumption among machines is critical to reducing the TWT.

Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints

This paper develops a new multi-objective hybrid flexible flowshop problem with some useful constraints including of the limited waiting times (LWT) between every two successive operations, unrelated parallel machines at least one stage, sequence- and machine-dependent set-up times and due dates of jobs. A mathematical model for this problem as a mixed-integer programming is provided to simultaneously minimise the total weighted tardiness and maximum completion times. We applied two metaheuristic methods based on the Pareto approach, multi-objective Particle swarm optimisation and strength Pareto evolutionary algorithm II, to solve our problem and achieve the non-dominated Pareto frontier. To assess the performances of the presented algorithms and compare their results, a set of test problems in small and large sizes are created and then executed on the algorithms. Owing to the sensitivity of the values of parameters in the metaheuristic algorithms, a response surface methodology (RSM) as a strength statistical tool adjusts the parameters of both algorithms separately for small- and large-sized problems. The computational evaluation demonstrated that multi-objective Particle swarm optimisation is an effective and appropriate algorithm for our investigated problem.

Fuzzy optimisation of multi-objective job shop scheduling based on inventory information

Job shop scheduling problems are one of the oldest combinatorial optimisation problems being studied. In this paper, fuzzy processing times of operations and fuzzy due dates of jobs are considered to incorporate fuzziness in the problem. Percentage of inventory consumption and profit earned form the orders are also considered in this fuzzy multi-objective job shop scheduling problem. Fuzzy inference system (FIS) is used to calculate the job weights based on the percentage of inventory consumption for a particular job and profit can be earned from the jobs. Average weighted tardiness, number of tardy jobs, total flow time and idle times of machines are considered as objectives which should be minimised. In this paper, genetic algorithm (GA) is used as a heuristic technique with specially encoded chromosomes that denotes the complete schedule of the jobs. A local search technique, simulated annealing (SA) is also used to compare the results obtained in two different methods. Different problem sizes has been tested and the fitness function values and computation times of the problems for each method is compared.

Job Shop Scheduling with Capacity Adjustment (An Efficient Search Method Using the Mixture of GA and Priority Rule)

In this paper, we propose an efficient scheduling method for a job shop environment in which production capacity is adjustable by adding overtime in each machine of each production shift. The primal objective of scheduling is to meet due-dates of jobs and the secondary one is to minimize overtime. To achieve these two objectives efficiently, the schedule is searched to eliminate tardy jobs at first by allowing large overtime. Then the overtime is minimized. The method using the mixture of genetic algorithm and priority rule is applied for this scheduling. In this method, the characteristics of priority rules are the key for improving performance. In particular, when minimizing overtime after finding schedules without tardiness, it is important to maintain more individuals meeting due dates even after genetic operations are applied. For this purpose, we propose a scheduling method using the priority rules which are modified (CR+SPT) rule and ATC rule. Numerical experiments show the effectiveness of the proposed method.

Single machine scheduling to minimize the absolute maximum lateness with shortening job processing times

In this paper we consider a single machine scheduling problem with shortening job processing times. The objective is to find a schedule that minimizes the absolute maximum lateness, where the due dates of jobs are a common due date for all jobs. For the cases of common due date is a given constant and a variable, we show that the problem can be solved in polynomial time, respectively.

Job due-date assignment with earliness and tardiness penalties and stochastic arrival and service times

We consider the problem of assigning job due-dates when the objective is to minimise the total expected earliness and tardiness penalty. This type of problem is important because modern production and distribution systems discourage both tardy and early deliveries. This paper extends a general due-date policy, the so-called SL rule, to incorporate not only tardiness but also earliness. We provide optimal policy parameters for the linear and quadratic penalty cases in an M/M/1 system.

A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine

This paper considers a classic model of weighted single machine scheduling problem and aims to improve it to a real-world application through fuzzy set theory. For this purpose, processing times and due dates of jobs are defined as fuzzy numbers. In the proposed model, two objectives are considered to be minimized: average tardiness and number of tardy jobs. The objectives are converted into fuzzy statement through fuzzy arithmetic. Because the problem is NP-hard, it is proposed to solve the model through three well-known meta-heuristic algorithms as Simulated Annealing, Tabu Search, and Genetic Algorithm albeit with some modifications. Comparative analysis of algorithms is attainable through different experimented results on generated benchmark problems with different sizes and difficulties. Efficiency of the developed algorithms is analyzed on the different versions of the model which come from the assumption of different parameters and/or components. Algorithms' behaviors to managerial insights are also considered in some sensitivity analysis experiments. The obtained results show the applicability of the proposed model in real-world scheduling problems.

Scheduling a burn-in oven with non-agreeable release times and due dates

We consider the problem of scheduling semiconductor burn-in operations, where burn-in ovens are modelled as batch processing machines. Most of the studies assume that ready times and due dates of jobs are agreeable (i.e., ri < rj implies di ≤ dj). In many real world applications, the agreeable property assumption does not hold. Therefore, in this paper, scheduling of a single burn-in oven with non-agreeable release times and due dates along with non-identical job sizes as well as non-identical processing of time problem is formulated as a Non-Linear (0-1) Integer Programming optimisation problem. The objective measure of the problem is minimising the maximum completion time (makespan) of all jobs. Due to computational intractability, we have proposed four variants of a two-phase greedy heuristic algorithm. Computational experiments indicate that two out of four proposed algorithms have excellent average performance and also capable of solving any large-scale real life problems with a relatively low computational effort on a Pentium IV computer.

The single-machine early-tardy Problem with Idle Time and Time Windows Permitted

In this article, a single-machine weighted earliness and tardiness problem with arbitrary time windows is considered. Idle time between two adjacent jobs is permitted and the due dates of jobs may be unequal. The dominance rules can be utilized to develop a relationship matrix which allows the branching algorithm to eliminate a high percentage of nonfeasible solutions. After this matrix is combined with a branching strategy, a procedure which can solve the problem is proposed.

Multi-objective genetic algorithm for single machine scheduling problem under fuzziness

This paper presents a new multi-objective approach to a single machine scheduling problem in the presence of uncertainty. The uncertain parameters under consideration are due dates of jobs. They are modelled by fuzzy sets where membership degrees represent decision maker’s satisfaction grade with respect to the jobs’ completion times. The two objectives defined are to minimise the maximum and the average tardiness of the jobs. Due to fuzziness in the due dates, the two objectives become fuzzy too. In order to find a job schedule that maximises the aggregated satisfaction grade of the objectives, a hybrid algorithm that combines a multi-objective genetic algorithm with local search is developed. The algorithm is applied to solve a real-life problem of a manufacturing pottery company.

A robust approach for the single machine scheduling problem

This paper describes a robust approach for the single machine scheduling problem 1|r i |L max . The method is said to be robust since it characterizes a large set of optimal solutions allowing to switch from one solution to another, without any performance loss, in order to face potential disruptions which occur during the schedule execution. It is based on a dominance theorem that characterizes a set of dominant sequences, using the interval structure defined by the relative order of the release and the due dates of jobs. The performance of a set of dominant sequences can be determined in polynomial time by computing the most favorable and the most unfavorable sequences associated with each job, with regard to the lateness criterion. A branch and bound procedure is proposed which modifies the interval structure of the problem in order to tighten the dominant set of sequences so that only the optimal sequences are conserved.

Mixed binary integer programming formulations for the flow shop scheduling problems. A case study: ISD projects scheduling

In this paper, we consider a flow shop scheduling problem under different conditions. The model is formulated in a form of mixed binary integer programming. The primary goal of this paper is to develop operation schedules that specify the completion time of operations. The objective function of our model is formulated as a minimization of the makespan, the weighted mean flow-time or the weighted mean tardiness. There are several constraints which are involved in our modeling such as the due dates of jobs, the jobs ready times, etc. For these objective functions and constraints, seven mixed binary integer programming models are proposed. We demonstrate the implementation of our proposed models using different numerical examples. As a case study, we apply the proposed models for an information systems development (ISD) center to schedule the ISD projects using some real data.