Due Window Research Articles

Minimizing Conditional Mean Tardiness and Mean Earliness on a Single Machine with the due Windows Approach

A Multicriteria scheduling problem that involves four due date-based performance measures; the total tardiness, proportion of tardy jobs, total earliness as well as the proportion of early jobs was reduced to an equivalent bicriteria problem of minimizing conditional mean Tardiness and conditional mean earliness. A schedule that optimizes these measures tend towards an ideal Just-In-Time (JIT) schedule. Two solution methods named GOA 1 and GOA 2 were proposed to solve the equivalent reduced problem. A single machine and due window approach was explored. The ideal JIT schedule with zero tardiness and zero earliness was set as the optimal and used for benchmarking the proposed solution as well as other methods found in the literature. The results show that the proposed heuristics yielded results that are not significantly different from the optimal in some instances.

Common due window assignment and single machine scheduling with delivery time, resource allocation, and job-dependent learning effect

Common due window assignment and single machine scheduling with delivery time, resource allocation, and job-dependent learning effect

Proportionate Flow Shop Scheduling with Job-dependent Due Windows and Position-dependent Weights

In this paper, we deal with the different due windows assignment proportionate flow shop problem with position-dependent weights, where the sequence is a permutation. The objective is to determine an optimal job sequence and due windows of all jobs such that the weighted sum of earliness–tardiness, the starting time and size of all due windows is to be minimized, where the weight is not related to the job but to the position in which some job is scheduled, i.e., position-dependent weights. According to a series of optimal properties, we prove that the problem can be solved in polynomial time [Formula: see text], where [Formula: see text] is the number of jobs.

The distributed no-idle permutation flowshop scheduling problem with due windows

The distributed no-idle permutation flowshop scheduling problem has gained significant attention as a prominent area of research in recent years, particularly in industries where setup operations are so expensive that reactivating the machines is not cost-effective. This study addresses an extension of the distributed permutation flowshop scheduling problem with no-idle and due window constraints. The aim is to determine the job assignments to the factories and their sequences in each factory that provide the minimum total weighted earliness and tardiness (TWET) penalties considering due windows. This study is the first to formulate this problem, offering four different mathematical models, and presents a benchmark to examine different problem cases that may arise in practical applications. Furthermore, to effectively solve the diverse problem instances, two hybrid metaheuristic algorithms based on the Iterated Greedy are proposed. These metaheuristics exhibit promising capabilities, enabling the solution of problem instances involving up to 500 jobs. To assess the effectiveness of the proposed models and algorithms, extensive numerical experiments are conducted, facilitating a thorough evaluation and comparison of their performances.

Just‐in‐time scheduling problem with due windows and release dates for precast bridge girders

AbstractAs the prefabrication construction method plays an increasingly important role in the construction of cross‐sea bridges, coordinating the schedule between off‐site prefabrication and on‐site assembly becomes crucial and challenging. This paper studies a just‐in‐time single‐machine scheduling problem with due windows and release dates, which originates from the production and supply process of large segments of precast steel box girders in bridge construction. To solve this problem, we equivalently decompose it into the independent and same type of subproblems and formulate a mixed‐integer linear mathematical programming model. Furthermore, we propose a branch‐and‐bound algorithm with the initialization of a novel linear early and tardy dispatching rule with due windows to solve the problem exactly. The dispatching rule, based on the local dominance criteria with due windows, is designed and used to improve the upper bound. Moreover, a lower bound is obtained by further decomposing and relaxing the subproblems. Finally, the parameter of the dispatching rule is calibrated, and the best search strategy of the branch‐and‐bound algorithm is determined. The comprehensive computational experiments carried out show the efficiency and effectiveness of the proposed branch‐and‐bound algorithm.

Delivery Times Scheduling with Deterioration Effects in Due Window Assignment Environments

In practical problems, in addition to the processing time of the job, the impact of the time required for delivering the service to customers on the cost is also considered, i.e., delivery time, where the job processing time is a simple linear function of its starting time. This paper considers the impact of past-sequence-dependent delivery times (which can be referred to as psddt) on the studied objectives in three types of due windows (common, slack and different due windows). This serves to minimize the weighted sum of earliness, tardiness, starting time and size of due window, where the weights (coefficients) are related to the location. Through the theoretical analysis of the optimal solution, it is found that these three problems can be solved in time O(NlogN), respectively, where N is the number of jobs.

Single machine SLK/DIF due window assignment problem with general job-dependent positional effect and outsourcing operations

This paper considers the SLK/DIF due window assignment problems on a single machine with a general job-dependent positional effect, where the actual processing time of a job is subject to a job-dependent and positional effect. Production schedulers may decide to outsource/reject some jobs by paying the corresponding costs. Based on whether to accept the tardiness jobs, we consider two different objectives under slack and different due window assignment, respectively. The first objective function is to minimize the weighted sum of earliness, tardiness, due window starting time, due window size, and outsourcing costs, while the second objective function is to minimize the cost function that includes earliness, due window starting time, due window size and outsourcing costs. We study the structural properties of two due window assignment methods and develop polynomial-time solution algorithms for the considered problems. A numerical example proves the advantage of the outsourcing decision and the distinction between two different objectives.

Formulations and an adaptive large neighborhood search for just-in-time scheduling of unrelated parallel machines with a common due window

In this paper, we address an unrelated parallel machine scheduling problem inspired by aspects of semiconductor manufacturing and other production systems. The objective is to minimize the total sum of weighted earliness and tardiness penalties when jobs are subject to a common restrictive due window. We conceptualize the problem, present two mixed-integer linear programming (MILP) formulations, and give necessary but not sufficient conditions for an optimal schedule. Since optimal schedules do not necessarily start at time zero, we derive two constructive heuristics coupled with a shift-forward procedure that determines leading idle times based on the elimination of straddling jobs. We propose a novel earliness–tardiness extension of the adaptive large neighborhood search (ET-ALNS) with several ad hoc removal/insertion operators and an embedded procedure to accelerate the calculation of the objective function. We carry out extensive computational experiments on a new set of 600 benchmark instances and compare our methods against two modern solvers with an incorporated warm-start mechanism as well as other state-of-the-art algorithms designed for closely related problems. A careful statistical analysis is conducted and results show that ET-ALNS outperforms the remaining methods by a significant margin.

A property-based hybrid genetic algorithm and tabu search for solving order acceptance and scheduling problem with trapezoidal penalty membership function

A property-based hybrid genetic algorithm and tabu search for solving order acceptance and scheduling problem with trapezoidal penalty membership function

Elucidating two-stage flowshop multiprocessor scheduling problems using a hybrid genetic algorithm

In recent decades, genetic algorithms (GAs) have often been applied as heuristic techniques at various settings entailing production scheduling. However, early convergence is one of the problems associated with this approach. This study develops an efficient local search rule for the target-oriented rule in traditional GAs. It also addresses the problem of two-stage multiprocessor flowshop scheduling (FSP) by viewing the due window and sequence-dependent setup times as constraints faced by common flowshops with multiprocessor scheduling suites in the actual production scenario. Using the simulated data, this study verifies the effectiveness and robustness of the proposed algorithm. The results of data testing demonstrate that the proposed method may outperform other algorithms, including a significant hybrid algorithm, in addressing the problems considered.

A Group-Dependent Due Window Assignment Scheduling Problem with Controllable Learning Effect

In this paper, we study a single machine scheduling problem with group-dependent due window assignment and further incorporate autonomous and induced learning effects. Here, autonomous learning refers to learning by doing, while induced learning denotes that proactive investments can promote the learning effect, i.e., the learning effect is controllable. The proactive spending could include any management efforts like professional training programs, among others. The objective is to find optimal strategies of due window assignments, sequence of groups and jobs, and level of induced learning that optimize the total cost comprising the due window-related penalty costs and the investment cost. We present a polynomial-time algorithm capable of solving this problem and an improved idea to further reduce the time complexity. In addition, a detailed numerical example is conducted. Our study shows that the learning effect can be tuned to fit the demand of the manufacturing system better and lead to a more flexible operating system.

RETRACTED: Enhancement of two-stage flow shop multiprocessor scheduling problems using a target-oriented genetic algorithm

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Forward Shift Heuristic for Minimization of Number of Early-Tardy Jobs in Just-In-Time Scheduling in a Flow Shop with Distinct Due Windows

Forward Shift Heuristic for Minimization of Number of Early-Tardy Jobs in Just-In-Time Scheduling in a Flow Shop with Distinct Due Windows

Dispatching Rules for Minimizing Deviation from JIT Schedule Using the Earliness -Tardiness Scheduling Problem with Due Windows Approach

In Earliness-Tardiness (E/T) scheduling approach, the Just-In-Time (JIT) schedule is a schedule with zero earliness and zero tardiness. However, this is an optimal schedule and even notional in some instances where tardiness and earliness are inevitable. However, minimizing the deviation at the upper region (tardiness) and the lower region (earliness) from the JIT schedule is a challenge. This work proposes solutions. Two proposed heuristics; TA1 and TA2 as well as some existing heuristics were explored to solve simulated problems ranging from 5≤n≤400 and the results obtained were benchmarked against the JIT schedule. The results obtained show that one of the heuristics, TA2 yielded JIT schedules for many problem sizes at the lower and upper deviation than other solution methods.

The Due Window Assignment Problems with Deteriorating Job and Delivery Time

This paper considers the single machine scheduling problem with due window, delivery time and deteriorating job, whose goal is to minimize the window location, window size, earliness and tardiness. Common due window and slack due window are considered. The delivery time depends on the actual processing time of past sequences. The actual processing time of the job is an increasing function of the start time. Based on the small perturbations technique and adjacent exchange technique, we obtain the propositions of the problems. For common and slack due window assignment, we prove that the two objective functions are polynomial time solvable in O(nlogn) time. We propose the corresponding algorithms to obtain the optimal sequence, window location and window size.

A discrete learning fruit fly algorithm based on knowledge for the distributed no-wait flow shop scheduling with due windows

The distributed no-wait flow shop scheduling problem with due windows (DNWFSPDW) is a novel and considerable model for modern production chain and large manufacturing industry. The object of total weighted earliness and tardiness (TWETdw) is a common cost indicator in application. A discrete knowledge-guided learning fruit fly optimization algorithm (DKLFOA) is proposed in this study to minimize TWET in DNWFSPDW. A knowledge-based structural initialization method (KNEHdw) is proposed to construct an effective initial solution. In the KNEHdw, the property that the job has no waiting time between processing machines in the no-wait flow shop scheduling problem is abstracted as knowledge to instruct jobs to be placed in possible positions. The swarm center expands from a single individual to an elitist swarm in the vision search stage. A probability knowledge model is established based on the sequence relationship of jobs in the elite population. The feedback information in the iterative process using the probabilistic knowledge model leads the population to search in the direction with a high success rate. The inferior individuals are allocated to the corresponding elite individuals for the local search in the olfactory search stage. The knowledge of weight in due windows is utilized to avoid invalid search during the iteration process. The variable neighborhood descent (VND) strategy is adopted in the local search to enhance the accuracy of the proposed algorithm and jump out of the local optimal. The design of experimental method (DOE) is introduced to calibrate the parameters in the algorithm. The simulation results show that DKLFOA has advantages for solving DNWFSPDW problems comparing with the state-of-the-art algorithms.

Single machine scheduling with common assignable due date/due window to minimize total weighted early and late work

• We study single-machine scheduling problems with assignable due date/due window. • The objective includes penalties for weighted early and tardy work. • We provide complexity analysis of the problem in hand. • For the hard variant of the problem, we provide easy special cases. Traditional scheduling models assume that due dates are predefined and the aim is to find a schedule that minimizes a given scheduling criterion with respect to the given set of due dates. A more recent trend consists of models that focus on coordinating two sets of decisions: due date assignment to customers and determining a job schedule. We follow this trend by analyzing a single machine scheduling problem, where the scheduler is tasked with assigning a common due date to all jobs in order to minimize an objective function that includes job-dependent penalties due to early and late work. We show that the problem is solvable in linear time if the common due date value is unbounded, and in O ( n log n ) time if it is bounded from above. We then extend the analysis to the case where a common due window has to be assigned to all jobs. We show that when the location of the due window is unbounded, the problem is solvable in O ( n log n ) time (and further in linear time if the length of the due window is unbounded as well). However, it becomes NP -hard when it is bounded. We complement our analysis by ( i ) providing a pseudo-polynomial time algorithm to solve this hard variant of the problem, and ( i i ) study two special cases of this hard variant that are solvable in polynomial time.

Modeling and solving a real world machine scheduling problem with due windows and processing set restrictions

Motivated by a real-world problem situation faced by a large manufacturing company in the last step of their production process, we model and solve an offline, deterministic, non-preemptive, identical, parallel machine scheduling problem with processing set restrictions, availability constraints, release dates, bounded tardiness, blocking constraints and due windows. The objective is to minimize the total tardiness while penalizing the assignment of jobs to larger than necessary machines. We develop a mixed integer linear program and we propose a heuristic solution approach relying on the well known large neighborhood search paradigm which relies on tailored destroy and repair operators, embedded into a simulated annealing framework. The proposed algorithm is tested on artificial instances as well as on real-world data provided by the company and compared to optimal solutions obtained by means of CPLEX. The developed tool is now used for decision support by the company partner.

A property-based genetic algorithm for order acceptance and scheduling with common due window

In a competitive make-to-order production environment, limited production capacity and strict delivery requirement prompt the manufacturer to make decisions of order acceptance and scheduling simultaneously with maximization of the total net profit (TNP). In this context, this paper studies an order acceptance and scheduling (OAS) problem in a single machine environment, and the earliness/tardiness penalties are exsiting when an order is not delivered on time under the common due window. To solve this problem, the membership function of earliness/tardiness penalties is designed and a mathematical model for maximizing the TNP is established. According to the characteristics of the problem, three properties that are related to rejecting orders, sequencing orders and determining the starting time of orders are derived separately. Based on these, a property-based genetic algorithm (P-GA) is designed to search for the optimal solution. In numerical experiments, the effectiveness and superiority of the proposed P-GA are demonstrated by comparing with the solutions that are obtained by other two heuristic algorithms.

Effective Heuristics and an Iterated Greedy Algorithm to Schedule Identical Parallel Machines Subject to Common Restrictive Due Windows

Effective Heuristics and an Iterated Greedy Algorithm to Schedule Identical Parallel Machines Subject to Common Restrictive Due Windows