Date Assignment Problem Research Articles

An Exploration of Multitasking Scheduling Considering Interruptible Job Assignments, Machine Aging Effects, the Influence of Deteriorating Maintenance, and Symmetry

The unique topic of allocating and scheduling tasks on a single machine in a multitasking environment is the main emphasis of this research, which also takes into account the effects of worsening maintenance and job-dependent aging effects. In this scenario, the performance and efficiency of the machine in handling different tasks should be symmetric, without significant bias due to the nature or size of the tasks. In a multitasking environment, waiting for jobs can disrupt the processing of the primary job being currently handled. As a result, the actual time required to complete a task becomes erratic and contingent upon the duration of the disruption. In addition to figuring out the best time for maintenance, where to put the due-window, and how big it should be in a multitasking environment, the primary objective is to minimize the costs associated with meeting due-window regulations. To tackle this problem, we propose two optimal algorithms. Additionally, we conduct numerical experiments to compare our approach with the classic due date assignment problem. Interestingly, we observe that in most cases, the average and minimum percentage costs tend to increase as the quantity of jobs increases. However, it is noteworthy that, when the number of jobs is relatively small, specifically when it does not exceed 20, there are instances where these costs decrease with an increase in the number of jobs.

A heuristic for single machine common due date assignment problem with different earliness/tardiness weights

A heuristic for single machine common due date assignment problem with different earliness/tardiness weights

A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem

Particle Swarm Optimization (PSO) has many successful applications on solving continuous optimization problems. It has been adapted to solve discrete optimization problems using different variants, such as integer PSO (IPSO), discrete PSO (DPSO) and integer and categorical PSO (ICPSO). ICPSO, a recent PSO variant, uses probability distributions instead of the solution values. In this study, we applied ICPSO algorithm to solve dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA) problem which is a higher integration level of well-known problems which are integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Briefly, due date assignment function is integrated to IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA performs scheduling function in a dynamic environment in where jobs arrive to shop floor in any time. The objective of DIPPSDDA problem is to minimize the earliness, tardiness and given due dates length. Since the experimental results show that ICPSO does not find better solutions, crossover and mutation operators used in genetic algorithm were implemented to ICPSO, namely modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO provides better performance as compared to genetic algorithm, ICPSO and modified discrete PSO.

Dynamic integrated process planning, scheduling and due-date assignment using ant colony optimization

This paper presents two well-known meta-heuristics which are Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) to solve the dynamic integrated process planning, scheduling and due date assignment problem (DIPPSDDA) in which jobs arrive to the shop floor randomly. In this study, it is aimed to find the best combination of dispatching rule, due date assignment rule and route of all job with the objective of minimizing earliness, tardiness and due-dates of each jobs. 8 different size shop floors for the comparison of the GA and ACO algorithms performances have been developed. As a result of the experimental study, it was concluded that ACO algorithm outperformed GA algorithm. In addition, it has been suggested that integrated approaches can provide more global manufacturing efficiency than individual approaches.

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.

Single machine common flow allowance group scheduling with learning effect and resource allocation

We introduce a single-machine group scheduling job-dependent due date assignment problem with learning effects and resource allocation in which each job is assigned an individual due date based on a common flow allowance. The actual processing time of a job depends not only on its scheduled position but also on its resource allocation. A job completed earlier (later) than its due date will incur an earliness (tardiness) penalty. Our goal is to find the optimal sequence for both the groups and jobs, together with the optimal due date assignment and the optimal resource allocation to minimize the total cost that comprises the earliness and tardiness penalties, and common flow allowance and resource allocation. We give polynomial time algorithm to solve these problems.

SOLVING PROCESS PLANNING, WEIGHTED APPARENT TARDINESS COST DISPATCHING, AND WEIGHTED PROCESSING PLUS WEIGHT DUE-DATE ASSIGNMENT SIMULTANEOUSLY USING A HYBRID SEARCH

Process planning, scheduling, and due date assignment play crucial role in manufacturing systems in terms of efficient and flexible production. Integration of the production functions has been studied in the literature. In this context, there are hundreds of works on integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA) problems. In this paper, we extend the existing literature works on integrated process planning, scheduling and due date assignment (IPPSDDA) using a hybrid search algorithm. Since the scheduling problem is in the NP-Hard problem class without any integration, integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid meta-heuristics are used to solve integrated problem. We used hybrid and random search techniques while solving the integrated problem. Full integration with hybrid search is found as the best combination. So, hybrid search outperformed random search and ordinary solutions were very poor compared to the search results.

Due date assignment and scheduling with time and positional dependent effects

This paper studies a due date assignment and single machine scheduling problem. In this model, the actual processing time of a job depended both on its start time and position in a sequence. Each job has to be assigned a due date by which it must be completed. The goal is to determine schedule the jobs and the due dates so as to minimize the the criteria that includes the cost of changing the due dates, the total cost of discarded jobs and the total earliness of the scheduled jobs. For the proposed problems, polynomial-time algorithms are presented separately for the CON and SLK due-date assignment method versions.

Integrated process planning, WSPT scheduling and WSLK due-date assignment using genetic algorithms and evolutionary strategies

Process planning, scheduling and due-date assignment are three important manufacturing functions in our life. They all try to get local optima and there can be enormous loss in overall performance value if they are handled separately. That is why they should be handled concurrently. Although integrated process planning and scheduling with due date assignment problem is not addressed much in the literature, there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment. Most of the works in the literature assign common due date for the jobs waiting and due dates are determined without taking into account of the weights of the customers. Here process planning function is integrated with weighted shortest processing times (WSPT) scheduling and weighted slack (WSLK) due date assignment. In this study unique due dates are given to each customer and important customers gets closer due dates. Integration of these three functions is tested for different levels of integration with genetic algorithms, evolutionary strategies, hybrid genetic algorithms, hybrid evolutionary strategies and random search techniques. Best combinations are found as full integration with genetic search and hybrid genetic search. Integration of these three functions provided substantial improvements in global performance.

Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects

This paper investigates a multi-objective parallel machine scheduling problem under fully fuzzy environment with fuzzy job deterioration effect, fuzzy learning effect and fuzzy processing times. Due dates are decision variables for the problem and objective functions are to minimise total tardiness penalty cost, to minimise earliness penalty cost and to minimise cost of setting due dates. Due date assignment problems are significant for Just-in-Time (JIT) thought. A JIT company may want to have optimum schedule by minimising cost combination of earliness, tardiness and setting due dates. In this paper, we compare different approaches for modelling fuzzy mathematical programming models with a local search algorithm based on expected values of fuzzy parameters such as job deterioration effect, learning effect and processing times.

Algorithms for the joint multitasking scheduling and common due date assignment problem

In this paper, we investigate a joint multitasking scheduling and common due date assignment problem on a single machine, for which examples can be found in product delivery process in logistics. Multitasking allows the machine to perform multiple tasks. The multitasking phenomenon has been observed in various practical domains, including manufacturing and administration. In multitasking settings, each waiting job interrupts a currently in-processing job, causing an interruption time and a switching time. In common due date assignment problems, the objective is to determine the optimal value of this due date with the purpose of minimising a total penalty function, which is associated with service quality. For the problem with general interruption functions, analytical properties are obtained to reduce the search space of the optimal solutions. For the cases with linear interruption functions, we develop a polynomial-time algorithm. Numerical experiments have been conducted to validate the efficiency of our proposed algorithm. Computational results also demonstrate an interesting phenomenon that in some cases, the optimal solutions under multitasking are superior to the counterparts without multitasking. Besides, we also devise a mixed integer programme for the cases with linear interruption function.

Common due date assignment with generalized earliness/tardiness penalties

We consider the single-machine common due date assignment problem with generalized earliness/tardiness penalties and show that the problem can be solved optimally in polynomial time by dynamic programming. We also present a faster linear time algorithm for a special case of the problem. A number of extensions with compressible job processing times, two assigned due dates and additional terms in the objective function are also analyzed and solved optimally in polynomial time.

Common due date assignment and cumulative deterioration scheduling on a single machine

ABSTRACTThis article addresses a single-machine scheduling and common due date assignment problem in which the actual processing time of a job is a linear increasing function of the total basic processing times of already processed jobs. The aim is to determine simultaneously the common due date and job schedule that will minimize a cost penalty function including the due date assignment cost, total earliness penalties and a weighted number of tardy jobs. The problem is shown to be -hard even if there is no earliness penalty. Moreover, a pseudo-polynomial time algorithm and a fully polynomial time approximation scheme are proposed to solve the problem. An time algorithm is designed to solve the special case when all jobs have identical tardiness penalties.

Proportionate flow shop: New complexity results and models with due date assignment

AbstractIt is known that the proportionate flow shop minimum makespan problem is solved optimally by any permutation job sequence. We show that the problem is at least ordinary NP‐hard when missing operations are allowed and present some solvable cases. We then consider the standard proportionate flow shop problem (with no missing operations) and show that the solution algorithms for a class of single‐machine due date assignment problems can be extended/generalized to the corresponding proportionate flow shop problems. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 98–106, 2015

Single Machine Multiple Common due Dates Assignment Based on Common Flow Allowance and Resource-Dependent Processing Times

This paper studies a multiple common due date assignment problem on a single machine. The job-dependent due dates are obtained based on common flow allowance criteria. We assume that the processing time of a job is controllable by the resource amount assigned to it. The objective is to find the optimal multiple common dues, the set of jobs assigned to each due date, the sequence of jobs and resource allocation scheme to minimize a total cost based on earliness and tardiness of jobs, the common dues and resource cost. We propose an optimal algorithm to solve the problem.

Single Machine Scheduling and Due Date Assignment with Past-Sequence-Dependent Delivery Times and Position-Dependent Processing Times

This paper considers single machine scheduling and due date assignment problems in which the jobs need to be delivered to customers after processing. It is assumed that the delivery times are proportional to the length of the already processed jobs, and a job's processing time depends on its position in a sequence. The objective functions include total earliness, the weighted number of tardy jobs and the cost of due date assignment. We analyze the problems with two different due date assignment methods and conclude that the problems are polynomial time solvable. We provide a dynamic programming algorithm with O(n3) times for the problems.

Single-machine scheduling and due date assignment with rejection and position-dependent processing times

This paper considers a single-machine scheduling and due date assignment problem in which the processing time of a job depends on its position in a processing sequence and jobs can be rejected by incurring penalties. The objective is to minimize the sum of the scheduling criterion of the accepted jobs and the total penalty of the rejected jobs. We first consider the problem with the common due date assignment method where the scheduling criterion is a cost function that includes the costs of earliness, tardiness, and due date assignment. We provide a polynomial-time algorithm to solve the problem. We then provide a unified model for solving the single-machine scheduling problem with rejection and position-dependent processing times. Finally, we extend the results to the setting involving various due date assignment methods.

Due date assignment in single machine with stochastic processing times

This paper considers two different due date assignment and sequencing problems in single machine where the processing times of jobs are random variables. The first problem is to minimise the maximum due date so that all jobs are stochastically on time. It is shown that sequencing the jobs in decreasing service level (DSL) order optimally solves the problem. The results are then extended for two special cases of flow shop problem. The other problem is to minimise a total cost function which is a linear combination of three penalties: penalty on job earliness, penalty on job tardiness, and penalty associated with long due date assignment. The assignment of a common due date and distinct due dates are investigated for this problem. It is shown that the optimal sequence for the case of common due date is V-shaped.

Single-machine due date assignment problem with deteriorating jobs and resource-dependent processing times

We study a single-machine earliness–tardiness scheduling problem with due date assignment, in which the processing time of a job is a function of its starting time and its resource allocation. We analyze the problem with two different processing time functions and three different due date assignment methods. The goal is to minimize an integrated objective function, which includes earliness, tardiness, due date assignment, and total resource consumption costs. For each combination of due date assignment method and processing time function, we provide a polynomial-time algorithm to find the optimal job sequence, due date values, and resource allocations.

Common due date assignment and scheduling with a rate-modifying activity to minimize the due date, earliness, tardiness, holding, and batch delivery cost

e consider a single-machine batch delivery scheduling and common due date assignment problem. In addition to making decisions on sequencing the jobs, determining the common due date, and scheduling job delivery, we consider the option of performing a rate-modifying activity on the machine. The processing time of a job scheduled after the rate-modifying activity decreases depending on a job-dependent factor. Finished jobs are delivered in batches. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find a common due date for all the jobs, a location of the rate-modifying activity, and a delivery date for each job to minimize the sum of earliness, tardiness, holding, due date, and delivery cost. We provide some properties of the optimal schedule for the problem and present polynomial algorithms for some special cases.