Bi-criteria Scheduling Problem Research Articles

Heuristic Approaches for Solving bi-objective Single Machine Scheduling Problems

In this research paper, n jobs have to be scheduled on one-machine to minimize the sum of maximum earliness and maximum tardiness. We solved a series of bi-criteria scheduling problems that related to minimize the sum of the maximum earliness and tardiness. Three new algorithms were presented, two for hierarchical objective and one for the simultaneous objective. Using the results of these algorithms, we minimize the sum of maximum earliness and maximum tardiness. This objective considered as one of the NP-hard problem, and it is also irregular, so this objective missed some helpful properties of regularity. The proposed algorithms had simple structures, and simple to implement. Lastly, they tested for different n.

Approximation algorithms for bicriteria scheduling problems on identical parallel machines for makespan and total completion time

We consider bicriteria scheduling problems with identical machines in parallel and two popular but conflicting objectives, namely, the makespan and the total completion time. Assuming no prioritization of the two objectives, we are interested in the simultaneous optimization of the two objectives and approximation algorithms relative to an ideal schedule – which may not exist – that has both objectives at their minimum. For the problem with a given number of machines, we propose a fast (ρ1,ρ2)-approximation algorithm where ρ1 and ρ2 represent approximation ratios with regard to the makespan and the total completion time, respectively. We also analyze the problem’s inapproximability by proposing a lower bound of the approximation ratio, which cannot be improved by any algorithm.

A Bicriterion Approach to Due Date Assignment Scheduling in Single-Machine with Position-Dependent Weights

We study single-machine bicriteria scheduling problems with position-dependent weights. Due date costs and penalty costs can affect the decision-maker’s profit. Therefore, how to optimize the benefits and balance of these costs for decision-makers is the focus of this research. This paper discusses three models: weighted-sum scheduling problem, constrained scheduling problem, Pareto scheduling problem. It analyzes the properties of bicriteria schedule, determines the optimal due dates (usually referred to as different due dates: DIF), and gives the corresponding polynomial solvable algorithms.

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

Tabu search for a parallel-machine scheduling problem with periodic maintenance, job rejection and weighted sum of completion times

We consider in this work a bicriteria scheduling problem on two different parallel machines with a periodic preventive maintenance policy. The two objectives considered involve minimization of job rejection costs and weighted sum of completion times. They are handled through a lexicographic approach, due to a natural hierarchy among the two objectives in the applications considered. The main contributions of this paper are first to present a new problem relevant to practice, second, to develop a mixed-integer-linear-program model for the problem, and third, to introduce two generalizable tabu-search metaheuristics relying on different neighborhood structures and solution spaces. Computational results for 120 instances (generated from a real case) are reported to empirically demonstrate the effectiveness of the proposed metaheuristics.

Single-machine slack due-window assignment scheduling with multiple maintenance activities and position-and-resource-dependent processing times

This paper studies the slack due-window assignment method for single-machine scheduling problems with position-and resource-dependent processing times, for which the actual processing time is a continuous bivariate non-increasing convex function involving position and resource allocation. Only the slack due-window assignment method will be used. Two linear combination problems concerning position-dependent effects and resource allocation will be studied, in which the machine requires multiple maintenance activities. We present the optimal properties or design optimal algorithms to minimise the combination of the makespan, the total completion time and the total absolute deviation of completion time and to minimise the combination of the total costs of tardiness, the slack due date, the earliness cost, and the resource amount. Finally, we also consider the bi-criteria scheduling problem with nonmaintenance activities to minimise the combination of the total resource allocation cost, the weighted number of tardy jobs, and slack due-window assignment costs. A pseudo-polynomial time algorithm is designed to solve the bi-criteria scheduling problem.

Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost

A bi-criteria scheduling problem for parallel identical batch processing machines in semiconductor wafer fabrication facilities is studied. Only jobs belonging to the same family can be batched together. The performance measures are the total weighted tardiness and the electricity cost where a time-of-use (TOU) tariff is assumed. Unequal ready times of the jobs and non-identical job sizes are considered. A mixed integer linear program (MILP) is formulated. We analyze the special case where all jobs have the same size, all due dates are zero, and the jobs are available at time zero. Properties of Pareto-optimal schedules for this special case are stated. They lead to a more tractable MILP. We design three heuristics based on grouping genetic algorithms that are embedded into a non-dominated sorting genetic algorithm II framework. Three solution representations are studied that allow for choosing start times of the batches to take into account the energy consumption. We discuss a heuristic that improves a given near-to-optimal Pareto front. Computational experiments are conducted based on randomly generated problem instances. The varepsilon -constraint method is used for both MILP formulations to determine the true Pareto front. For large-sized problem instances, we apply the genetic algorithms (GAs). Some of the GAs provide high-quality solutions.

Approximation ratio of LD algorithm for multi-processor scheduling and the Coffman–Sethi conjecture

Coffman and Sethi proposed a heuristic algorithm, called LD (Longest Decreasing), for multi-processor scheduling, to minimize makespan over flowtime-optimal schedules. The LD algorithm is an extension of a very well-known list scheduling algorithm, Longest Processing Time (LPT) list scheduling, to this bicriteria scheduling problem. Coffman and Sethi conjectured (in 1976) that the LD algorithm has the following precise worst-case performance bound: 54−34(4m−1), where m is the number of machines. In this paper, utilizing some recent work by the authors and Huang (2016), which exposed some very strong combinatorial properties of various presumed minimal counterexamples to the conjecture, we provide a proof of this conjecture. The problem and the LD algorithm have connections to some other fundamental problems (such as the assembly line-balancing problem) and algorithms.

Efficient Heuristics for Total Completion Time and Maximum Lateness Bicriteria Scheduling Problems

This paper considers the bicriteria scheduling problem of minimizing the total completion time and maximum lateness with zero release dates. The problem is NP hard, though Early Due Date (EDD) and Shortest Processing Time (SPT) rules yielded optimal solutions for maximum lateness and total completion time, respectively if each criterion were to be considered singly. Thus, the values of each of the criteria for the two proposed heuristics; SII and PI were compared to the optimal solution of the sub problems. Results of computational experiment on job sizes varies from 5-100 jobs showed that the two proposed solution methods yielded results not significantly different from the optimal. This is because the two heuristic yielded results not significantly from the optimal sub-problems for the two performance measures at 95% significant level.

Multistep Crossover Genetic Algorithm for Bi-criteria Parallel Machine Scheduling Problems

This paper propounds the bi-criteria scheduling problem with identical parallel machines, volatile due dates and processing times. A robust and simple structured nature inspired approach has been applied for minimisation of maximum tardiness as the primary and the number of tardy jobs as the secondary criteria. The optimal values of both criteria are evaluated on the trot confined with a constraint that optimal value of secondary criteria does not infringe the primary criteria in the opposite sense of the requirement. To unfold such kind of NP hard optimisation problems, genetic algorithm (GA) has shown a great advantage in solving the combinatorial optimisation problems in view of its characteristic that has high efficiency and is the best fit for practical application. In this paper, a multi-step crossover fusion operator of genetic algorithms (MSXF) has been introduced and applied to a set of randomly generated problems of different sizes. The results obtained are correlated with the other crossover operators. Computational results show that MSXF operator outperforms all the most every time on randomly generated problems analogous to the parameter under consideration.

New Solution Methods for Single Machine Bicriteria Scheduling Problems: Minimizing the Total Flowtime and Maximum Tardiness with Zero Release Dates

This paper considers the bicriteria scheduling problem of minimizing the maximum tardiness and total flowtime on single machine with zero release dates. The problem is NP hard, though Early Due Date (EDD) and Shortest Processing Time (SPT) rules yielded optimal solutions for maximum tardiness and total flowtime, respectively if each criterion were to be considered singly. Thus, the values of each of the criteria for the two proposed heuristics; AA and SA were compared to the optimal solution of the sub problems. Results of computational experiment on job sizes varies from 5-150 jobs showed that the SA heuristic is preferred over AA. This is because the heuristic SA yielded maximum tardiness and total flowtime not significantly different from the EDD and SPT results, respectively at 99% significant level. Though AA yielded optimal for total flowtime, the maximum tardiness results is significantly different from the EDD results.

Minimization of Total Tardiness and Total Flowtime on Single Machine with Non-Zero Release Dates

This paper considers the bicriteria scheduling problem of minimizing the composite function of total tardiness and total flow time on single machine with non-zero release dates. The problem is strongly Non Polynomial (NP). Thus, two heuristics SA7 and SA8 were proposed to solve the problem. The generalized algorithm and the branch and bound procedure were also implemented. The four solution methods were tested on a total of 800 randomly generated problems ranging from 5 to 1000 jobs. Results based on effectiveness and efficiency show that the SA7 heuristic is the most suited solution method to solve the problem.

Meta-Heuristic Algorithms to Solve Bi-Criteria Parallel Machines Scheduling Problem

Parallel machine scheduling problems are classified as NP-hard problems. The direct solutions for these problems are not available and meta-heuristic algorithms are required to be used to find near-optimal solutions. In this paper, the formulations of multi-objective Artificial Bee Colony algorithm by using combination of weighted objectives, secondary storage for managing possible solutions and Genetic algorithm have been developed and applied to schedule jobs on parallel machines optimizing bi-criteria namely maximum tardiness and weighted flow time. The results obtained indicate that proposed algorithm outperforms other multi-objective algorithms in optimizing bi-criteria scheduling problems on parallel machines. Further, the sequential optimization of bi-criteria using Early Due Date (EDD) followed by Genetic Algorithm (GA) has also been investigated. The efficiencies of the proposed algorithms have been verified by numerical illustrations and statistical tests.

Worst-case performance analysis of some approximation algorithms for minimizing makespan and flowtime

In 1976, Coffman and Sethi conjectured that a natural extension of LPT list scheduling to the bicriteria scheduling problem of minimizing makespan over flowtime-optimal schedules, called the LD algorithm, has a simple worst-case performance bound: $$\frac{5m-2}{4m-1}$$5m-24m-1, where m is the number of machines. We study the structure of potential minimal counterexamples to this conjecture, provide some new tools and techniques for the analysis of such algorithms, and prove that to verify the conjecture, it suffices to analyze the following case: for every $$m \ge 4$$mź4, $$n \in \{4m, 5m\}$$nź{4m,5m}, where n is the number of jobs.

Bicriteria scheduling problems involving job rejection, controllable processing times and rate-modifying activity

This paper addresses the bicriteria scheduling problems with simultaneous consideration of job rejection, controllable processing times and rate-modifying activity on a single machine. A job is either rejected, in which case a rejection penalty will be incurred, or accepted and processed on the machine. The rate-modifying activity is an activity on the machine that changes the processing times of the jobs scheduled after the activity. The processing time of a job scheduled after the rate-modifying activity decreases with a job-dependent factor. The processing time of each job can also be controlled by allocating extra resource which is either a linear or a convex function of the amount of a common continuously divisible resource allocated to the job. The objective is to determine the rejected job set, the accepted job sequence, the time (location) of the rate-modifying activity and the resource allocation that jointly find the trade-off between two criteria, where the first criterion is measured as the sum of total completion time and resource consumption cost while the second criterion is the total rejection cost. We consider four different models for treating the two criteria. The computational complexity status and solution procedures are provided for the problems under consideration.

On the equivalence of single machine earliness/tardiness problems with job rejection

We present optimal algorithms for single-machine scheduling problems with earliness criteria and job rejection and compare them with the algorithms for the corresponding problems with tardiness objectives. We present an optimal O(nlogn) algorithm for minimizing the maximum earliness on a single machine with job rejection. Our algorithm also solves the bi-criteria scheduling problem is which the objective is to simultaneously minimize the maximum earliness of the scheduled jobs and the total rejection cost of the rejected jobs. We also show that the optimal pseudo-polynomial time algorithm for the total tardiness problem with job rejection can be used to solve the corresponding total earliness problem with job rejection.

Bounded serial-batching scheduling for minimizing maximum lateness and makespan

For the serial-batching scheduling problem to minimize maximum lateness and makespan simultaneously, when the batch capacity b is unbounded, we have presented an O(n2)-time algorithm. In this paper, we concentrate on the corresponding bounded model of the bicriteria scheduling problem. We obtain an O(n6)-time algorithm to find all Pareto optimal solutions. Moreover, for the special case where the processing times and deadlines are agreeable, we present an O(n3)-time algorithm to find all Pareto optimal solutions. On the other hand, as a result of our main algorithm, we solve the open problem of minimizing maximum lateness on the single bounded serial-batching machine.

Efficient meta-heuristics based on various dominance criteria for a single-machine bi-criteria scheduling problem with rejection

In this paper, we try to fill in the gap between theory and practice in production scheduling by defining a new term as “rejection” and treating the corresponding scheduling problem with multi-objective optimization approach. We study a bi-objective single machine scheduling problem with rejection. At the beginning of scheduling time horizon, scheduler needs to decide which job shall be rejected due to the resource constraints regarding two objective functions: minimization of total weighted completion time of accepted jobs and total rejection penalty of rejected jobs. We develop different algorithms to find the best estimation of Pareto-optimal front for this problem. In order to improve the quality of the solutions, on the one hand, and facilitate the process of selecting best solution for the final decision maker, on the other hand, we integrate various dominance criteria into our proposed algorithms. Finally we compare the performance of those methods by testing on a large set of instances and highlight the advantages and weak points of each one.

BATCHING MACHINE SCHEDULING WITH BICRITERIA: MAXIMUM COST AND MAKESPAN

In this paper, the problem of minimizing maximum cost and makespan simultaneously on an unbounded parallel-batching machine is considered. An unbounded parallel-batching machine is a machine that can handle any number of jobs in a batch and the processing time of a batch is the largest processing time of jobs in the batch. The main goal of a multicriteria problem is to find Pareto optimal solutions. We present a polynomial-time algorithm to produce all Pareto optimal solutions of this bicriteria scheduling problem.

Fast approximation algorithms for bi-criteria scheduling with machine assignment costs

We consider parallel machine scheduling problems where the processing of the jobs on the machines involves two types of objectives. The first type is one of two classical objective functions in scheduling theory: either the total completion time or the makespan. The second type involves an actual cost associated with the processing of a specific job on a given machine; each job-machine combination may have a different cost. Two bi-criteria scheduling problems are considered: (1) minimize the maximum machine cost subject to the total completion time being at its minimum, and (2) minimize the total machine cost subject to the makespan being at its minimum. Since both problems are strongly NP-hard, we propose fast heuristics and establish their worst-case performance bounds.