Multi-criteria Scheduling Research Articles

Using benchmarks to study an intelligent and multicriteria scheduling service

The implementation of new production organization methods become essential in the evolution of companies' context. These methods include scheduling. In this context, scheduling is generally multicriteria. To solve this problem, we need multicriteria optimization methods to search all optimal solutions and multicriteria decision making methods to choose the best compromise between potential solutions. The objective here, is to evaluate the performance of a multicriteria decision making method used, in real time, in the holonic, isoarchic and multicriteria model (named PROSIS (Product, Resource, Order, Simulation, Isoarchic System)) through academic benchmarks. After presenting the control based on an intelligent scheduling service and existing multicriteria scheduling methods, the choice of the `Analytic Hierarchy Process' (AHP) method is justified. After that, the implementation of the scheduling service in PROSIS is described. Finally, the benchmarks allowing to study the performance of the multicriteria scheduling are presented before analysing the results.

Multi-criteria optimisation-based dynamic scheduling for controlling FMS

This study deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Most studies that address this issue use some sort of adaptive scheduling that enables the FMS to cope with the randomness and variability efficiently. The methods presented in the literature are usually based on heuristics and use simple dispatching rules. They do not consider changing the decision criteria dynamically as the system conditions change. In contrast to previous studies, the present study focuses on developing a control mechanism for dynamic scheduling that is based on incremental optimisation. This means that each time a scheduling decision is made, the local optimisation problem is solved such that the next jobs to be processed on machines are selected. The objective function (dominant decision criterion) for this optimisation problem is selected dynamically based on production order requirements, actual shop-floor status and system priorities. The proposed multi-criteria optimisation-based dynamic scheduling methodology was evaluated and compared with some known scheduling rules/policies. The results obtained demonstrate the superiority of the suggested methodology as well as its capability to cope with a multi-criteria environment.

Research Outline on Reconfigurable Manufacturing System Production Scheduling Employing Fuzzy Logic

This paper presents a research project carry out at Faculty of Engineering, University Putra Malaysia in the area of fuzzy scheduling. The fuzzy based scheduling model, in this paper, will only deal with the job assignment problem in Reconfigurable Manufacturing System (RMS). The model will select the best alternative machine with multi-criteria scheduling through an approach based on a fuzzy logic. This paper aim to show the framework of research and methodology, which aspire to be constructing based on.

A Generalized Algorithm for Solving Multicriteria Scheduling Problems

In this paper, the scheduling problem involving optimization of multiple criteria (or objectives) is explored. There are many variants of the problem. The particular variant, in which the objectives are aggregated into a scalar function (with each criterion having weight which denotes its relative importance), is considered. An algorithm which can be used to solve very large classes of the multicriteria scheduling problem is proposed. The proposed algorithm and two solution methods selected from the literature were evaluated on a total of 900 randomly generated multicriteria scheduling problems (ranging from 10 to 500 jobs). Two variants of the release dates (0 – 24 and 0 – 49) are utilized. Results show that the proposed algorithm performed better than the selected solution methods when the total completion time criterion is much more important than the other criteria. However, when the total completion time criterion is much less important than the other criteria, the selected solution methods outperformed the proposed algorithm. The results are consistent under the two variants of the release dates.

Multi-criteria scheduling: an agent-based approach for expert knowledge integration

In this work, we present an agent-based approach to multi-criteria combinatorial optimization. It allows to flexibly combine elementary heuristics that may be optimal for corresponding single-criterion problems. We optimize an instance of the scheduling problem 1|d j |∑C j ,L max and show that the modular building block architecture of our optimization model and the distribution of acting entities enables the easy integration of problem specific expert knowledge. We present a universal mutation operator for combinatorial problem encodings that allows to construct certain solution strategies, such as advantageous sorting or known optimal sequencing procedures. In this way, it becomes possible to derive more complex heuristics from atomic local heuristics that are known to solve fractions of the complete problem. We show that we can approximate both single-criterion problems such as P m |d j |∑U j as well as more challenging multi-criteria scheduling problems, like P m ||C max,∑C j and P m |d j |C max,∑C j ,∑U j . The latter problems are evaluated with extensive simulations comparing the standard multi-criteria evolutionary algorithm NSGA-2 and the new agent-based model.

Single-machine multi-agent scheduling problems with a global objective function

In this paper, we consider the problem of scheduling independent jobs when several agents compete to perform their jobs on a common single processing machine. Each agent wants to minimise its cost function, which depends exclusively on its jobs and we assume that a global cost function concerning the whole set of jobs has to be minimised. This cost function may correspond to the global performance of the workshop or to the global objective of the company, independent of the objectives of the agents. Classical regular objective functions are considered and both the ε-constraint and a linear combination of criteria are used for finding compromise solutions. This new multi-agent scheduling problem is introduced into the literature and simple reductions with multicriteria scheduling and multi-agent scheduling problems are established. In addition, the complexity results of several problems are proposed and a dynamic programming algorithm is given.

REVIEW ON JOB-SHOP AND FLOW-SHOP SCHEDULING USING

Scheduling is widely studied and complex combinatorial optimization problems. A vast amount of research has been performed in this particular area to effectively schedule jobs for various objectives. The multi-criteria scheduling problem is one of the main research subjects in the field of modern manufacturing where most of them are considered as NP-hard. This paper discusses the more recent literature on scheduling using multi criteria decision making (MCDM). This article addresses both job-shop and flow-shop scheduling problem.DOI: http://dx.doi.org/10.3329/jme.v41i2.7508

Ant colony algorithm for multi-criteria job shop scheduling to minimize makespan, mean flow time and mean tardiness

In the real world situation many scheduling problems faced by decision maker are involved more than one aspect and therefore multiple criteria analysis is required. This paper presents ant colony algorithm for solving the multi-objective Job Shop Scheduling Problem (JSP). The objectives considered in this study include the minimization of makespan, mean flow time, and mean tardiness. The proposed algorithm is tested on many benchmark problems up to 15 jobs × 10 machine. The results obtained have shown that the proposed algorithm is a feasible and effective approach for the multiple-objective problem.

Aspect of selective rerouting in multicriteria scheduling of flexible manufacturing

In this paper, a Multi-Criteria Scheduling Algorithm (MCSA) based on swapping of dispatching rules and Selective Rerouting (SR) approach based on lateral entry of critical jobs in the queue of alternate machine is used. Further, the effect of simultaneous application of MCSA and SR has been investigated. The system performance has been evaluated using mean flow time, maximum flow time, mean tardiness and maximum tardiness as the performance measures. Simulation studies using ProModel ® simulation software were carried out at different breakdown levels for a range of part complexity and part mix, in three types of flexible job shops with Job/Machine ratio less than, equal to and greater than unity involving a total of 799 simulation runs. Each run of simulation spans for a period of 2000 completed jobs. The results establish that simultaneous application of MCSA and SR improves the system performance between 12.17 % to 28.73 % and 30.51 % to 47.67 % over that when SR and MCSA respectively are applied individually. The improvement is more at low breakdown levels and high part complexity.

Bi-Criteria Scheduling of Scientific Grid Workflows

The drift towards new challenges in Grid computing including scientific workflow management implies the need for new, robust, multicriteria scheduling algorithms that can be applied by the user in an intuitive way. Currently existing bi-criteria scheduling approaches for scientific workflows are usually restricted to certain criterion pairs and require the user to define his preferences either as weights assigned each criterion or as fixed constraints defined for one criterion. The first approach has the drawback that combining multiple criteria into a single objective function is not always intuitive to the end-user, while the second requires a priori knowledge about the result of the first criteria scheduling result. We propose a new bi-criteria scheduling specification method defining for the secondary criterion a sliding constraint as a function of the primary criterion. We model the problem as an extension of the multiple-choice knapsack problem and propose a general bi-criteria scheduling heuristic called dynamic constraint algorithm (DCA) based on dynamic programming. We show through simulation that in most of the experimental cases DCA outperforms two existing algorithms designed for the same problem at the expense of an increased execution time, which is still relatively low for workflows of medium size. Finally, we confirm our simulation results for a real-world hydrological application executed in the Austrian Grid environment.

Methods for activating the decision-making process that is used for controlling flexible manufacturing systems

This paper deals with controlling flexible manufacturing systems (FMS) operating in volatile production environments. Shnits et al. (Shnits, B., Rubinovitz, J., and Sinreich, D., 2004. Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system. International Journal of Production Research, 42 (17), 3457–3472.) and Shnits and Sinreich (Shnits, B. and Sinreich, D., 2006. Controlling flexible manufacturing systems based on a dynamic selection of an appropriate operational criteria and scheduling policy. International Journal of Flexible Manufacturing Systems, 18 (1), 1–27.) developed a multi-criteria dynamic scheduling mechanism for controlling an FMS that can cope with such environments. An important part of this mechanism functioning, which impinges directly on its performance, is the activation of its decision-making process. This study continues the research presented in the above-mentioned papers and proposes different triggering methods for activating the control system decision-making process. The operational conditions for each suggested triggering method were analysed and a comparative analysis between these methods was performed. It was revealed that the highly dynamic triggering method, which activates the decision-making process right before a resource becomes available, outperformed the triggering methods that use a predefined scheduling period.

Multicriteria, multi-user scheduling in grids with advance reservation

In this paper, we propose a new method for multi-user, multicriteria job scheduling in Grid environments with QoS guarantees concerning time and cost. The main goal of our method is to find a fair schedule of jobs that were submitted by multiple users. To obtain a schedule which is satisfactory for each user we aim at finding a set of advance reservations (ARs) for multiple users at once. This goal is achieved by adequate use of the Ordered Weighted Averaging (OWA) operator and Multiobjective Evolutionary Algorithm (MOEA) with carefully designed problem representation and operators. We also propose a data structure and algorithm used to manage and search for resource availability time slots. Efficiency and usefulness of our approach was demonstrated by computational experiments conducted within a simulation environment.

A multi-criteria cross-docking scheduling with just-in-time approach

This paper addresses a scheduling problem of inbound and outbound trailers in a cross-docking system according to just-in-time approach. In the cross-docking system, commodities are unloaded from inbound trailers, sorted and categorized based on their specifications and loaded onto outbound trailers for delivery to demand points in a distribution network. One of the most fundamental issues in such systems is to establish coordination between the performance of inbound and outbound trailers in that they can be scheduled, and the product items can be assigned to trailers effectively. As we observe the JIT concept, the punctuality and exactness of product deliveries are vital for the system’s performance. To satisfy this target, this paper considers a multi-criteria scheduling in which primary objective is to minimize earliness and tardiness simultaneously via a unified objective function. Three meta-heuristics are applied for this matter, and their solutions’ quality and the acquired elapsed time are compared with each other in each case.

Multi-criteria Scheduling of Precedence Task Graphs on Heterogeneous Platforms

Latency, fault tolerance and reliability are important requirements for several applications that are time critical in nature: such applications require guarantees in terms of latency, even when processors are subject to failures. In this paper, we propose a fault-tolerant scheduling heuristic for mapping precedence task graphs on heterogeneous systems. Our approach is based on an active replication scheme, capable of supporting e arbitrary fail-silent/fail-stop processor failures, and hence valid results will be provided even if e processors fail. First we focus on a bi-criteria approach, where we aim at minimizing the latency given a fixed number of failures supported in the system, or the other way round. Next we derive a more complex algorithm in which we not only minimize latency and support a fixed number of failures, but also improve the overall reliability. Major achievements include low complexity of the new algorithms, and a drastic reduction of the number of additional communications induced by the replication mechanism. Experimental results demonstrate that our heuristics, despite their lower complexity, outperform their direct competitor, the fault-tolerance based active replication scheduling algorithm FTBAR.

Proactive, dynamic and multi-criteria scheduling of maintenance activities.

Proactive, dynamic and multi-criteria scheduling of maintenance activities.

Proactive, dynamic and multi-criteria scheduling of maintenance activities

In maintenance services skills management is directly linked to the performance of the service. A good human resource management will have an effect on the performance of the plant. Each task which has to be performed is characterised by the level of competence required. For each skill, human resources have different levels. The issue of making a decision about assignment and scheduling leads to finding the best resource and the correct time to perform the task. To solve this problem, managers have to take into account the different criteria such as the number of late tasks, the workload or the disturbance when inserting a new task into an existing planning. As there is a lot of estimated data, the managers also have to anticipate these uncertainties. To solve this multi-criteria problem, we propose a dynamic approach based on the kangaroo methodology. To deal with uncertainties, estimated data is modelled with fuzzy logic. This approach then offers the maintenance expert a choice between a set of the most robust possibilities.

Solving multi-criteria scheduling flow shop problem through compromise programming and satisfaction functions

Abstract The multi-criteria scheduling problem is one of the main research subjects in the field of multiple objectives programming. Several procedures have been developed to deal with this type of problem where some conflicting criteria have to be optimized simultaneously. The aim of our paper is to propose an aggregation procedure that integrates three different criteria to find the best sequence in a flow shop production environment. The compromise programming model and the concept of satisfaction functions will be utilized to integrate explicitly the manager’s preferences according to the deviations between the achievement and the aspiration levels of the following criteria: Makespan, total flow time and total tardiness.

A fitness-based weighting mechanism for multicriteria flowshop scheduling using genetic algorithms

The most effective approximate methods to tackle the problem of scheduling jobs in a permutation flowshop with the bicriteria of makespan and maximum tardiness are based on genetic algorithms (GA). In these methods, the performance of the GA is improved by applying local search to all offsprings belonging to the current population. To do so, the two objectives must be aggregated into a single objective, which is usually accomplished by weighting them into a scalar function. Since the weighting scheme is considered to be critical for the performance of the algorithm, several weighting mechanisms have been suggested in the literature. In this paper, we propose two new weighting schemes and conduct an extensive computational experience under a variety of parameter settings in order to show their effectiveness as compared to existing ones.

A multicriteria approach to two-level hierarchy scheduling in grids

In this paper we address a multicriteria scheduling problem for computational Grid systems. We focus on the two-level hierarchical Grid scheduling problem, in which at the first level (the Grid level) a Grid broker makes scheduling decisions and allocates jobs to Grid nodes. Jobs are then sent to the Grid nodes, where local schedulers generate local schedules for each node accordingly. A general approach is presented taking into account preferences of all the stakeholders of Grid scheduling (end-users, Grid administrators, and local resource providers) and assuming a lack of knowledge about job time characteristics. A single-stakeholder, single-criterion version of the approach has been compared experimentally with the existing approaches.

A multi-criteria scheduling with due-window assignment problem

We study a multi-criteria scheduling and due-window assignment problem, in which the cost components are: flow-time, earliness, tardiness and due-window cost. Jobs completed within the due-window are assumed to be on time, and no earliness ∖ tardiness cost is incurred. The due-window starting time is a given parameter, whereas the due-window completion time (i.e., the window size) is determined during sales negotiations with the customer, and is therefore a decision variable. The problem is shown to be solved (finding both the schedule and the due-window size) in polynomial time.