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Scheduling by Applying Scheduling by Applying Tabu SearchTabu Search::A Textile CaseA Textile Case

Prepared by:Prepared by:Ali Orhan AYDINAli Orhan AYDIN

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Scope of the PresentationScope of the Presentation

To present a studyTo present a study

which which

explains job scheduling exampleexplains job scheduling example

in a textile systemin a textile system

by applying Tabu Searchby applying Tabu Search

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IntroductionIntroduction

Systems are set of components which are related by Systems are set of components which are related by some form of interaction, and which act together to some form of interaction, and which act together to achieve some objectives. achieve some objectives. One of the most important man-made systems is One of the most important man-made systems is production systemproduction system..Major aim of the most of the manufacturing and Major aim of the most of the manufacturing and service systems is to make profit.service systems is to make profit.Therefore, they produce goods and services by using Therefore, they produce goods and services by using scarce resources. scarce resources. To achieve this purpose efficiently use of resources To achieve this purpose efficiently use of resources becomes key succession factor. becomes key succession factor. Scheduling promises in helping production systems to Scheduling promises in helping production systems to pursue this goal.pursue this goal.

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Scheduling is a series of activities aim of which Scheduling is a series of activities aim of which is to assign jobs and/or resources to men and/or is to assign jobs and/or resources to men and/or machines in production systemsmachines in production systems..

By performing these activities, it is targeted to By performing these activities, it is targeted to minimize production time and costs, by minimize production time and costs, by providing information to a production system on providing information to a production system on what to make, when to make, with which staff, what to make, when to make, with which staff, and on which machineand on which machine..

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MMany tools and approaches are proposed to any tools and approaches are proposed to achieve a good scheduleachieve a good schedule..

Pioneer tool for scheduling and planning isPioneer tool for scheduling and planning is Gantt ChartGantt Chart..

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Most basic method for scheduling purposes is Most basic method for scheduling purposes is to use dispatching rules. to use dispatching rules. – SIRO: Service in Random OrderSIRO: Service in Random Order– ERD: Earliest Release Date FirstERD: Earliest Release Date First– EDD: Earliest Due Date FirstEDD: Earliest Due Date First– MS: Minimum Slack FirstMS: Minimum Slack First– WSPT: Weighted Shortest Processing Time FirstWSPT: Weighted Shortest Processing Time First– LPT: Longest Processing Time FirstLPT: Longest Processing Time First– SST: Shortest Setup Time FirstSST: Shortest Setup Time First– CP: Critical PathCP: Critical Path– LNS: Largest Number of SuccessorsLNS: Largest Number of Successors– SQNO: Shortest Queue at the Next OperationSQNO: Shortest Queue at the Next Operation

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TThere are also some composite dispatching here are also some composite dispatching rulerules.s.– ATC: Apparent Tardiness Cost is a rule that ATC: Apparent Tardiness Cost is a rule that

combines WSPT and MScombines WSPT and MS..– ATCS: Apparent Tardiness Cost with Setups is a rule ATCS: Apparent Tardiness Cost with Setups is a rule

that combines WSPT, MS and SSTthat combines WSPT, MS and SST..

All of these dispatching rules prioritize all the All of these dispatching rules prioritize all the jobs that are waiting for processing on a jobs that are waiting for processing on a machine. machine.

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To find optimum schedule there are exact To find optimum schedule there are exact optimization methods. optimization methods.

If the scheduling problem is inherently easy If the scheduling problem is inherently easy linear programs can be used to solve them.linear programs can be used to solve them.– IInteger programnteger programmingming– BBranch-and-bound methodsranch-and-bound methods– CCutting plane methods utting plane methods – SSome hybrid methodsome hybrid methods

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On the other hand, scheduling problems are usually On the other hand, scheduling problems are usually Non-Deterministic Polynomial-Time Hard (NP-Hard)Non-Deterministic Polynomial-Time Hard (NP-Hard)..

In some cases, solving NP-Hard problems may take In some cases, solving NP-Hard problems may take enormous time. Usually, in real life that amount of enormous time. Usually, in real life that amount of computer time is not available. Therefore, finding computer time is not available. Therefore, finding optimal solution is nearly impossibleoptimal solution is nearly impossible..

MMany methods proposed to find a good solution to any methods proposed to find a good solution to scheduling problems of production systems in a scheduling problems of production systems in a relatively short timerelatively short time..

Those solutions are acceptable and feasible solutions Those solutions are acceptable and feasible solutions that presumably is not far from optimalthat presumably is not far from optimal..

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MMany methods proposed to find a good solution any methods proposed to find a good solution to scheduling problems of production systems in to scheduling problems of production systems in a relatively short timea relatively short time..

In such cases, beam search, local search and In such cases, beam search, local search and global search heuristics can be appliedglobal search heuristics can be applied..

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Local Search HeuristicsLocal Search Heuristics– Hill-ClimbingHill-Climbing– Min-ConflictsMin-Conflicts– Min-Conflicts-Random-WalkMin-Conflicts-Random-Walk– Steepest-Descent-Random-WalkSteepest-Descent-Random-Walk– GSATGSAT– WalkSatWalkSat– Simulated Annealing Simulated Annealing – Tabu-Search. Tabu-Search.

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Global Search HeuristicsGlobal Search Heuristics– Evolutionary algorithms Evolutionary algorithms

Genetic algorithms Genetic algorithms

Memetic algorithmsMemetic algorithms

– Population based algorithms. Population based algorithms. Particle swarm algorithmsParticle swarm algorithms

Ant colony algorithms Ant colony algorithms

Bees algorithmsBees algorithms

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All of these approaches have some advantages All of these approaches have some advantages and disadvantages. and disadvantages.

It is a fact that global search heuristics requires It is a fact that global search heuristics requires more time to achieve acceptable solution. more time to achieve acceptable solution.

On the other hand, global and local search On the other hand, global and local search heuristics give nearly the same results. heuristics give nearly the same results.

Therefore, in this paper one of the local search Therefore, in this paper one of the local search heuristic tabu search is applied in scheduling heuristic tabu search is applied in scheduling problem of a textile system.problem of a textile system.

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Literature Review: GreediesLiterature Review: Greedies

Among many heuristic algorithms local search Among many heuristic algorithms local search algorithms are reviewed in this section. algorithms are reviewed in this section. In local search, an initial configuration (valuation of In local search, an initial configuration (valuation of variables) is generated and the algorithm moves from variables) is generated and the algorithm moves from the current configuration to a neighborhood the current configuration to a neighborhood configurations until a solution (decision problems) or a configurations until a solution (decision problems) or a good solution (optimization problems) has been found or good solution (optimization problems) has been found or the resources available are exhausted. the resources available are exhausted. Usually, local search algorithms are called as greedy Usually, local search algorithms are called as greedy algorithms; since, they try to reach global maximum or algorithms; since, they try to reach global maximum or minimum by searching the space locally.minimum by searching the space locally.Local search algorithms move from solution to solution Local search algorithms move from solution to solution in the space of candidate solutions (the search space) in the space of candidate solutions (the search space) until a solution deemed optimal is found or a time bound until a solution deemed optimal is found or a time bound is elapsed. is elapsed.

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General Psuedo Code of local-search General Psuedo Code of local-search algorithmsalgorithms

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Hill-climbing is probably the most known Hill-climbing is probably the most known algorithm of local search. algorithm of local search.

Algorithm of hill-climbing Algorithm of hill-climbing

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Psuedo Code of Min-Conflicts Psuedo Code of Min-Conflicts

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Min-Conflicts-Random-Walk algorithm Min-Conflicts-Random-Walk algorithm

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Steepest-Descent-Random-Walk algorithmSteepest-Descent-Random-Walk algorithm

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Tabu Search algorithmTabu Search algorithm

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Tabu search is applied in many resource Tabu search is applied in many resource allocation problems like: allocation problems like: – cell formation problem cell formation problem – designing manufacturing cells designing manufacturing cells – optimization of Process Plansoptimization of Process Plans– Parallel Flowshop Scheduling Parallel Flowshop Scheduling – project scheduling project scheduling – flow-shop scheduling flow-shop scheduling – job shop schedulingjob shop scheduling

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There are also hybrid algorithms which are There are also hybrid algorithms which are developed by combining tabu search and other developed by combining tabu search and other heuristics to find better solutions to the heuristics to find better solutions to the problems. problems. – Tabu Search and Simulated Annealing is combined Tabu Search and Simulated Annealing is combined

by Zolfaghari and Liang by Zolfaghari and Liang – Tabu Search and Genetic Algorithm is hybridized by Tabu Search and Genetic Algorithm is hybridized by

Ombuki and Ventresca Ombuki and Ventresca – Tabu and Scatter Search is combined by Blazewicz, Tabu and Scatter Search is combined by Blazewicz,

Glover, and KasprzakGlover, and Kasprzak

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Moreover, like it is aimed in this paper, tabu Moreover, like it is aimed in this paper, tabu search is used in scheduling jobs in textile search is used in scheduling jobs in textile manufacturing systems and proved to be manufacturing systems and proved to be efficient. efficient.

In their paper, Tucci and Rinaldi [31] describes In their paper, Tucci and Rinaldi [31] describes a typical fabric production system and applies a typical fabric production system and applies tabu search. tabu search.

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Applying Tabu Search: Applying Tabu Search: A Textile CaseA Textile Case

Aim of this paper is to apply tabu search in a Aim of this paper is to apply tabu search in a textile manufacturing industry. textile manufacturing industry.

Specifically, fabric production is taken under Specifically, fabric production is taken under consideration. In such systems, to produce consideration. In such systems, to produce fabric first raw strings are dyed. Afterwards, they fabric first raw strings are dyed. Afterwards, they are tied to conics and sent to fabric department are tied to conics and sent to fabric department to be weaved. Final stage is quality control and to be weaved. Final stage is quality control and while controlling fabric, they are rolled on while controlling fabric, they are rolled on cylinders.cylinders.

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This paper focuses on scheduling jobs in This paper focuses on scheduling jobs in weaving process while trying to minimize weaving process while trying to minimize maximum lateness on single machine. maximum lateness on single machine.

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Bill-of-Material of such fabric productBill-of-Material of such fabric product

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As an example, here 20 jobs are describedAs an example, here 20 jobs are described..

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Length of ordered fabrics and total processing Length of ordered fabrics and total processing time of each job.time of each job.

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Due dates of jobs.Due dates of jobs.

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In the frame of the given information, initial In the frame of the given information, initial solution for the problem is obtained by applying solution for the problem is obtained by applying dispatching rule Earliest Due Date. dispatching rule Earliest Due Date.

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Afterwards, Tabu Search is applied. 20 Afterwards, Tabu Search is applied. 20 iterations are performed by using OpenTS open iterations are performed by using OpenTS open source code in JAVA environment. source code in JAVA environment.

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ConclusionConclusion

Tabu Search is an effective algorithm to achieve Tabu Search is an effective algorithm to achieve job scheduling objectives like maximum job scheduling objectives like maximum lateness/tardiness minimization. lateness/tardiness minimization.

It requires less processing time than global It requires less processing time than global search heuristics. search heuristics.

As the job size increase, time requirement also As the job size increase, time requirement also increases to generate a schedule. increases to generate a schedule.

Therefore, Tabu Search can be suggested to Therefore, Tabu Search can be suggested to mass production systems; since, they deal with mass production systems; since, they deal with many jobs. many jobs.

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ConclusionConclusion

The study just tried to simply explain the Tabu The study just tried to simply explain the Tabu Search concept. Search concept.

The problem provided is oversimplified. The problem provided is oversimplified.

Actual textiles systems deal with much more Actual textiles systems deal with much more higher amount of jobs. higher amount of jobs.

Because, it is aimed to show how Tabu Search Because, it is aimed to show how Tabu Search can be used in minimize maximum tardiness can be used in minimize maximum tardiness problem. problem.

In this manner, a good feasible solution but not In this manner, a good feasible solution but not optimal is found. optimal is found.

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