memetic mo ant colony algorithm for tsalbp

A MULTIOBJECTIVE MEMETIC ANT COLONY OPTIMIZATIONALGORITHM FOR THE 1/3 VARIANT OF THE TIME ANDSPACE ASSEMBLY LINE BALANCING PROBLEM

Manuel Chica, Óscar Cordón, Sergio Damas, Joaquín Bautista12th April 2011

Visit www.nissanchair.com/TSALBP

Summary

1. Introduction2. SALBP and TSALBP3. The memetic MACS proposal

1. General structure2. The MACS global search3. Local search operators

4. Experiments5. Conclusions and future work

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Introduction

The optimization of assembly lines is of great importance in the production and operation research context.

The time and space assembly line balancing problem (TSALBP) is a realistic extension of the well-known simple assembly line balancing problem (SALBP).

We present a memetic MACS proposal with two multiobjective (MO) local search (LS) methods to solve the 1/3 variant of the TSALBP.

The new proposal results are compared with a GRASP algorithm using multiobjective performance indicators in 9 problem instances.

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SALBP and TSALBP (I)

An industrial process is divided into a set V of n tasks. Each task j requires an operation time tj and has a set of direct predecessors (problem constraint).

The SALBP involves grouping these tasks in m workstationsminimizing the cycle time (C) or the number of stations (m).

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SALBP and TSALBP (II)

Importance of the area in assembly line balancing.

TSALBP formulations include: The area of each task, aj ; j=1,…,n The available area for any station, A

Hence, TSALBP has a multicriteria nature involving three different objectives: The cycle time of the plant (C) The number of stations (m) The available area (A)

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SALBP and TSALBP (III)

The existence of these three objectives creates the following problem taxonomy:

There are 4 TSALBP multiobjective variants

One of the most realisticvariants in the automotive industry: TSALBP-1/3 (A and m)

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Memetic metaheuristics have demonstrated its good performance because of the combination of global search behaviour and the local optimizer.

We present a multiobjective memetic algorithm:

a) With a powerful global search metaheuristic: MACS.

b) A MO local search approach with two local search methods, one per objective.

The set of constraints associated to TSALBP encourages the use of constructive memetic metaheuristics to solve it.

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The memetic MACS proposal


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General structure (I)


General structure (II)

The function to be optimised by the local search is a scalarization of the objective function vector:

Weights are created at random for solution: λ1, λ2

If λ1 > λ2 then LS operator for objective A is applied. Otherwise, LS operator for objective m. If no minimization, the other is also launched afterwards.

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A TSALBP solution is an assignment of tasks to different stations satisfying the constraints.

We have to give a sequence of tasks and how these tasks are split up into different stations to fully specify the assignment.

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The MACS global search (I)


The MACS global search (II)

Pareto-based MOACO algorithms have shown good performance in several problems.

MACS is an extension of the ACS which considers an external Pareto archive. The pseudo-random transition rule is considered:

Same transition rules as ACS

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The MACS global search (III)

The pheromone trail information is associated to a pair (task, station).

The initial pheromone value 0 is obtained from two single-objective greedy algorithms.

No heuristic information used!

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A new mechanism to close a station is used to induce diversity, following a multi-colony approach:

The MACS global search (IV)12131415161718


Local search operators (I)

Both LS operators are based on movements of tasks between their feasible stations.

Repeated 20 iterations on each solution obtained by MACS.

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Local search operators (II)14151617181920


Local search operators (III)15161718192021


Experiments (I)

Nine real-like problem instances with different features have been selected: arc111, barthol2, barthold, scholl…

Multiobjective performance indicators: unary HVR, binary C, and graphical representation of the aggregated Pareto fronts.

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Experiments (II)

We compare the new proposal with MACS (no memetic). Also against a multiobjective GRASP algorithm :

Builds the solution with a random selection of the next task to be included in the current station between the candidates using heuristic information.

Makes use of an external Pareto archive and a restricted candidate list (RCL) .

A mechanism to close stations using different thresholds.

Two similar LS operators applied when solution is built.

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Experiments (III)

MACS

Memetic MACS

Memetic MACS

GRASP

Clear dominace of Memetic

MACS

P1 and P9 GRASP is better

P2, P3 and P7 memetic MACS is

better

No clear dominance in P4, P5, P6 and P8

Binary C performance indicator

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Experiments (IV)

According to HVR, memetic MACS and GRASP are the best algorithms depending on the problem instance.

Memetic MACS is better than GRASP in P2, P3, P7 and P8. But worse in P1, P4, P5, and P6.

MACS is obviously worse than GRASP and memetic MACS.

HVR performance indicator

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Experiments (V)20212223242122


A novel memetic MACS was developed and applied to tackle the TSALBP-1/3.

Its behaviour is clearly superior to the MACS global search. However, there is no clear conclusion about which algorithm is better regarding the comparison between GRASP and memetic MACS.

As future work, we will consider:

a) Designing new EMO and memetic algorithms.

b) Adding interactive procedures to include preferences.

c) Application of Wilcoxon statistical tests to the results.

Conclusions and future work21

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Thanks for your attention

memetic mo ant colony algorithm for tsalbp

Technology

novel memetic macs

p7 memetic macs isbetter

local search methods

thememetic macs proposal

tsalbp solution

mo local search approach

tsalbp formulations

tsalbp iiimportance