DOLOSO EXTORSIÓN SECUESTRO

Meta-heuristics techniques are mostly applied in the optimisation of multiproduct systems because of the number of parameters involved in them. A Genetic Algorithm library offered by the simulation modelling software is applied to determine the stage by stage optimal settings of the basestock (ܵ) and/or the Kanbans (ܭ) for the strategies. The solution search space for each stage’s setting is predetermined by conducting preliminary evaluations to identify the reasonable ranges within which to carry out their optimisation evaluations, as described in Section 2.8.1. An objective function is then specified to the genetic algorithm library which generates alternative settings, simulate and evaluate them until a 95% target ܵܮ for both products is achieved with the lowest average system inventory level.

The minimisation-type objective function was formulated as follows:

ܯ݅݊ܥ݋ݏݐ ൌ  ቄܹܫܲ‹ˆܵܮͳܽ݊݀ܵܮʹ ൒ ͲǤͻͷ

ܺ‘–Š‡”™‹•‡ (4)

where ܺ is a penalty cost for not meeting the target ܵܮ In the objective function in equation (4), it is verified if a parameter setting achieved up to the target ܵܮ of 95% for both products. If it does, its objective function is calculated based on the ܹܫܲ. Otherwise, a penalty cost,ܺ, is assigned to the objective function. The value of ܺ is chosen to be significantly greater than the maximum possible ܹܫܲ so

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that such parameter setting becomes an unattractive candidate for further evaluation. The same optimisation approach is followed for all the strategies.

In general, the optimisations are done in an order of increasing complexity of the strategies, starting with the CONWIP – the easiest of them all, followed by the BSCS and the KCS – the single parameter per stage strategies. Then, as a means of facilitating the optimisation process of the two parameter per stage strategies, their link to some of the single parameter per stages strategies are exploited, by initialising their solution search spaces based on the optimised settings obtained for the single parameter per stage strategies. In this vein, despite the DKAP policies of the EKCS and the GKCS having more optimisation parameters, they are optimised before the SKAP because they have been reported in Sections 2.2.5 and 2.2.6 to exhibit more direct relation to the single parameter per stage strategies than the SKAP.

Similarly, since the EKCS was developed as a combination of the BSCS and the KCS, the initial search for its ܵ settings is conducted close to the values obtained for the BSCS. From this search, it was observed that the EKCS would require at least the same ܵ level as that of the optimised BSCS in order to achieve the target ܵܮ, irrespective of its number of Kanbans. It should be noted that because the philosophy of the EKCS is to keep Kanbans attached to the basestock parts while they wait in a stage’s output buffers, the simulation model has been implemented such that the initialised finished parts of the set basestock level, ܵ, have a corresponding number of Kanbans, ܭ. Therefore, the Kanban number being optimised for the EKCS’s DKAP and SKAP are the extra unattached Kanbans, ܣܭ, where

ܣܭ ൌ ܭȂ ܵ (5)

Based on the above approach, an initial evaluation reduced the solution space of the CONWIP strategy to a range of 20 – 27 from which the final optimised settings of 24 and 23 units were then obtained for the number of cards needed for Products 1 and 2respectively. Similarly, for the BSCS and the KCS, the optimal settings shown in Table 3-5 and Table 3-6 respectively were achieved from the evaluation of the solution search spaces shown in the tables. A notable observation from the optimisation results is that the largest number of Kanbans and basestocks are set at the last stages. These are consistent with the observations of previous studies that more Kanbans and basestock

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are needed at the last stage to absorb demand variations and minimise demand backlog [6, 29]. Moreover, since there was no differentiation in the value of the items at the early stages of processing and those at the final stages, it was more effective to set all the basestock at the last stage.

Table 3-5: Scenario 1: BSCS Solution Space and Optimal Settings

Manufacturing Stage

Product 1 ࡿ Product 2 ࡿ

Range Opt. Range Opt.

1 0 – 2 0 0 – 2 0

2 0 – 3 0 0 – 3 0

3 22 – 26 24 22 – 25 24

Table 3-6: Scenario 1: KCS Solution Space and Optimal Settings

Manufacturing Stage

Product 1 ࡷ Product 2 ࡷ

Range Opt. Range Opt.

1 1 – 3 2 1 – 3 2

2 7 – 10 8 7 – 10 8

3 14 – 20 18 14 – 18 15

The optimised settings for the DKAP and SKAP of the EKCS and the GKCS are presented in Table 3-7 to Table 3-10, along with their respective solution search spaces.

Table 3-7: Scenario 1: EKCS DKAP Sample Space and Optimised Settings

Manufacturing Stage

Product 1 Product 2

࡭ࡷ ࡿ ࡭ࡷ ࡿ

Range Opt. Range Opt. Range Opt. Range Opt.

1 4 – 8 6 0 – 2 0 4 – 8 6 0 – 2 0

2 6 – 10 9 0 – 3 0 6 – 10 8 0 – 3 0

3 13 – 17 13 22 – 24 24 13 – 16 12 22 – 24 24

Table 3-8: Scenario 1: EKCS SKAP Sample Space and Optimised Settings

Manufacturing Stage

Product 1 Shared Settings Product 2

ࡿ ࡭ࡷ ࡿ

Range Opt. Range Opt. Range Opt.

1 0 – 2 0 6 – 10 7 0 – 2 0

2 0 – 3 0 15 – 17 16 0 – 2 0

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Table 3-9: Scenario 1: GKCS DKAP Sample Space and Optimised Settings

Manufacturing Stage

Product 1 Product 2

ࡷ ࡿ ࡷ ࡿ

Range Opt. Range Opt. Range Opt. Range Opt.

1 1 – 3 1 0 – 1 0 1 – 3 1 0 – 1 0

2 7 – 10 10 0 – 2 0 8 – 9 9 0 – 2 0

3 18 – 22 22 24 – 25 25 18 – 20 20 23 – 25 25

Table 3-10: Scenario 1: GKCS SKAP Sample Space and Optimised Settings

Manufacturing Stage

Product 1 Shared Settings Product 2

ࡿ ࡷ ࡿ

Range Opt. Range Opt. Range Opt.

1 0 – 2 0 1 – 6 1 0 – 2 0

2 0 – 3 0 16 – 19 17 0 – 2 0

3 24 – 25 25 40 – 46 42 23 – 25 25