Modelling and resolving the joint problem of capacity expansion and allocation with multiple resources and a limited budget in the semiconductor testing industry

Wang, K.-J.; Hou, Tie

doi:10.1080/0020754031000109152

Cited by 41 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bilgin and Azizoglu [10] adopted a tabu search algorithm to find the optimum allocation of the scarce time, tool magazine capacities and the limited quantities of tool to meet the demand of the production. Wang and Hou [17] used genetic algorithm to effectively distribute resources such as tester, handlers, load board and tooling. Beside capacity allocation based on the tool and machine, material requirement planning, production and transportation have to be bought in for supply chain planning.…”

Section: Related Researchmentioning

confidence: 99%

Capacity Allocation Optimization using Offline Learning Differential Evolution Hyper-Heuristic

Fiqri¹,

Feng²

2018

JCSCM

View full text Add to dashboard Cite

Hyper-heuristics are high-level computational techniques to select or generate heuristics for solving complex problems, such as capacity planning involving finite resource allocation. This research proposes a hyper-heuristics method based on two-level differential evolution (DE) termed as HMDE. It optimizes four parameters in the hyper stage, namely mutation factor, crossover rate, number of generations, and number of iterations to derive a comparatively superior capacity planning meta-heuristics, also in the form of DE. The effect of demand seasonality onto its performance is examined and benchmarked against Genetic Algorithm (GA). As it is statistically proven from the results, HMDE has shortened the runtime and produced higher profit on average in comparison to the GA over a test case containing twelve months of seasonal data.

show abstract

Section: Related Researchmentioning

confidence: 99%

Capacity Allocation Optimization using Offline Learning Differential Evolution Hyper-Heuristic

Fiqri¹,

Feng²

2018

JCSCM

View full text Add to dashboard Cite

show abstract

“…Resource estimation tactic define α f (t) as (4) when applying (1) to calculate the concession range during the negotiation, where res f (t) = max (0, t f max − t):…”

Section: B Negotiation Tacticsmentioning

confidence: 99%

“…For instance, in semiconductor-testing industry, the cost of a single machine set ranges from US$ 300 000 to US$ 2 million, and it is common to have several factories, each with hundreds of machines, spread over the world [1]. Furthermore, the setting up of a 300-mm wafer-fabrication factory costs around US$ 2 billion [2], and its resource-related cost is the largest expenditure, accounted for approximately 60-70% of the total investment.…”

Section: Introductionmentioning

confidence: 99%

A Negotiation-Based Capacity-Planning Model

Wang

2012

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

Capacity planning deals with the conflicts among multiple factories. This study employs a negotiation framework to allow autonomous budget allocation among factories and make full use of manufacturing resources capacity scattered over individual factories. Factories are modeled as intelligent entities that exchange offer messages with one another. This study investigates the effects of the attitudes of a factory, while it bargains with other factories over the budget. Furthermore, individual factories apply a capacity-planning optimization model and a genetic algorithm to revise their capacity plan right after receiving new messages from other factories. This paper makes a contribution in successfully building a negotiation-based capacity-planning model applied to a multiple-factory environment. The outcome of the experiments shows the efficiency of the proposed model and the effect of different negotiation attitudes.Index Terms-Autonomous negotiation, capacity planning. NOMENCLATURE NOTATIONS NotationsDefinition F p Capital in the end of period p. F p ∈ R + . p ∈ P , a set of periods over the planning horizon. I p Capital interest rate in period p. e m Unit cost of purchasing a resource type m. d m Unit salvage value of phasing out a resource type m. d m can be a positive or negative value. K m Number of in-house resource type m during the planning horizon. K m ∈ Z + . K 0,m Number of resource type m in the initial period. X p,m ,z Number of resource type m associated with resource acquisition alternative z in period p. X p,m ,z ∈ Z + . c m ,j Product-resource capabilities for product j associated with main resource type m. This is a 0-1 parameter. c m ,j = 1 if main resource type m can conduct product j; c m ,j = 0 otherwise. Q p,m ,j Quantity of product j produced by main resource type m in period p. Q p,m ,j ∈ Z + . r m ,j Theoretical throughput of product j conducted by resource type m. Working hours of resource type m in period p. y p,m Target utilization of resource type m in period p. c m ,m 2Resource configuration capabilities regarded with main resource type m and auxiliary resource type m2. This is a 0-1 parameter. c m ,m 2 = 1 if auxiliary resource type m2 can cooperate with main resource m, c m ,m 2 = 0 otherwise. Q p,m 2,j

show abstract

“…With regard to capacity-size decisions, most deterministic single-site models consider capacity expansion as the only option to adapt capacity to demand, assuming non decreasing market demand over time (the most recent are Rajagopalan & Swaminathan, 2001;Wang & Lin, 2002;Çatay et al, 2003;Wang & Hou, 2003;Zhang et al, 2012;Lim et al, 2013). Atamtürk & Hochbaum (2001) highlights the opportunity of outsourcing as another way to meet a growing demand.…”

Section: Literature Reviewmentioning

confidence: 99%

Single-site strategic capacity planning considering renewal, maintenance, inventory, taxes and cash flow management

Benedito

Corominas

Martínez

et al. 2016

Journal of the Operational Research Society

View full text Add to dashboard Cite

This paper deals with strategic capacity planning of a single-site manufacturing system.We propose a MILP model that includes relevant business aspects and possibilities, some of which are only partially or not at all found in the literature. Specifically, we consider decisions on expansion, reduction and renewal of production capacity, and acquisition of storage capacity. In addition, we model aspects such as a) maintenance costs and unit variable costs depending, respectively, on age and characteristics of facilities, b) seasonality of the demand, and c) cash flow management, including taxes and, therefore, depreciation of assets. The model maximises the after-tax cash balance at the end of the planning horizon. We also present a computational experiment with 54 instances to show that the model can be solved for a wide range of sizes in a reasonable computing time using comercial software.

show abstract

Modelling and resolving the joint problem of capacity expansion and allocation with multiple resources and a limited budget in the semiconductor testing industry

Cited by 41 publications

References 8 publications

Capacity Allocation Optimization using Offline Learning Differential Evolution Hyper-Heuristic

Capacity Allocation Optimization using Offline Learning Differential Evolution Hyper-Heuristic

A Negotiation-Based Capacity-Planning Model

Single-site strategic capacity planning considering renewal, maintenance, inventory, taxes and cash flow management

Contact Info

Product

Resources

About