The machine loading and tool allocation problem in a flexible manufacturing system

Abstract

I n this paper the machine loading and tool allocation problem of an FMS is discussed. A mathematical model is developed to determine the routings of parts through the machines and to allocate appropriate cutting tools to each machine to achieve minimum overall machining cost Computational experience with this model is presented under various system and operation parameter values. C'otnl~~t;~tionRI rrfinrments haw4 on lagrangean relasation are also disrussrd.

Tool allocation and machining conditions optimization for CNC machines

“…2 for the operation (12, 1), and also in Table 4 for the operation (11,6). We found a better solution by decreasing the number of tool requirements, which slightly increased the cost of SMOP but decreased the overall cost measure for the multiple operation case.…”

“…In Table 4, this procedure is illustrated for the Volume-11 and Tool-6 pair, i.e. operation (11,6), as an example.…”

“…Therefore, we first allocate Tool-6, then continue with the Tool-3. For this purpose, all possible perturbations of the Tool-6 for its Table 4 Finding the minimum cost measure for operation (11,6) Ns= 30 parts, Co= $0.5/min, andHPma x = 5 hp.…”

Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

“…Table 1 is review of literature on mathematical modelling of loading problem of FMS. Stecke (1983b) Grouping and loading Need to decrease computational time Ammons et al (1985) General loading problem for discrete optimization Heuristics improves computational efficiency & effectiveness Berrada & Stecke (1986) Minimize Stecke (1983a) Maximize throughput and machine utilizations Future need to develop efficient heuristic algorithms for more real life solution Stecke (1986) Optimal allocation ratios Developed queueing network model where information is suppressed Greene & Sadowski (1986) Minimize make span, flow time and lateness Identified variables and constraints necessary to solve real world program Sarin & Chen (1987) Minimize machining cost Lagrangian relaxation is proposed Ventura et al (1988) Minimize make-span Heuristic algorithms are proposed Henery et al (1990) Balancing of workload and maximize flexibility Mathematical solution was found impractical Rajamani & Adil (1996) Routeing flexibility Routing flexibility is required for rigid loading schedules Nayak & Acharya (1998) Minimize number of batches heuristic has been proposed Ozdamarl & Barbarosoglu (1999) Minimize the holding cost GA-SA hybrid heuristics were developed Lee & Kim (2000) Minimize maximum workload Better performance with partial grouping than total grouping, solved by heuristics Kumar & Shanker (2000) Genetic algorithms for constrained optimization GA shows near-optimum performance and need of modern heuristic techniques Kumar & Shanker (2001) Balancing of workloads Results are in agreement with previous findings Yang & Wu (2002) Balancing of workloads Tested for small size test problems only Gamila & Motavalli (2003) Minimize total processing time Used computer generated data for validation Tadeusz (2004) Minimize inter-station transfer time Very high computational effort is required for realistic problems Chan et al (2004) Minimize system unbalance and maximize throughput Validated only for small set of test problems Require further extension of research Chen & Ho (2005) Minimize flow time & tool cost and workload unbalancing multi-objective genetic algorithm (GA) is proposed Bilgin & Azizoglu (2006) Optimization of total processing time near-optimal solution in reasonable time Nagarjuna et al (2006) Minimize system u...…”

A methodology for cutting-tool management through the integration of CAPP and scheduling

“…Loading problem can be decomposed into part allocation and tool allocation problems. Tool allocation problem consists of assigning tools to operations, and most authors have used mathematical programming approaches to solve this problem, using variables 0-1 to represent tool requirements (Stecke 1983, Sarin and Chen 1987, Atan and Pandit 1996, Akturk 1999. Mendes et al (2003) proposed integration of computer aided process planning with tool allocation through alternative process planning.…”