A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

A new mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm

“…(11) states, as much, the given time must be time slot(s) of the assigned machinetool combination, m l. Eq. (12) assures that once operation o starts, it will be terminated consecutively on time slot(s) without interruption. Constraint (13) shows that each operation o of each part type p can be performed in a time slot k at most with one machinetool combination, m l. Constraint (14) states that with each machine-tool combination, m l, at each time slot k, at most one operation o can be processed.…”

“…In this eld, many researchers have considered only operation assignment to machines [7], and some others have considered a tool role in their studies [2,[8][9][10]. Three policies can be used when tool roles are considered in these systems: rst, a part movement policy, in which tools cannot move around the machine in the planning horizon [8,9,[11][12][13]; second, a tool movement policy, in which tools can move around the machines and parts remain on a machine in the whole planning horizon [10]; and third, a part and tool movement policy, in which parts and tools can move around the machines simultaneously with their corresponding Automated Guided Vehicles (AGVs) [2,14]. As mentioned earlier, scheduling problems of parts (orders) in the shop oor are more operative than allocation problems.…”

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 flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

“…Therefore, some heuristics or metaheuristic algorithms are proposed [8][9][10]. A Flexible Manufacturing System (FMS) can be de ned as an integrated arrangement of Numerical Control (NC) machine tools, some supplementary production equipment, and a material handling system designed to simultaneously manufacture low to medium volumes of a wide variety of high-quality goods at low cost [11]. FMS is generally classi ed into four di erent categories: Single Flexible Machines (SFMs), Flexible Manufacturing Cells (FMCs), MultiMachine FMSs (MMFMSs), and Multi-Cell FMSs (MCFMSs) [12].…”