Abstract:
In the hierarchical production planning system, Aggregate Production Planning (APP)
falls between the broad decisions of long-range planning and the highly specific and
detailed short-range planning decisions. At the very first portion of this study develops an
interactive Possibilistic Linear Programming (PLP) approach for solving the multiproduct,
multi-period aggregate production planning (APP) with imprecise forecast
demand, related operating costs, and capacity. The proposed approach attempts to
minimize total costs with reference to inventory levels, labor levels, overtime,
subcontracting and backordering levels, and labor, machine and warehouse capacity. This
proposed approach used the strategy of simultaneously minimizing the most possible
value, the most pessimistic value & also most optimistic value of the imprecise total
costs. This thesis paper work also demonstrated an interactive Fuzzy Based Genetic
Algorithm (FBGA) approach. Here the author employed different unique genetic
algorithm parameters scrupulously for solving nondeterministic polynomials problems
like APP problems. On the later portion of this study develops an interactive Multi-
Objective Genetic Algorithm (MOGA) approach for solving the multi-product, multiperiod
aggregate production planning (APP) with forecasted demand, related operating
costs, and capacity. Here several genetic algorithm parameters are considered for solving
NP-hard problem (APP problem) and their relative comparisons are focused to choose the
most auspicious combination for solving multiple objective problems.
For reinforcing & accelerating the decision making for the decision maker a case study
was considered in a Ready Made garments manufacturer in Bangladesh named as Comfit
Composite Knit Limited (CCKL). A total of five years previous data were collected from
the concerned CCKL Company. The data were predominantly from sewing section of
different floors. The demand data were uncertain in nature since the buyers are
sophisticated to change their order volume within short notice. The manpower used & the
Standard Minute Value calculations are left behind this thesis work. All the mathematical
measures to calculate the sewing or machining time per unit garment are not covered in
this paper because all those were directly collected from the factory representatives. At
the bottom portion of this thesis paper there employed a Fuzzy Based Particle Swarm
Optimization (FBPSO) approach to choose the most auspicious algorithm for successful
APP problems. Two variants of PSO were targeted & solved by using C languages. In
nutshell a total of four very recent & potential heuristic algorithms are developed & used
in this thesis paper so that the researchers as well as the manufacturer can have the
guideline to implement successful aggregate production planning problem.
