Meta-heuristic approach to supply chain optimization in an integrated hierarchical production planning system

Abstract

The total supply chain of any enterprise is composed of three main scctions: backward linkage, furv,rard linkage and im;idc value-chain. The backward link"gc is a function or inward supply management, with its inherent uncertainly. The internal value chain is basically a hybrid function of several materials management functions. The two most important of these functions arc complex issues of uncertain inventory control and NP-hard type production scheduling problem. The forward side is composed of multi-variable interactive system, where variables interact with each other to control market demand. An internal material planning is one of the most complex tasks in an industry. Pre~ence of a large number of variables, operating in uncertain environment, is Ihe main rea~on behind ~uch complexity. As a result, optimization in a materials planning system requires a great deal of simplification, A material planning is thus suggested in several levC!s, starting from long-range aggregate planning, going through disaggrcgated Master Production Scheduling, individnal ~omponent planning and finally ending to shop !loor scheduling. Each individual level ha~ its own form of complexity. The first level of complexi!y starts in converting an aggregate production planning system into disaggrega!ed master production scheduling. The master production scheduling is esscntially the output of aggregate planning where master production scheduling process drives the material requirements planning (MRP) sy,tcm. The determination of net requirements is the core of MRP proees~ing. Lot-~i7jng is a major aspect of the MRP process, A lot-sizing problem involves decisions to determine the quantity and timing of production for N different items over a horizon of T period,. In the present work, it has been assumed that only one machine of each type i~ available with a fixed capacity in each period, The objective is to minimize the sum of set-up and inventory carrying cos!..'>for all items without incurring backlogs. In case of a single item production only an optimal solution algorithm exists. l3ut for medium~size and multi-item problems, optimal solution algorithms arc not available. It has been proved that even the two-item problem with con~!ant capacity is NP-hard (Nondeterministic polynomial-hard). This has increased the importance of searching for good heurj~lie solutions, In the presenl research I work, heuristic mc!hods have been developed and implemented to solve the multi-item, single level, limited capacity lot-sizing problem, bypassing paramekrs to the next step of planning, Production scheduling is the most complex step in the hierarchical production planning system. That is why the production scheduling problems have reccived ample attention from both rescarchers and practitioners, because an efficient produdion schedule can achieve reduction of production cost and inventory cost, increasc in prollt and incre<lsein 'on-time' delivery to customcrs. A Pareto-optimal algorillun is developed in this research work for a scheduling problem on a single machine with periodic mainlenance and non-preemptive jobs. In literature, most of the scheduling problems address only one objectivc function; while ill the real world, such problems are always associated with morc than one objective. In this work, both multi-objeclive functions and multi-maintenance period~ arc considered for a single machine scheduling problem. On the other hand, pcriodic maintenance schedules are also considered in the model. The objective of the modd addres,ed in this work is to minimize the weighted function of the total job now time, the maximum tardiness, and the machine idle time in a single machine environment. Thc parametric analysis of the trade-offs of all solutions with all possible weighted eombill<ltionof the criteria has been carried oul. i\ neighborhood search heuristic has been developed abo. 'Ihe computational results have shown that the modified Pareto-optimal algorithm provides a better solution lhan the neighborhood search heuristic and this shows the efficiency of thc modified Pareto-optim<llalgorithm. For forward side optimization, distribution system parametcrs have becn identified that affcct ~ubseqt1cnt marketing. The parameter of distribution for optimization has been selceted with Multi Criteria Decision Making (MCDM) technique. Fin<lllya distribution plan has been optimized lIsing optimization-ba>cd'Transportation algorithm'