Research On Integrated Modeling And Optimization Methods For Production And Distribution Problems In Supply Chain

The integration of production and distribution in supply chain is an importantacademic research hot at home and abroad. Like modern enterprise integratedobjective, the objective of production and distribution is to integrate the productionschedule and distribution route problem and minimize the total cost of productionscheduling and distribution routing problems. So the integration of productiondistribution has become an important task in supply chain management. If theproduction and distribution activities with each other are not coordinated, this willaffect the enterprise operating performance. So the supply chain must developeffective integrated planning to seek solution. This paper focuses on the work ofseveral production distribution problems research. From the angle of the global supplychain and supply chain operation of various levels to explore production scheduling,distribution center allocation, distribution route and their relationship, including allsorts of model and the corresponding solving algorithm is discussed, and thesimulation results verify, comparise and analysis the process.The detail work consistsof:Firstly, coordination of the supply chain in the environment of multi-distributionand multi-customer has been realized, and an optimization model for location-routingproblem is proposed. The integrated optimization of facility selection and vehiclerouting problem is studied by considering the fuzzy demand of customers. In practice,supply chain always operates in uncertainty circumstance, the dissertation establishesthe fuzzy chance-constrained programming model, and transforms it into equivalentmodel with adjustable parameters.And also the genetic algorithm (GA) and tabusearch (TS) are introduced into the location routing problem. Finally, the validity ofthe model and algorithm is demonstrated by a numerical example.Secondly, a novel mixed-integer programming distribution lot-sizing model withtime-varying demand has been developed. And then the paper develops an efficienttwo-phase heuristic method based on the genetic algorithm, in which a combinedmulti-period demand ordering policy, rather than the lot-for-lot ordering policyappeared in the literature is adopted. The experimental results indicate that the goodperformance of the proposed method has been verified through a comparison with theoptimal solution method. It is also shown that the performance of the proposed combined multi-period demand ordering policy is superior to that of the lot-for-lotordering policy.Thirdly, a nonlinear mathematical model to consider production scheduling andvehicle routing with time windows for perishable food products in the sameframework is proposed. The demands at retailers are assumed stochastic andperishable goods will deteriorate once they were produced. Thus the revenue of thesupplier is uncertain and depends on the value and the transaction quantity ofperishable products when they are carried to retailers. The objective of this model is tominimize the expected total cost of the supplier. The optimal production quantities,the time to start producing and the vehicle routes can be determined in the modelsimultaneously. Furthermore, the paper elaborates a solution algorithm composed ofthe constrained Nelder–Mead method and a heuristic for the vehicle routing with timewindows to solve the complex problem. Computational results indicate algorithm iseffective and efficient.Fourthly, this work develops a fuzzy multi-objective linear programming(FMOLP) model with piecewise linear membership function to solve integratedmulti-time period production/distribution planning decisions problems with fuzzyobjectives. The original multi-objective linear programming designed in this workmodel attempts to simultaneously minimize total costs, total delivery time and rate ofshortage. Then an interactive multi-level programming method based on fuzzy theoryfor solving the model is designed. Based on Bi-level programming a new model fornegotiating supply chain cooperation is studied, and fuzzy theory based solutionmethod is also proposed, and an interactive method based on fuzzy theory for solvingthe model is designed in considering the objective tolerance.To solve the problem, itproposes a two-loop interactive algorithm to get satisfactory solution by iterativelyadjusting parameters. In detail, the algorithm includes two interactive procedures: Theformer is major for the preference of the decision maker, realized by fuzzymembership functions reflecting goals and decisions attainments; the latter is for theimprecision of parameters, described by possibility degree. Experimental resultvalidates the feasibility of the algorithm.