الفهرس يوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
Increased global competition, rising costs, and rapid technological change are among the factors in today’s business environment that have turned management focus towards <~hain management. Improving efficiency and cutting costs at all stages of the tchain are viewed as critical issues to remain competitive. From the military to
And automobile manufacturers, organizations are achieving competitive
Improving efficiency and lowering costs associated with supply chain management
Through the supply chain view point, monitoring the relationships among the whole (chain is a necessity. Getting products to customers faster and more reliably than.. ’tors is now seen as a requirement rather than as a competitive advantage. Buyers products that are delivered on time and as ordered. Accomplishing these tasks requires that the production and distribution decisions be made daily to balance ion, holding and delivery costs in an optimal framework.
A large amount of literature was published in the area of integrating production and
ft,ution decisions. Due to the complex nature of integrated production-distribution tems, and because there are many variants to include in any problem that extend its
”,”’.
, ’1hot all the variants could be easily considered in a single model. Thus, researchers
>’to confine their problem’s formulation relaxing some of the parameters so as to be ,fto solve it. Two of the parameters that are known to affect the formulation and the ’on are the consideration of multi-time periods, and multi-commodity .
. aim of the present work is to develop a mixed integer linear programming model that solve the problem of integrated production-distribution planning. The proposed multi¬odity capacitated vehicle routing model with multi-time periods (MCCVRMT) some of the limitations that were found in literature. The model considers the
her of products to be produced in a single facility (depot) in the planning periods. Th uction capacity of the plant, the demand for each product, the vehicle capacity, th t of producing one unit, the inventory holding cost, the cost per unit distance travele the fixed vehicle cost are all given.
The output of the model is to determine the quantities to be produced from each product at each planning period, the quantities to be delivered from each product to every location, the accumulated quantities to be delivered up to each location, the number of routes/vehicles needed, the optimal routing sequence of customers’ deliveries and the optimum amounts of inventory to be stored from each product at each planning period. The output is reached while minimizing the overall costs of production and distribution in an integrated manner. The model is built in a step by step approach, tested and verified by referencing to a benchmark case, different problem instances are examined and has been illustrated. The main contribution of the present model is the resultant cost reduction due to integrating production and distribution decisions in a single model as compared to the separately, or even sequentially solved cases. As well as the improvement of the decision making process in terms of quality and speed of decision making.
The MCCVRMT model is applied in a real case study in one of the leading logistics providers and food processing companies. The company is facing the problem of determining the quantities to be produced from each product in each period and the immediate distribution of these products to customers so as to eliminate the inventory stored in large refrigerators. This will help to reduce both; the cost of keeping large amount of inventory, and the energy consumption associated with operating the refrigerators. The company also faces a situation where a critical decision shall be made. A truck dealer is offering the company two alternatives; the first alternative is to rent trucks based on a larger variable transportation cost with no fixed charge on trucks. The second alternative is to rent trucks with low variable transportation cost and fixed cost charged with each truck being rented. The proposed model succeeded in providing the company with the optimum decision to be taken in both issues.
Finally, the conclusions and recommendations for future work are presented.