6 research outputs found
Price changing and inventory sharing in supply chain management
The main task of supply chain management is to balance efficiency and effectiveness. Numerous operational management strategies are used to make a supply chain efficient, one such is inventory management. In this paper, we will consider a particular part of a supply chain consisting comprising a manufacturer and a retailer with the goal of minimizing associated inventory costs. We will focus on an inventory of final products determined as the difference between supply and demand and are expressed as a function of price, inflation rate and change in inflation rate resulting in the possible speculations. The manufacturer’s inventory cost is a function of these same variables, with the retailer’s inventory cost having the same function in addition to the margin. The problem is formulated as a dynamic game to share the speculation problem. The optimization problems to be solved are optimal control theory problems with objective functions in the form of integral functional with the integrand depending on the state function and its first and second derivative
Recommended from our members
Evolutionary Approach for a Multi–Objective Spare Parts Location–Inventory Problem with Time–Based Service Levels
Facility location-allocation decisions and inventory stocking decisions are very important in spare parts logistics. Both sets of decisions affect total costs in the system and the service levels that can be achieved by establishing distances between customers and distribution centers (DCs) in facility location-allocation, and by determining the availability of parts in inventory decisions. There is a trade-off between total costs and service levels that decision makers have to explicitly take into account when designing a spare parts logistics (SPL) system. The integration of the location-inventory decisions along with the multiple objectives will lead to an approach in which the sub-optimality of solutions obtained separately using a sequential approach can be overcome. However, this integration also increases the complexity of the problem. In this research, a nonlinear multi-objective optimization model was formulated in which the objectives are to minimize cost and maximize the service level. The goal is determining the number and location of DCs, the allocation of customer demands to DCs, and the safety stock for parts to maintain at DCs. A time-based service level requirement is considered by allocating customers to DCs that can serve demands of those customers within a specified time window. To solve this formulation, a non-dominated sorting genetic algorithm II (NSGA-II) solution method was implemented and used to obtain Pareto optimal solutions for problems of different sizes. A full factorial experimental design was implemented to analyze the impact of factors on the problem and the performance of the solution approach. Computational results and general insights about the problem are presented