Efficient material flow in mixed model assembly lines

Abstract

In this study, material flow from decentralized supermarkets to stations in mixed model assembly lines using tow (tugger) trains is investigated. Train routing, scheduling, and loading problems are investigated in parallel to minimize the number of trains, variability in loading and in routes lengths, and line-side inventory holding costs. The general framework for solving these problems in parallel contains analytical equations, Dynamic Programming (DP), and Mixed Integer Programming (MIP). Matlab in conjunction with LP-solve software was used to formulate the problem. An example was presented to explain the idea. Results which were obtained in very short CPU time showed the effect of using time buffer among routes on the feasible space and on the optimal solution. Results also showed the effect of the objective, concerning reducing the variability in loading, on the results of routing, scheduling, and loading. Moreover, results showed the importance of considering the maximum line-side inventory beside the capacity of the train in the same time in finding the optimal solution.

Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

“…Therefore, only a partial comparison can be made. For example, the effectiveness of DP to solve MIP problems was found in some studies such as Alnahhal and Noche [54,56]. Moreover, many studies found that variable quantity size and period between deliveries give the optimal solution [57], which was exactly found in this study.…”

“…So, in an intermediate step, if we know the optimal solution for a period of time (from week 1 to week j − 1), then in the next step, if we need to find the optimal lot allocation for the same period, there is no need to repeat the solution since the model "memorizes" the best solution found before. In this case, the time needed to find the final solution is minimized [54]. Some previous studies used the same idea such as Emde and Boysen [55] and Alnahhal and Noche [56].…”

Planning models for continuous supply of parts in assembly systems

“…Faccio et al (2013a) suggested a method for determining the optimal number of kanbans in supermarkets, while Faccio et al (2013b) developed a general framework to design and simulate supermarket and milk run-based component feeding systems. Kilic et al (2012) classified milk runs distribution systems, while Droste and Deuse (2011) and Alnahhal and Noche (2013) developed models for milk runs planning. Satoglu and Sahin (2013) developed a mathematical model and heuristic method to design milk run systems in order to feed assembly line just in time minimizing inventory and material handling costs.…”

Simulation-based efficiency analysis of an in-plant milk-run operator under disturbances

“…As a result, it was concluded that it is more advantageous to use milk-run trains in the multiple routed vehicles method than a one routed vehicle method. Alnahhal and Noche [1] investigated the routing, scheduling and loading problems together in the milk-run tow train to decrease the number of trains, the variability in loading and route lengths and the inventory holding costs. In that study, they did not introduce any stochastic parameters and solve the problem using dynamic programming and mixed integer programming.…”