Responsive assemble-to-order supply chains demand efficient coordination between production and distribution functions. This paper investigates the case of customer orders assigned to configuration cells located in different geographical regions. Complete orders are directly shipped from the cells to customers located in distinct locations. The objective is to minimize the total cost of production and transportation, and the percentage of tardy deliveries simultaneously. The problem is formulated as a bi-objective optimization problem. Four heuristics are developed for generating a set of Pareto solutions. Extensive experiments ranging from small to large-scale instances are performed. Results show the heuristics generate good, feasible non-dominant solutions, which are especially critical for decision makers. Our results also demonstrate that there is no clear dominant heuristic and that relative heuristic performance is dependent on problem size. Sensitivity analysis of critical parameters reveals additional insights.

Scheduling assemble-to-order systems with multiple cells to minimize costs and tardy deliveries

Paletta, Giuseppe;
2018-01-01

Abstract

Responsive assemble-to-order supply chains demand efficient coordination between production and distribution functions. This paper investigates the case of customer orders assigned to configuration cells located in different geographical regions. Complete orders are directly shipped from the cells to customers located in distinct locations. The objective is to minimize the total cost of production and transportation, and the percentage of tardy deliveries simultaneously. The problem is formulated as a bi-objective optimization problem. Four heuristics are developed for generating a set of Pareto solutions. Extensive experiments ranging from small to large-scale instances are performed. Results show the heuristics generate good, feasible non-dominant solutions, which are especially critical for decision makers. Our results also demonstrate that there is no clear dominant heuristic and that relative heuristic performance is dependent on problem size. Sensitivity analysis of critical parameters reveals additional insights.
2018
Assemble to order systems; Late deliveries; Multi-criteria decision making; Production planning; Scheduling; Supply chain planning; Transportation; Computer Science (all); Engineering (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/273470
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