Optimization model for injection molding production scheduling in kitchen-appliance manufacturing: A real-world case study

M. Sankar; M. Ramakrishnan; M. Sankar; M. Ramakrishnan

doi:10.3934/jimo.2026161

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 10: 4627-4675. doi: 10.3934/jimo.2026161

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Research article

Optimization model for injection molding production scheduling in kitchen-appliance manufacturing: A real-world case study

M. Sankar ¹,
M. Ramakrishnan ^{2
,
,}

1.
Vidyaa Vikas College of Engineering and Technology, Tiruchengode, Namakkal District, Tamil Nadu, India
2.
K.S. Rangasamy College of Technology, Tiruchengode, Namakkal District, Tamil Nadu, India

Received: 09 July 2026 Revised: 10 August 2026 Accepted: 21 August 2026 Published: 01 September 2026
90C11, 90B35, 90B30, 90C90, 90C27

Production scheduling in injection molding is challenging when production capacity depends not only on machines, but also on secondary shared tooling such as molds. In this paper, we present a real-world case study of a mixed-integer programming model developed for a kitchen-appliance manufacturing company to generate feasible and cost-efficient injection molding production schedules. The proposed formulation integrated machine assignment, mold allocation, sequence-dependent setup decisions, production quantities, inventory balance, subcontracting, planned stoppages, mold maintenance, shift calendars, holidays, and grouped customer-level demand fulfillment within a single optimization framework. The novelty of the study lies in representing the coupled machine–tooling–demand structure of an industrial molding environment in a unified model, rather than treating capacity planning, mold usage, sequencing, and demand fulfillment as separate planning problems. Although motivated by injection molding in kitchen-appliance manufacturing, the modeling approach is broadly applicable to industries where production depends on secondary shared tooling, including automotive components, consumer durables, electrical products, packaging, medical devices, metal forming, die casting, and other high-mix manufacturing environments. To the best of our knowledge, this is the first unified mixed-integer programming formulation for injection-molding production scheduling that integrates shared molds, sequence-dependent setups, operational disruptions, inventory, subcontracting, and grouped customer demand.
- production scheduling,
- optimization model,
- mixed-integer programming,
- sequence-dependent setups,
- integrated planning models,
- injection molding
Citation: M. Sankar, M. Ramakrishnan. Optimization model for injection molding production scheduling in kitchen-appliance manufacturing: A real-world case study[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 4627-4675. doi: 10.3934/jimo.2026161

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Abstract

Production scheduling in injection molding is challenging when production capacity depends not only on machines, but also on secondary shared tooling such as molds. In this paper, we present a real-world case study of a mixed-integer programming model developed for a kitchen-appliance manufacturing company to generate feasible and cost-efficient injection molding production schedules. The proposed formulation integrated machine assignment, mold allocation, sequence-dependent setup decisions, production quantities, inventory balance, subcontracting, planned stoppages, mold maintenance, shift calendars, holidays, and grouped customer-level demand fulfillment within a single optimization framework. The novelty of the study lies in representing the coupled machine–tooling–demand structure of an industrial molding environment in a unified model, rather than treating capacity planning, mold usage, sequencing, and demand fulfillment as separate planning problems. Although motivated by injection molding in kitchen-appliance manufacturing, the modeling approach is broadly applicable to industries where production depends on secondary shared tooling, including automotive components, consumer durables, electrical products, packaging, medical devices, metal forming, die casting, and other high-mix manufacturing environments. To the best of our knowledge, this is the first unified mixed-integer programming formulation for injection-molding production scheduling that integrates shared molds, sequence-dependent setups, operational disruptions, inventory, subcontracting, and grouped customer demand.

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