Heuristics for scheduling unrelated parallel machines
Computers and Operations Research
Exact and Approximate Algorithms for Scheduling Nonidentical Processors
Journal of the ACM (JACM)
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Parallel machine earliness and tardiness scheduling with proportional weights
Computers and Operations Research
Solving Real-World Linear Programs: A Decade and More of Progress
Operations Research
Heuristic methods for the identical parallel machine flowtime problem with set-up times
Computers and Operations Research
Scheduling unrelated parallel machines with sequence-dependent setups
Computers and Operations Research
Parallel machine total tardiness scheduling with a new hybrid metaheuristic approach
Computers and Operations Research
A GRASP for Parallel Machine Scheduling with Time Windows
INFORMS Journal on Computing
Computers and Operations Research
Computers and Operations Research
Computers and Industrial Engineering
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Herein we present a case of production planning in a woodturning company. The company wishes to plan the turning of various types of products of different radii in a set of parallel machines (lathes) and with the following principal conditions: for each type of product there is a minimum production lot size; some lathes cannot manufacture every type of product; the production capacity of a lathe depends on the lathe itself and the type of product to be manufactured; the products are classified into families according to radius; and there is an intra-family setup time (for manufacturing different products that have the same radius) and an inter-family setup time (for consecutively manufacturing products that have different radii), which is longer; part of the production can be subcontracted; each type of product can be manufactured on different lathes and/or subcontracted; and the operators can work overtime, during which additional time they can simultaneously operate multiple lathes. The goal is to meet the demand at minimum cost, which includes the cost of any overtime plus that of any subcontracting. The problem was modelled and solved by mixed-integer linear programming (MILP). The company considers the results to be satisfactory.