Mikael Nyberg

Nyberg, Mikael; Björk, Kaj-Mikael.
Abstract In this paper, we study an integrated production and outbound distribution scheduling model with one manufacturer and one customer. The manufacturer has to process a set of jobs on a single machine and deliver them
in batches to the customer. Each job has a release date and a delivery deadline. The objective of the problem is to issue a feasible integrated production and distribution schedule minimizing the transportation cost subject to the production release dates and delivery deadline constraints. We consider three problems with different ways how a job can be produced and delivered: non-splittable production and delivery (NSP–NSD) problem, splittable production and
non-splittable delivery problem and splittable production and delivery problem. We provide polynomial-time algorithms that solve special cases of the problem. One of these algorithms allows us to compute a lower bound for the NP-hard
problem NSP–NSD, which we use in a branch-and-bound (B&B) algorithm to solve problem NSP–NSD. The computational results show that the B&B algorithm outperforms a MILP formulation of the problem implemented on a commercial solver.

Keywords Single machine scheduling · Production and delivery · Release dates · Deadlines · Transportation costs ·Branch-and-bound

Nyberg, Mikael; Björk, Kaj-Mikael
Abstract

This paper presents two hybrid methods to solve a production planning problem. The problem is found in a producer of food-sweeteners, with several different kinds of products. The case study plant is found in the USA. The aim is to improve convergence for a big optimization problem. The first method uses a Genetic Algorithm in combination with an existing rigorous MILP model. The fitness value of each chromosome is evaluated by solving the MILP model with a highly reduced number of binary variables. The second method is a Tabu search that also uses the rigorous MILP to evaluate each node. The meta-heuristic models are presented and used to solve a few case problems and the results are discussed. Furthermore the two methods are compared in terms of solution times and convergence rate.

Key words

Genetic Algorithm, Tabu search, hybrid method, meta-heuristic, soft computing, production planning

M Nyberg, KM Björk
Abstract

This paper presents a new MILP (Mixed Integer Linear Programming) model to solve a complex production planning problem. The problem is found in a producer of food-sweeteners, with several different kinds of products. The case study plant is found in the USA. The process is fairly complex and the alternatives are vast. A discrete-time production planning model is presented and found suitable for the solution of the problem as well as a discussion how to reduce complexity in large processes without loosing good solutions.


Keywords:

Mixed Integer Linear Problem, discrete-time model, production planning, inventory optimization, supply chain