Integrated Production and Distribution Planning for Södra Cell AB

Gunnarsson, Helene; Rönnqvist, Mikael; Carlsson, Dick

doi:10.1007/s10852-006-9048-z

Cited by 37 publications

(28 citation statements)

References 6 publications

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“…As such, a real problem situation for the case company has been conceptualized and modelled into a scientific mathematical model, in line with Mitroff et al (1974). This model is partly based on a previous model developed by Gunnarsson et al (2007) and modified by Gunnarsson and Rönnqvist (2008). In this dissertation the previous model is referred to as the Mill Mission model, which in a modified form is currently in use at the case company for supply chain planning purposes.…”

Section: Studymentioning

confidence: 99%

“…This study had a starting point in the Mill Mission model previously developed by Helene Lidestam (see e.g. Gunnarsson et al, 2007;Gunnarsson and Rönnqvist, 2008). Thus, Helene Lidestam has partly been involved in the modelling phase as well as in the writing process of Paper 2 and Paper 3.…”

Section: Author's Statementmentioning

confidence: 99%

“…Gunnarsson et al (2006) further on analyse the distribution planning problem and include routing issues as well as terminal locations in a mixed integer programming model. The whole supply chain is later on considered by Gunnarsson et al (2007) that also present a model (Mill Mission) with a one year planning period. The Mill Mission model is further developed by Gunnarsson and Rönnqvist (2008) by also considering multiple time periods and the use of heuristics.…”

Section: Including Energy When Planning Production and Supply Chainmentioning

confidence: 99%

“…The production and the supply chain of the case company Södra Cell, has previously been studied numerous times (e.g. Carlsson and Rönnqvist, 2005;Gunnarsson et al, 2006;Gunnarsson et al, 2007;Gunnarsson and Rönnqvist, 2008;Lidestam and Rönnqvist, 2011), but none of these studies have taken the energy issues into special consideration as done with Energy Mission in Paper 2 and Paper 3. Plenty of modelling opportunities, where the energy system interacts with the production processes in many different ways, have also been found for the case company involved in the third study presented in Paper 4 and Paper 5.…”

Section: How Can Process Industries Include Energy Related Issues In mentioning

confidence: 99%

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Planning production and supply chain in energy intensive process industries

Waldemarsson

2014

Linköping Studies in Science and Technology. Dissertations

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To make a difference among the energy intensive process industries, this dissertation addresses production planning and supply chain planning problems related to industrial energy management issues. The energy issue is turning more and more important from different angles, involving price as well as environmental problems due to climate change leading to political pressure on all energy users. The process industry sector is one of the largest users of energy, and thus important to analyse. Process industries are also capital intensive and operate on large and expensive process equipment, making it imperative to plan their production well in order to reach preferable capacity utilisation. Therefore this dissertation strives to locate the most important energy management issues for the long term profitability of process industries, and investigates the symbiotic effects of including energy issues in production and supply chain planning.Three different studies at three case companies are carried out, analysed, and presented in five papers. The cases represent the process industry sectors: chemicals, pulp, and steel. Both qualitative case study methodologies as well as quantitative mathematical modelling and optimisation approaches have been practiced. The research questions are analysed from both an energy system and from a production process point of view, separately as well as combined. Energy is somewhat considered to be the main workforce for process industries and this dissertation exemplifies some of its most important dimensions in this context. Several prerequisites for putting energy management on the strategic agenda are located in a specialty chemical industry where the importance of introducing a strategic perspective on energy, the way energy is used, and the possibilities of increasing alternative revenue from utilising by-and/or co-products differently are pinpointed. Approaches for including energy issues in planning processes are also suggested in terms of a MILP model for the entire supply chain of a pulp company, including decisions on purchase and transportation of raw materials, production allocation, energy mix, and distribution. Another example is presented based on the perspectives of economics of scale and lot sizing through economic order quantity principles in a steel company. By using real company data, energy smart approaches in planning and scheduling are developed with respect to the most important intersections between the production processes and their supporting energy system. The accumulated resource intensity and embedded energy could, and probably should, hence be more fairly reflected in the product price. The research finally shows some possible impact with including energy issues in a production and supply chain planning model. By planning differently, production prioritisations can be done, and it is not only possible without any large investments, but also prosperous with savings on both energy and money within reach.To conclude, planning of production and supply ch...

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Section: Studymentioning

confidence: 99%

Section: Author's Statementmentioning

confidence: 99%

Section: Including Energy When Planning Production and Supply Chainmentioning

confidence: 99%

Section: How Can Process Industries Include Energy Related Issues In mentioning

confidence: 99%

See 2 more Smart Citations

Planning production and supply chain in energy intensive process industries

Waldemarsson

2014

Linköping Studies in Science and Technology. Dissertations

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“…Fahimnia et al [2] classified the studies into seven clusters according to the SCS complexity. These clusters are given as follows: Cluster 1: single-product models [25][26][27][28]; Cluster 2: multi-product, single-plant models [29][30][31]; Cluster 3: multiple-products, multiple-plants, single-or no-warehouse models [32][33][34]; Cluster 4: multiple-products, multipleplants, multiple-warehouses, single-/no-end-user models [35][36][37]; Cluster 5: multiple-products, multiple-plants, multiple-warehouses, multiple end users, single-transport-path models [20,38,39]; Cluster 6: multiple-products, multipleplants, multiple-warehouses, multiple-end-users, multipletransport-paths, no time period models [40,41]; Cluster 7: multiple-products, multiple-plants, multiple-warehouses, multiple-end-users, multiple-transport-paths, multible-period-models [2]. To the best of the author's knowledge, a new cluster (Cluster 8) can be added to these classifications: Cluster 8: multiple-products, multiple-plants, multiple-warehouses, multiple-end-users, single-transport-path, multiple-periods models [42,43].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Production-Distribution Problem in Supply Chain Management under Stochastic and Fuzzy Uncertainties

Sakallı

2017

Mathematical Problems in Engineering

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Production-Distribution Problem (PDP) in Supply Chain Management (SCM) is an important tactical decision. One of the challenges in this decision is the size and complexity of supply chain system (SCS). On the other side, a tactical operation is a midterm plan for 6-12 months; therefore, it includes different types of uncertainties, which is the second challenge. In the literature, the uncertain parameters were modeled as stochastic or fuzzy. However, there are a few studies in the literature that handle stochastic and fuzzy uncertainties simultaneously in PDP. In this paper, the modeling and solution approaches of PDP which contain stochastic and fuzzy uncertainties simultaneously are investigated for a SCS that includes multiple suppliers, multiple products, multiple plants, multiple warehouses, multiple retailers, multiple transport paths, and multiple time periods, which, to the best of the author's knowledge, is not handled in the literature. The PDP contains deterministic, fuzzy, fuzzy random, and random fuzzy parameters. To the best of the author's knowledge, there is no study in the literature which considers all of them simultaneously in PDP. An analytic solution approach has been developed by using possibilistic programming and chance-constrained programming approaches. The proposed modeling and solution approaches are implemented in a numerical example. The solution has shown that the proposed approaches successfully handled uncertainties and produce robust solutions for PDP.

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