Production–distribution planning in supply chain considering capacity constraints

Lee, Young Hae; Kim, Sook Han

doi:10.1016/s0360-8352(02)00063-3

Cited by 186 publications

(68 citation statements)

References 10 publications

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“…In terms of the solution techniques, it is possible to find mathematical programming-based approaches or solved directly by a commercial solver (e.g., [3-5, 10-16, 21, 23, 29-37]); a vast number of contributions that propose heuristic techniques, motivated by the complexity of the resulting problem [18,19,22,26,32,[38][39][40][41][42][43][44][45][46][47]; and simulation-based approaches to tackle the problem [48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

et al. 2018

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This paper addresses a biobjective production-distribution planning problem. The problem is formulated as a mixed integer programming problem with two objectives. The objectives are to minimize the total costs and to balance the total workload of the supply chain, which consist of plants and depots, considering that it represents a company vertically integrated. In order to solve the model, we propose an adapted biobjective GRASP to obtain an approximation of the Pareto front. To evaluate the performance of the proposed algorithm, numerical experimentations are conducted over a set of instances used for similar problems. Results indicate that the proposed GRASP obtains a relatively small number of nondominated solutions for each tested instance in very short computational time. The approximated Pareto fronts are discontinuous and nonconvex. Moreover, the solutions clearly show the compromise between both objective functions.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

et al. 2018

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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%

“…For heuristic techniques, since analytic techniques have a limitation on solving large-scale PDP, the researchers developed heuristic techniques that obtain feasible solution close to an optimal solution [16,35,47]. For simulation modeling, simulation is a very useful tool to analyze the system's behavior and performance criteria when the considered system is very complex to solve analytically [30,48]. For genetic algorithms (GA), they are effective algorithms that use direct and stochastic search methods to solve large-scale problems [49,50].…”

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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“…The architecture includes the equation of continuous portion in the supply chain and describes how these equations can be used in supply-chain simulation models. Lee and Kim [11] proposed a hybrid approach combining analytic and simulation models. Joines et al [12] studied a supply-chain simulation optimization methodology employing a GA to optimize system parameters.…”

Section: Introductionmentioning

confidence: 99%

A simulation approach for production-distribution planning with consideration given to replenishment policies

Lim

Jeong

Kim

et al. 2005

Int J Adv Manuf Technol

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One of the key issues in the current research area of supply-chain networks is a production-distribution plan taking into account a multi-facility, multi-product, and multi-period problem. The purpose of this study is to determine the optimal production-distribution plan in a network with a bill of material (BOM).First, we present a mathematical model to determine the capacities of facilities including the manufacturing plant and distribution center (DC). Next, with consideration given to the uncertainty and complexity of solving such a network, we apply a simulation approach. The simulation model is developed to analyze a production-distribution plan that satisfies customer demands for higher customer satisfaction and lower total relevant costs, while taking replenishment policies into consideration.The computational results for a simple example of the network represent the developed mathematical and simulation model and show the prospects of using this approach. The multifactor analysis of variance (ANOVA) test results clearly indicate that all three factors have a significant effect on the grouping output. Indices:r index of raw materials (i = 1, 2, ..., R) q index of sub-assembled products (q = 1, 2, ..., Q) p index of final assembled products ( p = 1, 2, ..., P)s rst fixed cost of r at s at time period t h rst unit inventory holding cost of r at s at time period t p rst unit production cost of r at s at time period t s q ft fixed cost of q at f at time period t h q ft unit inventory holding cost of q at f at time period t h r ft unit inventory holding cost of r at f at time period t p q ft unit production cost of q at f at time period t s pat fixed cost of p at a at time period t h pat unit inventory holding cost of p at a at time period t h qat unit inventory holding cost of q at a at time period t h rat unit inventory holding cost of r at a at time period t p pat unit production cost of p at a at time period t s pdt fixed cost of p at d at time period t h pdt unit inventory holding cost of p at d at time period t CT rs ft unit transporting cost of r from s to f at time period t CT rsat unit transporting cost of r from s to a at time period t CT q fat unit transporting cost of q from f to a at time period t CT padt unit transporting cost of p from a to d at time period t CT pdct unit transporting cost of p from d to c at time period t D pct demand for c for p at time period t PT rqs processing time of r for q at s PT rps processing time of r for p at s PT qp f processing time of q for p at f PT pa processing time of p at a C st total available production capacity of s at time period t C ft total available production capacity of f at time period t 594 C at total available production capacity of a at time period t C dt total available production capacity of d at time period t R qp q's quantity required of p R rp r's quantity required of p R rq r's quantity required of q M arbitrary large numberVariables:X rsqt production amount of r at s at the end of period t for q X rspt production amount of r at s at the e...

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Production–distribution planning in supply chain considering capacity constraints

Cited by 186 publications

References 10 publications

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

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

A simulation approach for production-distribution planning with consideration given to replenishment policies

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