Strategic level three-stage production distribution planning with capacity expansion

Yılmaz, Pınar; Çatay, Bülent

doi:10.1016/j.cie.2006.05.004

Cited by 57 publications

(14 citation statements)

References 19 publications

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“…An LP model was framed to minimize the total cost which included manufacturing, inventory holding, and distribution costs along with capacity expansion, retaining and decreasing costs. Yilmaz and Catay [16] solved a mixed integer linear programming (MILP) production-distribution problem using CPLEX and three linear relaxation heuristics. The costs associated with the production, transportation, inventory holding, and capacity expansion were minimized.…”

Section: Production-distribution Planningmentioning

confidence: 99%

“…This is in the absence of hiring new workers. Interestingly, the algorithms utilize the entire overtime hours (see constraint 16) to arrive at the solutions with almost zero change in labor levels and zero percentage underutilization simultaneously. But the total cost values (obtained using simulation-based AHP-DPSO and simulation-based AHP-BCGA algorithms) are high due to over production.…”

Section: Production-distribution Plans Obtained Using Simulation-basementioning

confidence: 99%

See 1 more Smart Citation

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

Varthanan¹,

Murugan²,

Kumar

et al. 2011

Int J Adv Manuf Technol

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Integrated production-distribution plan considering three major objectives, viz., total cost minimization, change in labor level reduction, and underutilization minimization, is developed for a renowned bearing manufacturing industry in India. The total cost minimization objective minimizes the regular, overtime, and outsourced production costs along with inventory holding, backorder, hiring/layingoff, and trip-wise distribution costs. The multi-criteria model is solved using a novel simulation-based analytic hierarchy process (AHP)-discrete particle swarm optimization (DPSO) algorithm. The solutions of the AHP-DPSO algorithm are verified using the AHP-binary-coded genetic algorithm solutions. The proposed simulation-based AHP-DPSO solutions are found to be superior. Demand is assumed to vary uniformly, and the simulation-based AHP-DPSO algorithm is used for obtaining the best production-distribution plan that serves as a trade-off between holding inventory and backordering products. In addition to bearing manufacturing industry dataset, two other test datasets are also solved.

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Section: Production-distribution Planningmentioning

confidence: 99%

Section: Production-distribution Plans Obtained Using Simulation-basementioning

confidence: 99%

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

Varthanan¹,

Murugan²,

Kumar

et al. 2011

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…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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“…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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Strategic level three-stage production distribution planning with capacity expansion

Cited by 57 publications

References 19 publications

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

Development of simulation-based AHP-DPSO algorithm for generating multi-criteria production–distribution plan

Optimizing a Biobjective Production‐Distribution Planning Problem Using a GRASP

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

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