Combinatorial optimization and Green Logistics

Sbihi, Abdelkader; Eglese, Richard W.

doi:10.1007/s10479-009-0651-z

Cited by 233 publications

(129 citation statements)

References 64 publications

Supporting

Mentioning

122

Contrasting

Unclassified

Order By: Relevance

“…The objective function (15) consists in the minimization of the total expected cost and comprises first-stage costs related to manufacturing and remanufacturing production and manufacturing setup; and second-stage costs are associated with remanufacturing setup, inventories, disposal, and backlogging. Constraints (16) to (21) are equivalent to their deterministic versions, but must be satisfied for every scenario ξ.…”

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

“…Objective function (22) minimizes the same costs as the previous objective function (15), considering now only the stochastic joint setup. The other constraints are similar to those from previous models.…”

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

“…Thus, the aim of this paper is to incorporate uncertainty regarding demand, return, and setup costs in hybrid manufacturing/remanufacturing systems to determine the production lot sizing of both types of processes. Our mathematical model is mostly related to Sbihi and Eglese [15]; however, differently from their models, we consider additional practical characteristics that together have also been neglected by the literature so far: (i) disposal option of part of the returned products, since some of them have no rework condition due to physical or economic issues [16]; (ii) both disposal cost and disposal capacity; (iii) both manufacturing and remanufacturing costs; (iii) multiple products; (iv) backlogging option; (v) uncertainty in demand, return of products and setup costs via scenariobased two-stage stochastic programming; and (vi) a riskaverse perspective of the stochastic program to address the fluctuations of the realizations of the random variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Macedo

Alem

Santos³

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper presents mixed-integer programming models to hybrid manufacturing and remanufacturing lotsizing problems in which remanufactured products are treated as new ones, so that both manufactured and remanufactured products compete to meet the demands. Differently from previous studies, we consider an environment with multiple products, both manufacturing and remanufacturing costs, disposal, backlogging, and the inherent uncertainties of demands, return rates of usable products, and setup costs. In order to deal with these uncertainties, we propose a scenario-based two-stage stochastic programming model that assumes production and setup as first-stage decision variables, whereas inventory, disposal, and backlogging are defined as second-stage decision variables.We also analyze a risk-averse model in an attempt to reduce the dispersion of the second-stage costs. The main results of the present study indicate that setup costs for remanufacturing can be decisive in choosing between manufacturing or remanufacturing. Even though remanufacturing costs are lower, the process is still largely dependent on return rates and low storage costs for returned products.

show abstract

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

Section: Deterministic Mixed-integer Programming Models For Lot Sizinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Macedo

Alem

Santos³

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…This approach aims at increasing part substitution capability (Bolmsjo and Olsson 2005;Geroliminis and Daganzo 2005). The objective of small series reproduction is to improve customization flexibility by offering a series of adjustable reproduction processes (Sbihi and Eglese 2010). Some researchers introduced an optimal recovery policy for reusable items to lower inventory levels and increase the flexibility of parts recycling processes (Chao et al 2002;Dhouib et al 2012).…”

Section: Introductionmentioning

confidence: 99%

An adaptable optimizer for green component design

Tsai

Chen

et al. 2014

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

This paper proposes an adaptive mechanism for improving the availability efficiency of green component design (GCD) process. The proposed approach incorporates a wide range of GCD strategies to increase availability of the recycled/reused/remanufactured components. We have also designed a self-adjusting mechanism to enhance the versatility and generality of a genetic algorithm (GA) to improve GCD availability efficiency. The mechanism allows refinement of the GA parameters for the selections of operators in each generation. Our research contribution includes the development of a novel mechanism for the evaluation of optimal selections of reproduction strategies, adjustment and optimization of the crossover and mutation rates in evolutions, and design of Taguchi Orthogonal Arrays with a GA optimizer. The effectiveness of the proposed algorithms has been examined in a GCD chain. From the experimental results, we can conclude that the proposed approach resulted in better reproduction optimization than the traditional ones.

show abstract

“…In the last decade, many researchers have been attracted by production planning aspects in closed-loop supply chains (Fleischmann, Bloemhof Ruwaard, Dekker, van der Laan, van Nunen, and van Wassenhove 1997;Guide and Van Wassenhove 2001;Rubio, Chamorro, and Miranda 2008;Sbihi and Eglese 2010). In addition to forward-oriented supply chains from the manufacturer to the customer, the reverse direction also has to be taken into account in the field of closed-loop supply chains.…”

Section: Introductionmentioning

confidence: 99%

A Column-Generation Approach for a Short-Term Production Planning Problem in Closed-Loop Supply Chains

Sahling

2013

Bus Res

View full text Add to dashboard Cite

We present a new model formulation for a multi-product lot-sizing problem with product returns and remanufacturing subject to a capacity constraint. The given external demand of the products has to be satisfied by remanufactured or newly produced goods. The objective is to determine a feasible production plan, which minimizes production, holding, and setup costs. As the LP relaxation of a model formulation based on the well-known CLSP leads to very poor lower bounds, we propose a column-generation approach to determine tighter bounds. The lower bound obtained by column generation can be easily transferred into a feasible solution by a truncated branch-and-bound approach using CPLEX. The results of an extensive numerical study show the high solution quality of the proposed solution approach.

show abstract

Combinatorial optimization and Green Logistics

Cited by 233 publications

References 64 publications

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs

An adaptable optimizer for green component design

A Column-Generation Approach for a Short-Term Production Planning Problem in Closed-Loop Supply Chains

Contact Info

Product

Resources

About