Abstract
In this study, we propose to solve a biobjective tactical integrated production-distribution planning problem for a multisite, multiperiod, multiproduct, sea-air intermodal supply chain network under uncertainties. Two random parameters are considered simultaneously: product replenishment orders and production capacity, which are modelled via a finite set of scenarios, using a two-stage stochastic approach. A corresponding mathematical model is developed, coded, and solved using the LINGO 18.0 software optimisation tool. This model aims to simultaneously minimise the total costs of production in both regular and overtime, inventory, distribution, and backordering activities and maximise the customer satisfaction level over the tactical planning horizon. The AUGMECON technique is applied to handle with the multiobjective optimisation. The applicability and the performance of the proposed model are tested through a real-life case study inspired from a medium-sized Tunisian textile and apparel company. Sensitivity analysis on stochastic parameters and managerial insights for the studied supply chain network are argued based on the empirical findings.
Highlights
Based on the abovementioned assumptions and description, the two-stage stochastic programming model for the tactical production and distribution planning problem could be formulated as a multiobjective integer linear programming model. e objectives correspond to minimisation of the overall costs of the supply chain and maximisation of customer services that can be rendered to customers in terms of the rate of on-time deliveries
Conclusion and Perspectives is paper presents a new formulation of an integrated production-distribution planning problem in a stochastic multisite, multiproduct, multiperiod, and intermodal supply chain context
Two objectives are considered: minimising the total costs and maximising the customer satisfaction level in terms of orders delivered on time
Summary
TC: total costs; MILP: mixed-integer linear programming; D: demand; 2STSP: two-stage stochastic programming; P: profit; MINLP: mixed-integer nonlinear programming; C: cost; RO: robust optimisation; CSL: customer satisfaction level; LP: linear programming; Pr: price; FP: fuzzy programming; Q: quality; MIP: mixed-integer programming; CAP: capacities; SP: stochastic programming; TDT: total delivery time; ILP: integer linear programming; PT: production time; VTC: variance of total costs; STT: setup time; EI: environmental impacts; MA: memetic algorithm; FR: financial risk; OSL: IBM Optimization Subroutine Library; NSGAII: nondominated sorting genetic algorithm II; PROD: productivity. All the orders should be fully met at the end of the tactical planning horizon since lost sales are not permitted Both stochastic demand and stochastic production capacity are considered in this problem, and we assume that these uncertainties can be modelled by defining several different scenarios with a given probability of occurrence. Cs of possible realisations, called scenarios with known probabilities δ(s), and basically, the decisions are made in two different stages In this approach, first-stage decisions are made “here and ” at the beginning of the planning horizon, prior to the realisation of the random events. E presence of uncertainty is correlated with the stochastic nature of the demand and production capacity associated with the second-stage decisions. Based on the abovementioned assumptions and description, the two-stage stochastic programming model for the tactical production and distribution planning problem could be formulated as a multiobjective integer linear programming model. e objectives correspond to minimisation of the overall costs of the supply chain and maximisation of customer services that can be rendered to customers in terms of the rate of on-time deliveries
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