An optimum closed loop supply chain network model in a stochastic product life cycle context

Nowadays, closed loop supply chain network (CLSCN) receives considerable attention due to the growing awareness of the environmental destruction and depletion of natural resources. The establishment of a CLSCN is considered as a strategic decision that requires a lot of effort and intensive capital resources. Therefore, it is very crucial to make CLSCN design decisions taking into account multiple facets of uncertainties. Literature reviews to date reveals that uncertainties in product life cycle (PLC) or what has been called “product diffusion” have been vastly ignored. Particularly, the deterministic nature of the proposed diffusion models is a severe defect that can hinder the involvement of real-world uncertainties in design of a CLSCN. This study is an attempt to fill this gap by developing a costefficient CLSCN model for a product with dynamic and stochastic diffusion into the market that leads to an optimum design of the targeted CLSCN. Firstly, a geometric Brownian motion (GBM)-based diffusion forecast method was proposed and validated using a conventional approach namely, Holt’s method. Then, a two-stage stochastic programming mathematical model for optimum design of the targeted CLSCN was developed. The developed stochastic CLSCN model provides the optimum design of the targeted CLSCN utilizing the values predicted for the product diffusion through the PLC based on the proposed forecast method. The developed mathematical model addresses two types of decisions namely, “here and now” and “wait and see” decisions within the PLC. The “here and now” decisions were made in the first stage. The results show optimum values for decisions concerning configuration of the CLSCN as well as dynamic capacity allocation and expansion decisions through the PLC. However, the “wait and see” decisions are made in the second stage within the frame provided by the first-stage solutions. Here, the results portray optimum values for decisions concerning with the flow quantities between the CLSCN facilities, backorder and inventory levels, and recovery of returns through the PLC. In order to test the applicability of the developed CLSCN model, the mathematical model was coded by CPLEX software and solved for secondary data from the case study from previous case study in literature. Finally, a sensitivity analysis was performed to investigate the effect of diffusion uncertainty on the total cost of the CLSCN, its configuration, and production capacity allocations and expansions. The results of the sensitivity analysis revealed that, for the higher levels of diffusion uncertainty, the total cost imposed to the supply chain increases due to the increase in the allocated production capacity as well as the increase in the number of involved facilities

A simulation-based product diffusion forecasting method using geometric Brownian motion and spline interpolation

This study addresses the problem of stochasticity in forecasting diffusion of a new product with scarce historical data. Demand uncertainties are calibrated using a geometric Brownian motion (GBM) process. The spline interpolation (SI) method and curve fitting process have been utilized to obtain parameters of the constructed GBM-based differential equation over the product's life cycle (PLC). The constructed stochastic differential equation is coded as the forecast model and is simulated using MATLAB. The results are several sample demand paths generated from simulation of the forecast model. To evaluate the forecasting performance of the proposed method it is compared with Holt's model, using actual data from the semiconductor industry. The comparison results confirm the applicability of the proposed method in the semiconductor industry. The method can be helpful for policy-makers who require the prediction of uncertain demand over a time horizon, such as decisions associated with aggregate production planning, capacity planning, and supply chain network design. Especially for the semiconductor industry with intensive capital investment the proposed approach can be useful for making decisions associated with capacity allocation and expansion

A deterministic aggregate production planning model considering quality of products

Aggregate Production Planning (APP) is a medium-term planning which is concerned with the lowest-cost method of production planning to meet customers' requirements and to satisfy fluctuating demand over a planning time horizon. APP problem has been studied widely since it was introduced and formulated in 1950s. However, in several conducted studies in the APP area, most of the researchers have concentrated on some common objectives such as minimization of cost, fluctuation in the number of workers, and inventory level. Specifically, maintaining quality at the desirable level as an objective while minimizing cost has not been considered in previous studies. In this study, an attempt has been made to develop a multi-objective mixed integer linear programming model that serves those companies aiming to incur the minimum level of operational cost while maintaining quality at an acceptable level. In order to obtain the solution to the multi-objective model, the Fuzzy Goal Programming approach and max-min operator of Bellman-Zadeh were applied to the model. At the final step, IBM ILOG CPLEX Optimization Studio software was used to obtain the experimental results based on the data collected from an automotive parts manufacturing company. The results show that incorporating quality in the model imposes some costs, however a trade-off should be done between the cost resulting from producing products with higher quality and the cost that the firm may incur due to customer dissatisfaction and sale losses

A Multiobjective Fuzzy Aggregate Production Planning Model Considering Real Capacity and Quality of Products

In this study, an attempt has been made to develop a multiobjective fuzzy aggregate production planning (APP) model that best serves those companies whose aim is to have the best utilization of their resources in an uncertain environment while trying to keep an acceptable degree of quality and customer service level simultaneously. In addition, the study takes into account the performance and availability of production lines. To provide the optimal solution to the proposed model, first it was converted to an equivalent crisp multiobjective model and then goal programming was applied to the converted model. At the final step, the IBM ILOG CPLEX Optimization Studio software was used to obtain the final result based on the data collected from an automotive parts manufacturing company. The comparison of results obtained from solving the model with and without considering the performance and availability of production lines, revealed the significant importance of these two factors in developing a real and practical aggregate production plan

Identifying Significant Factors of Brick Laying Process through Design of Experiment and Computer Simulation: a Case Study

Improving performance measures in the construction processes has been a major concern for managers and decision makers in the industry. They seek for ways to recognize the key factors which have the largest effect on the process. Identifying such factors can guide them to focus on the right parts of the process in order to gain the best possible result. In the present study design of experiment (DOE) has been applied to a computer simulation model of brick laying process to determine significant factors while productivity has been chosen as the response of the experiment. To this end, four controllable factors and their interaction have been experimented and the best factor level has been calculated for each one. The results indicate that three factors, namely, labor of brick, labor of mortar and inter arrival time of mortar along with interaction of labor of brick and labor of mortar are significant

A multiobjective aggregate production planning model for lean manufacturing: Insights from three case studies

This article proposes a multiobjective mathematical model to optimize the multiperiod aggregate production planning (APP) of multiproduct companies. Although there are many studies of lean manufacturing (LM), its integration with APP has not been studied. The present article is intended to integrate APP and LM, including an analysis of market winners, market qualifiers, and waste. The model's objective functions include the cost, lead time, and waste minimization in addition to maximizing the product quality. A solution procedure is suggested to solve the model using IBM CPLEX 12.4 software. The model is investigated in three different case studies to check its applicability and generalizability. According to the obtained results, the proposed model provides an optimized APP with regard to the major concerns of LM, including waste, overproduction, time, and sourcing. In addition, according to the sensitivity analysis, lean weighting of the objective functions provides a better output than using equal weighting

Multi-objective Aggregate Production Planning Model Considering Overtime and Outsourcing Options Under Fuzzy Seasonal Demand

This paper investigates a novel fuzzy multi-objective multi-period Aggregate Production Planning (APP) problem under seasonal demand. As two of the main real-world assumptions, the options of workforce overtime and outsourcing are studied in the proposed Mixed-Integer Linear Programming (MILP) model. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/ unemployment costs, and maximize the customers' satisfaction level. To deal with demand uncertainty, triangular fuzzy numbers are considered for demand parameters. Then the proposed model is validated by solving an illustrative example using a Weighted Goal Programming (WGP) method and CPLEX solver. Finally, it is demonstrated that uncertain conditions and considering real-world assumptions can yield different results in developing a practical aggregate production plan. Moreover, a sensitivity analysis is then performed to provide qualitative managerial insights and decision aids