This article proposes an integrated approach towards the design optimization and production planning of cellular manufacturing systems as a part of closed-loop supply chains in an effort to make manufacturing enterprises sustainable. For industrial applications both at the system design and operation stages, a mixed integer linear programming (MILP) model, to integrate the production planning problem in cellular manufacturing systems and the tactical planning of a closed-loop supply chain, has been developed. The cellular manufacturing system in the proposed mathematical model has several features including dynamic cell configuration, multi-period production settings, machine capacity, machine acquisition, machine procurements, and multiple units of identical machines as well as considering different cost parameters such as production cost, operational cost of the machines, and subcontracting cost of the part demands; mainly targeted to be used in industry at the operational level. In addition, several activities such as acquisition, disassembly, setup for disassembly, and disposition of the returned products have been considered on the reverse flow of the closed-loop supply chain of the proposed mathematical model, which would lead to further industrial applications mainly at the integrated design stage of manufacturing and supply chain systems in addition to the potential applications at the operational level.
Recently, renewable energy resources such as solar energy have been significantly utilized in various sectors, regarding world population growth and the increasing use of fossil fuel and non-renewable resources, and consequently, the increased rate of environmental pollution. Implementing photovoltaic systems is regarded as one of the methods of using solar energy, which countries have highly considered in recent years. With the limited lifetime of photovoltaic systems, addressing the forward and reverse supply chain of these systems plays a significant role in increasing their efficiency. To this end, the present study seeks to develop a two-objective mixed-integer non-linear model, including minimizing total costs and minimizing the negative environmental impacts, aiming to design a closed-loop supply chain network for photovoltaic systems. In this study, the augmented e-constraint method was employed to convert the current two-objective programming model into a single-objective one. Finally, the proposed model was implemented in a case study in Iran to evaluate the model's efficiency. The results indicated that solar power plants should be built in areas with higher solar energy and lower cost. Also, the model dynamics could increase the number of constructed solar power plants and their electricity generation capacity over the time horizon, followed by increased demand for annual electricity generated by solar energy.
This paper introduces a new method for organizational performance measurement. The model integrates the QEST (Quality factor + Economic, Social & Technical dimensions) and the BSC (Balanced Scorecard) models. Although the model is originally defined to measure performance in the banking industries, it can be used for almost any organization and any multi-attribute decision-making problem. It not only has a new look to the BSC perspectives but enables organizations to obtain a more reliable assessment. The model takes advantage of the QEST-3D and the BSC that lets the proposed model be easy to generalize. The research’s motivation is to obtain performance measurement based on not only the values of the indicators but the proportion of the values of the leading indicators and the lagging ones obtained by BSC. The financial perspective is considered a lagging indicator, while the other indicators are considered leading. To obtain reliability analysis, the model has been tested in 17 branches of a bank in Kurdistan province for three recent years, as a real case. The results that are compared with what is obtained from the pre-qualified TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) successfully illustrate the accuracy and practicality of the proposed model. The results show that based on the correct logic proposed in the proposed method for separating and analyzing the perspectives taken from the BSC, the strategy-oriented ranking made by the proposed method is more reliable and logical than the ranking performed by the TOPSIS.
Proper transportation and distribution of commodities plays a pivotal role in the expenditures of supply chains. In this paper, a clustered vehicle routing problem with pick-up and delivery is studied. A fleet of distinct vehicles is concurrently responsible for distribution of medicines and collection of their wastes. Collected wastes should be sent to a waste center. To solve the problem, a bi-objective mathematical model is presented. Fairness of travelled distances among drivers and transportation expenses are two objective functions considered in the model. Since the proposed problem is NP-hard, a three-step hybrid approach is developed to solve the problem. First, K-medoids clustering algorithm allocates customers to subsets based on their coordinates. Second, a mathematical model is used for routing vehicles within each cluster. Third, NSGA-II is used to produce final result using the outcome of step 2. Extensive numerical results indicate the superiority of the proposed approach against the NSGA-II.
This study develops a mathematical model for the designing of a supply chain network. The uncertain nature of demand and lead time is incorporated into the concerned model. This motivates us to deploy the queuing concept to deal with uncertainties and analysing the number of orders, number of shortages and average of on-hand inventory. Then, in accordance with the outputs of the queuing analysis, a mixed integer nonlinear programming model is devised to design the distribution network of a supply chain. The decisions to be made are facility locations, demand allocations along with inventory management decisions. The objective function of the model aims at minimising the total supply chain costs encompassing location, transportation and inventory costs. Notably, we assume that each facility manages its inventory policy based on a policy and stock outs result in lost sales. Inasmuch as the developed problem is difficult to solve by means of exact methods, tailored hybrid solution algorithms based on simulated annealing and genetic algorithm are employed to overcome the computational complexity of the developed model. Finally, Using the real information of the Telecommunication infrastructure company, we evaluate the proposed model and the management insights are reported.
