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Supply risk is one of the undeniable risks in a supply chain that can affect the supply chain process and lead to disruptions in customer satisfaction or distributor reliability. This research employs a closed-loop supply chain network for the plastic bottle industry, followed by formulating a multi-objective mathematical model by considering several assumptions. The model seeks to optimize total costs, supply risk, and customers' satisfaction (distributor's reliability). The revised multi-choice goal programming approach is also applied to solve the model and verify it through a case study. Moreover, the best–worst method as a robust multi-criteria decision-making tool is used to find the values of the parameters of supply risk. Afterward, the sensitivity analysis checks the proposed framework's robustness and shows its reaction under various conditions. Our results indicate the effectiveness of the proposed framework and offer insights that can be used to improve an organization's status. [ABSTRACT FROM AUTHOR]

• A Goal Programming model to optimize a waste paper recycling network was elaborated. • The model considers objectives related to sustainability, multiple products and periods. • Vehicle routing, different-capacity vehicles and multiple trips are highlights. • The possibility of storing material at the collection points is considered. • The formulation provides relevant solutions to assist decision-makers. Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. [ABSTRACT FROM AUTHOR]

• Presenting a novel mixed-integer programming model for designing a sustainable lateral resupply blood supply chain network. • Addressing a blood inventory-routing problem with supply and demand uncertainties. • Considering ABO and Rh factor and the interchangeability of blood products. • Developing a scenario-based stochastic optimization model and revised multi-choice goal programming approach to solve. The vibrant subject of blood banking and blood distribution is addressed where lateral resupply of blood products is allowed. In this paper, we studied a blood supply chain network design consisting of four conventional levels each of which corresponding to one entity in the real world: donors, blood collection facilities, blood centers, and hospitals. The blood supply chain network considered two important properties: ABO-Rh factors and shelf lives of blood products. Moreover, an integrated inventory system for sharing hospitals' inventory levels is considered to investigate the two performance indicators of the network in terms of cross-matching and outdated units. This type of inventory system is known as "Lateral Resupply" permitting a hospital to satisfy its demand by the other hospitals' inventories in the absence of the required product at the blood center and its excess in any hospital. The proposed mixed-integer mathematical programming model entails three objectives referring to the three underlying concepts of sustainability, additionally, to cope with uncertainties and multi objectiveness, a scenario-based optimization approach and revised multi-choice goal programming technique were employed, respectively. Finally, the results confirmed that the lateral resupply virtue improved the performance indicators. [ABSTRACT FROM AUTHOR]

This paper presents an integrated platelet supply chain where demand is age-differentiated according to the type of patient. At first, considering the apheresis method and traditional production platelet method, two mixed-integer programming models are developed based on FIFO and LIFO issuing policies. Since in practice fresh platelets have been preferred, a bi-objective model has been then developed in which the first objective maximizes the freshness of the units delivered and the second minimizes the total cost. To cope with uncertain demand, a robust optimization approach is presented and the application of the proposed model is discussed in a case study. [ABSTRACT FROM AUTHOR]

This study aims to design a multi-echelon, multi-objective supply chain model that incorporates new product development and its effects on supply chain configuration. To survive in a highly competitive industry, strategies to either collaborate or compete with rival firms within a network should be considered in the new product development process, and it is crucial to pay great attention to customers’ needs and interests. Considering the imprecise nature of some critical parameters plays an important role in making suitable strategic decisions. This fact requires considering uncertainties of the environment such as customer demands and supplier capacities. In this study, a supply chain involving multiple suppliers, manufacturers, distributors and customers and addressing a multi-objective, multi-period and multi-product aggregate procurement and production planning problem is considered. The first objective function aims to maximize the profit of the supply chain, including that associated with new product development. The second objective function considers customer satisfaction, and the third one maximizes the production of the developed and new products. To address real-world planning problems involving noisy, incomplete or erroneous data, the supplier capacity parameters of the supply chain and demand fluctuations are subject to uncertainty, which is modeled by fuzzy stochastic programming. Finally, the proposed multi-objective model is solved as a single-objective mixed integer programming model by applying the revised multi-choice goal programming method. In addition, a numerical example is provided to demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, a bi-objective optimization model is proposed for a dynamic virtual cellular manufacturing system and supply chain design. The presented model considers important manufacturing features thoroughly such as multi-plants and facility locations, multi-markets allocations, multi-period production planning under uncertain demand of products and capacities of resources. The proposed model is converted into an equivalent auxiliary crisp model. Then, to solve the model, a revised multi-choice goal programming approach is applied for finding a favorite solution. The aims of the proposed model are to minimize the total cost of supply chain design which includes holding cost, outsourcing cost, maintenance and overhead cost of machines, fixed cost, external transportation cost and minimizing the total number of exceptional elements and movements of the labors between active plants. The Computational results are explained in terms of a real case study as well as the validity of the proposed model solution method. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the potential use of revised multi-choice goal programming approach for modelling and solving economic- environmental power generation and dispatch problems in a thermal power plant operation and planning horizon. In the proposed approach, the objective functions and certain constraints having the characteristics of nonlinear programming are first transformed into their equivalent linear forms to employ linear goal programming methodology in the process of solving the problem. Then, in the model formulation, the objectives with multiplicity of aspiration levels are converted into the standard form of goals in goal programming approach to arrive at a most satisfactory decision in the multiobjective decision making environment. In the solution process, minsum goal programming approach is addressed for achievement of objective goals within their tolerance ranges specified in the decision making context. To illustrate the potential use of the approach, the proposed model is tested on IEEE 6-Generator 30-Bus system. In addition, the model solution is compared with the solutions obtained by using other approaches studied previously. [Copyright &y& Elsevier]

Abstract: In this paper, we consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0–1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works. [Copyright &y& Elsevier]