Multi-period planning of fish breeding chains and investigation of its efficiency under demand uncertainty

A stochastic model for developing speculation-postponement strategies and modularization concepts in the global supply chain with demand uncertainty

In this paper, we present a multicut version of the Benders decomposition method for solving two-stage stochastic linear programming problems, including stochastic mixed-integer programs with only continuous recourse (two-stage) variables. The main idea is to add one cut per realization of uncertainty to the master problem in each iteration, that is, as many Benders cuts as the number of scenarios added to the master problem in each iteration. Two examples are presented to illustrate the application of the proposed algorithm. One involves production-transportation planning under demand uncertainty, and the other one involves multiperiod planning of global, multiproduct chemical supply chains under demand and freight rate uncertainty. Computational studies show that while both the standard and the multicut versions of the Benders decomposition method can solve large-scale stochastic programming problems with reasonable computational effort, significant savings in CPU time can be achieved by using the proposed multicut algorithm.

In global supply chains, coordination of decision making in decentralized supply chain has received much attention. The total profit in the decentralized situation is inferior to the optimal profit in the centralized situation. In order to improve the total profit in the decentralized situation, it is required to optimize the coordination of the production planning for supplier and retailer under different leader-follower relation under demand uncertainty. In this paper, the multi-period production planning problem for the supplier and the retailer is formulated as the nonlinear bilevel programming problem under uncertain demand. We propose the replacement of leader-follower relation in multi-period supply chain planning. In the proposed model, the leader can be changed dynamically in the middle of periods. The effectiveness of the replacement of leader-follower relation to the total profit in the bilevel production planning is examined by some numerical experiments.

The urban transport planning process has four main activities: Network design, Timetable construction, Vehicle scheduling and Crew scheduling; each activity has subactivities. In this paper the authors work with the subactivities of timetable construction: minimal frequency calculation and departure time scheduling. The authors propose to solve both subactivities in an integrated way. The developed mathematical model allows multi-period planning and it can also be used for multimodal urban transportation systems. The authors consider demand uncertainty and the authors employ fuzzy programming to solve the problem. The authors formulate the urban transportation timetabling construction problem as a bi-objective problem: to minimize the total operational cost and to maximize the number of multi-period synchronizations. Finally, the authors implemented the SAUGMECON method to solve the problem.

As the largest archipelago nation in the world, Indonesia’s logistics system has not shown excellence according to the parameters of logistics performance index and based on logistics costs percentages from overall GDP. This is due to the imbalances of trading on the western and eastern regions in Indonesia, which impacts the transportation systems costs to and from the eastern regions. Therefore, it is imperative to improve the competitiveness of Indonesian maritime logistics through maritime logistics network design. This research will focus on three levels of decision making in logistics network design, which include type of ships in the strategic level, shipping routes in the tactical level, and container allocation in the operational level with implementing butterfly routes in Indonesia’s logistics networking problems. Furthermore, this research will analyze the impact of Pendulum Nusantara and Sea Toll routes against the company profits and percentages of containers shipped. This research will also foresee how demand uncertainties and multi-period planning should affect decision making in designing the Indonesian Liner Shipping Network.

PurposeThe purpose of this research paper is to discuss cellular manufacturing is discussed under conditions of changing product demand. Traditional cell formation procedures ignore any changes in demand over time from product redesign and other factors. However given that in today's business environment, product life cycles are short, a framework is proposed that creates a multi‐period cellular layout plan including cell redesign where appropriate.Design/methodology/approachThe framework is illustrated using a two‐stage procedure based on the generalized machine assignment problem and dynamic programming. This framework is conceptually compared to virtual cell manufacturing, which is useful when there is uncertainty in demand rather than anticipated changes in demand. A case study is used to explain how the concept would work in practice.FindingsOne major characteristic of the proposed method is that it is flexible enough to incorporate existing cell formation procedures. It is shown through an example problem that the proposed two‐stage method is better than undergoing ad hoc layout changes or ignoring the demand changes when shifting or cell rearrangement costs exist. It also sheds some insight into cellular manufacturing under dynamic conditions.Originality/valueThis paper should be useful to both researchers and practitioners who deal with demand changes in cellular manufacturing.

Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewable production, and energy prices. Therefore, this work proposes a novel model for the multi-period planning of EDSs and DERs considering conditional value at risk (CVaR) to manage fluctuations in generation cost and carbon emissions. The proposed mathematical model aims to minimize the net present cost related to investment, operation, and risk. Unlike previous approaches, uncertain behavior of demand growth per planning period is addressed, and the risk is evaluated from two perspectives: planning costs and carbon taxes. Investments in substations, lines, renewable distributed generation, EV charging stations, and energy storage systems are considered. The uncertainties associated with the variability of renewable generation and demand are modeled through a set of scenarios. Finally, the model was evaluated using the 24 and 54-bus EDS. Thus, the proposal is a flexible tool that can be used for different purposes (e.g., carbon taxes, budget limits).

This paper examines the effect of incorporating multi-period network augmentation into the survivable network design process. The framework presented can handle variable restorability requirements, potential economies of scale, and technology shifts. By utilizing a multi-period planning model, shifts and uncertainties in demand and policy can be accounted for. This study examined the design of survivable networks using both demand-wise shared protection and shared backup path protection (representing opposite ends of the mesh network efficiency spectrum) with topology augmentation over multiple time horizons.

Multi-period planning of fish breeding chains and investigation of its efficiency under demand uncertainty

The rapid integration of renewable energy sources (RES) and distributed energy resources (DER) into modern power systems introduces significant challenges for distribution network expansion planning (DNEP). These challenges primarily arise from the inherent uncertainty in renewable generation and load demand, which traditional deterministic and robust optimization models struggle to address effectively. This study proposes a hybrid stochastic optimization framework that integrates Monte Carlo simulation with scenario reduction techniques to improve decision-making under uncertainty. The framework employs stochastic programming to minimize investment and operational costs while ensuring system reliability across diverse scenarios. Monte Carlo simulation models high-dimensional uncertainties, and scenario reduction methods enhance computational efficiency by selecting representative scenarios. By leveraging interdisciplinary methods, including optical sensing technologies for real-time monitoring and data acquisition, and intelligent grid management, the framework supports the development of smart cities, where energy systems must be sustainable, resilient, and efficient. Case studies on a modified IEEE 33-bus system demonstrate the framework’s ability to achieve significant cost savings—up to 9% compared to deterministic methods—while improving reliability by 15%. Sensitivity analysis further reveals the framework’s robustness to variations in scenario numbers and distributions. This scalable and computationally efficient approach provides a practical solution for real-world power system planning, particularly in the context of smart cities transitioning toward higher renewable penetration and intelligent energy infrastructures. Future research directions include exploring advanced scenario generation techniques and extending the framework to dynamic, multi-period planning under evolving uncertainties.

In this paper the multiperiod planning and scheduling of multiproduct plants under demand uncertainty is addressed. The proposed stochastic model, allowing for uncertain product demand correlations, is an extension of the deterministic model introduced by Birewar and Grossmann (Ind. Eng. Chem. Res. 1990, 29, 570). The stochastic model involves the maximization of the expected profit subject to the satisfaction of single or multiple product demands with prespecified probability levels (chance-constraints). The stochastic elements of the model are expressed with equivalent deterministic forms, eliminating the need for discretization or sampling techniques. This implies that problems with a large number of possibly correlated uncertain product demands can be efficiently handled. The resulting equivalent deterministic optimization models are MINLP's with convex continuous parts. An example problem involving 20 correlated uncertain product demands is addressed. A sequence of different models is considered whic...

Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions

A kit is a specific collection of components and/or tools needed for completing a procedure or producing a product. A multiperiod material planning system for production kits under the demand and procurement lead-time uncertainty is considered when component sharing among kits is plausible. Simulation experiments show that component sharing improves the system's service measure of average kit availability and average backorder quantity per period. Also, carrying component safety stock only enhances the benefits of component sharing by reducing the average backorder quantity at the expense of increased inventories, but does not improve the average kit availability.

This paper presents a joint optimization framework for multi-period planning in a Manufacturing–Remanufacturing–Transport Supply Chain (MRTSC), focusing on carbon emission reduction and economic efficiency. A novel Mixed Integer Linear Programming (MILP) model is developed to coordinate procurement, production, remanufacturing, transportation, and returns under environmental constraints, aligned with carbon tax policies and the Paris Agreement. To address uncertainty in future demand and the number of returned used products (NRUP), a two-stage approach combining forecasting and optimization is applied. Among several predictive methods evaluated, a hybrid SARIMA/VAR model is selected for its accuracy. The MILP model, implemented in CPLEX, generates optimal decisions based on these forecasts. A case study demonstrates notable improvements in cost efficiency and emission reduction over traditional approaches. The results show that the proposed model consistently maintained strong service levels through flexible planning and responsive transport scheduling, minimizing both unmet demand and inventory excesses throughout the planning horizon. Additionally, the findings indicate that carbon taxation caused a sharp drop in profit with only limited emission reductions, highlighting the need for parallel support for cleaner technologies and more integrated sustainability strategies. The analysis further reveals a clear trade-off between emission reduction and operational performance, as stricter carbon limits lead to lower profitability and service levels despite environmental gains.

This study conducts a comparative analysis of three inventory review policies within a multi-stage supply chain framework to determine the best policies to adopt. Optimizing supply chain inventory planning is challenging due to uncertain demand. The problem involves minimizing the overall operating costs and determining the optimal reorder plan for the operational network. Three models are developed, employing a multi-period planning approach that considers supply flows, and inventory levels at facilities. To test the models, a real-world supply chain case study is solved. The uncertain demand faced by retailers is addressed by defining the optimal safety stock that guarantees a given service level at each facility. Using the guaranteed service approach to model time delays in distribution flows, we effectively capture the stochastic nature of demand uncertainty. We developed multi-period planning formulations that indicate the precise amount and timing of inventory replenishments. Additionally, this work emphasizes the importance of lead time in multi-period planning modeling, a topic often overlooked in the literature. The combination of inventory planning with inventory policies enables better operational decisions for supply chain managers. The results obtained allow the decision makers to choose the best inventory policy option based on the conditions of their supply chain.