Multi-period Supply Chain Network Design Research Articles

A multi-product and multi-period supply chain network design problem with price-sensitive demand and incremental quantity discount

Demand is an important factor in supply chain network design (SCND). Among the many factors that affect demand, the impact of price cannot be underestimated. This study, for the first time, provides enterprises with direct multi-period pricing and demand decision solutions in SCND by simultaneously considering the impact of price-sensitive demand and incremental quantity discounts on the network. In this way, a mixed-integer nonlinear programming model is established based on the relationships between price and demand functions to maximize the overall profit of the supply chain. Due to the nonlinear characteristics of the model, this study proves the existence of the optimal solution of the problem and the feasibility of implementing the following two algorithms by analyzing the nature of the problem. The first algorithm is based on the second-order cone programming (SOCP) method, and the second algorithm is based on the outer-approximation method. The results of numerical experiments at different scales show that the SOCP method can obtain the optimal solution for small and medium-scale experiments. In contrast, the outer-approximation method can find approximate optimal solutions within a reasonable time for large-scale experiments. The results confirm that considering incremental quantity discount can significantly enhance the profitability of supply chain networks in long-term planning. Finally, some future research directions in the field of SCND are discussed.

Customer Prioritization Integrated Supply Chain Optimization Model with Outsourcing Strategies.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.

Multi-stage, multi-product and multi-period milk supply chain network design: a case of coastal region of Odisha

Due to the complexity of supply chain networks in today’s business environment, it is important to consider several strategic planning decisions along with the classical location-allocation decisions to achieve an integrated system. In this paper, a milk supply chain network design (SCND) problem of coastal region Odisha is considered. The proposed model is focussed on multi-stage, multi-product and multi-period supply chain network design of milk of a dairy industry. The network design includes the strategic decision-making approach of influence area, order quantity, replenishment cycle time, preservation factor and number of proposed distribution centre of milk products. This supply chain network design generic model of the milk products is developed as a mixed integer non-linear programming (MINLP) model. It is solved by using piecewise nonlinear optimization approach for location-allocation decisions of the clusters. The sensitivity analysis is also conducted to check the importance and influence of the model parameters on total supply chain network profit per unit time of the multi-milk product. Through a real-world dairy industrial case of multi products, this proposed SCND generic model is demonstrated and then customized model is developed for the case analysis. It is observed that the total supply chain network profit per unit time for the proposed model is more than that of existing model. Hence the strategic conditions like the service region must be greater than the influence area and imposing preservation effort for the multi-milk product can be a mechanism for revenue growth of the supply chain network design.

Multi-stage, multi-product and multi-period milk supply chain network design: a case of coastal region of Odisha

Due to the complexity of supply chain networks in today’s business environment, it is important to consider several strategic planning decisions along with the classical location-allocation decisions to achieve an integrated system. In this paper, a milk supply chain network design (SCND) problem of coastal region Odisha is considered. The proposed model is focussed on multi-stage, multi-product and multi-period supply chain network design of milk of a dairy industry. The network design includes the strategic decision-making approach of influence area, order quantity, replenishment cycle time, preservation factor and number of proposed distribution centre of milk products. This supply chain network design generic model of the milk products is developed as a mixed integer non-linear programming (MINLP) model. It is solved by using piecewise nonlinear optimization approach for location-allocation decisions of the clusters. The sensitivity analysis is also conducted to check the importance and influence of the model parameters on total supply chain network profit per unit time of the multi-milk product. Through a real-world dairy industrial case of multi products, this proposed SCND generic model is demonstrated and then customized model is developed for the case analysis. It is observed that the total supply chain network profit per unit time for the proposed model is more than that of existing model. Hence the strategic conditions like the service region must be greater than the influence area and imposing preservation effort for the multi-milk product can be a mechanism for revenue growth of the supply chain network design.

Robust supply chain network design with multi-products for a company in the food sector

The paper aims to solve a problem faced by a company competing in the snacks market in Turkey. In line with the growth in this market, the company needs to make important decisions over the next few years about the timing and location of a new plant, its initial capacity, the timing and amount of additional capacity to be installed at the new and existing plants, the assignment of demand points to plants and the amount of raw materials to be shipped from suppliers to the plants in each period. The objective is to minimize the total cost of various components. The problem is formulated as a multi-period supply chain network design model with multi products. The resulting mixed-integer linear programming model is solved by the commercial solver CPLEX. This model enables us to carry out all analyses requested by the company in an efficient way. After this deterministic model is solved on the basis of a 9% annual increase in demand, it is extended to a minimax regret model to deal with uncertainty in demand quantities. The results suggest that opening the new plant in the city of İzmir is indeed a robust solution that is unaffected in different scenarios that are based on three distinct demand increase rates. Even though the location of the new plant remains unchanged with respect to scenarios, the optimal robust solution differs from the optimal solution of each scenario in terms of the capacity expansion decisions. After all obtained results had been communicated to the company managers and executives, the new plant construction was started in 2016 very close to the city that the mathematical model had determined. The new plant is expected to start operating in 2018.

Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain

This paper addresses a multi-stage and multi-period supply chain network design problem in which multiple commodities should be produced through different subsequent levels of manufacturing processes. The problem is formulated as a two-stage stochastic program under stochastic and highly time-variable demands. To deal with the stochastic demands, a Latin Hypercube Sampling method is applied to generate a fan of scenarios and then, a backward scenario reduction technique reduces the number of scenarios. Weighted mean-risk objectives by using different risk measures and minimax objective are examined to obtain risk-averse and robust solutions, respectively. Computational results are presented on a real-life case study to illustrate the applicability of the proposed approaches. To compare these different decision-making situations, a simulation approach is used. Furthermore, by several test problems, the performance of the stochastic model is investigated and the scenario generation method is evaluated in terms of in-sample and out-of-sample stability. Finally, sensitivity analysis on main parameters of the problem is performed to drive some managerial insights.

A new robust optimization approach for integrated multi-echelon, multi-product, multi-period supply chain network design under process uncertainty

Cost-effective supply chain management through integrating strategic decisions (e.g., network design) along with tactical decisions (e.g., production, distribution, and inventory decisions) under various markets, logistics, and production uncertainties is a critical issue for companies in different industries. In this paper, robust design and planning of a multi-echelon, multi-product, multi-period supply chain network considering process uncertainty is addressed. At first, a new robust optimization approach is developed which is capable to take into account two possibly disjoint uncertainty sets for each uncertain parameter. Then this framework is applied to the supply network design problem and the associated robust counterpart is derived. A simulation-based procedure is also proposed to determine appropriate uncertainty budgets. Finally, the applicability of the presented approach is tested through an illustrative example.

The CAT metaheuristic for the solution of multi-period activity-based supply chain network design problems

This paper proposes an agent-based metaheuristic to solve large-scale multi-period supply chain network design problems. The generic design model formulated covers the entire supply chain, from vendor selection, to production–distribution sites configuration, transportation options and marketing policy choices. The model is based on the mapping of a conceptual supply chain activity graph on potential network locations. To solve this complex design problem, we propose Collaborative Agent Team (CAT), an efficient hybrid metaheuristic based on the concept of asynchronous agent teams (A-Teams). Computational results are presented and discussed for large-scale supply chain networks, and the results obtained with CAT are compared to those obtained with the latest version of CPLEX.

A multi-stage stochastic supply network design problem with financial decisions and risk management

In this paper, a multi-period supply chain network design problem is addressed. Several aspects of practical relevance are considered such as those related with the financial decisions that must be accounted for by a company managing a supply chain. The decisions to be made comprise the location of the facilities, the flow of commodities and the investments to make in alternative activities to those directly related with the supply chain design. Uncertainty is assumed for demand and interest rates, which is described by a set of scenarios. Therefore, for the entire planning horizon, a tree of scenarios is built. A target is set for the return on investment and the risk of falling below it is measured and accounted for. The service level is also measured and included in the objective function. The problem is formulated as a multi-stage stochastic mixed-integer linear programming problem. The goal is to maximize the total financial benefit. An alternative formulation which is based upon the paths in the scenario tree is also proposed. A methodology for measuring the value of the stochastic solution in this problem is discussed. Computational tests using randomly generated data are presented showing that the stochastic approach is worth considering in these types of problems.