A hedging policy for carriers’ selection under availability and demand uncertainty

Production distribution systems are increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. Production distribution involves a multistage supply chain network that consists of factories, distribution centers, retailers, and various customers. Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the production distribution problem with certain demands or a single period. To fill the gap, this study aims to integrate the extended priority-based discrete particle swarm optimization and novel extended priority-based hybrid genetic algorithm for solving flexible multistage production distribution under uncertain demands in multiple periods. In particular, triangular fuzzy demands are considered for minimizing the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. For validation, we designed numerical experiments to compare the proposed approaches with LINGO computational software (for small problems) and conventional genetic algorithms (for normal problems) in real settings. The experimental results demonstrated practical viability of the proposed approaches.

Logistics network is increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. The logistics network distribution involves a multistage supply chain that consists of the flexible forward directions (i.e., factories, distribution centers, retailers, and various customers) and the flexible backward directions (i.e., re-manufacturing and reuse). Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the traditional multistage forward directions problem with certain demands or a single period. To fill the gap, this chapter proposes the hybrid genetic algorithm approaches for solving flexible, multiple periods, multiple stages, and forward/reverse logistics network. In particular, triangular fuzzy demands are considered to minimize the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. The experimental results demonstrated practical viability for the proposed approaches.

In this paper, secure communication over a buffer-aided two-hop communication link, in which a source sends data to a destination with the aid of a trusted half-duplex relay node in the presence of a passive eavesdropper, is studied. It is assumed that there is no direct link between the source and the destination, and the buffer-aided relay forwards the information to the destination by employing the decode-and-forward (DF) scheme. Both the source and the relay transmissions are assumed to be vulnerable to the eavesdropper. The perfect channel side information (CSI) of the network is assumed to be available at the source and the relay. Initially, conventional relaying protocols where equal partition of the time for the reception and transmission of the relay is considered. Then, the optimal time fraction with the optimal power control policy that maximizes the secrecy throughput is identified. Subsequently, the optimal joint link selection and power control policy that maximizes the secrecy throughput is derived. Numerical results demonstrate that the joint link selection and power control scheme can significantly improve the achievable secrecy throughput of the buffer-aided two-hop communication system.

Optimal selection and release problem in software testing process: A continuous time stochastic control approach

<div class="section abstract"><div class="htmlview paragraph">This paper presents an energy-optimal plan for the allocation of a heterogeneous fleet of delivery vehicles in a dynamic network of multiple distribution centers and fulfillment centers. Each distribution center with a heterogeneous fleet of delivery vehicles is considered as a hub connected with the fulfillment centers through the routes as spokes. The goal is to minimize the overall energy consumption of the fleet while meeting the demand of each of the fulfillment centers. To achieve this goal, the problem is divided into two sub-problems that are solved in a hierarchical way. Firstly, for each spoke, the optimal number of vehicles to be allocated from each hub is determined. Secondly, given the number of allocated delivery vehicles from a hub for each spoke, the optimal selection of vehicle type from the available heterogeneous fleet at the hub is done for each of spokes based on the energy requirement and the energy efficiency of the spoke under consideration. Furthermore, the dynamic nature of the network is also addressed. As the network changes due to the number of demanded or available vehicles, or drop out of distribution center(s) or fulfillment center(s), for example, due to unavailability of a distribution center or no requirement from a fulfillment center, the plan is updated accordingly in the next iteration. To check the effectiveness of the plan, three distribution centers and ten fulfillment centers are considered on a real-world map. The results show that the proposed plan is the most optimal plan in terms of total energy consumption among all the possible plans for the tested scenario.</div></div>

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.

This paper considers a single-sourcing network design problem for a three-level supply chain. For the first time, a novel mathematical model is presented considering risk-pooling, the inventory existence at distribution centers (DCs) under demand uncertainty, the existence of several alternatives to transport the product between facilities, and routing of vehicles from distribution centers to customer in a stochastic supply chain system, simultaneously. This problem is formulated as a bi-objective stochastic mixed-integer nonlinear programming model. The aim of this model is to determine the number of located distribution centers, their locations, and capacity levels, and allocating customers to distribution centers and distribution centers to suppliers. It also determines the inventory control decisions on the amount of ordered products and the amount of safety stocks at each opened DC, selecting a type of vehicle for transportation. Moreover, it determines routing decisions, such as determination of vehicles' routes starting from an opened distribution center to serve its allocated customers and returning to that distribution center. All are done in a way that the total system cost and the total transportation time are minimized. The Lingo software is used to solve the presented model. The computational results are illustrated in this paper.

Order picking is the most expensive operation in a distribution center. Due to a large amount of labor used in order picking, the cost associated with the labor is high. The objective of this paper is to build a simulation model that would help distribution center managers to forecast their throughput by optimizing the worker configuration. The research uses a hierarchical approach to build a simulation model. The simulation model is divided into small submodels. The submodels are completely independent of each other. The submodels can be combined to make various different complete models. In this research, the submodels are used to build a simulation model of an actual industrial distribution center. The model is then run for twenty-four hours and the results are compared with the flat simulation model of the same distribution center.

In this paper, a new mathematical model is developed to optimize replenishment policies and inventory costs of a two-echelon supply chain system of kerosene product under demand uncertainty. The system consists of a fuel depot at the upper echelon and four petrol stations at the lower echelon. The petrol stations face stochastic stationary demand where inventory replenishment periods are uniformly fixed over the echelons. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for the inventory item. The replenishment cost, holding cost and shortage costs are combined with demand and inventory positions in order to generate the inventory cost matrix over the echelons. The matrix represents the long run measure of performance for the decision problem. The objective is to determine in each echelon of the planning horizon an optimal replenishment policy so that the long run inventory costs are minimized for a given state of demand. Using weekly equal intervals, the decisions of when to replenish additional units are made using dynamic programming over a finite period planning horizon. A numerical example demonstrates the existence of an optimal state-dependent replenishment policy and inventory costs over the echelons. DOI:10.12660/joscmv8n2p67-76URL: http://dx.doi.org/10.12660/joscmv8n2p67-76

PurposeThis work aims to measure the performance of an electric motors supply chain (SC) by using the Triple E performance measurement model to assess three performance dimensions simultaneously: efficiency, efficacy and environmental impact.Design/methodology/approachThis research presents a real case of a Brazilian company as an example for applying the Triple E performance measurement model and the discrete-event simulation to assess the performance of an SC.FindingsPerformance measurements for the three dimensions were the following: (1) for efficiency, a high inventory cost in the SC, driven by the distribution center (DC) (49.7% total cost); an order cycle time operating with an average of 21.7 days and a logistic channel transport capacity of 88%; (2) for efficacy, a service level of 98% for all channels; (3) for environmental impact, the SC emits a total of 395,733 kg of CO2 annually, with the DC and the regional distribution center (RDC2) being the largest emitters.Originality/valueThis work allowed to measure the performance of an SC interface (manufacturing process, a DC and three regional distribution centers). It was possible to measure the inventory holding cost and out-of-stock inventory costs, order cycle time, SC service level, transport capacity utilization and CO2 emissions in the transportation process of each SC stage and the whole SC. This study can be used as a decision support guide for academics and practitioners to measure and improve the SC performance.

In this paper, we consider a strategic distribution network design problem for a chain enterprise, in which the decision maker needs to decide the number and location of the distribution centers (DCs) and system inventory control decisions - such as economic order quantity and safety stock decisions. In the chain enterprise, stores face random customer demand, and each store and DC, respectively, maintains a certain amount of safety stock in order to achieve a certain service level for the customers or the stores it serves. The objective is to minimize the total cost that includes location costs, inventory costs, and distribution costs in the chain enterprise. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. In a numerical application, we found that the benefits of having an integrated distribution design framework that includes location, inventory, and routing decisions in the same optimization model.

In this work, we propose a novel multi-period location-inventory problem that assumes demand uncertainty, periodic review, backlogging, and lead times. The problem stems from the need to strategically manage distribution centers (DCs) to enhance the resilience of the supply chain, while providing a high service level to customers. A trade-off is sought between distribution resilience and stock-out risks on the one hand, and financial resources on the other hand. A progressive phase-in of the DCs is considered. The problem is cast as a two-stage decision-making process under uncertainty. A mixed-integer linear programming model is formulated. Two resilience indicators are adopted to access the results of a series of computational tests. Based on the experiments, it was found that the financial resources required to establish DCs throughout the planning horizon directly affect the long-term resilience of the supply chain. Nevertheless, the model proposed in this study can ensure that the supply chain resilience remains at a satisfactory level within the given financial resources.

The energy harvesting D2D-enabled cellular network (EH-DCN) has emerged as a promising approach to address the issues of energy supply and spectrum utilization. Most of existing works mainly focus on the throughput, while the information freshness, which is critical to the time-sensitive applications, has been rarely explored. Considering above facts, we aim to develop an optimal mode selection and resource allocation (MSRA) policy that maximizes the long-term overall throughput of a time-varying dynamic EH-DCN, subject to an age of information (AoI) constraint. As the MSRA policy involves both continuous variables (i.e., bandwidth, power, and time allocations) and discrete variables (i.e., mode selection and channel allocation), the optimization problem is proved to be nonconvex and NP-hard. To solve the nonconvex NP-hard problem, we exploit a deep reinforcement learning (DRL) approach, called MSRA twin delayed deep deterministic policy gradient (MSRA-TD3). The MSRA-TD3 employs a double critic network structure to better fit the reward function, and could effectively mitigate the overestimation of Q-value in deep deterministic policy gradient (DDPG), which is a classical DRL algorithm. It is worth noting that in the design of the MSRA-TD3, we use the throughput of user equipments (UEs) at the previous time slot as a state to bypass the channel state information estimation resulting from the time-varying dynamic environment, and take the weights of throughput and AoI penalty into the reward function to evaluate two performance. Simulations demonstrate that the established MSRA-TD3 algorithm achieves better performance in terms of throughput and AoI than comparison DRL algorithms.

Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations

In this paper, the authors propose a reliability based design optimization approach for optimal inventory design in emergency operation, considering reliability target. In this context, the reliability of an inventory system is defined as the probability that the available inventory level meets all population demand during crisis period. Inventory support for emergency operations is complicated by uncertainties of demands and duration of crisis period. The inventory system is considered as a structure that undergoes an external load represented by the demand of population during crisis period. Moreover, the supply chain resists to this load by its strength represented by the quantity of products available at the distribution center. Reliability based design optimization approach is proposed to perform an optimal design of inventory system in emergency operation without making any particular assumption on distribution of population demand and duration crisis period. The simulation software SIMIO is used to validate the proposed method by conducing simulation analysis using several examples with different distributions of population demand and duration crisis period. The results indicate clearly that the proposed method gives an optimal design with reliability much closer to target value.