A Two-Warehouse Model for Deteriorating Items with Holding Cost under Particle Swarm Optimization

A deterministic inventory model has been developed for deteriorating items and Particle Swarm Optimization (PSO) having a ramp type demands with the effects of inflation with two-warehouse facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Particle Swarm Optimization (PSO) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Particle Swarm Optimization (PSO). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters

One vendor-one retailer in vendor managed inventory problem with stochastic demand

One of the basic problems in supply chain operation is lack of information exchanges related to inventory between vendor and retailer. Vendor managed inventory (VMI) provides a good approach to handle this problem. VMI has been proven to reduce cost and improve customer service level. This research aim is to develop a VMI model for the system with one vendor and one retailer to minimise the total system cost. The model is developed for (t, q) policy where the retailer’s cycle time is fixed. Due to the complexity nature of the model, simulation-optimisation using genetic algorithm is employed to determine the decision variables which are the retailer’s lot size, the vendor’s lot size, and the number of replenishments in a vendor cycle. Numerical experiments are conducted to show how the proposed model works. Sensitivity analysis is also conducted to understand the effects of some input parameters

Optimizing Vendor-Buyer Inventory Model with Exponential Quality Degradation for Food Product Using Grey Wolf Optimizer

Inventory is an essential factor in the supply chain. Inventory problems are increasingly complex for perishable products such as food. This study proposes a Single Vendor-Single Buyer (SVSB) model for food products by considering exponential quality degradation. The objective function of this problem is to maximize the Joint Total Profit (JTP) of the SVSB system. The frequency of ordering raw materials (m), the frequency of delivery of the finished product (n), and the time of the inventory cycle (T) were the three (3) decision variables introduced in the study. This study proposes the Grey Wolf Optimizer (GWO) algorithm as an optimization tool for SVSB problems. A case study was conducted on a food company in Indonesia. Sensitivity analysis on costs, revenue, and JTP was also presented. The results showed that raw materials' quality degradation level affected JTP. The results also suggested that the GWO algorithm performs better than the Genetic Algorithm (GA) to optimize the SVSB inventory model

One vendor and multiple retailers system in vendor managed inventory problem with stochastic demand

In many supply networks, the retailers are reluctant to share information about demand and inventory level to the vendor. This might lead to many difficulties for the vendor in establishing his own order/production plan. Vendor managed inventory (VMI) policy can help to solve that problem. By applying VMI, information sharing is not really a problem for the vendor anymore and this policy have been proven to help reduce total inventory cost as well as improve customer service level in the supply network. In this research, a VMI model for the system with one vendor and multiple retailers will be developed. The main target of the model is to determine the retailer’s lot size, the vendor’s lot size, the retailer cycle time, and the number of replenishments in a vendor cycle so as to minimise the total system cost. For solution purpose, simulation-optimisation technique using genetic algorithm is employed to help find optimal solutions for the decision variables. Numerical experiments are conducted to show the applicability of the proposed model. Sensitivity analysis is also conducted to examine the effects of some input parameters on the optimal solution

Essays on Shipment Consolidation Scheduling and Decision Making in the Context of Flexible Demand

This dissertation contains three essays related to shipment consolidation scheduling and decision making in the presence of flexible demand. The first essay is presented in Section 1. This essay introduces a new mathematical model for shipment consolidation scheduling for a two-echelon supply chain. The problem addresses shipment coordination and consolidation decisions that are made by a manufacturer who provides inventory replenishments to multiple downstream distribution centers. Unlike previous studies, the consolidation activities in this problem are not restricted to specific policies such as aggregation of shipments at regular times or consolidating when a predetermined quantity has accumulated. Rather, we consider the construction of a detailed shipment consolidation schedule over a planning horizon. We develop a mixed-integer quadratic optimization model to identify the shipment consolidation schedule that minimizes total cost. A genetic algorithm is developed to handle large problem instances. The other two essays explore the concept of flexible demand. In Section 2, we introduce a new variant of the vehicle routing problem (VRP): the vehicle routing problem with flexible repeat visits (VRP-FRV). This problem considers a set of customers at certain locations with certain maximum inter-visit time requirements. However, they are flexible in their visit times. The VRP-FRV has several real-world applications. One scenario is that of caretakers who provide service to elderly people at home. Each caretaker is assigned a number of elderly people to visit one or more times per day. Elderly people differ in their requirements and the minimum frequency at which they need to be visited every day. The VRP-FRV can also be imagined as a police patrol routing problem where the customers are various locations in the city that require frequent observations. Such locations could include known high-crime areas, high-profile residences, and/or safe houses. We develop a math model to minimize the total number of vehicles needed to cover the customer demands and determine the optimal customer visit schedules and vehicle routes. A heuristic method is developed to handle large problem instances. In the third study, presented in Section 3, we consider a single-item cyclic coordinated order fulfillment problem with batch supplies and flexible demands. The system in this study consists of multiple suppliers who each deliver a single item to a central node from which multiple demanders are then replenished. Importantly, demand is flexible and is a control action that the decision maker applies to optimize the system. The objective is to minimize total system cost subject to several operational constraints. The decisions include the timing and sizes of batches delivered by the suppliers to the central node and the timing and amounts by which demanders are replenished. We develop an integer programing model, provide several theoretical insights related to the model, and solve the math model for different problem sizes

The problem of production-distribution under uncertainty based on Vendor Managed Inventory

In this paper, a problem of managed inventory by the vendor in the production-distribution supply chain is presented based on the scenario. The main purpose of presenting the model of maximizing producer profit in a three-level supply chain network consisting of various strategic and tactical decisions under uncertainty. Due to the nonlinearity and NP-Hardness of the problem, meta-heuristic genetic algorithms, Whale optimization algorithm and league champions algorithm have been used. The results of problem solving show the high efficiency of meta-heuristic algorithms compared to accurate methods in solving the above model. So that the maximum percentage of relative differences between the methods mentioned with GAMS is less than 1%.Also, by solving the sample problems in larger sizes, it was observed that the league champions algorithm has the highest efficiency in terms of achieving the optimal value of the target function in a shorter time than the other algorithms used, with a useful weight of 0.998

A two-storage model for deteriorating items with holding cost under inflation and Genetic Algorithms

A deterministic inventory model has been developed for deteriorating items and Genetic Algorithms (GA) having a ramp type demands with the effects of inflation with two-storage facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Genetic Algorithms (GA) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Genetic Algorithms (GA). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters