Stochastic inventory-routing problem with lateral transshipment for perishable product

Convoy movement problem (CMP) is an important optimisation problem, especially occurring in military logistics, which focuses on routing convoys concurrently in a network. The CMP is subject to constraints for preventing certain interactions between convoys such that the objectives related to travel times of them are minimised. We propose a Lagrangian relaxation (LR) approach to efficiently obtain a lower bound on the optimal solution. Our LR approach does not have integrality property, and hence the lower bound obtained is always tigher than the lower bound obtained using the LP relaxation. As CMP is an NP-complete problem, lower bounds are useful to evaluate the solutions obtained by a heuristic. From our computational experimentation, it is found that the proposed LR approach is superior to the existing LR approach.

Through incorporating option mechanism, quantity flexible strategy and quick respond strategy into perishable products' supply chain, this paper establishes the gamble models of retailer's initial order, option purchase and manufacturer production under two production and ordering modes. If manufacturer and retailer share products demand information, manufacturer can decide first period's proper production through retailer's initial order and option purchase in order to make his own profit and the whole supply chain optimization. Comparing with two flexible production modes without option mechanism, retailer may get higher expected profit and reduce his managing risk in the condition of the whole supply chain's optimal expected profit without changed; comparing with two period production modes having option mechanism and only one ordering opportunity, retailer will purchase less option and may get higher expected profit and manufacturer may also get higher expected profit.

This work is motivated by the need to solve the inventory routing problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers′ inventories and decide when and how much inventory should be replenished at each customer. The inventory routing problem attempts to coordinate inventory replenishment and transportation in such a way that the cost is minimized over the long run. We formulate a Markov decision process model of the stochastic inventory routing problem and propose approximation methods to find good solutions with reasonable computational effort. We indicate how the proposed approach can be used for other Markov decision processes involving the control of multiple resources.

We consider an inventory routing problem with stochastic demand where the demand is expressed in a probabilistic sense and it is dynamic in which the demand is gradually revealed at the end of each period. The problem is known as dynamic and stochastic inventory routing problem (DSIRP). DSIRP distribution network consists of a supplier and a set of retailers is considered. A limited number of single product is assumed to be produced by the supplier and a holding cost per unit product is incurred at both the supplier. The demand is assumed to follow the binomial probability distribution. We adopt an order-up-to level inventory policy and each unit of positive inventory at the retailer is incurred a holding cost, while the unmet demand (stock-out) is penalized. We assumed that the transportation of the product from supplier to the retailers is handled by a third party. The aim of DSIRP is to minimize the total expected cost which includes the inventory cost at supplier, inventory cost at retailers, stockout cost at retailers and transportation cost. The DSIRP is modeled as a Markov decision process (MDP) where the state in period t is represented as the inventory level for all retailers. A hybrid rollout algorithm where a Mixed Integer Linear Programming of the problem is used to obtain the control in each state. We also proposed an Artificial Bee Colony (ABC) algorithm to obtain a feasible and better controls. Computational experiments is conducted using an existing benchmark problem.

A Lagrangian relaxation (LR) approach was presented which is with machine capacity relaxation and operation precedence relaxation for solving a flexible job shop (FJS) scheduling problem from the steelmaking-refining-continuous casting process. Unlike the full optimization of LR problems in traditional LR approaches, the machine capacity relaxation is optimized asymptotically, while the precedence relaxation is optimized approximately due to the NP-hard nature of its LR problem. Because the standard subgradient algorithm (SSA) cannot solve the Lagrangian dual (LD) problem within the partial optimization of LR problem, an effective deflected-conditional approximate subgradient level algorithm (DCASLA) was developed, named as Lagrangian relaxation level approach. The efficiency of the DCASLA is enhanced by a deflected-conditional epsilon-subgradient to weaken the possible zigzagging phenomena. Computational results and comparisons show that the proposed methods improve significantly the efficiency of the LR approach and the DCASLA adopting capacity relaxation strategy performs best among eight methods in terms of solution quality and running time.

This thesis provides tools for decision support systems for perishable goods logistics. It also provides an approach for enterprises to estimate the benefit of adopting the proposed logistic systems. Perishable products are produced, transported, and consumed all over the world. Thanks to perishable goods supply chains, we can enjoy safe, fresh, and affordable products. Nevertheless, it is estimated that one third of the agricultural products produced globally for human consumption end up wasted, which amounts to 1.3 billion tonnes per year. This also means that one third of the resources and greenhouse gas emissions for producing and transporting these products are in vein. The wastage of these fresh products happens throughout the supply chains, which are often caused by the perishing nature and inefficiencies of supply chain planning. For instance, congestions at a certain location or over supply at a retailer may result in spoilage. Disruptions such as malfunctioning of cooling equipment can also contribute to wastage in supply chains. Recent technological developments provide new insights into supply chain management, allowing further waste reduction. With sensors and communication technologies, information of products such as location and freshness can be made known to supply chain planners in real-time. Thus the research question of this thesis is given real-time information of perishable goods logistics, in what ways can perishable goods supply chain players better control and coordinate logistic processes to reduce loss of perishable products? Traditional mathematical methods cannot capture enough details when describing perishable goods in their supply chains. Therefore, this thesis proposes a general framework in a system and control fashion to describe and control logistic operations. This general framework consists of a quality-aware modeling method and a model predictive control strategy. The quality-aware modeling method considers the perishable goods in the supply chain as a system, with quality and logistic features. The model predictive control strategy observes the system and steers the system in a manner that the wastage can be minimized. In this thesis, the proposed method is used in case studies of supply chains with three different commodities, namely bananas, starch potatoes, and cut roses. In each case study, because each commodity is unique in its physiological nature, it perishes in a unique way. Therefore, the supply chain takes care of the commodity in a way that may not suit other commodities. As a result, the logistic features of the supply chains are also different from each other. This requires the systems to be described differently, and control architectures can also vary. Results from the case studies show that the general framework can improve the effectiveness of supply chain logistic planning, and to reduce wastage. The improvements are quantified to illustrate the benefit of making full use of the information on perishable goods in supply chains.

Cloud-based Auction Tower for Perishable Supply Chain Trading

In recent decades, changes in people’s lifestyles and occupations have led to new food consumption patterns around the world, with a notable growth in the demand for ready meals and meat products. Accordingly, the food industry has tried to transform the global food culture by promoting its more profitable products to become able to set a higher price for meat. Considering the short lifetime of products in perishable food supply chains, inventory decisions are considered crucial. In addition, the demand for perishable food products is greatly affected by their freshness. In this paper, we develop a multi-objective mixed-integer non-linear programming model for a four-level sustainable supply chain (SC) of a perishable product with price-dependent demand and deterioration rates. The SC consists of suppliers, a production center (PC), distribution centers (DCs), and retailers. We aim to ascertain the optimal pricing policy and cycle length to maximize profit, achieve specific social objectives, and minimize the total cost and adverse environmental impacts. The proposed model determines the deterioration rate of each product according to its expiration date, the optimal location of supply-side facilities, and the flow rate between the facilities based on the selected mode of delivery. The products are delivered to the retailers by a mechanized transportation system either directly from the PC or indirectly through the DCs. Finally, an actual case study is provided to demonstrate the applicability of the model and our theoretical results under real-world conditions. We solve the case study by a preemptive fuzzy goal programming method and perform several sensitivity analyses on the results. By performing simultaneous sensitivity analyses on the demand and expiration dates, the optimal values of the problem’s parameters are determined. The optimal values help decision-makers make optimal decisions regarding the selling price of products and replenishment times. The model is applicable to supply chains of any perishable items with expiration dates.

In this work we introduce a green inventory routing problem termed the Stochastic Inventory Routing Problem on Electric Roads (S-IRP-ER), in which a hybrid vehicle navigates a road network with charging opportunities in some road sections, to cover the non-stationary stochastic demand of a single product for a set of retailers in the network. We model the problem using isochrone graphs to represent the real road network. In an isochrone graph nodes are located such that the time to travel any arc is constant all over the network. This allows for tracking the battery level of the vehicle serving retailers, as it charges and discharges continuously while travelling. We formulate a mathematical programming heuristics and prove its effectiveness. We use our model on a realistic instance of the problem, showcasing the different strategies that a vehicle may follow depending on fuel costs in relation to the costs of electricity.

We consider an inventory routing problem (IRP) in the liquefied natural gas (LNG) supply chain, called the LNG-IRP. Here, an actor is responsible for the LNG production and inventory management at the liquefaction plants, the routing and scheduling of a heterogeneous fleet of LNG ships, as well as the inventories and sales at the regasification terminals. Furthermore, all ports have a limited number of berths available for loading and unloading. The LNG-IRP is more complicated than many other maritime inventory routing problems because a constant rate of the cargo evaporates in the tanks each day and is used as fuel during transportation. In addition, a variable number of tanks are unloaded at the regasification terminals. We introduce a new path flow formulation for this problem arising from a novel decomposition scheme based on parts of a ship schedule, called duties. A ship schedule for the entire planning horizon can be divided into duties consisting of a visit to a liquefaction plant, then one or two visits to a regasification terminal before ending in a liquefaction plant. The solution method suggested is based on a priori generation of duties, and the formulation is strengthened by valid inequalities. The same problem was previously solved by a branch-price-and-cut algorithm for a schedule-based formulation. Computational results show that the new formulation provides tighter bounds than the previous schedule-based formulation. Furthermore, on a set of 27 benchmark instances, the proposed algorithm clearly outperforms the previous branch-price-and-cut algorithm both with regard to computational time and the number of problems solved within a 10-h time limit.

We consider a supply chain system involved in a perishable product with a fixed lifetime. The retailer is using a periodical reviewed (s, S) policy to manage the inventory and sell the products with backlogs. The supplier and the retailer coordinate the system using a revenue-sharing contract in order to improve the channel performance and maximize the total profit of the system. We show that coordination between supplier and retailer with revenue-sharing contract is possible to improve the performance of the system. Considering the risk of product expiration, the optimal coordination contract is different for system selling perishable product compared with normal long lifetime product.

An alternative framework to Lagrangian relaxation approach for job shop scheduling

Accepted by: M. Zied BABAI In recent years, global disruptions, such as the COVID-19 pandemic, have significantly exposed vulnerabilities in supply chains, particularly in the handling of perishable goods. Addressing these vulnerabilities, this study focuses on assessing risks in perishable supply chains by employing a novel decision-making approach that integrates the outranking method with a visualization technique. This study makes a methodological contribution to scholarly work by integrating VIseKriterijumska Optimizacija I Kompromisno Resenje, which provides the best compromise ranking, with Graphical Analysis for Interactive Assistance, which enhances clarity through detailed visual representation. The integration is further reinforced by a sensitivity analysis, ensuring robust and reliable findings. From a practical perspective, this study evaluates the supply chain performance of nine perishable items based on eight critical risks. The outcomes provide insights into the risk intensity of supply chains disruptions for each perishable product, facilitating the development of effective management policies. These findings offer valuable managerial insights, equipping researchers and practitioners with essential tool to formulate effective strategies and enhance decision-making processes during undesirable circumstances.

Supply chain network design problem is increasingly showing its importance, especially the perishable supply chain. This research develops a multi-objective mathematical model to design four-echelon intermodal multi-product perishable supply chain configuration in order to ensure a balance of the three pillars of sustainable development: economy, environment, and society. The optimization objective functions of the model are, respectively, minimizing costs, delivery time, emissions, and the supply-demand mismatch in time. The model addresses particular problems in the supply chain of fresh fruits, which is more challenging compared to other types of perishable products due to its seasonal characteristics. The study proposes a new approach that combines and standardizes the above objective functions into a single weighted objective function. The solution from the model supports the decision-making process at both strategic and tactical levels. Strategically, the model supports decisions about the location, size of facilities, product flows, and workforce level. Tactically, the decision variables provide information on harvest time, delivery time, the delivery route, and mode of transport. To demonstrate its practical applicability, the model is applied to Mekong Delta region, Vietnam, where a variety of fruit types, large yields, and high distribution demand in this region make designing a shared supply chain desirable for its overall economic, environmental, and social concerns. Moreover, sensitivity analysis regarding weights of different objectives is performed to assess possible changes in supply chain configurations. Application of this model to other perishable products, the addition of modes of transport, social policy, and uncertainty parameters may be suggested for future research.

This paper examines supply chain design strategies for a specific type of perishable product—fresh produce—using melons and sweet corn as examples. Melons and other types of produce reach their peak value at the time of harvest; product value deteriorates exponentially post‐harvest until the product is cooled to dampen the deterioration. Using the product's marginal value of time (MVT), the rate at which the product loses value over time in the supply chain, we show that the appropriate model to minimize lost value in the supply chain is a hybrid of a responsive model from post‐harvest to cooling, followed by an efficient model in the remainder of the chain. We also show that these two segments of the supply chain are only loosely linked, implying that little coordination is required across the chain to achieve value maximization. The models we develop also provide insights into the use of a product's MVT to develop supply chain strategies for other perishable products.