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Addresses a number of issues relating to determining whether products should be ordered independently and therefore shipped as a single‐product order, or co‐ordinated and shipped as a group, or multiproduct, order from a single source. Factors which might influence the decision include the level or volume of demand, the distribution of demand across products, the weight of items and the attractiveness of the quantity discount offered. Uses an optimal inventory‐theoretic model, that incorporates transport weight breaks and quantity discounts, to assess when product orders should be combined and what products should be ordered separately. The effects of these decisions on the order interval, the number of order groupings, the proportion of items ordered independently, the proportion of attractive discounts forgone in favour of consolidation, and the relative cost savings, are examined using an extensive set of simulated data that are based on a firm in the automobile industry supply chain.

Evolving production technologies are altering the cost structures on which many supporting inventory ordering systems are based; for example, fixed costs compared with variable costs are increasing significantly. Unfortunately, many inventory ordering formulations consider only the variable portions of inventory ordering costs and inventory holding costs. To address this deficiency, departs from traditional categorizations and offers an inventory classification schema based on the functional roles served by the inventory items. Functional roles of inventory include transition, buffer, investment, maintenance, supplies and dead stock. Extending the schema, assesses the implications each functional role has for inventory cost containment, emphasizing the impact of evolving production technologies on inventory ordering policies and their relevance to functional roles.

This paper presents a study on the impacts of information sharing and ordering co‐ordination on the performance of a supply chain with one capacitated supplier and multiple retailers under demand uncertainty. In particular, a computer model is proposed to simulate inventory replenishment decisions by the retailers and production decisions by the supplier under different demand patterns and capacity tightness. It is found that information sharing and ordering co‐ordination significantly impact the supply chain performance in terms of both total cost and service level. It is also found that the value of sharing information and ordering co‐ordination is significantly affected by demand patterns and capacity tightness. Guidelines are developed for companies to share information and co‐ordinate orders under different conditions. These guidelines can help companies reduce costs and improve customer service levels in the supply chain.

Consolidation, the grouping of several small shipments into one at a designated location, can reduce total logistics cost. Total logistics cost includes consolidation, transportation and inventory costs. Identifying where cost‐saving opportunities exist is often confused by the interrelated nature of these various costs.

This paper proposes an integrated approach that seeks to jointly optimize project scheduling and material lot sizing decisions for time-constrained project scheduling problems.

A mixed integer linear programming model is devised, which utilizes the splitting of noncritical activities as a mean toward leveling the renewable resources. The developed model minimizes renewable resources leveling costs along with consumable resources related costs, and it is solved using IBM ILOG CPLEX optimization package. A hybrid metaheuristic procedure is also proposed to efficiently solve the model for larger projects with complex networks structure.

The results confirmed the significance of the integrated approach as both the project schedule and the material ordering policy turned out to be different once compared to the sequential approach under same parameter settings. Furthermore, the integrated approach resulted in substantial total costs reduction for low values of the acquiring and releasing costs of the renewable resources. Computational experiments conducted over 240 test instances of various sizes, and complexities illustrate the efficiency of the proposed metaheuristic approach as it yields solutions that are on average 1.14% away from the optimal ones.

This work highlights the necessity of having project managers address project scheduling and materials lot sizing decisions concurrently, rather than sequentially, to better level resources and minimize materials related costs. Significant cost savings were generated through the developed model despite the use of a small-scale example which illustrates the great potential that the integrated approach has in real life projects. For real life projects with complex network topology, practitioners are advised to make use of the developed metaheuristic procedure due to its superior time efficiency as compared to exact solution methods.

The sequential approach, wherein a project schedule is established first followed by allocating the needed resources, is proven to yield a nonoptimized project schedule and materials ordering policy, leading to an increase in the project's total cost. The integrated approach proposed hereafter optimizes both decisions at once ensuring the timely completion of the project at the least possible cost. The proposed metaheuristic approach provides a viable alternative to exact solution methods especially for larger projects.

Dynamic lot sizing is the problem of determining the quantity and timing of ordering items while satisfying the demand over a finite planning horizon. This paper considers the problem with two practical considerations: minimum order size and lost sales. The minimum order size is the minimum amount of items that should be purchased and lost sales involve situations in which sales are lost because items are not on hand or when it becomes more economical to lose the sale rather than making the sale. The objective is to minimize the costs of ordering, item , inventory holding and lost sale over the planning horizon. To solve the problem, we suggest a heuristic algorithm by considering trade-offs between cost factors. Computational experiments on randomly generated test instances show that the algorithm quickly obtains near-optimal solutions.

In order to achieve competitive advantage over the physical marketplace, the e-retailers are insisted on endowing with lenient return policies. The piece-wise returns-and-reordering process incurs excessive buffering and unwanted logistics costs which raises overall fulfillment charges. The objective of this study is to re-design e-retail distribution policy by providing temporal storage at logistics service provides' (LSP) location. The impact of recurrent returns on pricing and profit margins are also investigated over time continuum.

A framework is developed to reduce the non-value added (NVA) storage and distribution efforts by providing collaborative buffering between LSP and e-retailer. The knapsack based buffering approach is tested and compared with traditional e-retail distribution practices. The revenue sharing concept is mathematically modelled and implemented in GAMS, which finally validated through multiple return scenarios.

The proposed model outperforms the existing one under all scenarios with different configuration settings of re-ordering, profit margins, and buffer time windows. The distribution cost is found, linearly related to the necessary product buffering space. The findings help to re-design sustainable return policies for individual products so that maximum customer value can be yield with minimum costs.

This study helps to determine the NVA efforts incurred while storing and delivering multi-time returned products to ensure desired service levels. The revenue sharing model provides pricing strategies for e-retail practitioners deciding which product should store in what quantity for how much time at the shipping agency location so that it fulfils the re-ordering at least waiting and sufficient buffering.

The proposed model extends the role of LPSs as temporary buffer providers to reduce returns-and-reordering fulfilment efforts in the e-retail network. This Collaborative framework offers an opportunity to amend the distribution contracts and policies time by time that enhances e-retailer's performance and customer satisfaction.

A new policy for joint optimization of age replacement and spare provisioning has been proposed by incorporating a continuous review (s, S) type inventory policy, where s is the reorder level and S is the maximum stock level. Gives cost formulations for a single operating unit situation and outlines simulation procedure to determine optimal values of the decision variables by minimizing total cost of replacement and inventory. Studies the behaviour of this policy for a large number of case problems and highlights the effects of different cost elements, item failure characteristics and lead time characteristics. Also determines, for all case problems, optimal (s, S) policies to support Barlow‐Proschan age policy. Simulation results clearly indicate that the jointly optimal policy is more cost‐effective than Barlow‐Proschan policy.

Presents a mathematical model to assist companies in their decision to switch from the economic order quantity (EOQ) to the just‐in‐time (JIT) purchasing policy. Determines an upper limit for the JIT purchase price of an item below which the manufacturer will be better off using JIT purchasing. Also determines the annual demand level at which the costs of EOQ and JIT purchasing will be equal (the indifference point). For demand levels above this indifference point EOQ is the less costly method while JIT is preferable for demand levels below this point. The model also predicts that JIT will be preferred for inventory items with higher purchase price, holding costs, or ordering cost.

This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.