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The paper examines the perspectives of linear-quadratic (LQ) optimal control in steering the process of goods distribution in logistic systems with multiple transportation options. In the considered class, the distribution centre governs the stock replenishment process of subordinate depots, from which un uncertain market demand is served. The centre is linked with the depots via shared supply channels with different characteristics regarding delay, reliability, and capacity, e.g. train vs truck delivery. The design objective is a rule of dynamical channel allocation – how many goods to send in a period using a given mode – so that balanced, cost-efficient system performance and high customer service rate are achieved. The received goods are inspected for quality defects and rejected when faulty. Thus, one needs to cope with two major sources of uncertainty: unpredictable demand variations and channel imperfections. A multi-variable LQ optimal controller is designed and presented in closed form for detailed analytical and numerical treatment. It is formally shown that despite perturbations, the controller always establishes a non-negative and upper-bounded replenishment signal, and the stock level does not cross the reference value. Conditions for warehouse space selection and obtaining full demand satisfaction at the depots are specified and formally proved. [ABSTRACT FROM AUTHOR]

Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM. [ABSTRACT FROM AUTHOR]

Blockchain possesses the potential of transforming global supply chain management. Gartner predicts that blockchain could be able to track $2 T of goods and services in their movement across the globe by 2023, and blockchain will be a more than $3 trillion business by 2030. Nowadays, a growing number of blockchain initiatives are disrupting traditional business models in each sector. In this paper, we provide a timely and holistic overview of the state-of-the-art, challenges, gaps and opportunities in global supply chain and trade operations for both the private sector and governmental agencies, by synthesising a wide range of resources from business leaders, global international organisations, leading supply chain consulting firms, research articles, trade magazines and conferences. We then identify collaborative schema and future research directions for industry, government, and academia to jointly work together in ensuring that the full potential of blockchain is unleashed amidst the socioeconomic, geopolitical and technological disruptions that global supply chains and trade are facing. [ABSTRACT FROM AUTHOR]

Efficient planning of freight transportation requires a comprehensive look at wide range of factors in the operation and management of any transportation mode to achieve safe, fast, and environmentally suitable movement of goods. In this regard, a combination of transportation modes offers flexible and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to develop models and algorithms in Transport Management System software packages. This paper discusses the principles of green logistics required in designing such models and algorithms which truly represent multiple modes and their characteristics. Thus, this research provides a unique practical contribution to green logistics literature by advancing our understanding of the multi-objective planning in intermodal freight transportation. Analysis based on a case study from hinterland intermodal transportation in Europe is therefore intended to make contributions to the literature about the potential benefits from combining economic and environmental criteria in transportation planning. An insight derived from the experiments conducted shows that there is no need to greatly compromise on transportation costs in order to achieve a significant reduction in carbon-related emissions. [ABSTRACT FROM AUTHOR]

This paper presents an integrated storage assignment method for low-level picker-to-parts order picking warehouses taking into account economic and ergonomic objectives. Three different pallet rack layouts are studied in this paper, namely (a) picking from full pallets on the floor, (b) picking from half-pallets on the floor, and (c) picking from half-pallets on the upper rank of the shelf. First, cost functions are developed to assess the total order picking performance impact of these different pallet rack layouts. Second, with regard to workload, the metabolic cost and energy expenditure rates for picking from the different rack layouts under study are derived. Third, for assessing the working posture during order picking, the Ovako Working Posture Analysing System index is used where the required data is collected using a motion capturing system. The developed models are combined to propose a heuristic storage assignment procedure that supports the decision of which item to store on which pallet. The developed storage assignment method is then applied to an industrial case study. The results of the paper support warehouse managers in assessing the order picking storage assignment from an ergonomics viewpoint and in estimating its impact on financial order picking performance. [ABSTRACT FROM AUTHOR]

This paper investigates strategies for improving supply chain delivery timeliness when the delivery time follows an asymmetric Laplace distribution. Delivery performance is measured using a cost-based analytical model which evaluates the expected cost for early and late delivery. This paper presents a set of propositions that define the effect of changes to the parameters of the delivery time distribution on the expected penalty cost for untimely delivery when a supplier uses an optimally positioned delivery window to minimise the expected cost of untimely delivery. The scale parameter increases the expected penalty cost, skewness decreases the cost and the location parameter has no effect on the expected penalty cost. The effects are illustrated in a numerical example with real-world supplier data. The results can be used in developing strategies for improving delivery performance from a supplier's perspective and define how the delivery time distribution parameters can be modified to decrease the expected penalty cost of untimely delivery. The paper proposes a general approach to modelling delivery performance improvement and can be applied to other delivery time distribution forms. The approach can serve as guidance for practitioners undertaking a programme to improve delivery performance. [ABSTRACT FROM AUTHOR]

After a disaster happens, emergency response operations are critical to save humans’ lives and properties. The limited resources and time requirements call for coordinated supply chain operations. This paper studies supply chain operations for rescue kits in disaster reliefs, motivated by a real-world application. The objective is to minimise the total tardiness and peak tardiness of product delivery over the multi-period planning horizon. One major challenge is the lack of reliable prediction of customer demand in disasters. In order to cope with demand uncertainty while maintaining the tractability of the optimisation model, we decompose the demand into two components: a relatively stable base demand predicted by historical data and unpredictable demand surges. For the base demand, an optimisation model is developed to optimise the production and distribution operations, as well as the inventory replenishment policy for manufacturers and distribution centres, so as to minimise the total tardiness. For the demand surges, we propose to deploy supply chain flexibility to cope with the uncertainty. An empirical study shows the effectiveness of increasing supply chain flexibility and suggests some managerial insights on configuring such flexibility in emergency operations. [ABSTRACT FROM AUTHOR]

We model and derive an algorithm for the assort-packing and distribution problem (APDP) in the fashion apparel industry. Assort-packing is a common method used to reduce the distribution lead time between manufacturers and retailers, wherein different items are packed together in one box according to the retailers’ demands. Specifically, in the fashion industry, multiple box types are configured by varying the size distribution of the same apparel product. In this study, we propose an optimisation model to quantitatively solve the APDP with respect to how many box types should be configured, how to configure the size distribution in each box, and how many of each type of box should be sent to stores. In particular, the modelling and algorithm development focus on the size-assorted homogeneous-article configuration in the APDP, a widely used assort-packing method in the fashion industry. We show that the APDP is NP-hard, and propose a heuristic algorithm in which a pre-processing step significantly reduces the search space. Numerical studies of the industry scale are conducted and show that the algorithm outperforms the benchmarking algorithms. [ABSTRACT FROM AUTHOR]

A variety of industries handling perishable goods is faced with the challenge of reducing lead times in order to ensure the best possible freshness of products at the point of sales. Considering a short-term multi-day planning horizon, the implementation of quantitative optimisation approaches with a continuous-time representation is most appropriate for planning and scheduling. However, as the quality measures are usually standardised by discrete grades, coordination take thresholds of maximum delivery times into account. For this purpose, a new mixed-integer linear programming model is developed. It enables to assess complete material flows, whose formal composition originates in a method based on power sets on the one hand, but additionally allows for the exact scheduling of partial material flows between sites. Specific transportation conditions that need to be imposed with respect to shelf-life are included. The efficiency of the model is confirmed by comparison with an equivalent mixed-integer linear formulation that uses path variables for modelling complete material flows. Besides an illustrative example motivated by the real-life problem of a fresh produce company supplying a wholesaler group, both model formulations are validated within a numerical analysis composed of 10 scenarios with different numbers of suppliers, warehouses, markets and product variants. Each scenario includes six instances with randomly generated data. As a result of the computations using high-performance hardware and software, it was shown that the formulation based on power sets was superior in each instance, as it enabled determining optimal solutions within significantly reduced computation times. [ABSTRACT FROM AUTHOR]

Service operations management of metropolitan gas networks at operational level implies the optimisation of decisions related to logistic activities, taking into account multi-objectives and operational constraints. This paper proposes a metaheuristic approach for the operational planning of the daily logistic activities based on vehicle routing with time window model. Experimental results for a real planning case in a gas distribution network demonstrate the approach effectiveness. [ABSTRACT FROM AUTHOR]

Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model and solve a rich order picking problem under weight, fragility and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective ofminimising the distance travelled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap,mid-point, S-shape and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighbourhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and 7 aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS metaheuristic and derive some important insights for managing order picking in this kind of warehouses. [ABSTRACT FROM AUTHOR]

In order to differentiate from competitors in terms of customer service, warehouses accept late orderswhile providing delivery in a quick and timely way. This trend leads to a reduced time to pick an order. This paper introduces workload forecasting in a warehouse context, in particular a zone picking warehouse. Improved workforce planning can contribute to an effective and efficient order picking process. Most order picking publications treat demand as known in advance. As warehouses accept late orders, the assumption of a constant given demand is questioned in this paper. The objective of this study is to present time series forecasting models that perform well in a zone picking warehouse. A real-life case study demonstrates the value of applying time series forecasting models to forecast the daily number of order lines. The forecast of order lines, along with order pickers' productivity, can be used by warehouse supervisors to determine the daily required number of order pickers, as well as the allocation of order pickers across warehouse zones. Time series are applied on an aggregated level, as well as on a disaggregated zone level. Both bottom-up and top-down approaches are evaluated in order to find the best-performing forecasting method. [ABSTRACT FROM AUTHOR]

E-commerce companies often use manual order-picking systems in their warehouses since these systems can provide the required flexibility and scalability.Manual systems have been widely studied, but the operating policiesmay require significant changes for e-commerce settings. First, to maintain consumers' loyalty, it is important to maintain delivery reliability even on the busiest days. When the number of order pickers in an area increases, however, more delays due to interactions may occur. For example, travel speed may need to be lowered when order pickers pass each other in narrow aisles. Second, many products sold through e-commerce are returned by consumers. Before these returned products can be sold again, they must be reintegrated in the stock. This paper presents hybrid genetic algorithms to determine routes for simultaneous pickup of products in response to consumers' orders and delivery of returned products to storage locations. Furthermore, interactions between the order pickers are considered in the routing decisions. The developed algorithms use specific warehouse problem characteristics. We identify the mix of pickups and deliveries to realise the highest savings in practice. It is shown that order-picker interactions can be a significant cause for delay and should be accounted for in the routing. [ABSTRACT FROM AUTHOR]

Distribution networks often manage products with varying life cycles, where demand for some products is relatively stable throughout the year (basic) and the demand for others is short-lived (fashion). Beyond the coordination of inventory and transportation decisions, decisions at the warehouse must be considered as its resources are frequently shared by both product classes simultaneously. For this two-product class distribution planning problem, we focus on characterising three real-world distribution strategies observed in industry and evaluating them based on total distribution cost and warehouse measures (e.g. workforce plan and workload variation) against a benchmark ILS-based heuristic. Experimental results suggest that there are in fact strategies in industry that under specific system configurations may provide competitive solutions compared to the benchmark heuristic on large problem instances (e.g. 200 stores, 1000 products, and 28 days). Several managerial insights are derived to compare such distinct warehouse strategies and the corresponding impact on the network. [ABSTRACT FROM AUTHOR]

The popularity of round-the-clock online shopping urges the rapid growth of e-commerce, which substantially generates additional parcels for the distribution on the forward side. On the other hand, collecting the returned goods on the revere side is also increasingly becoming a preoccupation, particularly in the crowd and dense metropolitan areas. Inspired by the concepts ofCrowdsourcingandPhysical Internet, we propose an innovative solution to collect the e-commerce returned goods from final consumption points back to retailers. As an alternative to traditional ways, this solution delivers the returned goods and passengers in an integrated way by leveraging the extra loading capacity and constant mobility provided by taxis that are already reserved to transport passengers. Thus, it could simultaneously migrate the negative economical, environmental and social impacts of reverse flows management. To address the issue of the returned goods collection, we first conduct the qualitative and quantitative study, and further investigate the feasibility and viability of the solution based on three real-world datasets, which consist of locations of shops, a road network and a large-scale trajectory data generated by over 7000 taxis in a month in the city of Hangzhou, China. Three collection strategies are proposed and evaluated. Experimental results generate several useful insights into the implementability and managerial issues of the proposed solution. [ABSTRACT FROM AUTHOR]

This paper is motivated by the performance evaluation of circulating vertical conveyor systems (CVCSs). CVCSs are bulk queues of transportation type. These material handling systems feature generally distributed inter-arrival times, which can be longer than the bulk service time. This leads to interdependencies between the number of arrivals in consecutive service intervals and the number of loads in the queue. We propose a new discrete-time approach for the steady-state analysis of such bulk service queues of transportation type with general arrival and service processes and finite server and limited queue capacities. The approach is based on a finite Markov chain that generates complete probability distributions for the key performance measures, including the queue length, waiting time and departing batch size. The proposed approach is exact in the cases of discrete-time slots, e.g. as in communication systems. We investigate the discretisation error that arises if the approach is used as an approximation for the continuous time using a numerical comparison to a discrete-event simulation. Moreover, we examine the impact of arrival stream variability on the system performance and compare the positive effects of a higher frequency of server visits with the effects arising from larger pickup capacities. [ABSTRACT FROM AUTHOR]

Auction logistics centre (ALC) performs transshipment operation on auction products from their inbound-from-supplier transporters to their outbound-to-client transporters with goods trading functions. Major third-party trading service providers have solved technological problems of dealing with millions of simultaneous biddings. But logistics that fulfils the massive and lumpy auction demands in the centre is still challengeable. The lack of process visibility and synchronised schedule has made the congestion on material flow, especially for the trolley loading and auction trading stages. Space resource is wasted and auction products deteriorate as holding time increases. This paper aims to provide a first demonstration of scheduling for auctions of perishable goods using Physical Internet (PI). PI-enabled scheduling is vital to facilitate the decision-making process while ensuring required throughput time with large trading volumes. A PI-ALC is created to automate the flow of information and enable the flexible implementation of scheduling. Following the hybrid flowshop classification, a timely operation scheduling model is developed. A heuristic-based solution approach is proposed to minimise either makespan or value loss using a set of dispatching rules. Simulation experiments show that the dispatching–picking mechanisms have statistically significant interaction impacts on both performance criteria. Decision-makers should strike a balance between minimising makespan and value loss based upon the growth in the frozen buffer size. Finally, the sensitivity analyses justify that schedulers can flexibly select dispatching rules under various demand patterns and operation time windows, as well as system configurations and trolley sizes. [ABSTRACT FROM AUTHOR]

Order pickers and individual differences between them could have a substantial impact on picking performance, but are largely ignored in studies on order picking. This paper explores the role of individual differences in picking performance with various picking tools (pick by voice, RF-terminal picking and pick to light) and methods (parallel, zone and dynamic zone picking). A unique realistic field experiment with 101 participants (academic students, vocational students and professional pickers) is employed to investigate the influence of individual differences, especially the Big Five personality traits, on picking performance in terms of productivity and quality. The results suggest that (PbV) performs better than RF-terminal picking, and that Neuroticism, Extraversion, Conscientiousness and the age of the picker play a significant role in predicting picking performance with voice and RF-terminals. Furthermore, achieving higher productivity appears to be possible without sacrificing quality. Managers can increase picking performance by incorporating the insights in assigning the right pickers to work with a particular picking tool or method, leading to increased picking performance and reduced warehousing costs. [ABSTRACT FROM PUBLISHER]

In this paper, imperfect multi-item production inventory models are considered over a finite time horizon with known dynamic demands. The production rates are functions of time which are taken as control variables. In the production process, reliability plays an important role to improve the quality of products and to decrease the defective rate. The said defective units are partially or fully reworked. The unit production cost is a function of production rate and also dependent on raw material cost, development cost due to reliability and wear-tear cost. There is a constraint on the total production cost termed as budget constraint which is crisp/imprecise/random in nature. The objective of the present investigation is to fix the optimum reliabilities of the production system to have maximum return. Thus, the models are formulated as optimal control problems for the maximisation of profit and solved using Hamiltonian (Pontryagin’s Maximum Principle), fixed-final time and free-final state system, Kuhn–Tucker conditions and Generalised Reduced Gradient Method. Several particular cases are derived from the general model. The models are illustrated numerically and graphically and some managerial decisions are derived. [ABSTRACT FROM PUBLISHER]

In a supply chain’s order fulfilment process, it is often the warehouse that plays a central role in making the right product available to the right customer at the right time. This paper aims to improve warehouse performance by deriving an effective design method for the simultaneous determination of warehouse layout and the warehouse’s control policies. The authors consider layout variables for the warehouse such as the number of primary aisles utilised, the number of cross-aisles and the aisle length under several different design philosophies. Concurrently, control policies such as storage policies and routing are considered. Simulation is utilised to determine the performance of the various resulting scenarios. A screening and selection procedure is employed to reduce the required number of replications while achieving a predetermined precision in identifying the best configuration. The approach is applied for an industrial partner in this research and the results of experimentation are compared to a baseline scenario which describes a proposed new facility in the Netherlands. The results reveal a large potential for performance improvement. [ABSTRACT FROM PUBLISHER]

In many industries, distribution activities are realised in a dynamic environment including uncertainties. Besides, adding transportation mode alternatives, inventory-stocking opportunities in wholesalers, unmet demand permission in distribution centres, etc. increase the difficulty of problem modelling and solving for large-scale networks. In this study, the problem of physical distribution network (DN) design with profit maximisation objective function is modelled to tackle with realistic cases. Two-stage stochastic mixed-integer programming method is used to handle the uncertainties and to consider the probable scenarios. The first-stage decisions of the proposed model are related with the selection of facility location in strategic level, and the second-stage decisions are related with the transported and stocked products or unmet demand quantities. Here, a multi-product, two-echelon, multi-mode and multi-period network model is applied to a hypothetically created problem, inspired from the physical DN of home appliance companies. Various scenarios including stochastic demand and price data with different realisation probabilities are used in the model. The motivation of this study is the lack of reaching a global optimum result using transportation modes as stochastic parameters, considering their own lead times and capacities. Finally, various results are obtained for different cases and analysed in detail. [ABSTRACT FROM PUBLISHER]

Although it is an essential link in any supply chain, transportation is often neglected in inventory decisions. In reordering decisions, for example, this omission may well result in unexpected losses, as transportation and quantity discounts can be tricky. In this paper, we study the problem of optimal coordination of these discounts from the perspectives of the parties who offer them, rather than of those who take them. Using a non-cooperative model of a three-party supply chain, we show that cooperation can benefit each member individually, and the supply chain overall. In fact, cooperation can generate a non-empty core – that is, there may be a surplus that can be allocated so that all parties and all coalitions of parties, are induced to continue to cooperate. We provide a detailed algorithm, based on the Shapley value, to allocate the surplus fairly and illustrate it with numerical examples. [ABSTRACT FROM AUTHOR]

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty. [ABSTRACT FROM PUBLISHER]

The standard literature on inventory modelling is rarely differentiating between the inventory records and the physical inventory. In the recent years, some empirical studies highlighted that errors and inventory perturbations may occur in the inventory system. Such errors result in a gap between what the informational system (IS) shows and what is actually available for sales and used to satisfy the demand. The impact of such errors is particularly important in a wholesaling/e-retailing context where customer’ demands are remotely satisfied based on the inventory records shown in theIS. These errors could be modelled by an additive or multiplicative structure depending on the link between the error variability and the ordered quantity. The aim of the paper is to extend the existing literature by developing an inventory framework that permits the analysis and the performance improvement of an inventory system subject to a multiplicative errors setting. The multiplicative and stochastic settings also known as the stochastically proportional modelling of errors is not well developed in the literature despite the fact that such an assumption bears considerable association with reality. We provide comprehensive analytical and numerical studies and we also complete our contribution by a comparison between the additive and the multiplicative error settings where we derive interesting managerial insights about the impact of wrongly modelling errors. We also focus on the benefit of applying our results compared with the case where errors are ignored or not known. [ABSTRACT FROM PUBLISHER]

In a manufacturing system, a just-in-time (JIT) procurement and supply system is important for reducing cost and responding to customer’s requirement quickly. Successful implementation of a JIT system needs supplier/manufacturer cooperation in small lot-size delivery and inbound logistics cost reduction. In this study, an integrated optimal model of inventory lot-sizing vehicle routing of multisupplier single-manufacturer with milk-run JIT delivery is established. A novel method for computing transportation cost is proposed. Because the integrated model is a NP-hard problem, a meta-heuristic algorithm of ant colony optimisation is developed for solving the model. Numerical examples are used to demonstrate and test the effectiveness of the model and the algorithm. The results show that integrated model with milk-run delivery can reduce the total cost and realise the JIT production and procurement philosophy which emphasises small lot-size production and delivery. The results highlight the importance of cooperation between suppliers and manufacturers in JIT production practices. [ABSTRACT FROM PUBLISHER]

The paper investigates newsvendor problem for a dyadic supply chain in which both the supplier and the retailer have the preference of status-seeking with fairness concerns. Nash bargaining solution is introduced as the fairness reference point and equilibrium results are derived. The effects of fairness-concerned status-seeking behaviors on optimal decisions as well as channel efficiency are further analyzed. It is shown that the channel efficiency will decrease because of such behavioral preference. The retailer’s share will be larger when the supplier concerns fairness less, and the supplier’s sensitivity to fairness plays a relatively more important role for the channel efficiency. Additionally, another interesting managerial insight is concluded that fairness concerns will not change the status of channel coordination in certain conditions. More specifically, those contracts able (unable) to coordinate fairness-neutral supply chain, based on affine transformations with scale factors within certain ranges, still succeed (fail) to coordinate the fairness-concerned. Furthermore, several insights on bargaining powers are given as well. [ABSTRACT FROM PUBLISHER]

The survival of a company nowadays depends on answering to a customer-driven economy, and therefore relies on the performance of its entire network of partners. Competition is no longer among companies nor among supply chains, but rather between networks of companies which form a value network. Thus, the need has arisen to analyse the performance of a network of companies, and include the customer-perceived value in the strategic decision-making process. This paper proposes a framework and a tool to model, simulate and analyse a value network as a decision support system. The method extends the SimulValor approach and language. The discrete event simulation tool relies on a developed value network simulation library. This paper presents a case study in the shoemaking industry to validate the proposed approach. [ABSTRACT FROM PUBLISHER]

The standardisation of packaging and containers for product handling has potential benefits from transportation, material handling and environmental perspectives. Likewise, standardised modular containers play an important role in the Physical Internet (PI). To support implementation of standard modular containers, we introduce a mathematical model to select a requisite number of modular containers to pack a set of products to maximise space utilisation. A decomposition-based solution methodology is developed and presented. Computational results from industry-based problem instances indicate that using standardised modular containers results in increased space utilisation at the unit load level. This finding substantiates the use of modular containers in moving toward the PI. [ABSTRACT FROM PUBLISHER]

This paper describes an integer linear programming model conceived as an alternative to a traditional material requirements planning (MRP) system for extending the concept of supply chain synchronisation upstream in a multi-tier supply chain. In this model, we assume there is an incumbent application for transmitting original equipment manufacturer (OEM) requirements to first-, second- and third-tier suppliers. The proposed model is regarded as being embedded within a web-enabled, multi-tier, supply chain information system that provides the application for transmitting the production requirements. The principal motivation for having second- and third-tier suppliers that are synchronised with OEM and first-tier activity is the significant inventory, lead time and responsiveness gains that can potentially be achieved. Here, inventory is considered as a whole across a supply chain, and stock-outs are prohibited for the first-tier supplier. For illustration purposes, an example based on a real, automotive case study is provided. The model results proved better in terms of inventory and bullwhip reduction than those found in a previous simulation-based approach. Also, a comparison of the proposed case results with those of a conventional MRP application is provided. [ABSTRACT FROM AUTHOR]

The inventory-routing problem (IRP) is a typical logistics optimisation problem that supply chains, implementing vendor managed inventory (VMI), are confronted with. It combines inventory control and vehicle routing. The main objective of the IRP is to jointly determine optimal quantities of the product to be delivered to the retailers, delivery periods and optimal vehicle routes for the shipment of these quantities. This paper considers a multiperiod inventory-routing problem with stochastic stationary demand rates (MP-SIRP). The problem is first formulated as a linear mixed-integer stochastic program for which we propose a deterministic equivalent approximation model (MP-DAIRP). This latter model can be decomposed into two well-know subproblems: an inventory allocation subproblem and a vehicle routing subproblem. The stochastic aspect of the demand is accounted for in the inventory allocation subproblem. The vehicle routing subproblem is solved as a deterministic mixed-integer problem. Lagrangian relaxation is used to determine close to optimal feasible solutions for the MP-DAIRP. Results of the proposed Lagrangian relaxation approach on some numerical examples are reported and thoroughly discussed. [ABSTRACT FROM AUTHOR]

In this paper, we empirically test the theoretical model underlying the Toyota Way (TW), based on data obtained from Toyota’s logistics, sales and marketing functions across 27 countries. TW is the result of Toyota attempting to codify its culture to the global community. Using structural equation modelling techniques we show that the TW-associated measures challenge, kaizen, genchi genbutsu, respect and teamwork do adequately operationally define the TW; the first three measures corresponding to the construct ‘process improvement’ and the final two measures corresponding to the construct ‘people development’. Empirically, people development is found to have no direct effect on how the TW is deployed across a business unit. However, people development is found to be indirectly related to TW deployment through the mediating effect of process improvement. Our study provides quantitative evidence that while the intangible aspects of the TW (modelled as people development) may not directly relate to the results, they are an integral component of a complete implementation of the TW and related ‘Lean’ systems. By logical extension, this provides support for adoption of a holistic and long-term strategy, integrating soft and hard elements, by those organisations attempting to implement and sustain Toyota-style systems. [ABSTRACT FROM PUBLISHER]

Vendor selection in supply chain is a multi-criteria problem that involves a number of quantitative and qualitative factors. This paper deals with a concrete problem of flour purchase by a company that manufactures bakery products. The selection process is driven by the price, reliability and quality objectives of the buyer and is subject to the capacity constraints of the vendors in the situation where all suppliers offer several kinds of flour (multiproduct situation). Vendors often offer quantity discounts, i.e. the purchasing price of flour depends on the quantity ordered. The multiproduct situation complicates the mathematical formulation of the problem and in this context it is much more convenient to consider the discounts of the money volume of business with every vendor. This problem has been solved using an integration of analytic hierarchy process (AHP), weighted sum model (WSM) and fuzzy multi-objective mixed-integer programming to define the optimum quantities among the selected suppliers. [ABSTRACT FROM AUTHOR]

Aim: Recent mediatic recalls of products illustrate the losses in terms of cost and reputation induced by non-conformities reaching end customer. The purpose of this paper is to master the propagation of quality non-conformities in manufacturing processes. Most of the time, the control methods implemented to ensure the quality performance of a production system present weaknesses, inherent to their constitution. They can let non-conformities propagate along the value stream and even to the end customer inducing costly recalls for manufacturer. This paper presents a method to master non-conformities propagation in a production system. Method: In order to calibrate the proposition, two case studies have been issued on non-conformities propagation in a low-volumes industry in the energy field. Results: The proposed method combines two levels of analysis and associated improvement tools. At the event level, it proposes to build the control chart associated with the non-conformities propagation distance. At the system level, it proposes a propagation matrix to analyse the permeability of the global detection system and to identify where improvement effort has to be driven. Conclusion: The empirical results of the case studies indicate that the method is adapted in low-volume contexts. [ABSTRACT FROM AUTHOR]

We propose a new optimisation approach to address a multi-period, inventory control problem for an assembly system under stochastic environment. Significant drawback in a construction of a real-life model for a multi-product, multi-component assembly system is the fact that the shortage cost is typically known for a final product while the optimisation is carried out on assembly components. In the cases when the same type of components assemble different types of finished products, this becomes insuperable obstacle for most of the known approaches. In addition, restrictive assumptions about component demand are common for number of published models. Therefore, we develop a framework, which has the ability to incorporate real shortage costs of the final products within the optimisation process and to incorporate more realistic assumptions about product demand. The aim is to define a completely new optimisation approach in the case of complex component’s interdependencies in assembly systems. The inclusion of the proposed model in the optimisation process is shown by the example of a known simple stochastic demand model. Simple SQL procedures have been presented which benefits from the relational structure of the problem. [ABSTRACT FROM PUBLISHER]

An integrated multi-stage scheduling and routing problem with alternative machines at each supply chain (SC) stage and non-preemptive operations is studied. The peculiarity of the considered problem is the consideration of both continuous flows in operations and discrete assignments. Such problems are typical in the process industry. Taking into account the basic aspects that pertain to this case, a dynamic decomposition of the problem on the basis of optimal programme control is proposed. A particular feature and novelty of the presented study is the consideration of non-preemptive operations and their execution dynamics. The solution is based on a combined application of discrete and continuous optimization. With the special properties of the proposed scheduling model, it becomes possible to develop an optimal solution to the integrated scheduling and routing of jobs. Since the schedule generation is formulated in control terms, the models of scheduling and adaptation can be integrated. From the practical point of view, such integration can be advantageous subject to further dynamic analysis of the robustness, stability, and adaptability of SC schedules. [ABSTRACT FROM AUTHOR]

Given increased pressure to provide short delivery times, minimising customer order throughput time is a very important objective in warehousing operations. There are many factors that may affect the performance of an order picking system, such as layout of the warehouse, the storage strategy, the routing policy, the zoning method and the batching policy. In this study, we propose a simple travel time model and analyse the effect of order batching on the expected customer order throughput time with variable time window batching. In addition, we discussed the impact of some parameters on the batch size and the expected customer order throughput time. In the practice of Wuhan Dong Hon Logistics co. Ltd, the storage rack in the warehouse has two levels: the low storage level and the high storage level. These two levels are visited by pickers and forklifts, respectively. The classification improves the performance of order picking. In this study, we build the model based on this practice. [ABSTRACT FROM AUTHOR]

Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms. [ABSTRACT FROM AUTHOR]

We consider the problem of designing a distribution network for a logistics provider that acquires products from multiple facilities and then delivers those products to many retail outlets. Potential locations for consolidation facilities that combine shipments for cost reduction and service improvements are considered. The problem is formulated with direct shipment and consolidation opportunities. A novel mathematical model is derived to solve a complex facility location problem determining: (i) the location and capacity level of warehouses to open; (ii) the distribution route from each production facility to each retailer outlet; and (iii) the quantity of products stocked at each warehouse and retailer. A genetic algorithm and a specific problem heuristic are designed, tested and compared on several realistic scenarios. [ABSTRACT FROM PUBLISHER]

A supply chain ecosystem consists of the elements of the supply chain and the entities that influence the goods, information and financial flows through the supply chain. These influences come through government regulations, human, financial and natural resources, logistics infrastructure and management, etc., and thus affect the supply chain performance. Similarly, all the ecosystem elements also contribute to the risk. The aim of this paper is to identify both performances-based and risk-based decision criteria, which are important and critical to the supply chain. A two step approach using fuzzy AHP and fuzzy technique for order of preference by similarity to ideal solution has been proposed for multi-criteria decision-making and illustrated using a numerical example. The first step does the selection without considering risks and then in the next step suppliers are ranked according to their risk profiles. Later, the two ranks are consolidated into one. In subsequent section, the method is also extended for multi-tier supplier selection. In short, we are presenting a method for the design of a resilient supply chain, in this paper. [ABSTRACT FROM PUBLISHER]

We consider a decentralised supply chain with demand uncertainty, wherein a manufacturer sells a single product that consists of two components to the end consumer through an independent distributor. Assume that one component has a random yield, the manufacturer may rely on the spot market to replenish any shortage components after the random yield is realised. We identify the optimal product order, component production and replenishment decisions under two different situations. One is the traditional arrangement that the distributor decides the product procurement quantity and the manufacturer chooses the production quantity. The other is vendor-managed inventory (VMI) arrangement in which the manufacturer is responsible for both the order and the production decisions. The results show that the role of spot market can effectively offset the negative effects from production yield’s, market demand’s and component spot price’s variances. Consequently, the spot market leads to the cooperation between the manufacturer and the distributor, and improves the channel’s overall performance, in particular under the VMI arrangement when the wholesale price is sufficiently high. [ABSTRACT FROM PUBLISHER]

This paper deals with the integrated facility location and supplier selection decisions for the design of supply chain network with reliable and unreliable suppliers. Two problems are addressed: (1) facility location/supplier selection; and (2) facility location/supplier reliability. We first consider the facility location and supplier selections problem where all the suppliers are reliable. The decisions concern the selection of suppliers, the location of distribution centres (DCs), the allocation of suppliers to DCs and the allocation of retailers to DCs. The objective is to minimise fixed DCs location costs, inventory and safety stock costs at the DCs and ordering costs and transportation costs across the network. The introduction of inventory costs and safety stock costs leads to a non-linear NP-hard optimisation problem. To solve this problem, a Lagrangian relaxation-based approach is developed. For the second problem, a two-period decision model is proposed in which selected suppliers are reliable in the first period and can fail in the second period. The corresponding facility location/supplier reliability problem is formulated as a non-linear stochastic programming problem. A Monte Carlo optimisation approach combining the sample average approximation scheme and the Lagrangian relaxation-based approach is proposed. Computational results are presented to evaluate the efficiency of the proposed approaches. [ABSTRACT FROM PUBLISHER]

A joint economic production quantity (EPQ) and delivery quantity model for a production system is investigated in this paper. More specifically, an EPQ policy is implemented in the production system, while a smaller shipping quantity is periodically dispatched to the customer. The production system is also responsible for the shipment cost, i.e. a delivery price-based procurement from the customer. The considered cost includes setup cost to launch the batch production, inventory carrying cost, and transportation cost, where the transportation cost is a function of the delivery quantity. A per unit time cost model is developed and analysed to determine the optimal production and delivery quantities. Under some mild conditions, it can be shown that the joint cost function is convex with respect to the production quantity; and the number of delivery is an integer in each replenishment cycle. Computational study has demonstrated the significant impact of the joint decision model on the operating cost. In particular, the reduction in total cost can be more than 15% when inventory carrying costs, and/or transportation costs, are high. [ABSTRACT FROM PUBLISHER]

Warehouse design has become important due to its impact on service to customers and total logistics costs. Order picking is the key activity of a warehouse and an appropriate design will directly affect its overall performance. The increasing complexity of warehouses means that the main operating strategies such as storage location assignment, batching and routing need to be considered simultaneously. A Design of Experiment approach aided by Discrete Event Simulation could help to meet these new picking design process requirements, especially when various performance measures need to be compared. Some of the trade-offs that managers must keep in mind when designing order picking systems are shown, describing a single-case study carried out at a retail distributor. [ABSTRACT FROM PUBLISHER]

Automated storage and retrieval systems (AS/RS) are devices that allow intensive storage of materials. They can improve the supply chain performance, assuring more available volume for storage, lower labour costs and higher handling throughput of warehousing. Furthermore, the automated control allows the probability of errors in storage and retrieval to be minimised, along with the probability of product damage during movements. The purpose of the paper is to find the best solution in order to find the optimal dwell point policy, among different rules, able to minimise the travel time and distance travelled by stacker cranes, and consequently warehousing costs. An original and innovative model was developed in order to investigate the dwell point position for random allocation of unit loads. A software platform was developed to validate the proposed model by computer simulations. The performance of the system was analysed in a parametric/continuous way, varying at the same time the number of spans and levels, the height of the input/output point and the interval between requested missions. The results show that the developed model allowed convenience areas to be identified among the policies in which the travel time, distance travelled, and consequently warehousing costs are minimised, by varying different parameters. [ABSTRACT FROM PUBLISHER]

Many of the existing methods and models of supply chain planning and control consider only one supply chain structure and assume it's more or less static in nature. In practice, supply chains have a multi-structural semantics and those multiple interrelated structures (organisational, functional, informational, financial, topological, technological, product, and energy structures) are dynamic and subject to many planned and disturbance-based changes. These changes are tackled by control activities which make planning an adaptive process. Although the supply chain optimal planning domain for static structures has been extensively investigated, the domain of planning and control of supply chains with structure dynamics merits more systematic and critical attention. In this study, an original form of supply chain representation as a dynamic system with changing multi-structural characteristics is developed. The structure dynamics control is a dynamic interpretation of the supply chain (re)synthesis process and aims at both advancing the supply chain (re)planning domain and enlarging the scope of the supply chain analysis domain that is currently rather limited. The developed approach is based on an optimal program control theory, active modelling objects, and structural–mathematical concept blended with mathematical programming. In this paper, we describe its basic features and exemplify possible applications and extensions regarding real planning and control problems faced by international supply chains in recent years. [ABSTRACT FROM PUBLISHER]

Crossdock operations are designed to consolidate and quickly move goods from a variety of origins to a variety of destinations. The key performance characteristics used to measure crossdock performance are outgoing truck cube utilisation and load dwell time in the crossdock. These two measures are at odds with each other, and satisfying one comes at the expense of satisfying the other. The paper develops an integrated crossdock operations model that optimises this trade-off and accounts for incoming and outgoing truck door assignment, staging allocation/location, and load scheduling. The problem is further explored experimentally to determine factor contributions to maximising overall operations revenue. Finally, a genetic algorithm is developed to solve large-scale problems. [ABSTRACT FROM AUTHOR]

Over the last decade, supply chain coordination has attracted much attention in the context of industrial logistics systems, because of the role played in production support logistics. There is a growing interest in the analysis and measurement of performances, because performance analysis allows us to build supporting decision tools for the design, planning and management of the supply chain. This paper presents an analytical approach for performance analysis based on a decomposition principle. The approach is applied to a serial supply chain that consists of two levels of suppliers, a production plant and a distribution centre, and operates as a pull system. This configuration is typical for bulk materials such as grain. The method allows us to approximately calculate performance indicators (such as customer satisfaction and inventory levels) taking into account all the interactions between the different echelons of the supply chain. The accuracy of the approximations is confirmed by the results obtained via a simulation model. Numerical analysis allows us to outline interesting and non-intuitive behaviour of the performance indicators for different stages of the supply chain. [ABSTRACT FROM AUTHOR]

Recent changes in environmental requirements have forced manufacturing operations to face a new challenge, which is to develop green strategies to design supply chains and their processes. This study describes the specific features of green supply-chain design considering end-of-life products. We propose a mixed integer linear programming model to obtain the optimal closed-loop supply-chain (CLSC) network design, in which recovery process occurs in three different ways: material recovery, component recovery, and product recovery. An extensive analysis is supported by numerical results which inform CLSC managers about the opportunities of enhancing product returns in terms of quality and quantity. To define a better CLSC strategy to increase system profitability, the volume of product returns and corresponding quality conditions should be considered in the design of a CLSC network. The numerical study provides information for us to discuss the potential benefits of product-recovery processes and the reaction of system profitability to the changes in specific CLSC parameters. [ABSTRACT FROM AUTHOR]

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form. [ABSTRACT FROM AUTHOR]

Consider a supply chain where the retailer faces a stochastic demand and orders from the supplier, while the supplier manufactures new products and also remanufactures early returns to meet the order. The order and manufacturing quantity decisions are studied under three decentralised cases: (1) the Stackelberg case, where the manufacturing quantity is determined by the supplier after realising the order quantity from the retailer; (2) the Nash case, where the manufacturing and order quantities are determined simultaneously; (3) the inaccessible return information case, where the retailer does not have the distribution information of upstream returns. We find that in the Stakelberg case the order and manufacturing quantities are larger than in the inaccessible return information case, and the profits for the supplier and retailer are also higher. In contrast, the quantities and profits in the Nash case are the lowest of the three cases when the returns are negatively correlated to the demand. The centralised system is also explored by solving a two-step dynamic program. Computational results are reported to show the effects of system parameters. [ABSTRACT FROM AUTHOR]